No retraction, correction or concern found when last checked on Oct 20, 2026.

Intrinsically Disordered Proteins: An Overview

Rakesh Trivedi, Hampapathalu Adimurthy Nagarajaram

International Journal of Molecular Sciences · 2022 · Review · CC BY

Abstract

Many proteins and protein segments cannot attain a single stable three-dimensional structure under physiological conditions; instead, they adopt multiple interconverting conformational states. Such intrinsically disordered proteins or protein segments are highly abundant across proteomes, and are involved in various effector functions. This review focuses on different aspects of disordered proteins and disordered protein regions, which form the basis of the so-called “Disorder–function paradigm” of proteins. Additionally, various experimental approaches and computational tools used for characterizing disordered regions in proteins are discussed. Finally, the role of disordered proteins in diseases and their utility as potential drug targets are explored.

Full text

From PubMed Central, U.S. National Library of Medicine, as current on Oct 20, 2026. Paragraph numbers are ours, so an answer can point at one.

1. Introduction

1The functional aspects of genes have been attributed to RNAs and proteins. Of the two, proteins are the ones that bring about the majority of diverse cellular effector functions. Two paradigms concerning the structure and function of proteins have evolved, as shown in Figure 1. The first paradigm corresponds to the well-established, often assumed as the default for proteins, ‘structure–function paradigm’, which states that the three-dimensional native structure under physiological conditions is the prerequisite for a protein to function. The second paradigm is the recently established ‘disorder–function paradigm’ based on the proteins that perform cellular functions without attaining a stable three-dimensional structure under physiological conditions. The naturally occurring, biologically active proteins that appear to possess a high degree of conformational flexibility have been referred to as intrinsically disordered proteins (IDPs). In most instances, instead of the whole protein, only some regions in the protein are disordered and functional; such protein segments are known as intrinsically disordered regions (IDRs). Interestingly, these intrinsically disordered proteins/regions have endowed proteins with functional promiscuity.

1.1. Structure–Function Paradigm

2In the early stages, protein research was focused on studying globular (ordered) proteins; therefore, the principles laid to explain the protein structures were more specific to globular proteins. These fundamentals defining the protein structures received continuous experimental support throughout the 20th century from the multitude of protein structures submitted to repositories, such as that of the Protein Data Bank (PDB). There was no doubt about the relatedness of the protein structure and function. Hence, the central philosophy of structural biology was described as follows: “a protein requires a native folded structure to perform its biological function”.

3Proteins were initially considered as rigid, crystal-like biological molecules with no flexibility. This “static” view of protein function was supported by one of the best-understood protein functions, catalysis. This static view of proteins led to the assumption that the complementarity of shapes at the binding interface and the emerging non-covalent forces between molecules are the major drivers of interactions involving proteins. After Koshland proposed the “induced-fit model” of catalysis, the theory of presence of some degree of flexibility in protein structures to carry out their biological functions was accepted.

4The three-dimensional native structure of a protein, which is at the center of the structure–function paradigm, is encrypted in the protein’s amino acid sequence and is governed by the intricate balance of various physical forces between its atoms. Physical forces include strong covalent (peptide/amide) bonds, which connect atoms of the polypeptide sequence, and weaker attractive/repulsive forces between non-bonded atoms. All of these fundamental interactions act coherently and assist a polypeptide in attaining a well-defined three-dimensional structure under physiological conditions. A unique 3-D structure of a protein ensures a precise spatial arrangement of residues, which provides a particular physicochemical microenvironment required for the extremely specific catalysis of chemical reactions, binding of ligands, ion or small molecule translocation, or macromolecular complex assembly. Along with the deposition of a large number of protein structures into the PDB, the availability of the mechanistic details of a protein’s functions, such as enzyme catalysis, receptors, transporters, membrane channels, etc., also helped the acceptance of the structure–function paradigm. However, with increasing reports of the structural/functional data deviating from the classical structure–function paradigm, there was a need to reassess this well-established paradigm, which, in turn, led to the proposition of the disorder–function paradigm.

1.2. Disorder–Function Paradigm

5The early recognition of the significance of protein structures in their functioning is generally considered the prime reason for the prolonged negligence of protein disorder. Unusual behavior of proteins, such as missing electron density in the PDB structures, increased sensitivity to in vitro proteolysis, peculiar behavior during the purification process, etc., has always been observed, but in the light of the classical structure–function paradigm, these observations were initially considered as artifacts. This “dark side” of structural biology underwent an intensive reassessment against the backdrop of proteins’ considerable inherent flexibility and peculiar properties, and resulted in the recognition of the intrinsic structural disorder of proteins as a standard feature across proteomes. Altogether, the widespread occurrence of intrinsically disordered proteins or protein segments and their functional relevance has led to the introduction of a new generalization with respect to the protein structure and function, i.e., the ‘disorder–function paradigm’.

6Intrinsically disordered proteins (IDPs) or regions (IDRs) remain unfolded and carry out their biological role under physiological conditions. Just as in the case of ordered/globular proteins, the structural organizations of IDPs/IDRs are also governed by the same physical forces, albeit with some differences in their balance and emerging protein structure dynamics. Most of the early concepts regarding the structure of IDPs/IDRs came from the unfolding/folding reaction studies of globular proteins. At the level of genome organization, large proportions of gene sequences have also been observed to code for functional long amino acid stretches, which most likely either attain a non-globular conformation or exist as unfolded entities in solution as IDPs/IDRs.

7Over the years, our understanding of the protein structure and function has increased tremendously. However, with the increasing reports of the involvement of protein disorder in carrying out cellular functions and the development of computational tools to predict disordered residues of proteins, there has been an exponential increase in interest in studying IDPs/IDRs. This review article summarizes various features of intrinsically disordered proteins/regions that form the basis of this newly established ‘disorder–function paradigm’.

2. Intrinsic Protein Disorder

8Repeated occurrences of proteins with intrinsic flexibility and properties different from those of ordered/globular proteins gradually resulted in the development of the notion that non-rigid proteins are not exceptions. Around 2000, these naturally flexible proteins were accepted as a general category of proteins. The conformational flexibility of these “non-traditional” proteins was proposed to be the source of their biological functions. Over the years, various terms have been introduced by different authors to describe these proteins with inherent flexibility. It was only in recent years that the phrase “intrinsically disordered proteins” (IDPs) became more widely used than other terms. Most acceptably, “intrinsic protein disorder” defines the biologically active proteins or protein segments that exist as ensembles of unfolded, collapsed, extended, non-globular conformations at the secondary or tertiary structural level.

2.1. Natural Abundance of Intrinsically Disordered Proteins

9Information about IDPs was very sparse for a very long time. Until the reports on the experimentally characterized IDPs, it appeared improbable to have such a class of proteins in abundance. However, computational predictions revealed the possible widespread occurrence of IDPs and IDRs. The exhaustive analysis of 31 genomes spanning three kingdoms of life revealed that a considerable number of proteins contain regions with 40 or more consecutive disordered residues. The proportion of structural disorder was also found to increase progressively with genome complexity, from bacteria to archaea, and then to eukaryotes. While 33% of eukaryotic proteins have been reported to contain at least one functionally relevant long (>30 residues) intrinsically disordered region, archaean and eubacterial proteins possess only 2.0% and 4.2% of such functional IDRs, respectively. In viral proteomes, the total intrinsic disorder content is determined by the nature of the nucleic acid constituting the viral genome, and it decreases successively with an increase in the size of the viral proteome. Recently, 3133 unique proteins were experimentally validated to contain functional long disordered regions (at least 30 residues). Additionally, the degree of disorderedness in proteomes and essential proteins was estimated for various genomes, and a sharp increase was observed at the prokaryote/eukaryote boundary. Thus, the natural abundance of disordered proteins or protein segments across complex genomes suggests that, even though IDPs/IDRs fail to attain stable three-dimensional structures under physiological conditions, they are of high functional pertinence.

2.2. Sequence Characteristics of Intrinsically Disordered Proteins

10The propensity of a protein or protein segment to fold or remain unfolded under physiological conditions is encrypted in its amino acid sequence. In other words, the amino acid sequence composition determines whether a protein would be an ordered protein with a stable folded 3-D structure or an unfolded intrinsically disordered protein. Strong electrostatic repulsions due to a higher net charge and a lack of driving force for compaction due to low mean hydrophobicity are generally considered as the prime reasons for the unfolded, extended structure of IDPs/IDRs.

11An in-depth comparative analysis of the sequence composition of ordered and disordered proteins revealed that residues such as Ala, Arg, Gly, Gln, Glu, Lys, Pro, and Ser (referred to as disorder-promoting residues) occurred more frequently in IDPs/IDRs. In contrast, residues such as Asn, Cys, Ile, Leu, Phe, Val, Trp, and Tyr were more common in the ordered/structured segments of the proteins (referred to as order-promoting residues). Comparative studies of amino acid residues in disordered and ordered regions, physicochemical property-based scales (such as the coordination number, aromaticity, strand propensities, flexibility index, volume, helix propensities, etc.), and composition-based features (e.g., any combination that has one to four residues in the group) have led to the distinction between disordered and ordered regions in proteins. Compositional bias and sequence characteristics play a significant role in defining the interactions of disordered proteins/regions. Furthermore, the distinct compositional bias of the intrinsically disordered proteins/regions as compared with the ordered proteins/regions forms the basis for developing many disorder-predicting computational tools. For instance, tools involving the alignment of IDPs/IDRs use specific amino acid substitution scoring matrices reflecting the frequency of occurrence of different residues in the disordered regions of proteins.

2.3. Structural Aspects of Intrinsically Disordered Proteins

12In general, most proteins exist as a combination of both ordered and disordered segments in different proportions. Unlike the ordered regions of proteins, which exist as stable secondary/tertiary structures, the disordered regions fail to attain a stable three-dimensional native structure under physiological conditions. However, the structure of IDPs/IDRs can be best defined as the ensemble of functionally relevant interconverting transient structures on a fast time scale.

13Several previous studies reported that IDPs/IDRs are enriched with uncharged and polar amino acids, lacks bulky hydrophobic residues, and exist as dynamic heterogeneous ensembles of collapsed or extended structures. Furthermore, IDPs/IDRs exhibit different degrees of foldability, ranging from potentially foldable to not foldable at all.

14IDPs/IDRs do not possess a precise equilibrium value of the atomic coordinates and backbone Ramachandran angles over time; as a result, they appear as “protein clouds”. Despite being highly dynamic, the structures of such protein clouds are best described as a few low-energy conformations.

15In most cases, upon interaction with specific binding partners, a disordered protein/segment undergoes a disorder to order conformational transition (termed as ‘induced folding’),. Additionally, a disordered protein can bind to multiple partners to attain distinct conformations with each of them, which, in turn, enable it to interact with different targets. Moreover, there is also a “conformational preference” for the structure attained by IDPs upon binding. Post-translational modifications (PTMs) also enable IDPs/IDRs to attain diverse conformations, thus increasing the total repertoire of structures resulting from an IDP/IDR sequence. Ensembles of the conformations resulting from a single sequence make it possible for IDPs to perform multiple apparently unrelated biological functions (termed as ‘moonlighting’) required for the maintenance of life.

2.4. Functional Classification of Intrinsically Disordered Proteins

16Several classification schemes have been proposed over the years based on the functions performed by IDPs/IDRs. Tompa et al. annotated IDPs/IDRs in six different functional categories depending on the presence/absence and the strength of the binding of the disordered proteins/regions to their ligands. Later, this stratification was further extended to define eight functional classes of IDPs, namely entropic chains, modification sites, disordered chaperones, molecular effectors, molecular recognition assemblers, molecular recognition scavengers, metal sponges, and unknown, as shown in Figure 2. These functional subtypes can be present either alone or in combination within the same protein if the protein has several disordered regions. In the following sections, different functional classes are described in some detail with relevant examples.

2.4.1. Entropy Chains

17Entropic chains take advantage of their conformational flexibility and perform biological functions without becoming a structured entity. Examples of entropic chains include flexible linkers and spacers. Flexible linkers regulate the relative movement of domains positioned at the two ends of the linkers, whereas spacers specify the inter-domain distances. In addition to providing a means of distance regulation between domains/regions within proteins, both flexible linkers and spacers enable extraordinary freedom for inter-domain orientations. In general, the conformational ensembles generated through computational simulations and modeling are used to understand the flexibility and positioning of the domains in multidomain proteins.

18Several previous studies showed the significance of conformational flexibility for IDPs/IDRs to function. For instance, a mutation in the flexible linker region connecting light and heavy chains of multidomain antibody protein reduces the linker’s flexibility, disturbs the domain’s orientation, and limits their interactions. In another study, disordered and flexible linker regions of the host proteins have been shown to be the primary targets of the oncogenic adenoviral Early region 1A (E1A) protein. Replication protein A subunit (70 kDa) manifests a conserved dynamic behavior regardless of the insignificant sequence conservation, which is also evidence of the importance of flexibility from the perspective of functional relevance. The microtubule-associated protein 2 (MAP2) projection domain facilitates the spacing of cytoskeletal structures by repelling molecules that approach microtubules. The disordered regions in the mammalian CIP/KIP family of proteins act as flexible linkers or spacers, and have a conserved propensity to remain in the disordered state. Along with the kinetic properties of the binding partners, the linker flexibility and length are critical factors in determining the processivity of enzymes.

2.4.2. Modification Sites

19Most of the regulatory and signaling proteins possess IDRs, and the intrinsic flexibility of these regions as display sites affords them an advantage over ordered regions. Furthermore, the prevalence of charged side chains and low-complexity regions in the disordered proteins facilitates their multivalent electrostatic interactions with the charged lipid head groups in the membranes. Thus, both the composition of membranes and the disorderedness of IDPs/IDRs fine-tune the signaling pathways. In several independent studies, approximately 15% of all disordered proteins have been reported as lipid-binding proteins.

20The flexibility of IDRs helps them to undergo transient, but specific, interactions with enzymes carrying out various post-translational modifications. Moreover, this intrinsic flexibility of IDRs facilitates the easy entry and recognition of post-translational modifications within IDRs by effector molecules, leading to subsequent outcomes, such as protein stability, turnover, and localization within a cell upon binding. p53, p27, histone protein tails, and the CREB-kinase-inducible domain are a few examples of well-explored IDPs where PTMs are critical for functioning and regulation. Recently, phosphorylation at multiple sites within the disordered regions of four representative IDPs (Ash1, E-Cadherin, CTD2, and p130Cas) has been reported to regulate the IDR-mediated protein–protein interactions through conformational plasticity. Furthermore, site-specific phosphorylation within the intrinsically disordered AF1 domain of glucocorticoids receptor (GR) generates surfaces appropriate for the interaction of the AF1 domain with coregulatory binding proteins (BP), which, in turn, regulates the transcription of the downstream genes involved in GR signaling. Additionally, the methylation of arginine residues in low-complexity and disordered regions has been shown to regulate genome integrity, gene transcription, splicing, mRNA–protein complex biology, protein translation and stability, and phase separation.

2.4.3. Disordered Chaperones

21Proteins that help RNAs and other protein molecules to attain their functional folded state are known as chaperones. About half of the RNA chaperone sequences and one-third of the protein chaperones are disordered in nature. The ability of IDRs to adapt their structures according to different binding partners and increase the lifetime of the encounter complex during binding supports their chaperone activity. Disordered chaperones prevent toxic aggregation by quickly binding and solubilizing misfolded proteins. In addition, the transient binding of unfolded or misfolded substrate molecules to disordered chaperone regions helps them to fold/refold correctly in a thermodynamically permissible manner. hnRNP A1, GroEL, α-crystallin, Hsp33, etc. are a few well-known examples of IDPs with chaperone activity.

22RNA chaperones enriched with positively charged residues promote the compactness of nucleic acids conformation by acting as a counterion that locally shields repulsive electrostatic repulsions. For instance, the dimerization of the hepatitis C virus genome is facilitated by its core protein with chaperone activity. Additionally, disorder-enriched RNA binding proteins (RBP) have been found to stabilize the disorderedness of the interacting IDPs/IDRs and maintain their functional competence, thus making the disordered state of the human proteome available for drug targeting. Furthermore, IDRs present in the human chaperones Hsp110s, Hsp105α, and Apg-1 prevent general protein aggregation and amyloid genesis. With aging, the disordered chaperone-mediated quality control of intrinsically disordered proteome is lost, leading to the accumulation of protein aggregates responsible for neurodegenerative diseases. In plants, dehydrins, the largest group of disordered chaperones, are expressed at higher levels under various abiotic stress conditions, such as drought, osmotic stress, or high temperatures. An intricate interplay of ordered and disordered segments of these proteins is required for proper cellular protection. Additionally, disordered chaperones have been reported to lose flexibility upon binding to a substrate, which helps them to act as a molecular on/off switch, as characterized in Hsp33.

2.4.4. Molecular Recognition Effectors

23Effector functional class disordered regions bind permanently to other proteins and modify their actions. Such IDRs often undergo disorder to order conformational transition upon binding to their interacting partners, which is known as ‘coupled folding and binding’. The degree of folding upon the development of intermolecular contacts may vary significantly. The disordered protein partners may either fold completely or remain as an extended structure with short flanking ordered regions. Recently, a ubiquitin-binding domain (referred to as DisUBM) was identified in many proteins that remained mostly disordered in a free state, and attained an α-helical structure upon binding to ubiquitin. Such naturally occurring DisUBMs are considered as general affinity enhancers of IDPs that can bind to fold proteins with a possibility of ubiquitinylation. In general, disordered regions have a kinetic advantage over folded segments, even after intermolecular interactions are formed during the folding-upon-binding process.

24Effector-disordered regions can also alter the functioning of the other parts within the same protein in two different ways: (i) competitive interactions and (ii) allosteric modulation. For example, the disordered GTPase binding domain (GBD) of Wiskott–Aldrich Syndrome protein (WASP) controls its autoinhibition through competitive binding. Upon binding to cdc42 protein there is an enhanced interaction of the WASP GDB domain with the actin cytoskeleton regulatory machinery. However, GDB interaction with the actin machinery is inhibited as it folds back on the other region of WASP. In another study, IDRs of general transcription factors and transcriptional regulators were shown to compete for the folded TAZ1 domain of CREB-binding protein (CBP) in response to various types of stress stimuli. For instance, under oxygen-deficient conditions, the hypoxia-inducible transcription factor (HIF-1α) C-terminal transactivation domain drives the transcription of different critical adaptive genes. One such gene translates to HIF-1α negative feedback regulator protein CITED2, which represses the transcriptional activity of HIF-1α by directly competing for CBP’s TAZ1 binding site. Allosteric coupling in disordered regions can be represented by the binding of adenovirus E1A oncoprotein with retinoblastoma protein (pRb) and the TAZ2 domain of CBP. The binding of pRb to E1A increases the probability of CBP binding, whereas the initial binding of CBP to E1A decreases the possibility of pRb binding. In addition, the globular KIX domain of the CBP is known to bind cooperatively with CREB kinase-inducible transactivation domain (pKID), the activation domain of transcription factor c-Myb, and mixed-lineage leukemia (MLL) protein. The binding of the pKID and c-Myb to the KIX domain is mutually exclusive and regulated by the allosteric binding of MLL. These results also suggest that the allosteric coupling can also be determined by the stability of the individual protein conformations achieved when interacting partners are binding.

2.4.5. Molecular Assemblers

25As the name suggests, assemblers interact with multiple binding partners to encourage the formation of higher-order protein complexes, such as the ribosome, activated T-cell receptor complexes, and transcription pre-initiation complexes. An assembly of various interacting partners of large complexes is feasible due to the multiple types of disordered functional regions, such as short linear peptide motifs (SLiMs), molecular recognition features (MoRFs), etc. In fact, with an increase in the size of the protein complexes, the disorder content of the assembler protein increases. Furthermore, upon binding to their partner proteins, disordered assemblers maintain their open structure, allowing multiple proteins to bind to a single IDR. Therefore, the overall architecture of assemblies formed by IDPs depends on the disorder content, the available number of interaction sites, and the nature of the binding molecules.

26The assembler function of IDPs is an outcome of their multivalency and can be achieved in two distinct ways. Firstly, IDPs can act as the binder molecule by tethering all of the interacting proteins and stabilizing the complex. For example, ribosome assembly involves the cooperative binding of proteins enriched with IDRs and RNA. Secondly, IDPs can also regulate the spatiotemporal assembly of the different signaling partners by acting as a scaffold, such as the IDP-mediated formation of biomolecular condensates, nuclear pore complex, and cytoskeletal assemblies. Among all the functional categories, the scaffolding regions of the assemblers contain the highest degree of disorder.

2.4.6. Molecular Recognition Scavengers

27This functional class of intrinsically disordered proteins/regions stores and neutralizes small ligands. For example, caseins and other calcium-binding phosphoproteins (SCPPs) are highly disordered proteins that can act as scavengers by solubilizing calcium phosphate clusters in milk and other biofluids. The proline-rich salivary gland glycoproteins also belong to the molecular recognition scavengers class of IDPs; they function by binding and neutralizing the tannin molecules in digestive tracts. Plant dehydrins are highly disordered proteins expressed at elevated levels in the late stages of embryogenesis. The VviDHN4 isoform of dehydrin acts as a scavenger by removing reactive oxygen species from the cellular environment. Human peroxiredoxins-4 protein, most highly expressed and localized exclusively within the endoplasmic reticulum, acts as its major hydrogen peroxide scavenger. Other examples of IDPs with scavenger activity include chromogranin protein A, which is responsible for sequestering adrenaline and ATP in the medulla of the adrenal gland.

2.4.7. Metal Sponges

28IDPs/IDRs that are capable of storing and neutralizing heavy metals belong to the metal sponges category, such as the sulfonation of catecholamines in humans by catecholamine sulfotransferase enzyme (Sulfotransferase 1A3). This reaction is a detoxification pathway because it readily forms excretable water-soluble metabolites. Additionally, the SmbP protein of the ammonia-oxidizing bacterium Nitrosomonas Europaea, binds to divalent cations, especially copper, to prevent cellular toxicity. Similarly, the crystallin proteins from the bacteria Yersinia Pestis act as metal sponges by managing the cellular copper levels.

29All of the other IDPs/IDRs with no experimental support or evidence proving/disproving their role in protein function were assigned to the unknown class. Upon functional annotation, the members of this class are transferred to one of the above-mentioned functional categories.

2.5. Functional Elements of Intrinsically Disordered Proteins

30Various functional regions in intrinsically disordered regions have been revealed when studying different classes of functions carried out by IDRs. In general, the functional modules within IDRs have been classified into three categories: (i) Short Linear Motifs (SLiMs), (ii) Molecular Recognition Features (MoRFs), and (iii) Intrinsically Disordered Domains (IDDs). The classification and features of each of these functional modules are shown in Figure 2. The following sections discuss these functional modules of disordered proteins/regions interactions.

2.5.1. Short Linear Motifs

31Short Linear Motifs (SLiMs), also known as MiniMotifs or Linear Motifs (LMs), are 3–10-residue-long peptide segments that occur within IDPs. SLiMs typically comprise a stretch of loosely conserved amino acids interspersed with a few highly conserved residues. As a consequence, interactions mediated by an individual SLiM motif have weak affinities. However, multiple SLiMs frequently coordinate to produce higher-affinity dynamic interfaces. Even though SLiMs are short sequences with transient binding capabilities, they are indispensable to a protein’s binding precision and functioning. The Eukaryotic Linear Motif (ELM) initiative aims to characterize and annotate motifs, mostly focusing on, but not limited to, eukaryote proteins. At present, 1,000,000 SLiMs have been predicted across the human proteome, but only a small fraction of it has been explored in detail to date.

32Owing to their crucial role in regulating signal transduction and protein–protein interactions, SLiMs are considered as functional in almost every biological pathway. They can interact with a variety of biological entities, including globular protein domains, intrinsically disordered proteins or regions, RNA, lipids, etc. Broadly, SLiMs can be classified into two distinct groups: modification sites and ligands, with each having multiple subgroups. If a linear motif act as a modification site, catalytic domains of enzymes interact with SLiMs to catalyze cellular processes, such as post-translational processing, the addition or removal of moieties, and structural alteration of the peptide backbone. In contrast, when linear motifs behave as ligands, they act as complex-promoting motifs required for scaffolding and increasing the avidity of interactions with binding partners. Additionally, SLiMs can act as a docking motif to increase the specificity and efficiency of modification events, and as a targeting/trafficking motif to provide stability to proteins and facilitate their subcellular localization and trafficking. For example, in the family of Rho-activated kinases (Raks), kinase function activation and uniform subcellular localization is an outcome of cleavage by caspases at SLiMs identified in disordered regions. The binding of the catalytic and regulatory subunits of protein serine/threonine phosphatases (PPP) and their substrates depends on SLiMs. The interaction of human-origin recognition complex subunit ORC1 and licensing factor CDC6 during pre-initiation complex formation and its regulation by CDKs are also mediated by SLiMs.

33Furthermore, SLiMs can also act as molecular switches by allowing the mutually exclusive binding of different partners at the protein’s same or distinct SLiM sites. Such SLiM features are essential to bring about the context-dependent moonlighting functions of IDPs.

2.5.2. Molecular Recognition Features

34Molecular Recognition Features (MoRFs) or Elements (MoREs) are 10–70-residue-long peptide motifs that can occur in the disordered regions of proteins. These motifs undergo disorder-to-order transition upon binding with their partners (i.e., folding upon binding) and attain well-defined structures, such as α-helices (α-MoRFs), β-strands (β-MoRFs), γ-coils (γ-MoRFs), or mixtures of all of these conformations (complex-MoRFs). The final conformation achieved after transition may occur either due to the conformational preference of unbound MoRFs or the induced folding after binding. The structural preorganization of MoRFs is modulated mainly by the sequence itself. Therefore, sequence analysis of MoRFs from the structural, conformational, and interaction mechanism aspects will help to understand the properties of MoRFs. A recent detailed computational investigation of 868 complete proteomes showed that 29% of IDRs of bacteria and archaea and 21% of eukaryotes harbor MoRFs.

35p53 protein is a classic example of a protein with multiple MoRFs in a disordered state attaining multiple conformations upon interaction with different binding partners. MoRF at the N-terminus of p53 protein is the primary binding site of Mdm2 and the other 40 proteins. The MoRF between residues 40 and 60 is the binding site of the Mdm2 and RPA70 proteins. C-terminal MoRF binds to multiple proteins, including S100B and the cdk2–cyclin-A complex. Each of these unique combinations of MoRF and the interacting proteins results in a distinct conformation of p53.

36Moreover, the T-cell surface glycoprotein CD3 zeta chain has two MoRFs, referred to here as N-terminus and C-terminus MoRF. While tyrosine kinase, tyrosine phosphatase, and nef protein bind to N-terminal MoRF, the SH2 domain of the SHC protein binds to the C-terminus MoRF. Additionally, the ethylene response factor in plants has several identified MoRFs, and the binding of different interacting proteins to these motifs helps the plants to overcome multiple stresses and adapt to different biological niches. In conclusion, a single MoRF can bind structurally diverse sets of protein partners to give rise to multiple distinct conformations.

2.5.3. Intrinsically Disordered Domains

37Sequence-based approaches have identified various protein domains that are entirely or largely disordered both in isolation and solution. They are mostly involved in the DNA, RNA, and protein binding (e.g., Wiskott–Aldrich syndrome protein (WASP)-homology domain 2 (WH2) of actin-binding proteins). In general, the intrinsically disordered domains are a long region of disorder (>20 residues) with conserved function, conserved sequence, and conserved disorder. Moreover, at the genomic level, the protein domains acquired during evolution as an outcome of the extension of the existing exons or exonization of previously non-coding regions tend to be highly disordered. Therefore, the exonization of the previously non-coding regions could be one way of incorporating disordered segments into proteins. Some well-known examples of disordered domains include cell cycle regulatory proteins p21, p27, p57, etc. The disordered domains of these proteins have been experimentally and computationally verified.

38Interestingly, specific disordered regions have been observed to co-occur frequently with particular types of structured domains in the same sequence. Some globular domains require the presence of short, disordered regions in their vicinity, while others require disordered segments at specific locations with respect to the domain boundaries. These findings suggest that the functional module in proteins can comprise either disordered/ordered regions alone or in combination. For example, ER degradation-enhancing alpha-mannosidase-like proteins (EDEM) recognize misfolded proteins and direct them to the ER-associated protein degradation (ERAD) process. EDEM3 degrades the misfolded proteins with the help of both structured domains (mannosidase homology domain (GH47), intermediate (IMD), and protease-associated (PA)) and intrinsically disordered domains. Furthermore, the Bcl-2 family mediates apoptosis, and the maintenance of stem-like behavior of glioblastoma multiforme spheres by RBM14 protein also requires both structured and disordered domains. Therefore, studying the co-occurrence of structured domains and IDRs in the same protein may prove insightful in understanding the unannotated disordered segments of proteins with respect to their biological role.

3. Experimental Approaches for Assessing Intrinsic Protein Disorder

39Intrinsic protein disorder can be recognized and characterized by various direct and indirect (bio)physical methods. In contrast to direct techniques, which provide structural information about proteins, indirect approaches do not offer any structural details. Still, they suggest a behavior from which the disordered nature of the proteins can be inferred.

3.1. Indirect Methods

40Early understanding of the intrinsic structural disorders of proteins was based on a few simple techniques. In general, these indirect intrinsic disorder-identification approaches can quickly provide ample insight into the structural states of a protein or its segments.

41Because of the unusual amino acid composition and lack of a compact hydrophobic core, disordered proteins are evident during the purification process. Usually, the molecular mass (Mw) of IDPs estimated by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE) is higher by a factor of 1.2–1.8 in comparison with that measured by mass spectrometry. Indeed, due to the enrichment of acidic residues and extension in solution, IDPs bind less to SDS and migrate more slowly on the gel in comparison with globular proteins. The aberrant mobility of IDPs is also observed in size-exclusion chromatography (SEC) or gel-filtration (GF) experiments, as a result of which the apparent Mw of proteins with disordered regions is higher. Furthermore, the flexible regions of proteins are known to have increased sensitivity to proteolytic degradation. IDPs, which are more affected than ordered proteins, based on limited in vitro proteolysis, exhibit high inherent flexibility.

42Other peculiar biochemical behaviors of IDPs/IDRs include insensitivity to high temperatures and stability under acidic treatment. The resistance of IDPs/IDRs to boiling temperatures and acidic pH values has been ascribed to their lower contents of hydrophobic residues and enrichment of polar/charged residues, respectively. Neutralizing acidic groups at lower pH levels reduces the net charge on IDPs/IDRs, leading to their increased solubility and a more compact structural state. In contrast to IDPs/IDRs, the aggregation/precipitation of globular/ordered proteins occur at elevated temperatures and under low-pH conditions. While high-temperature conditions expose the hydrophobic core of ordered proteins, acidic conditions cause protonation of their negatively charged side chains, leading to charge imbalances, followed by the disruption of salt bridges and aggregate formation.

3.2. Direct Methods

43Several techniques provide both steady-state and dynamic structural information on IDPs/IDRs at the residue level. These methods capitalize on the significantly distinct conformational behavior of IDPs compared with that of globular proteins. Some of the most commonly used direct methods are as follows:

3.2.1. X-ray Crystallography

44The diffraction intensity and X-ray pattern scattered by electrons in the protein structure are used to construct a three-dimensional (3-D) model of electron density, which, in turn, is used to deduce the atomic nuclei positions in the protein molecule. Disordered regions in X-ray structures appear as missing regions. This method can provide protein structure resolution down to 1Å. Still, additional experimental support is required to be certain about the structural disorder, as missing electron density regions can also result from technical failures in crystallography.

3.2.2. Circular Dichroism (CD)

45Circular dichroism (CD) is an absorption spectroscopy-based approach that relies on measuring the difference in the absorption spectra of right-handed and left-handed circularly polarized light. Optically active chiral molecules preferentially absorb either right-handed or left-handed circularly polarized light. Near-UV (250–350 nm) and far-UV (190–230 nm) CD signals are generally used to determine different aspects of the structure of proteins in solutions. The near-UV CD spectrum represents the tertiary structure around aromatic residues Phe, Tyr, and Trp. While intense and detailed spectra characterize ordered proteins, those of IDPs are of low intensity and low complexity. The far-UV CD spectra of the secondary structural elements of proteins are quite distinct; therefore, they are used to determine the proportion of ⍺-helix, β-sheet, turn, PPII helix, and coil conformations in proteins. If the far-UV CD spectrum is indicative of predominantly coil conformations, it indicates the disordered nature of the protein. In the case of proteins having both disordered and ordered regions, the CD does not provide clear information, as it lacks residue-specific details.

3.2.3. Nuclear Magnetic Resonance (NMR)

46NMR is the most common quantitative technique used for studying IDPs. The spinning ability of the charged atomic nuclei forms the basis of the 3-D structure determination of proteins in solutions using NMR. The directions of these spins are random, but the application of the external magnetic field can align these nuclei in directions either parallel or antiparallel to the applied magnetic field. These two states of nuclei have different energy levels, a low-energy state and a high-energy state. The low-energy state attains a high-energy state upon irradiation with electromagnetic radiation, and free inductive decay (FID) is obtained as the nuclei undergo relaxation. Fourier transformation of FID results in a NMR spectrum with peaks from different types of nuclei in the molecule, which, in turn, is used to characterize the local covalent and spatial arrangement of atoms.

47In a protein, each nucleus of the individual residues experiences a different magnetic field depending on its microenvironment (referred to as the ‘shielding effect’ or ‘chemical shift’). The chemical shift of the peptide backbone (1H⍺, 13CO, 13C⍺, and 13Cβ) can be used to determine the secondary structure type of the given peptide segment. Amino acids in ordered proteins are packed in different kinds of chemical environments, as a result of which their NMR spectrum resembles a combination of spectra of various secondary structure elements. In contrast, the NMR spectra of disordered proteins with extensive conformational averaging appear as a summation of the random coil spectra of residues of proteins. In addition to the fine structural details, NMR also provides specific information at the residue level.

3.2.4. Small-Angle X-ray Scattering (SAXS)

48The SAXS technique can quickly define the structural characteristics of proteins of sizes ranging from a few kilo-Daltons to several giga-Daltons under various experimental setups. Briefly, this method involves exposing samples placed in quartz capillary tubes to a collimated monochromatic X-ray beam source and capturing scattered photons with a detector. Comparative analysis of the electron density distributions of the protein sample and pure solvent/buffer is then conducted to determine various parameters of the proteins in the solution, such as the molecular mass, volume, radius of gyration, folding state, etc.. Moreover, SAXS data can also be used to define protein flexibility and the intrinsically disordered state of proteins in solutions. The scattering profiles of the proteins obtained from SAXS experiments are most commonly represented as Kratky plots (s2I(s) as a function of s, where s and I represent the momentum transfer function and scattering intensity), which are used to obtain structural insights into the protein.

49In contrast to globular proteins’ bell-shaped Kratky’s plot with well-defined maxima, disordered protein-specific Kratky plots exhibit a plateau for a given range of the momentum transfer function (s), followed by a monotonic increase. Additionally, the experimentally determined radius of gyration (Rg) of IDPs from the SAXS curve can be directly compared with the theoretical or experimental Rg values of a globular and random coil for a given number of residues. The Rg values of IDPs lie between those of highly compact globular proteins (lowest Rg values) and completely disordered/unfolded proteins represented by random coils (highest Rg values). Altogether, this method offers fast structural characterization of proteins in solutions with a relatively easy sample preparation protocol and can capture data under near-native conditions. As the sensitivity of SAXS depends on the particle size, prior removal of the macromolecular aggregates during sample preparation using a method such as sedimentation or size-exclusion chromatography is suggested.

50Finally, it is worth mentioning that SAXS-based studies of IDPs can use valuable a priori complementary information from several other experimental and in silico protein structure determination methods. For instance, X-ray crystallography depicts the structured regions of a protein, while SAXS defines the protein segments with missing electron density. Similarly, NMR provides information about different domains/complex sub-units during analyses of bimolecular complexes and multi-domain proteins, and SAXS defines their relative inter-domain positions. Furthermore, other complementary techniques, such as CD, spectroscopy, chromatography, etc., and SAXS, can be used for the biophysical characterization of IDPs. A low-resolution protein structure defined through the ab initio modeling of SAXS data alone can be further refined using inputs from protein structure prediction tools, such as I-TASSER, CORAL, etc.. Recently, various protein structure determination/prediction techniques and SAXS have been used to characterize partially disordered mycobacterial ESX-secretion-associated protein K (EspK).

3.2.5. Cryo-Electron Microscopy (Cryo-EM)

51In the last five years, the research area involving the structural characterization of proteins and other biological entities has been revolutionized by the development of cryo-electron-microscopy-based techniques. These methods overcome the limitations of primary methods, i.e., X-ray crystallography and NMR, and allow the structural characterization of relatively large, structurally heterogeneous, flexible, and dynamic assemblies at sub-nanometer atomic resolution (below 4 Å). Typically, a cryo-EM workflow contains three main steps: (a) vitrification (rapid cooling without ice crystal formation) of specimens in an aqueous solution, (b) image acquisition at a low electron dose using electron microscopy, and (c) 3D model reconstruction and validation. Single-particle analysis (SPA) and sub-tomogram averaging (STA) models are most commonly used for the structural annotation of proteins. However, while the globular/ordered/structured regions of proteins can be structurally resolved using cryo-EM, the predicted intrinsically disordered regions in the proximity of flexible regions escape structural assignment. Therefore, similar to x-ray crystallography, a high degree of intrinsic disorder restricts the implementation of cryo-EM techniques. Alternatively, the structure and dynamics of IDPs/IDRs can be investigated by complementing higher-resolution NMR studies of IDRs with the modeling capabilities of cryo-EM. In conclusion, 3D cryo-EM maps in conjunction with high-resolution data from NMR can model IDPs under physiologically relevant conditions and provide insights into their functional behavior.

4. Computational Tools for Disorder Prediction

52The biased amino acid compositions and peculiar sequence characteristics of IDPs/IDRs have encouraged the development of various reliable computational tools for studying intrinsic protein disorders. As a result, disorder predictors have been grouped into three distinct classes based on the underlying concepts.

4.1. Propensity-Based Predictors

53In principle, a disorder predictor is classified as propensity-based if it depends on some essential physical or chemical characteristics of residues or on prior knowledge of the biological background of intrinsic protein disorder. Disorder-predicting tools, such as FoldIndex, NORSp, GlobPlot, CH plot, and PreLink belong to this category.

4.2. Machine Learning Algorithms (MLAs) Based Predictors

54This class of advanced predictors relies on algorithms trained on data sets of experimentally characterized disordered regions and can differentiate disorder and order encoding sequences. Currently, the experimentally characterized disordered proteins are publicly available on three databases: MobiDB (http://mobidb.bio.unipd.it/; accessed on 7 November 2022), IDEAL (https://ngdc.cncb.ac.cn/databasecommons/database/id/198; accessed on 7 November 2022), and DisProt (http://www.disprot.org/; accessed on 7 November 2022). PONDR, Spritz, DisEMBL, RONN, and DISOPRED are a few predictors that fit into this category.

55Recently, the field of protein structure prediction has been revolutionized by the development of the deep learning-based method AlphaFold. This software generates a per-residue confidence score (pLDDT) based on the protein’s amino acid sequence. The most recent version of this tool, i.e., Alphafold2, has been reported to achieve protein structure prediction accuracy competitive with that of experimental determination. However, this program gives a low confidence score (pLDDT < 50) for intrinsically unstructured or disordered proteins/regions, and the inconclusive predicted structure resembles a ribbon. In addition, this method does not anticipate the relative likelihood of diverse IDP conformations and the folding pathways followed by IDPs/IDRs attaining an ordered structure upon interaction with other biomolecules. At present, the AlphaFold Protein Structure Database is considered as the most complete and precise representation of the human proteome.

4.3. Inter-Residue Contact-Based Predictors

56Predictors based on the idea that IDPs/IDRs are disordered because they cannot make enough inter-residue contacts required to compensate for the loss of configurational entropy during folding are grouped together as inter-residue contact-based predictors. The above conclusions may be derived by either simple statistics involving contact numbers or through sophisticated techniques of determining the total stabilization energy of a protein. Computational tools, such as IUPred, FoldUnfold, and Ucon belong to this class.

57At present, there is no “best” disorder prediction computational tool. Therefore, to avoid the limitations of a given tool, prediction results from different disorder predictors relying on distinct principles should be combined to provide a consensus prediction, as implemented by meta-predictors (for example, PONDR-FIT). Alternatively, publicly available meta-servers (for example, MeDor and metaPRDOS can also be used for quick and simultaneous analysis of protein disorder using multiple predictors.

58In several recent articles, extensive comparisons of various computational disorder prediction methods’ performance and comprehensive online resources useful for studying IDPs/IDRs were provided.

5. Evolution of IDPs/IDRs

59The evolution of proteins involves changes in the form of insertions, deletions, or substitutions in their amino acid sequences. Over time, such changes can accumulate in the proteins, giving rise to taxonomic classes having substantial differences in their amino acid compositions. In general, the structure and function of proteins are well conserved, but several exceptions exist. Several previous studies suggested that, even if the protein sequence diverges extensively, the protein function is well-conserved. Hence, proteins are generally considered as the ‘chemical fingerprints’ of evolutionary history, as they manifest the underlying genetic changes as amino acid sequences.

60The evolution of intrinsic disorder exhibits a wavy pattern in which highly disordered primordial proteins with predominantly RNA-chaperone-like activities were slowly replaced with highly structured proteins. Later, because of its peculiar features regarding the regulation of complex cellular processes, protein disorder was reinvented at various succeeding evolutionary stages, resulting in the creation of more complex organisms from the last universal ancestor.

61Several mechanisms, such as de novo generation, horizontal gene transfer, and lateral gene transfer, can give rise to genes that encode IDPs. Approximately 14% of Pfam domains, predicted to be mostly disordered and shared by many protein families, appear to have originated from domain duplications and module exchange between genes. The high frequency of occurrence of tandemly repeated sequences in IDPs/IDRs suggests that the expansion of internal repeat regions (microsatellite and minisatellite coding regions) is another possible way by which the IDPs encoding genes arose. Looking at the exceptional functional variability conferred to IDPs/IDRs due to the genetic instability of repetitive elements, the mechanism of the extension of repeat elements appears as the frequent method of disorder spread during evolution and rapid genomic changes in adaptation. Furthermore, these IDPs/IDRs can also act as hot spots for mutations, leading to the loss of different functional modalities and thus resulting in various types of diseases, including cancer. Seera and Nagarajaram have recently shown that the disease-causing missense mutations within IDRs reduce the overall conformation heterogeneity of the IDRs as compared to their wild type counterparts, and the few ‘locked’ dominant conformations presumably limit their interaction with the cognate partners.

62Recent studies have shown that disordered protein segments are encoded by GC-enriched gene regions, which, in turn, directly correspond to the disorderedness of the encoded proteins. This GC enrichment is due to the prevalence of amino acids coded by GC-rich codons (G, A, R, and P) in the disordered regions of proteins. At the residue level, a relatively higher rate of evolutionary changes in the disordered regions of proteins was observed compared with that in the ordered/globular domains, as there were no structural constraints to maintaining a 3-D structure. However, in certain cases, structured domains and disordered regions of proteins have been observed to co-evolve at higher rates. Despite these rapid changes, the biological functions of the structured domains and disordered regions are always conserved. Hence, a deeper understanding of the conformation ensemble–function relationship will help to decipher the evolutionary trajectory of IDPs.

63Based on the conservation of sequences coding for protein disorder, disordered residues have been classified as constrained (both sequence and disorder are conserved) or flexible (only protein disorder is conserved). Together, constrained and flexible disorder residues are known as conserved disorder. On the other hand, if neither disorder nor the residues encoding it are conserved, such a disorder class is known as non-conserved disorder. This integrated structural and evolutionary approach has recently been used to define the determinants of the functional adaptability of the neutrophin family of proteins involved in neuronal development.

64Considering that the disordered regions in proteins have a distinct amino acid composition and evolutionary rate as compared with that of ordered regions, the substitution frequencies of residues in the disordered regions must also be distinct from those found in ordered regions. Thus, identifying the evolutionary and functional features of IDPs/IDRs has become a computational challenge, as most of the sequence analysis tools and parameter optimization procedures are aimed at ordered/structured regions of proteins. Recently, methods evaluating disordered proteins’ molecular features and sequence composition in a position-specific manner have been developed. These advancements have allowed researchers to pursue alignment-based evolutionary studies on IDPs/IDRs without aligning the residues discretely.

6. IDPs/IDRs in Diseases

65Like structured proteins, the expression, localization, and interactions of intrinsically disordered proteins (IDPs) are also highly coordinated and regulated. Multiple checkpoints at various stages of the expression of IDPs-specific genes (from transcript synthesis to protein degradation) ensure the availability of IDPs in appropriate quantities and for the desired duration, preventing any ectopic interactions. Several studies have shown the role of IDPs/IDRs in different human disorders, including diabetes, cancer, amyloidosis, neurodegenerative, and cardiovascular diseases. Some well-studied examples of IDPs associated with human disease are p53, Mdm2, PTEN, c-Myc, AF4, BRCA1, EWS, Bcl-2, c-Fos, HPV oncoproteins, etc.. Moreover, the deposition of ⍺-synuclein, tau, and amyloid-β proteins leads to Alzheimer’s disease, the accumulation of ⍺-synuclein results in Parkinson’s disease, and aggregates of PrPSC cause prion diseases. The expansion of CAG triplet repeats in disease genes, which introduces disorder, results in the family of polyQ diseases, such as Kennedy’s disease, Huntington’s disease, etc..

66In the last two decades, the role of IDPs in human diseases has been actively studied, giving rise to new mechanistic findings that have led to the formation of the D2 concept (‘Disorder to Disorders’). Several comprehensive reviews and thematic series articles have been published covering the significance of IDPs in diseases. For instance, Coskuner and Uversky described various hypotheses proposed to explain the molecular mechanisms of the pathogenesis of Alzheimer’s and Parkinson’s diseases and suggested the need for the development of new techniques through the integration of quantum and statistical mechanics, thermodynamics, bioinformatics, and machine learning approaches, which, in turn, may lead to the development of new experimental approaches. However, at present, there are several limitations and challenges associated with in silico studies of IDP-associated neurodegenerative disorders. Another study found that an NADH-stabilized 26S proteasomal complex could degrade IDPs efficiently. Therefore, the accumulation of disease-causing disordered proteins, such as tau, c-Fos, p53, etc., can be prevented by the selective degradation of IDPs in an ATP-independent manner. Moreover, the analysis of components of the ATP-dependent ubiquitin-proteasome degradation system (UPS) revealed the importance of the disorder content and MoRFs of the complex in neurodegenerative disorders and cancers. However, identifying key mutations, PTM sites, and functional motifs in the disordered regions, exploring the evolutionary history of IDPs involved in diseases, understanding the cooperative functioning of ordered and disordered domains, and dissecting the IDPs’ interactome are some of the many active research areas involving IDPs/IDRs and diseases.

7. IDPs/IDRs as Drug Targets

67With increasing evidence of their involvement in molecular functions complementing globular domains, essential biological processes, protein–nucleic acid interactions, protein–protein interactions, and diseases, IDRs/IDPs have emerged as one of the prime targets for drug discovery or repurposing. However, IDP characteristics, such as a lack of a sTable 3D structure, very high flexibility, conformational ensembles, susceptibility to proteolytic cleavage, protein aggregation, etc., limit the application of the most-established experimental assays and computational methods that would otherwise work for ordered/globular proteins. Therefore, IDP-specific drug screening/development is mainly a tradeoff between binding affinity/specificity and the alternation in the functioning of disordered proteins with other features, such as solubility, crowding, efflux, metabolism, etc., a potentially relevant role.

68Broadly, disordered proteins/regions have been used in drug development procedures by targeting their conformational changes, interactions, and self-aggregating behavior. For example, the inhibitor 10058F4 of Myc proto-oncogene protein (MYC) binds to MYC and prevents conformational disorder-to-order transition, which, in turn, blocks MYC-MAX complex-driven tumorigenesis. Similarly, Methyl-CpG-binding domain protein 2 (MBD2) inhibitors restrict the folding of MBD2 upon binding to its partner p66α. This MBD2-p66α is known to regulate the Mi-2/NuRD chromatin remodeling complex involved in promoting metastasis in various cancer cells through epithelial–mesenchymal transition (EMT). In contrast to ordered proteins, the protein–protein interactions involving IDPs offer uneven, shorter, compact, and more mimicable surfaces for the tighter binding of small drug molecules. In recent times, potential drug molecules have been designed to target either the disordered segment or the binding region of the interacting molecule. For instance, nutlins binding to Mdm2 prevent the interaction of Mdm2 with the disordered regions of p53, which activates the p53 pathway, leading to apoptosis, cell-cycle arrest, and the inhibition of the uncontrolled cell growth of human tumor xenografts. Additionally, an FDA-approved compound, trifluoperazine dihydrochloride, was found to bind to a disordered region of multifunctional protein nuclear protein 1 (NUPR1) and arrest pancreatic ductal adenocarcinoma (PDAC) development. Moreover, the disordered proteins from pathogens can also be targeted to interrupt their interaction with host proteins, which they utilize for their survival and pathogenesis. In a recent review, Santofimia et al. comprehensively described targeting IDPs in various protein–protein and protein–nucleic acid interactions involved in cancer. Furthermore, compounds, such as curcumin, rosmarinic acid, ferulic acid, and safranal, have also been reported to prevent the aggregation of α-synuclein protein by binding to monomers, thus inhibiting the polymerization of these proteins, which results in various neuronal malignancies. In summary, deciphering the sequence–ensemble–function relationship of IDPs/IDRs and the development of efficient computational modeling approaches will help to unravel the enormous potential of disordered proteins as drug targets.

8. Conclusions

69The spread and versatility of proteins’ functions carried by intrinsically disordered regions within them are phenomenal. The overview of the protein structure–function relationship presented in this review with a focus on various aspects of intrinsically disordered proteins/regions can be very helpful in understanding the fundamentals of biologically active structureless proteins. It also offers a novel perspective for characterizing proteins with unknown functions.

70Over the past two decades, the research field of protein disorder has witnessed a revolution with respect to the understanding of various aspects of IDPs/IDRs such as sequence, abundance, structure, function, regulation, evolution, etc. However, there is still a requirement for new theoretical, experimental, and computational models specific to intrinsically disordered regions in proteins that can explain both the diversifying behavior of IDPs and the unifying principle of protein structure–function relationships.

References 361

1.
Babu M.M., Kriwacki R.W., Pappu R.V. Structural biology. Versatility from protein disorder. Science. 2012;337:1460-1461.
Not in our library10.1126/science.1228775
2.
Van der Lee R., Buljan M., Lang B., Weatheritt R.J., Daughdrill G.W., Dunker A.K., Fuxreiter M., Gough J., Gsponer J., Jones D.T., et al. Classification of intrinsically disordered regions and proteins. Chem. Rev. 2014;114:6589-6631.
Not in our library10.1021/cr400525m
3.
Luo Y., Ye S., Li X., Lu W. Emerging Structure-Function Paradigm of Endocrine FGFs in Metabolic Diseases. Trends Pharmacol. Sci. 2019;40:142-153.
Not in our library10.1016/j.tips.2018.12.002
4.
Lin D.H., Hoelz A. The Structure of the Nuclear Pore Complex (An Update). Annu. Rev. Biochem. 2019;88:725-783.
Not in our library10.1146/annurev-biochem-062917-011901
5.
Uversky V.N. Protein intrinsic disorder and structure-function continuum. Prog. Mol. Biol. Transl. Sci. 2019;166:1-17.
Not in our library10.1016/bs.pmbts.2019.05.003
6.
Mak H., Thurston T.L.M. Interesting Biochemistries in the Structure and Function of Bacterial Effectors. Front. Cell Infect. Microbiol. 2021;11:608860.
Not in our library10.3389/fcimb.2021.608860
7.
Pinet L., Assrir N., van Heijenoort C. Expanding the Disorder-Function Paradigm in the C-Terminal Tails of Erbbs. Biomolecules. 2021;11:1690.
Not in our library10.3390/biom11111690
8.
Ghag G., Holler C.J., Taylor G., Kukar T.L., Uversky V.N., Rangachari V. Disulfide bonds and disorder in granulin-3: An unusual handshake between structural stability and plasticity. Protein Sci. 2017;9:1759-1772.
Not in our library10.1002/pro.3212
9.
Davey N.E. The functional importance of structure in unstructured protein regions. Curr. Opin. Struct. Biol. 2019;56:155-163.
Not in our library10.1016/j.sbi.2019.03.009
10.
Bondos S.E., Dunker A.K., Uversky V.N. On the roles of intrinsically disordered proteins and regions in cell communication and signaling. Cell Commun. Signal. 2021;19:88.
Not in our library10.1186/s12964-021-00774-3
11.
Dunker A.K., Babu M.M., Barbar E., Blackledge M., Bondos S.E., Dosztányi Z., Dyson H.J., Forman-Kay J., Fuxreiter M., Gsponer J., et al. What’s in a name? Why these proteins are intrinsically disordered. Intrinsically Disord. Proteins. 2013;1:e24157.
Not in our library10.4161/idp.24157
12.
Bickers S.C., Sayewich J.S., Kanelis V. Intrinsically disordered regions regulate the activities of ATP binding cassette transporters. Biochim. Biophys. Acta Biomembr. 2020;1862:183202.
Not in our library10.1016/j.bbamem.2020.183202
13.
Brodsky S., Jana T., Mittelman K., Chapal M., Kumar D.K., Carmi M., Barkai N. Intrinsically Disordered Regions Direct Transcription Factor In Vivo Binding Specificity. Mol. Cell. 2020;79:459-471.
Not in our library10.1016/j.molcel.2020.05.032
14.
Jamecna D., Antonny B. Intrinsically disordered protein regions at membrane contact sites. Biochim. Biophys. Acta Mol. Cell Biol. Lipids. 2021;1866:159020.
Not in our library10.1016/j.bbalip.2021.159020
15.
Ahrens J.B., Nunez-Castilla J., Siltberg-Liberles J. Evolution of intrinsic disorder in eukaryotic proteins. Cell. Mol. Life Sci. 2017;74:3163-3174.
Not in our library10.1007/s00018-017-2559-0
16.
Intrinsically Disordered Regions Can Contribute Promiscuous Interactions to RNP Granule Assembly · 2018
Not checked
17.
Macossay-Castillo M., Marvelli G., Guharoy M., Jain A., Kihara D., Tompa P., Wodak S.J. The Balancing Act of Intrinsically Disordered Proteins: Enabling Functional Diversity while Minimizing Promiscuity. J. Mol. Biol. 2019;431:1650-1670.
Not in our library10.1016/j.jmb.2019.03.008
18.
Chakrabarti P., Chakravarty D. Intrinsically disordered proteins/regions and insight into their biomolecular interactions. Biophysical Chemistry. 2022;283:106769.
Not in our library10.1016/j.bpc.2022.106769
19.
Citing a Data Repository: A Case Study of the Protein Data Bank. · 2015
Not checked
20.
Fischer E. Einfiuss der Configurationa Uf die Wirkung der Enzyme. Ber. Dtsch. Chem. Ges. 1894;11:3479-3483.
Not in our library10.1002/cber.189402703169
21.
Borgia A., Borgia M., Bugge K., Kissling V.M., Heidarsson P.O., Fernandes C.B., Sottini A., Soranno A., Buholzer K.J., Nettels D., et al. Extreme disorder in an ultrahigh-affinity protein complex. Nature. 2018;555:61-66.
Not in our library10.1038/nature25762
22.
Koshland D. Application of a theory of enzyme specificity to protein synthesis. Proc. Natl. Acad. Sci. USA. 1958;44:98-104.
Not in our library10.1073/pnas.44.2.98
23.
Mirzaie M. Identification of native protein structures captured by principal interactions. BMC Bioinform. 2019;20:604.
Not in our library10.1186/s12859-019-3186-6
24.
The Pfam protein families database in 2019 · 2018
No notice
25.
Lermyte F. Roles, Characteristics, and Analysis of Intrinsically Disordered Proteins: A Minireview. Life. 2020;10:320.
Not in our library10.3390/life10120320
26.
Yang J., Zeng Y., Liu Y., Gao M., Liu S., Su Z., Huang Y. Electrostatic interactions in molecular recognition of intrinsically disordered proteins. J. Biomol. Struct. Dyn. 2020;38:4883-4894.
Not in our library10.1080/07391102.2019.1692073
27.
Wright P.E., Dyson H.J. Intrinsically unstructured proteins: Re-assessing the protein structure-function paradigm. J. Mol. Biol. 1999;293:321-331.
Not in our library10.1006/jmbi.1999.3110
28.
Dunker A.K., Lawson J.D., Brown C.J., Williams R.M., Romero P., Oh J.S., Oldfield C.J., Campen A.M., Ratliff C.M., Hipps K.W., et al. Intrinsically disordered protein. J. Mol. Graph. Model. 2001;19:26-59.
Not in our library10.1016/s1093-3263(00)00138-8
29.
Tompa P. Intrinsically unstructured proteins. Trends Biochem. Sci. 2002;27:527-533.
Not in our library10.1016/s0968-0004(02)02169-2
30.
Uversky V., Gillespie J., Fink A. Why Are “Natively Unfolded” Proteins Unstructured Under Physiologic Conditions? Proteins Struct. Funct. Genet. 2000;41:415-427.
Not in our library10.1002/1097-0134(20001115)41:3<415::aid-prot130>3.0.co;2-7
31.
Wallin S. Intrinsically disordered proteins: Structural and functional dynamics. Res. Rep. Biol. 2017;8:7-16.
Not in our library10.2147/rrb.s57282
32.
Dunker A.K., Garner E., Guilliot S., Romero P., Albrecht K., Hart J., Obradovic Z., Kissinger G., Villafranca J.E. Protein disorder and the evolution of molecular recognition: Theory, predictions and observations. Pac. Symp. Biocomput. 1998;3:473-484.
Not in our library
33.
Uversky V.N., Dunker A.K. Understanding protein non-folding. Biochim. Biophys. Acta (BBA) Proteins Proteom. 2010;1804:1231-1264.
Not in our library10.1016/j.bbapap.2010.01.017
34.
Uversky V.N. Intrinsically disordered proteins from A to Z. Int. J. Biochem. Cell Biol. 2011;43:1090-1103.
Not in our library10.1016/j.biocel.2011.04.001
35.
The Mysterious Unfoldome: Structureless, Underappreciated, Yet Vital Part of Any Given Proteome · 2010
No notice
36.
Ward J.J., Sodhi J.S., McGuffin L.J., Buxton B.F., Jones D.T. Prediction and Functional Analysis of Native Disorder in Proteins from the Three Kingdoms of Life. J. Mol. Biol. 2004;337:635-645.
Not in our library10.1016/j.jmb.2004.02.002
37.
Dunker A.K., Obradovic Z., Romero P., Garner E.C. Intrinsic Protein Disorder in Complete Genomes. Genome Inform. 2000;11:161-171.
Not in our library
38.
Hu G., Wang K., Song J., Uversky V.N., Kurgan L. Taxonomic Landscape of the Dark Proteomes: Whole-Proteome Scale Interplay Between Structural Darkness, Intrinsic Disorder, and Crystallization Propensity. Proteomics. 2018;18:e1800243.
Not in our library10.1002/pmic.201800243
39.
Kumar N., Kaushik R., Tennakoon C., Uversky V.N., Longhi S., Zhang K.Y.J., Bhatia S. Comprehensive Intrinsic Disorder Analysis of 6108 Viral Proteomes: From the Extent of Intrinsic Disorder Penetrance to Functional Annotation of Disordered Viral Proteins. J. Proteome Res. 2021;20:2704-2713.
Not in our library10.1021/acs.jproteome.1c00011
40.
Monzon A.M., Necci M., Quaglia F., Walsh I., Zanotti G., Piovesan D., Tosatto S.C.E. Experimentally Determined Long Intrinsically Disordered Protein Regions Are Now Abundant in the Protein Data Bank. Int. J. Mol. Sci. 2020;21:4496.
Not in our library10.3390/ijms21124496
41.
Tompa P., Dosztanyi Z., Simon I. Prevalent Structural Disorder in E. coli and S. cerevisiae Proteomes. J. Proteome Res. 2006;5:1996-2000.
Not in our library10.1021/pr0600881
42.
Dunker A.K., Brown C.J., Lawson J.D., Iakoucheva L.M., Obradovic Z. Intrinsic Disorder and Protein Function. Biochemistry. 2002;41:6573-6582.
Not in our library10.1021/bi012159+
43.
Dunker A.K., Cortese M.S., Romero P., Iakoucheva L.M., Uversky V.N. Flexible nets. The roles of intrinsic disorder in protein interaction networks. FEBS J. 2005;272:5129-5148.
Not in our library10.1111/j.1742-4658.2005.04948.x
44.
Dyson H.J., Wright P.E. Intrinsically unstructured proteins and their functions. Nat. Rev. Mol. Cell Biol. 2005;6:197-208.
Not in our library10.1038/nrm1589
45.
Uversky V. What does it mean to be natively unfolded? Eur. J. Biochem. 2002;269:2-12.
Not in our library10.1046/j.0014-2956.2001.02649.x
46.
Uversky V.N. Natively unfolded proteins: A point where biology waits for physics. Protein Sci. 2002;11:739-756.
Not in our library10.1110/ps.4210102
47.
Dyson H.J., Wright P.E. Coupling of folding and binding for unstructured proteins. Curr. Opin. Struct. Biol. 2002;12:54-60.
Not in our library10.1016/s0959-440x(02)00289-0
48.
Radivojac P., Iakoucheva L.M., Oldfield C.J., Obradovic Z., Uversky V.N., Dunker A.K. Intrinsic Disorder and Functional Proteomics. Biophys. J. 2007;92:1439-1456.
Not in our library10.1529/biophysj.106.094045
49.
Romero P., Obradovic Z., Li X., Garner E., Brown C., Dunker A.K. Sequence complexity of disordered protein. Proteins Struct. Funct. Genet. 2001;42:38-48.
Not in our library10.1002/1097-0134(20010101)42:1<38::aid-prot50>3.0.co;2-3
50.
Williams R., Obradovi Z., Mathura V., Braun W., Garner E., Young J., Takayama S., Brown C.J., Dunker A.K. The protein non-folding problem: Amino acid determinants of intrinsic order and disorder. Pac. Symp. Biocomput. 2001;6:89-100.
Not in our library10.1142/9789814447362_0010
51.
Holt C., Raynes J.K., Carver J.A. Sequence characteristics responsible for protein-protein interactions in the intrinsically disordered regions of caseins, amelogenins, and small heat-shock proteins. Biopolymers. 2019;110:e23319.
Not in our library10.1002/bip.23319
52.
Kastano K., Mier P., Dosztanyi Z., Promponas V.J., Andrade-Navarro M.A. Functional Tuning of Intrinsically Disordered Regions in Human Proteins by Composition Bias. Biomolecules. 2022;12:1486.
Not in our library10.3390/biom12101486
53.
Trivedi R., Nagarajaram H.A. Amino acid substitution scoring matrices specific to intrinsically disordered regions in proteins. Sci Rep. 2019;9:16380.
Not in our library10.1038/s41598-019-52532-8
54.
Trivedi R., Nagarajaram H.A. Substitution scoring matrices for proteins—An overview. Protein Sci. 2020;29:2150-2163.
Not in our library10.1002/pro.3954
55.
Jarnot P., Ziemska-Legiecka J., Grynberg M., Gruca A. Insights from analyses of low complexity regions with canonical methods for protein sequence comparison. Brief Bioinform. 2022;23:bbac299.
Not in our library10.1093/bib/bbac299
56.
McFadden W.M., Yanowitz J.L. idpr: A package for profiling and analyzing Intrinsically Disordered Proteins in R. PLoS ONE. 2022;17:e0266929.
Not in our library10.1371/journal.pone.0266929
57.
Tompa P. Structure and Function of Intrinsically Disordered Proteins. 2010.
Not in our library
58.
Uversky V.N., Oldfield C.J., Dunker A.K. Showing your ID: Intrinsic disorder as an ID for recognition, regulation and cell signaling. J. Mol. Recognit. 2005;18:343-384.
Not in our library10.1002/jmr.747
59.
Uversky V. Intrinsic Disorder-based Protein Interactions and their Modulators. Curr. Pharm. Des. 2013;19:4191-4213.
Not in our library10.2174/1381612811319230005
60.
Uversky V.N. The most important thing is the tail: Multitudinous functionalities of intrinsically disordered protein termini. FEBS Lett. 2013;587:1891-1901.
Not in our library10.1016/j.febslet.2013.04.042
61.
Uversky V.N. Unusual biophysics of intrinsically disordered proteins. Biochim. Biophys. Acta (BBA) Proteins Proteom. 2013;1834:932-951.
Not in our library10.1016/j.bbapap.2012.12.008
62.
Morffy N., Strader L.C. Structural Aspects of Auxin Signaling. Cold Spring Harb. Perspect. Biol. 2022;14:a039883.
Not in our library10.1101/cshperspect.a039883
63.
Crick S.L., Jayaraman M., Frieden C., Wetzel R., Pappu R.V. Fluorescence correlation spectroscopy shows that monomeric poly- glutamine molecules form collapsed structures in aqueous solutions. Proc. Natl. Acad. Sci. USA. 2006;103:16764-16769.
Not in our library10.1073/pnas.0608175103
64.
Walters R.H., Murphy R.M. Examining Polyglutamine Peptide Length: A Connection between Collapsed Conformations and Increased Aggregation. J. Mol. Biol. 2009;393:978-992.
Not in our library10.1016/j.jmb.2009.08.034
65.
Mukhopadhyay S., Krishnan R., Lemke E.A., Lindquist S., Deniz A.A. A natively unfolded yeast prion monomer adopts an ensemble of collapsed and rapidly fluctuating structures. Proc. Natl. Acad. Sci. USA. 2007;104:2649-2654.
Not in our library10.1073/pnas.0611503104
66.
Wang X., Vitalis A., Wyczalkowski M.A., Pappu R.V. Characterizing the conformational ensemble of monomeric polyglutamine. Proteins Struct. Funct. Bioinform. 2006;63:297-311.
Not in our library10.1002/prot.20761
67.
Mukhopadhyay S. The Dynamism of Intrinsically Disordered Proteins: Binding-Induced Folding, Amyloid Formation, and Phase Separation. J. Phys. Chem. B. 2020;124:11541-11560.
Not in our library10.1021/acs.jpcb.0c07598
68.
Dunker A.K., Uversky V.N. Drugs for ‘protein clouds’: Targeting intrinsically disordered transcription factors. Curr. Opin. Pharmacol. 2010;10:782-788.
Not in our library10.1016/j.coph.2010.09.005
69.
Choy W.Y., Forman-Kay J.D. Calculation of ensembles of structures representing the unfolded state of an SH3 domain. J. Mol. Biol. 2001;308:1011-1032.
Not in our library10.1006/jmbi.2001.4750
70.
Strodel B. Energy Landscapes of Protein Aggregation and Conformation Switching in Intrinsically Disordered Proteins. J. Mol. Biol. 2021;433:167182.
Not in our library10.1016/j.jmb.2021.167182
71.
Dunker A.K., Obradovic Z. The protein trinity—Linking function and disorder. Nat. Biotechnol. 2001;19:805-806.
Not in our library10.1038/nbt0901-805
72.
Dunker A.K., Brown C.J., Obradovic Z. Identification and Functions of Usefully Disordered Proteins. Adv. Protein Chem. 2002;62:25-49.
Not in our library10.1016/s0065-3233(02)62004-2
73.
Toto A., Malagrino F., Visconti L., Troilo F., Pagano L., Brunori M., Jemth P., Gianni S. Templated folding of intrinsically disordered proteins. J. Biol. Chem. 2020;295:6586-6593.
Not in our library10.1074/jbc.rev120.012413
74.
Malagrino F., Visconti L., Pagano L., Toto A., Troilo F., Gianni S. Understanding the Binding Induced Folding of Intrinsically Disordered Proteins by Protein Engineering: Caveats and Pitfalls. Int. J. Mol. Sci. 2020;21:3484.
Not in our library10.3390/ijms21103484
75.
Malagrino F., Diop A., Pagano L., Nardella C., Toto A., Gianni S. Unveiling induced folding of intrinsically disordered proteins—Protein engineering, frustration and emerging themes. Curr. Opin. Struct. Biol. 2022;72:153-160.
Not in our library10.1016/j.sbi.2021.11.004
76.
Kriwacki R.W., Hengst L., Tennant L., Reed S.I., Wright P.E. Structural studies of p2lWa1CiPl/Sdil in the free and Cdk2-bound state: Conformational disorder mediates binding diversity. Proc. Natl. Acad. Sci. USA. 1996;93:11504-11509.
Not in our library10.1073/pnas.93.21.11504
77.
Uversky V.N. Analyzing IDPs in Interactomes. Methods Mol Biol. 2020;2141:895-945.
Not in our library10.1007/978-1-0716-0524-0_46
78.
Fuxreiter M., Simon I., Friedrich P., Tompa P. Preformed Structural Elements Feature in Partner Recognition by Intrinsically Unstructured Proteins. J. Mol. Biol. 2004;338:1015-1026.
Not in our library10.1016/j.jmb.2004.03.017
79.
Choi U.B., Sanabria H., Smirnova T., Bowen M.E., Weninger K.R. Spontaneous Switching among Conformational Ensembles in Intrinsically Disordered Proteins. Biomolecules. 2019;9:114.
Not in our library10.3390/biom9030114
80.
Follis A.V., Llambi F., Kalkavan H., Yao Y., Phillips A.H., Park C.G., Marassi F.M., Green D.R., Kriwacki R.W. Regulation of apoptosis by an intrinsically disordered region of Bcl-xL. Nat. Chem. Biol. 2018;14:458-465.
Not in our library10.1038/s41589-018-0011-x
81.
Miao Y., Tipakornsaowapak T., Zheng L., Mu Y., Lewellyn E. Phospho-regulation of intrinsically disordered proteins for actin assembly and endocytosis. FEBS J. 2018;285:2762-2784.
Not in our library10.1111/febs.14493
82.
Csizmok V., Forman-Kay J.D. Complex regulatory mechanisms mediated by the interplay of multiple post-translational modifications. Curr. Opin. Struct. Biol. 2018;48:58-67.
Not in our library10.1016/j.sbi.2017.10.013
83.
The structural context of posttranslational modifications at a proteome-wide scale · 2022
No notice
84.
Ye H., Han Y., Li P., Su Z., Huang Y. The Role of Post-Translational Modifications on the Structure and Function of Tau Protein. J. Mol. Neurosci. 2022;72:1557-1571.
Not in our library10.1007/s12031-022-02002-0
85.
Jeffery C.J. Moonlighting proteins. Trends Biochem. Sci. 1999;24:8-11.
Not in our library10.1016/s0968-0004(98)01335-8
86.
Jeffery C.J. Molecular mechanisms for multitasking: Recent crystal structures of moonlighting proteins. Curr. Opin. Struct. Biol. 2004;14:663-668.
Not in our library10.1016/j.sbi.2004.10.001
87.
Meng F., Kurgan L. High-throughput prediction of disordered moonlighting regions in protein sequences. Proteins. 2018;86:1097-1110.
Not in our library10.1002/prot.25590
88.
RNA–protein interactions: disorder, moonlighting and junk contribute to eukaryotic complexity · 2019
No notice
89.
Rafiee M.R., Zagalak J.A., Sidorov S., Steinhauser S., Davey K., Ule J., Luscombe N.M. Chromatin-contact atlas reveals disorder-mediated protein interactions and moonlighting chromatin-associated RBPs. Nucleic Acids Res. 2021;49:13092-13107.
Not in our library10.1093/nar/gkab1180
90.
Gsponer J., Babu M.M. The rules of disorder or why disorder rules. Prog. Biophys. Mol. Biol. 2009;99:94-103.
Not in our library10.1016/j.pbiomolbio.2009.03.001
91.
Tompa P. The interplay between structure and function in intrinsically unstructured proteins. FEBS Lett. 2005;579:3346-3354.
Not in our library10.1016/j.febslet.2005.03.072
92.
Sickmeier M., Hamilton J.A., LeGall T., Vacic V., Cortese M.S., Tantos A., Szabo B., Tompa P., Chen J., Uversky V.N., et al. DisProt: The Database of Disordered Proteins. Nucleic Acids Res. 2007;35:D786-D793.
Not in our library10.1093/nar/gkl893
93.
Piovesan D., Tosatto S.C.E. Mobi 2.0: An improved method to define intrinsic disorder, mobility and linear binding regions in protein structures. Bioinformatics. 2018;34:122-123.
Not in our library10.1093/bioinformatics/btx592
94.
Sequence Versus Composition: What Prescribes IDP Biophysical Properties? · 2019
Not checked
95.
Thomasen F.E., Lindorff-Larsen K. Conformational ensembles of intrinsically disordered proteins and flexible multidomain proteins. Biochem. Soc. Trans. 2022;50:541-554.
Not in our library10.1042/bst20210499
96.
Weber B., Hora M., Kazman P., Gobl C., Camilloni C., Reif B., Buchner J. The antibody light-chain linker regulates domain orientation and amyloidogenicity. J. Mol. Biol. 2018;430:4925-4940.
Not in our library10.1016/j.jmb.2018.10.024
97.
Murrali M.G., Felli I.C., Pierattelli R. Adenoviral E1A Exploits Flexibility and Disorder to Target Cellular Proteins. Biomolecules. 2020;10:1541.
Not in our library10.3390/biom10111541
98.
Daughdrill G.W., Narayanaswami P., Gilmore S.H., Belczyk A., Brown C.J. Dynamic behavior of an intrinsically unstructured linker domain is conserved in the face of negligible amino acid sequence conservation. J. Mol. Evol. 2007;65:277-288.
Not in our library10.1007/s00239-007-9011-2
99.
Cunningham C.C., Leclerc N., Flanagan L.A., Lu M., Janmey P.A., Kosik K.S. Microtubule-associated protein 2c reorganizes both microtubules and microfilaments into distinct cytological structures in an actin-binding protein-280-deficient melanoma cell line. J. Cell Biol. 1997;136:845-857.
Not in our library10.1083/jcb.136.4.845
100.
Fahmi M., Ito M. Evolutionary Approach of Intrinsically Disordered CIP/KIP Proteins. Sci. Rep. 2019;9:1575.
Not in our library10.1038/s41598-018-37917-5
101.
Szabo B., Horvath T., Schad E., Murvai N., Tantos A., Kalmar L., Chemes L.B., Han K.H., Tompa P. Intrinsically Disordered Linkers Impart Processivity on Enzymes by Spatial Confinement of Binding Domains. Int. J. Mol. Sci. 2019;20:2119.
Not in our library10.3390/ijms20092119
102.
Cornish J., Chamberlain S.G., Owen D., Mott H.R. Intrinsically disordered proteins and membranes: A marriage of convenience for cell signalling? Biochem. Soc. Trans. 2020;48:2669-2689.
Not in our library10.1042/bst20200467
103.
A model for hydrophobic protrusions on peripheral membrane proteins. · 2018
Not checked
104.
Deryusheva E., Nemashkalova E., Galloux M., Richard C.A., Eleouet J.F., Kovacs D., Van Belle K., Tompa P., Uversky V., Permyakov S. Does Intrinsic Disorder in Proteins Favor Their Interaction with Lipids? Proteomics. 2019;19:e1800098.
Not in our library10.1002/pmic.201800098
105.
Diella F., Haslam N., Chica C., Budd A., Michael S., Brown N.P., Trave G., Gibson T.J. Understanding eukaryotic linear motifs and their role in cell signaling and regulation. Front. Biosci. 2008;13:6580-6603.
Not in our library10.2741/3175
106.
Galea C.A., Wang Y., Sivakolundu S.G., Kriwacki R.W. Regulation of Cell Division by Intrinsically Unstructured Proteins: Intrinsic Flexibility, Modularity, and Signaling Conduits. Biochemistry. 2008;47:7598-7609.
Not in our library10.1021/bi8006803
107.
Hoermann B., Kohn M. Evolutionary crossroads of cell signaling: PP1 and PP2A substrate sites in intrinsically disordered regions. Biochem. Soc. Trans. 2021;49:1065-1074.
Not in our library10.1042/bst20200175
108.
Galea C.A., Pagala V.R., Obenauer J.C., Park C.G., Slaughter C.A., Kriwacki R.W. Proteomic studies of the intrinsically unstructured mammalian proteome. J Proteome Res. 2006;5:2839-2948.
Not in our library10.1021/pr060328c
109.
Bode A.M., Dong Z. Post-translational modification of p53 in tumorigenesis. Nat. Rev. Cancer. 2004;4:793-805.
Not in our library10.1038/nrc1455
110.
Kouzarides T. Chromatin modifications and their function. Cell. 2007;128:693-705.
Not in our library10.1016/j.cell.2007.02.005
111.
Jin F., Grater F. How multisite phosphorylation impacts the conformations of intrinsically disordered proteins. PLoS Comput. Biol. 2021;17:e1008939.
Not in our library10.1371/journal.pcbi.1008939
112.
Kumar R., Thompson E.B. Role of Phosphorylation in the Modulation of the Glucocorticoid Receptor’s Intrinsically Disordered Domain. Biomolecules. 2019;9:95.
Not in our library10.3390/biom9030095
113.
Lorton B.M., Shechter D. Cellular consequences of arginine methylation. Cell. Mol. Life Sci. 2019;76:2933-2956.
Not in our library10.1007/s00018-019-03140-2
114.
Schroeder R., Barta A., Semrad K. Strategies for RNA folding and assembly. Nat. Rev. Mol. Cell Biol. 2004;5:908-919.
Not in our library10.1038/nrm1497
115.
Young J.C., Agashe V.R., Siegers K., Hartl F.U. Pathways of chaperone-mediated protein folding in the cytosol. Nat. Rev. Mol. Cell Biol. 2004;5:781-791.
Not in our library10.1038/nrm1492
116.
Kovacs D., Tompa P. Diverse functional manifestations of intrinsic structural disorder in molecular chaperones. Biochem. Soc. Trans. 2012;40:963-968.
Not in our library10.1042/bst20120108
117.
Rogers J.M., Steward A., Clarke J. Folding and Binding of an Intrinsically Disordered Protein: Fast, but Not ‘Diffusion-Limited’. J. Am. Chem. Soc. 2013;135:1415-1422.
Not in our library10.1021/ja309527h
118.
Tompa P., Csermely P. The role of structural disorder in the function of RNA and protein chaperones. FASEB J. 2004;18:1169-1175.
Not in our library10.1096/fj.04-1584rev
119.
Lindner R.A., Kapur A., Mariani M., Titmuss S.J., Carver J.A. Structural alterations of alpha-crystallin during its chaperone action. Eur. J. Biochem. 1998;258:170-183.
Not in our library10.1046/j.1432-1327.1998.2580170.x
120.
Treweek T.M., Rekas A., Walker M.J., Carver J.A. A quantitative NMR spectroscopic examination of the flexibility of the C-terminal extensions of the molecular chaperones, αA- and αB-crystallin. Exp Eye Res. 2010;91:691-699.
Not in our library10.1016/j.exer.2010.08.015
121.
Holmstrom E.D., Liu Z., Nettels D., Best R.B., Schuler B. Disordered RNA chaperones can enhance nucleic acid folding via local charge screening. Nat. Commun. 2019;10:2453.
Not in our library10.1038/s41467-019-10356-0
122.
Lee H.M., Kwon S.B., Son A., Kim D.H., Kim K.H., Lim J., Kwon Y.G., Kang J.S., Lee B.K., Byun Y.H., et al. Stabilization of Intrinsically Disordered DKK2 Protein by Fusion to RNA-Binding Domain. Int. J. Mol. Sci. 2019;20:2847.
Not in our library10.3390/ijms20112847
123.
Yakubu U.M., Morano K.A. Suppression of aggregate and amyloid formation by a novel intrinsically disordered region in metazoan Hsp110 chaperones. J. Biol. Chem. 2021;296:100567.
Not in our library10.1016/j.jbc.2021.100567
124.
Liu A.Y., Minetti C.A., Remeta D.P., Breslauer K.J., Chen K.Y. HSF1, Aging, and Neurodegeneration. Adv. Exp. Med. Biol. 2022.
Not in our library10.1007/5584_2022_733
125.
Murvai N., Kalmar L., Szabo B., Schad E., Micsonai A., Kardos J., Buday L., Han K.H., Tompa P., Tantos A. Cellular Chaperone Function of Intrinsically Disordered Dehydrin ERD14. Int. J. Mol. Sci. 2021;22:6190.
Not in our library10.3390/ijms22126190
126.
Mitrea D.M., Kriwacki R.W. Regulated unfolding of proteins in signaling. FEBS Lett. 2013;587:1081-1088.
Not in our library10.1016/j.febslet.2013.02.024
127.
Bardwell J.C., Jakob U. Conditional disorder in chaperone action. Trends Biochem. Sci. 2012;37:517-525.
Not in our library10.1016/j.tibs.2012.08.006
128.
Tempra C., Scollo F., Pannuzzo M., Lolicato F., La Rosa C. A unifying framework for amyloid-mediated membrane damage: The lipid-chaperone hypothesis. Biochim. Biophys. Acta Proteins Proteom. 2022;1870:140767.
Not in our library10.1016/j.bbapap.2022.140767
129.
Sugase K., Dyson H.J., Wright P.E. Mechanism of coupled folding and binding of an intrinsically disordered protein. Nature. 2007;447:1021-1025.
Not in our library10.1038/nature05858
130.
Wright P.E., Dyson H.J. Linking folding and binding. Curr. Opin. Struct. Biol. 2009;19:31-38.
Not in our library10.1016/j.sbi.2008.12.003
131.
Gianni S., Jemth P. Affinity versus specificity in coupled binding and folding reactions. Protein Eng. Des. Sel. 2019;32:355-357.
Not in our library10.1093/protein/gzz020
132.
Zou R., Zhou Y., Wang Y., Kuang G., Ågren H., Wu J., Tu Y. Free Energy Profile and Kinetics of Coupled Folding and Binding of the Intrinsically Disordered Protein p53 with MDM2. J. Chem. Inf. Model. 2020;60:1551-1558.
Not in our library10.1021/acs.jcim.9b00920
133.
Morris O.M., Torpey J.H., Isaacson R.L. Intrinsically disordered proteins: Modes of binding with emphasis on disordered domains. Open Biol. 2021;11:210222.
Not in our library10.1098/rsob.210222
134.
Schuler B., Borgia A., Borgia M.B., Heidarsson P.O., Holmstrom E.D., Nettels D., Sottini A. Binding without folding—The biomolecular function of disordered polyelectrolyte complexes. Curr. Opin. Struct. Biol. 2020;60:66-76.
Not in our library10.1016/j.sbi.2019.12.006
135.
Dreier J.E., Prestel A., Martins J.M., Brøndum S.S., Nielsen O., Garbers A.E., Suga H., Boomsma W., Rogers J.M., Hartmann-Petersen R., et al. A context-dependent and disordered ubiquitin-binding motif. Cell. Mol. Life Sci. 2022;79:484.
Not in our library10.1007/s00018-022-04486-w
136.
Robustelli P., Piana S., Shaw D.E. Mechanism of Coupled Folding-upon-Binding of an Intrinsically Disordered Protein. J Am Chem Soc. 2020;142:11092-11101.
Not in our library10.1021/jacs.0c03217
137.
Kim A., Kakalis L., Abdul-Manan N., Liu G., Rosen M. Autoinhibition and activation mechanisms of the Wiskott-Aldrich syndrome protein. Nature. 2000;404:151-158.
Not in our library10.1038/35004513
138.
Berlow R.B., Dyson H.J., Wright P.E. Multivalency enables unidirectional switch-like competition between intrinsically disordered proteins. Proc. Natl. Acad. Sci. USA. 2022;119:e2117338119.
Not in our library10.1073/pnas.2117338119
139.
Ferreon A.C., Ferreon J.C., Wright P.E., Deniz A.A. Modulation of allostery by protein intrinsic disorder. Nature. 2013;498:390-394.
Not in our library10.1038/nature12294
140.
Radhakrishnan I., Perez-Alvarado G.C., Parker D., Dyson H.J., Montminy M.R., Wright P.E. Solution structure of the KIX domain of CBP bound to the transactivation domain of CREB: A model for activator: Coactivator interactions. Cell. 1997;91:741-752.
Not in our library10.1016/s0092-8674(00)80463-8
141.
De Guzman R.N., Goto N.K., Dyson H.J., Wright P.E. Structural basis for cooperative transcription factor binding to the CBP coactivator. J. Mol. Biol. 2006;355:1005-1013.
Not in our library10.1016/j.jmb.2005.09.059
142.
Berlow R.B., Dyson H.J., Wright P.E. Expanding the Paradigm: Intrinsically Disordered Proteins and Allosteric Regulation. J Mol. Biol. 2018;430:2309-2320.
Not in our library10.1016/j.jmb.2018.04.003
143.
Hilser V.J., Thompson E.B. Intrinsic disorder as a mechanism to optimize allosteric coupling in proteins. Proc. Natl. Acad. Sci. USA. 2007;104:8311-8315.
Not in our library10.1073/pnas.0700329104
144.
Flock T., Weatheritt R.J., Latysheva N.S., Babu M.M. Controlling entropy to tune the functions of intrinsically disordered regions. Curr. Opin. Struct. Biol. 2014;26:62-72.
Not in our library10.1016/j.sbi.2014.05.007
145.
Fuxreiter M., Tompa P., Simon I., Uversky V.N., Hansen J.C., Asturias F.J. Malleable machines take shape in eukaryotic transcriptional regulation. Nat. Chem. Biol. 2008;4:728-737.
Not in our library10.1038/nchembio.127
146.
Peng Z., Oldfield C.J., Xue B., Mizianty M.J., Dunker A.K., Kurgan L., Uversky V.N. A creature with a hundred waggly tails: Intrinsically disordered proteins in the ribosome. Cell. Mol. Life Sci. 2013;71:1477-1504.
Not in our library10.1007/s00018-013-1446-6
147.
Hegyi H., Schad E., Tompa P. Structural disorder promotes assembly of protein complexes. BMC Struct. Biol. 2007;7:65.
Not in our library10.1186/1472-6807-7-65
148.
Gunasekaran K., Tsai C.J., Kumar S., Zanuy D., Nussinov R. Extended disordered proteins: Targeting function with less scaffold. Trends Biochem. Sci. 2003;28:81-85.
Not in our library10.1016/s0968-0004(03)00003-3
149.
Cortese M.S., Uversky V.N., Dunker A.K. Intrinsic disorder in scaffold proteins: Getting more from less. Prog. Biophys. Mol. Biol. 2008;98:85-106.
Not in our library10.1016/j.pbiomolbio.2008.05.007
150.
Clerc I., Sagar A., Barducci A., Sibille N., Bernado P., Cortes J. The diversity of molecular interactions involving intrinsically disor dered proteins: A molecular modeling perspective. Comput. Struct. Biotechnol. J. 2021;19:3817-3828.
Not in our library10.1016/j.csbj.2021.06.031
151.
Shajani Z., Sykes M.T., Williamson J.R. Assembly of bacterial ribosomes. Annu. Rev. Biochem. 2011;80:501-526.
Not in our library10.1146/annurev-biochem-062608-160432
152.
Intrinsically disordered proteins play diverse roles in cell signaling · 2022
No notice
153.
Fung H.Y.J., Birol M., Rhoades E. IDPs in macromolecular complexes: The roles of multivalent interactions in diverse assemblies. Curr Opin Struct Biol. 2018;49:36-43.
Not in our library10.1016/j.sbi.2017.12.007
154.
Holt C. Unfolded phosphopolypeptides enable soft and hard tissues to coexist in the same organism with relative ease. Curr. Opin. Struct. Biol. 2013;23:420-425.
Not in our library10.1016/j.sbi.2013.02.010
155.
Vazquez-Hernandez M., Romero I., Sanchez-Ballesta M.T., Merodio C., Escribano M.I. Functional characterization of VviDHN2 and VviDHN4 dehydrin isoforms from Vitis vinifera (L.): An in silico and in vitro approach. Plant Physiol. Biochem. 2021;158:146-157.
Not in our library10.1016/j.plaphy.2020.12.003
156.
Elko E.A., Manuel A.M., White S., Zito E., van der Vliet A., Anathy V., Janssen-Heininger Y.M.W. Oxidation of peroxiredoxin-4 induces oligomerization and promotes interaction with proteins governing protein folding and endoplasmic reticulum stress. J. Biol. Chem. 2021;296:100665.
Not in our library10.1016/j.jbc.2021.100665
157.
Bidwell LM, McManus ME, Gaedigk A, Kakuta Y, Negishi M, Pedersen L, Martin JL Crystal structure of human catecholamine sulfotransferase. J. Mol. Biol. 1999;293:521-530.
Not in our library10.1006/jmbi.1999.3153
158.
Barney B.M., LoBrutto R., Francisco W.A. Characterization of a small metal binding protein from Nitrosomonas europaea. Biochemistry. 2004;43:11206-11213.
Not in our library10.1021/bi049318k
159.
Jobby M.K., Sharma Y. Calcium-binding crystallins from Yersinia pestis. Characterization of two single betagamma-crystallin domains of a putative exported protein. J. Biol. Chem. 2005;280:1209-1216.
Not in our library10.1074/jbc.m409253200
160.
Davey N.E., Van Roey K., Weatheritt R.J., Toedt G., Uyar B., Altenberg B., Budd A., Diella F., Dinkel H., Gibson T.J. Attributes of short linear motifs. Mol. BioSyst. 2012;8:268-281.
Not in our library10.1039/c1mb05231d
161.
Fuxreiter M., Tompa P., Simon I. Local structural disorder imparts plasticity on linear motifs. Bioinformatics. 2007;23:950-956.
Not in our library10.1093/bioinformatics/btm035
162.
Lee S., Kim D., Han J., Cha E., Lim J., Cho Y., Lee C., Han K. Understanding Pre-Structured Motifs (PreSMos) in Intrinsically Unfolded Proteins. Curr. Protein Pept. Sci. 2012;13:34-54.
Not in our library10.2174/138920312799277974
163.
Mohan A., Oldfield C.J., Radivojac P., Vacic V., Cortese M.S., Dunker A.K., Uversky V.N. Analysis of Molecular Recognition Features (MoRFs). J. Mol. Biol. 2006;362:1043-1059.
Not in our library10.1016/j.jmb.2006.07.087
164.
Oldfield C., Cheng Y., Cortese M., Romero P., Uversky V., Dunker A.K. Coupled Folding and Binding with R-Helix-Forming Molecular Recognition. Biochemistry. 2005;44:12454-12470.
Not in our library10.1021/bi050736e
165.
Hinds M.G., Smits C., Fredericks-Short R., Risk J.M., Bailey M., Huang D.C., Day C.L. Bim, Bad and Bmf: Intrinsically unstructured BH3-only proteins that undergo a localized conformational change upon binding to prosurvival Bcl-2 targets. Cell Death Differ. 2007;14:128-136.
Not in our library10.1038/sj.cdd.4401934
166.
Dinkel H., Michael S., Weatheritt R.J., Davey N.E., Van Roey K., Altenberg B., Toedt G., Uyar B., Seiler M., Budd A., et al. ELM—The database of eukaryotic linear motifs. Nucleic Acids Res. 2012;40:D242-D251.
Not in our library10.1093/nar/gkr1064
167.
Van Roey K., Uyar B., Weatheritt R.J., Dinkel H., Seiler M., Budd A., Gibson T.J., Davey N.E. Short Linear Motifs: Ubiquitous and Functionally Diverse Protein Interaction Modules Directing Cell Regulation. Chem. Rev. 2014;114:6733-6778.
Not in our library10.1021/cr400585q
168.
Dinkel H., Van Roey K., Michael S., Davey N.E., Weatheritt R.J., Born D., Speck T., Kruger D., Grebnev G., Kuban M., et al. The eukaryotic linear motif resource ELM: 10 years and counting. Nucleic Acids Res. 2014;42:D259-D266.
Not in our library10.1093/nar/gkt1047
169.
ELM 2016—data update and new functionality of the eukaryotic linear motif resource · 2015
No notice
170.
Tompa P., Davey N.E., Gibson T.J., Babu M.M. A million peptide motifs for the molecular biologist. Mol. Cell. 2014;55:161-169.
Not in our library10.1016/j.molcel.2014.05.032
171.
The eukaryotic linear motif resource – 2018 update · 2017
No notice
172.
Zhou X., Kops O., Werner A., Lu P., Shen M., Stoller G., Kullertz G., Stark M., Fischer G., Lu K. Pin1-Dependent Prolyl Isomerization Regulates Dephosphorylation of Cdc25C and Tau Proteins. Mol. Cell. 2000;6:873-883.
Not in our library10.1016/s1097-2765(05)00083-3
173.
Pop C., Salvesen G.S. Human Caspases: Activation, Specificity, and Regulation. J. Biol. Chem. 2009;284:21777-21781.
Not in our library10.1074/jbc.r800084200
174.
Kalderon D., Roberts B.L., Richardson W.D., Smith A.E. A Short Amino Acid Sequence Able to Specify Nuclear Location. Cell. 1984;39:499-509.
Not in our library10.1016/0092-8674(84)90457-4
175.
Schmid E.M., McMahon H.T. Integrating molecular and network biology to decode endocytosis. Nature. 2007;448:883-888.
Not in our library10.1038/nature06031
176.
Li J., McQuade T., Siemer A.B., Napetschnig J., Moriwaki K., Hsiao Y.S., Damko E., Moquin D., Walz T., McDermott A., et al. The RIP1/RIP3 necrosome forms a functional amyloid signaling complex required for programmed necrosis. Cell. 2012;150:339-350.
Not in our library10.1016/j.cell.2012.06.019
177.
Li P., Banjade S., Cheng H., Kim S., Chen B., Guo L., Llaguno M., Hollingsworth J., King D., Banani S., et al. Phase transitions in the assembly of multivalent signalling proteins. Nature. 2012;483:336-340.
Not in our library10.1038/nature10879
178.
Pfleger C., Kirschner M. The KEN box: An APC recognition signal distinct from the D box targeted by Cdh1. Genes Dev. 2000;14:655-665.
Not in our library10.1101/gad.14.6.655
179.
He J., Chao W.C., Zhang Z., Yang J., Cronin N., Barford D. Insights into Degron Recognition by APC/C Coactivators from the Structure of an Acm1-Cdh1 Complex. Mol. Cell. 2013;50:649-660.
Not in our library10.1016/j.molcel.2013.04.024
180.
Gagliardi P.A., Primo L. Irreversible Activation of Rho-activated Kinases Resulted from Evolution of Proteolytic Sites within Disordered Regions in Coiled-coil Domain. Mol. Biol. Evol. 2019;36:376-392.
Not in our library10.1093/molbev/msy229
181.
Brautigan D., Shenolikar S. Protein Serine/Threonine Phosphatases: Keys to Unlocking Regulators and Substrates. Annu. Rev. Biochem. 2018;87:921-964.
Not in our library10.1146/annurev-biochem-062917-012332
182.
Hossain M., Bhalla K., Stillman B. Multiple, short protein binding motifs in ORC1 and CDC6 control the initiation of DNA replication. Mol. Cell. 2021;81:1951-1969.
Not in our library10.1016/j.molcel.2021.03.003
183.
Faustova I., Loog M. SLiMs in intrinsically disordered protein regions regulate the cell cycle dynamics of ORC1-CDC6 interaction and pre-replicative complex assembly. Mol. Cell. 2021;81:1861-1862.
Not in our library10.1016/j.molcel.2021.04.016
184.
Gibson T.J. Cell regulation: Determined to signal discrete cooperation. Trends Biochem. Sci. 2009;34:471-482.
Not in our library10.1016/j.tibs.2009.06.007
185.
Van Roey K., Dinkel H., Weatheritt R.J., Gibson T.J., Davey N.E. The switches.ELM resource: A compendium of conditional regulatory interaction interfaces. Sci. Signal. 2013;6:rs7.
Not in our library10.1126/scisignal.2003345
186.
Zanzoni A., Ribeiro D.M., Brun C. Understanding protein multifunctionality: From short linear motifs to cellular functions. Cell Mol Life Sci. 2019;76:4407-4412.
Not in our library10.1007/s00018-019-03273-4
187.
The unfoldomics decade: an update on intrinsically disordered proteins · 2008
No notice
188.
Espinoza-Fonseca L.M. Reconciling binding mechanisms of intrinsically disordered proteins. Biochem. Biophys. Res. Commun. 2009;382:479-482.
Not in our library10.1016/j.bbrc.2009.02.151
189.
Das R.K., Crick S.L., Pappu R.V. N-Terminal Segments Modulate the α-Helical Propensities of the Intrinsically Disordered Basic Regions of bZIP Proteins. J. Mol. Biol. 2012;416:287-299.
Not in our library10.1016/j.jmb.2011.12.043
190.
Sharma R., Sharma A., Patil A., Tsunoda T. Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions. BMC Bioinform. 2019;19:378.
Not in our library10.1186/s12859-018-2396-7
191.
Yan J., Dunker A.K., Uversky V.N., Kurgan L. Molecular recognition features (MoRFs) in three domains of life. Mol. Biosyst. 2016;12:697-710.
Not in our library10.1039/c5mb00640f
192.
Chi S.W., Lee S.H., Kim D.H., Ahn M.J., Kim J.S., Woo J.Y., Torizawa T., Kainosho M., Han K.H. Structural Details on mdm2-p53 Interaction. J. Biol. Chem. 2005;280:38795-38802.
Not in our library10.1074/jbc.m508578200
193.
Borcherds W., Kashtanov S., Wu H., Daughdrill G.W. Structural divergence is more extensive than sequence divergence for a family of intrinsically disordered proteins. Proteins Struct. Funct. Bioinform. 2013;81:1686-1698.
Not in our library10.1002/prot.24303
194.
Katuwawala A., Peng Z., Yang J., Kurgan L. Computational Prediction of MoRFs, Short Disorder-to-order Transitioning Protein Binding Regions. Comput. Struct. Biotechnol. J. 2019;17:454-462.
Not in our library10.1016/j.csbj.2019.03.013
195.
Sun X., Malhis N., Zhao B., Xue B., Gsponer J., Rikkerink E.H.A. Computational Disorder Analysis in Ethylene Response Factors Uncovers Binding Motifs Critical to Their Diverse Functions. Int. J. Mol. Sci. 2019;21:74.
Not in our library10.3390/ijms21010074
196.
Tompa P., Fuxreiter M., Oldfield C.J., Simon I., Dunker A.K., Uversky V.N. Close encounters of the third kind: Disordered domains and the interactions of proteins. BioEssays. 2009;31:328-335.
Not in our library10.1002/bies.200800151
197.
Chen J.W., Romero P., Uversky V.N., Dunker A.K. Conservation of intrinsic disorder in protein domains and families: I. A database of conserved predicted disordered regions. J. Proteome Res. 2006;5:879-887.
Not in our library10.1021/pr060048x
198.
Chen J.W., Romero P., Uversky V.N., Dunker A.K. Conservation of Intrinsic Disorder in Protein Domains and Families: II. Functions of Conserved Disorder. J. Proteome Res. 2006;5:888-898.
Not in our library10.1021/pr060049p
199.
Zhou J., Oldfield C.J., Yan W., Shen B., Dunker A.K. Intrinsically disordered domains: Sequence ➔ disorder ➔ function relationships. Protein Sci. 2019;28:1652-1663.
Not in our library10.1002/pro.3680
200.
Buljan M., Frankish A., Bateman A. Quantifying the mechanisms of domain gain in animal proteins. Genome Biol. 2010;11:R74.
Not in our library10.1186/gb-2010-11-7-r74
201.
Russo A.A., Jeffrey P.D., Patten A.K., Massague J., Pavletich N.P. Crystal structure of the p27Kip1 cyclin-dependent-kinase inhibitor bound to the cyclin A-Cdk2 complex. Nature. 1996;382:325-331.
Not in our library10.1038/382325a0
202.
Galea C.A., Nourse A., Wang Y., Sivakolundu S.G., Heller W.T., Kriwacki R.W. Role of intrinsic flexibility in signal transduction mediated by the cell cycle regulator, p27 Kip1. J. Mol. Biol. 2008;376:827-838.
Not in our library10.1016/j.jmb.2007.12.016
203.
Pentony M.M., Jones D.T. Modularity of intrinsic disorder in the human proteome. Proteins Struct. Funct. Bioinform. 2010;78:212-221.
Not in our library10.1002/prot.22504
204.
Teraguchi S., Patil A., Standley D. Intrinsically disordered domains deviate significantly from random sequences in mammalian proteins. BMC Bioinform. 2010;11:S7.
Not in our library10.1186/1471-2105-11-s7-s7
205.
Manica G., Ghenea S., Munteanu C.V.A., Martin E.C., Butnaru C., Surleac M., Chiritoiu G.N., Alexandru P.R., Petrescu A.J., Petrescu S.M. EDEM3 Domains Cooperate to Perform Its Overall Cell Functioning. Int. J. Mol. Sci. 2021;22:2172.
Not in our library10.3390/ijms22042172
206.
Rautureau G.J., Day C.L., Hinds M.G. Intrinsically disordered proteins in bcl-2 regulated apoptosis. Int. J. Mol. Sci. 2010;11:1808-1824.
Not in our library10.3390/ijms11041808
207.
Kai M. Roles of RNA-Binding Proteins in DNA Damage Response. Int. J. Mol. Sci. 2016;17:310.
Not in our library10.3390/ijms17030310
208.
Blocquel D., Habchi J., Gruet A., Blangy S., Longhi S. Compaction and binding properties of the intrinsically disordered C-terminal domain of Henipavirus nucleoprotein as unveiled by deletion studies. Mol. BioSyst. 2012;8:392-410.
Not in our library10.1039/c1mb05401e
209.
Etoh Y., Simon M., Green H. Involucrin acts as transglutaminase substrate at multiple sites. Biochem. Biophys. Res. Commun. 1986;136:51-60.
Not in our library10.1016/0006-291x(86)90875-2
210.
Lynch W., Riseman V., Bretscher A. Smooth Muscle CaldesmonIs an Extended Flexible Monomeric Protein in Solution That Can Readily Undergo Reversible Intra- and Intermolecular Sulfhydryl Cross-linking. J. Biol. Chem. 1987;262:7429-7437.
Not in our library10.1016/s0021-9258(18)48255-5
211.
Weinreb P., Zhen W., Poon A., Conway K., Lansbury P.J. NACP, a protein implicated in Alzheimer’s disease and learning, is natively unfolded. Biochemistry. 1996;35:13709-13715.
Not in our library10.1021/bi961799n
212.
Huber R. Conformational flexibility in protein molecules. Nature. 1979;280:538-539.
Not in our library10.1038/280538a0
213.
Ohgushi M., Wada A. ‘Molten-globule state’: A compact form of globular proteins with mobile side chains. FEBS Lett. 1983;164:21-24.
Not in our library10.1016/0014-5793(83)80010-6
214.
Kuwajima K. A Folding Model of a-Lactalbumin Deduced from the Three-state Denaturation Mechanism. J. Mol. Biol. 1977;114:241-258.
Not in our library10.1016/0022-2836(77)90208-x
215.
Fasman G.D. Circular Dichroism and the Conformational Analysis of Biomolecules. 1996.
Not in our library
216.
Dyson H.J., Wright P.E. Insights into the structure and dynamics of unfolded proteins from nuclear magnetic resonance. Adv. Protein Chem. 2002;62:311-340.
Not in our library10.1016/s0065-3233(02)62012-1
217.
Dyson H.J., Wright P.E. Unfolded proteins and protein folding studied by NMR. Chem. Rev. 2004;104:3607-3622.
Not in our library10.1021/cr030403s
218.
Poulsen F.M. A Brief Introduction to NMR Spectroscopy of Proteins. 2002.
Not in our library
219.
Cavalli A., Salvatella X., Dobson C.M., Vendruscolo M. Protein structure determination from NMR chemical shifts. Proc. Natl. Acad. Sci. USA. 2007;104:9615-9620.
Not in our library10.1073/pnas.0610313104
220.
Kikhney A.G., Svergun D.I. A practical guide to small angle X-ray scattering (SAXS) of flexible and intrinsically disordered proteins. FEBS Lett. 2015;589:2570-2577.
Not in our library10.1016/j.febslet.2015.08.027
221.
Korasick D.A., Tanner J.J. Determination of protein oligomeric structure from small-angle X-ray scattering. Protein Sci. 2018;27:814-824.
Not in our library10.1002/pro.3376
222.
Da Vela S., Svergun D.I. Methods, development and applications of small-angle X-ray scattering to characterize biological macromolecules in solution. Curr. Res. Struct. Biol. 2020;27:164-170.
Not in our library10.1016/j.crstbi.2020.08.004
223.
Grawert T.W., Svergun D.I. Structural Modeling Using Solution Small-Angle X-ray Scattering (SAXS). J. Mol. Biol. 2020;432:3078-3092.
Not in our library10.1016/j.jmb.2020.01.030
224.
Jacques D.A., Trewhella J. Small-angle scattering for structural biology--Expanding the frontier while avoiding the pitfalls. Protein Sci. 2010;19:642-657.
Not in our library10.1002/pro.351
225.
Recent Developments in Data-Assisted Modeling of Flexible Proteins · 2021
Not checked
226.
Perez J., Vachette P., Russo D., Desmadril M., Durand D. Heat-induced unfolding of neocarzinostatin, a small all-β protein investigated by small-angle X-ray scattering. J. Mol. Biol. 2001;308:721-743.
Not in our library10.1006/jmbi.2001.4611
227.
Bernado P., Blackledge M. A Self-Consistent Description of the Conformational Behavior of Chemically Denatured Proteins from NMR and Small Angle Scattering. Biophys. J. 2009;97:2839-2845.
Not in our library10.1016/j.bpj.2009.08.044
228.
Bernado P., Svergun D.I. Structural analysis of intrinsically disordered proteins by small-angle X-ray scattering. Mol. Biosyst. 2012;8:151-167.
Not in our library10.1039/c1mb05275f
229.
Jeffries C.M., Svergun D.I., Owens R. High-Throughput Studies of Protein Shapes and Interactions by Synchrotron Small-Angle X-ray Scattering. Structural Proteomics. 2015;Volume 1261:277-301.
Not in our library10.1007/978-1-4939-2230-7_15
230.
Graewert M.A., Jeffries C.M. Sample and Buffer Preparation for SAXS. Adv. Exp. Med. Biol. 2017;1009:11-30.
Not in our library10.1007/978-981-10-6038-0_2
231.
David G., Perez J. Combined sampler robot and high-performance liquid chromatography: A fully automated system for biological small-angle X-ray scattering experiments at the Synchrotron SOLEIL SWING beamline. J. Appl. Crystallogr. 2009;42:892-900.
Not in our library10.1107/s0021889809029288
232.
Schulte T., Lofling J., Mikaelsson C., Kikhney A., Hentrich K., Diamante A., Ebel C., Normark S., Svergun D., Birgitta H.N., et al. The basic keratin 10-binding domain of the virulence-associated pneumococcal serine-rich protein PsrP adopts a novel MSCRAMM fold. Open Biol. 2014;41:30090.
Not in our library10.1098/rsob.130090
233.
Rodriguez Z.P. Conjugation of NMR and SAXS for flexible and multidomain protein structure determination: From sample preparation to model refinement. Prog. Biophys. Mol. Biol. 2020;150:140-144.
Not in our library10.1016/j.pbiomolbio.2019.08.009
234.
Gast K., Damaschun H., Eckert K., Schulze-Forster K., Maurer H.R., Muller-Frohne M., Zirwer D., Czarnecki J., Damaschun G. Prothymosin alpha: A biologically active protein with random coil conformation. Biochemistry. 1995;34:13211-13218.
Not in our library10.1021/bi00040a037
235.
I-TASSER server for protein 3D structure prediction · 2008
No notice
236.
New developments in the ATSAS program package for small-angle scattering data analysis · 2012
No notice
237.
Gijsbers A., Sánchez-Puig N., Gao Y., Peters P.J., Ravelli R.B.G., Siliqi D. Structural Analysis of the Partially Disordered Protein EspK from Mycobacterium Tuberculosis. Crystals. 2021;11:18.
Not in our library10.3390/cryst11010018
238.
Nogales E. The development of cryo-EM into a mainstream structural biology technique. Nat. Methods. 2016;13:24-27.
Not in our library10.1038/nmeth.3694
239.
Shoemaker S.C., Ando N. X-rays in the cryo-electron microscopy era: Structural biology’s dynamic future. Bichemistry. 2018;57:277-285.
Not in our library10.1021/acs.biochem.7b01031
240.
Hanske J., Sadian Y., Muller C.W. The cryo-em resolution revolution and transcription complexes. Curr. Opin. Struct. Biol. 2018;52:8-15.
Not in our library10.1016/j.sbi.2018.07.002
241.
Nwanochie E., Uversky V.N. Structure Determination by Single-Particle Cryo-Electron Microscopy: Only the Sky (and Intrinsic Disorder) is the Limit. Int. J. Mol. Sci. 2019;20:4186.
Not in our library10.3390/ijms20174186
242.
Cheng Y. Single-particle cryo-EM-How did it get here and where will it go. Science. 2018;361:876-880.
Not in our library10.1126/science.aat4346
243.
Musselman C.A., Kutateladze T.G. Characterization of functional disordered regions within chromatin-associated proteins. IScience. 2021;24:102070.
Not in our library10.1016/j.isci.2021.102070
244.
Advances in Single-Particle Electron Cryomicroscopy Structure Determination applied to Sub-tomogram Averaging · 2015
No notice
245.
Yan Z., Bai X., Yan C., Wu J., Li Z., Xie T., Peng W., Yin C., Li X., Scheres S.H.W., et al. Structure of the rabbit ryanodine receptor ryr1 at near-atomic resolution. Nature. 2015;517:50-55.
Not in our library10.1038/nature14063
246.
Gibbs E.B., Cook E.C., Showalter S.A. Application of NMR to studies of intrinsically disordered proteins. Arch. Biochem. Biophys. 2017;628:57-70.
Not in our library10.1016/j.abb.2017.05.008
247.
Geraets J.A., Pothula K.R., Schroder G.F. Integrating cryo-EM and NMR data. Curr. Opin. Struc. Biol. 2020;61:173-181.
Not in our library10.1016/j.sbi.2020.01.008
248.
Prilusky J., Felder C.E., Zeev-Ben-Mordehai T., Rydberg E.H., Man O., Beckmann J.S., Silman I., Sussman J.L. FoldIndex(C): A simple tool to predict whether a given protein sequence is intrinsically unfolded. Bioinformatics. 2005;21:3435-3438.
Not in our library10.1093/bioinformatics/bti537
249.
Liu J., Rost B. NORSp: Predictions of long regions without regular secondary structure. Nucleic Acids Res. 2003;31:3833-3835.
Not in our library10.1093/nar/gkg515
250.
Linding R., Russell R., Neduva V., Gibson T. GlobPlot: Exploring protein sequences for globularity and disorder. Nucleic Acids Res. 2003;31:3701-3708.
Not in our library10.1093/nar/gkg519
251.
Coeytaux K., Poupon A. Prediction of unfolded segments in a protein sequence based on amino acid composition. Bioinformatics. 2005;21:1891-1900.
Not in our library10.1093/bioinformatics/bti266
252.
Di Domenico T., Walsh I., Martin A.J., Tosatto S.C. MobiDB: A comprehensive database of intrinsic protein disorder annotations. Bioinformatics. 2012;28:2080-2081.
Not in our library10.1093/bioinformatics/bts327
253.
Fukuchi S., Sakamoto S., Nobe Y., Murakami S.D., Amemiya T., Hosoda K., Koike R., Hiroaki H., Ota M. IDEAL: Intrinsically Disordered proteins with Extensive Annotations and Literature. Nucleic Acids Res. 2012;40:D507-D511.
Not in our library10.1093/nar/gkr884
254.
Romero P., Obradovic Z., Dunker A.K. Folding minimal sequences: The lower bound for sequence complexity of globular proteins. FEBS Lett. 1999;462:363-367.
Not in our library10.1016/s0014-5793(99)01557-4
255.
Vullo A., Bortolami O., Pollastri G., Tosatto S.C. Spritz: A server for the prediction of intrinsically disordered regions in protein sequences using kernel machines. Nucleic Acids Res. 2006;34:W164-W168.
Not in our library10.1093/nar/gkl166
256.
Linding R., Jensen L., Diella F., Bork P., Gibson T., Russell R. Protein Disorder Prediction: Implications for Structural Proteomics. Structure. 2003;11:1453-1459.
Not in our library10.1016/j.str.2003.10.002
257.
Yang Z.R., Thomson R., McNeil P., Esnouf R.M. RONN: The bio-basis function neural network technique applied to the detection of natively disordered regions in proteins. Bioinformatics. 2005;21:3369-3376.
Not in our library10.1093/bioinformatics/bti534
258.
Ward J.J., McGuffin L.J., Bryson K., Buxton B.F., Jones D.T. The DISOPRED server for the prediction of protein disorder. Bioinformatics. 2004;20:2138-2139.
Not in our library10.1093/bioinformatics/bth195
259.
DISOPRED3: precise disordered region predictions with annotated protein-binding activity · 2014
No notice
260.
Highly accurate protein structure prediction with AlphaFold · 2021
No notice
261.
Zweckstetter M. NMR hawk-eyed view of AlphaFold2 structures. Protein Sci. 2021;30:2333-2337.
Not in our library10.1002/pro.4175
262.
AlQuraishi M. Protein-structure prediction revolutionized. Nature. 2021;596:487-488.
Not in our library10.1038/d41586-021-02265-4
263.
He X.H., You C.Z., Jiang H.L., Jiang Y., Xu H.E., Cheng X. AlphaFold2 versus experimental structures: Evaluation on G protein-coupled receptors. Acta Pharmacol. Sin. 2022.
Not in our library10.1038/s41401-022-00938-y
264.
Ruff K.M., Pappu R.V. AlphaFold and Implications for Intrinsically Disordered Proteins. J. Mol. Biol. 2021;433:167208.
Not in our library10.1016/j.jmb.2021.167208
265.
AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models · 2021
No notice
266.
Highly accurate protein structure prediction for the human proteome · 2021
No notice
267.
Dosztanyi Z., Csizmok V., Tompa P., Simon I. IUPred: Web server for the prediction of intrinsically unstructured regions of proteins based on estimated energy content. Bioinformatics. 2005;21:3433-3434.
Not in our library10.1093/bioinformatics/bti541
268.
Dosztanyi Z., Csizmok VTompa P., Simon I. The Pairwise Energy Content Estimated from Amino Acid Composition Discriminates between Folded and Intrinsically Unstructured Proteins. J. Mol. Biol. 2005;347:827-839.
Not in our library10.1016/j.jmb.2005.01.071
269.
Garbuzynskiy S.O., Lobanov M.Y., Galzitskaya O.V. To be folded or to be unfolded? Protein Sci. 2008;13:2871-2877.
Not in our library10.1110/ps.04881304
270.
Schlessinger A., Punta M., Rost B. Natively unstructured regions in proteins identified from contact predictions. Bioinformatics. 2007;23:2376-2384.
Not in our library10.1093/bioinformatics/btm349
271.
Xue BDunbrack R.L., Williams R.W., Dunker A.K., Uversky V.N. PONDR-FIT: A meta-predictor of intrinsically disordered amino acids. Biochim. Biophys. Acta. 2010;1804:996-1010.
Not in our library10.1016/j.bbapap.2010.01.011
272.
Lieutaud P., Canard B., Longhi S. MeDor: A metaserver for predicting protein disorder. BMC Genom. 2008;9:S25.
Not in our library10.1186/1471-2164-9-s2-s25
273.
Ishida T., Kinoshita K. Prediction of disordered regions in proteins based on the meta-approach. Bioinformatics. 2008;24:1344-1348.
Not in our library10.1093/bioinformatics/btn195
274.
Meng F., Uversky V.N., Kurgan L. Comprehensive review of methods for prediction of intrinsic disorder and its molecular functions. Cell. Mol. Life Sci. 2017;74:3069-3090.
Not in our library10.1007/s00018-017-2555-4
275.
Liu Y., Wang X., Liu B. A comprehensive review and comparison of existing computational methods for intrinsically disordered protein and region prediction. Brief Bioinform. 2019;20:330-346.
Not in our library10.1093/bib/bbx126
276.
Kurgan L. Resources for computational prediction of intrinsic disorder in proteins. Methods. 2022;204:132-141.
Not in our library10.1016/j.ymeth.2022.03.018
277.
Jordan I., Kondrashov F., Adzhubei I.A., Wolf Y., Koonin E., Kondrashov A., Sunyaev S. A universal trend of amino acid gain and loss in protein evolution. Nature. 2005;433:633-638.
Not in our library10.1038/nature03306
278.
Brooks D.J., Fresco J.R. Increased Frequency of Cysteine, Tyrosine, and Phenylalanine Residues Since the Last Universal Ancestor. Mol. Cell. Proteom. 2002;1:125-131.
Not in our library10.1074/mcp.m100001-mcp200
279.
Cristofari G., Darlix J.L. The ubiquitous nature of RNA chaperone proteins. Prog. Nucl. Acid Res. Mol. Biol. 2002;72:223-268.
Not in our library10.1016/s0079-6603(02)72071-0
280.
Doolittle W.F. Uprooting the tree of life. Sci. Am. 2000;282:90-95.
Not in our library10.1038/scientificamerican0200-90
281.
Theobald D.L. A formal test of the theory of universal common ancestry. Nature. 2010;465:219-222.
Not in our library10.1038/nature09014
282.
De novo gene birth · 2019
No notice
283.
Tompa P. Intrinsically unstructured proteins evolve by repeat expansion. BioEssays. 2003;25:847-855.
Not in our library10.1002/bies.10324
284.
Lise S., Jones D. Sequence Patterns Associated with Disordered Regions in Proteins. Proteins Struct. Funct. Genet. 2005;58:144-150.
Not in our library10.1002/prot.20279
285.
Brown C.J., Johnson A.K., Dunker A.K., Daughdrill G.W. Evolution and disorder. Curr. Opin. Struct. Biol. 2011;21:441-446.
Not in our library10.1016/j.sbi.2011.02.005
286.
Structural Disorder in Eukaryotes · 2012
No notice
287.
Meyer K., Kirchner M., Uyar B., Cheng J.Y., Russo G., Hernandez-Miranda L.R., Szymborska A., Zauber H., Rudolph I.M., Willnow T.E., et al. Mutations in Disordered Regions Can Cause Disease by Creating Dileucine Motifs. Cell. 2018;175:239-253.
Not in our library10.1016/j.cell.2018.08.019
288.
Pajkos M., Zeke A., Dosztányi Z. Ancient Evolutionary Origin of Intrinsically Disordered Cancer Risk Regions. Biomolecules. 2020;10:1115.
Not in our library10.3390/biom10081115
289.
Meszaros B., Hajdu-Soltesz B., Zeke A., Dosztanyi Z. Mutations of Intrinsically Disordered Protein Regions Can Drive Cancer but Lack Therapeutic Strategies. Biomolecules. 2021;11:381.
Not in our library10.3390/biom11030381
290.
Seera S., Nagarajaram H.A. Effect of Disease Causing Missense Mutations on Intrinsically Disordered Regions in Proteins. Protein Pept. Lett. 2022;29:254-267.
Not in our library10.2174/0929866528666211126161200
291.
Peng Z., Uversky V., Kurgan L. Genes encoding intrinsic disorder in Eukaryota have high GC content. Intrinsically Disord. Proteins. 2016;4:e1262225.
Not in our library10.1080/21690707.2016.1262225
292.
High GC content causes orphan proteins to be intrinsically disordered. · 2017
Not checked
293.
Brown C.J., Takayama S., Campen A.M., Vise P., Marshall T.W., Oldfield C.J., Williams C.J., Dunker A.K. Evolutionary Rate Heterogeneity in Proteins with Long Disordered Regions. J. Mol. Evol. 2002;55:104-110.
Not in our library10.1007/s00239-001-2309-6
294.
Homma K., Anbo H., Noguchi T., Fukuchi S. Both Intrinsically Disordered Regions and Structural Domains Evolve Rapidly in Immune-Related Mammalian Proteins. Int. J. Mol. Sci. 2018;19:3860.
Not in our library10.3390/ijms19123860
295.
Pancsa R., Zsolyomi F., Tompa P. Co-Evolution of Intrinsically Disordered Proteins with Folded Partners Witnessed by Evolutionary Couplings. Int. J. Mol. Sci. 2018;19:3315.
Not in our library10.3390/ijms19113315
296.
Proteome-wide signatures of function in highly diverged intrinsically disordered regions · 2019
No notice
297.
Bringing order to protein disorder through comparative genomics and genetic interactions · 2011
No notice
298.
Covaceuszach S., Peche L.Y., Konarev P.V., Lamba D. A combined evolutionary and structural approach to disclose the primary structural determinants essential for proneurotrophins biological functions. Comput. Struct. Biotechnol. J. 2021;19:2891-2904.
Not in our library10.1016/j.csbj.2021.05.007
299.
Millard P.S., Bugge K., Marabini R., Boomsma W., Burow M., Kragelund B.B. IDDomainSpotter: Compositional bias reveals domains in long disordered protein regions-Insights from transcription factors. Protein Sci. 2020;29:169-183.
Not in our library10.1002/pro.3754
300.
Cohan M.C., Shinn M.K., Lalmansingh J.M., Pappu R.V. Uncovering Non-random Binary Patterns Within Sequences of Intrinsically Disordered Proteins. J. Mol. Biol. 2022;434:167373.
Not in our library10.1016/j.jmb.2021.167373
301.
Lu A.X., Lu A.X., Pritisanac I., Zarin T., Forman-Kay J.D., Moses A.M. Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning. PLoS Comput. Biol. 2022;18:e1010238.
Not in our library10.1371/journal.pcbi.1010238
302.
Gsponer J., Futschik M.E., Teichmann S.A., Babu M.M. Tight Regulation of Unstructured Proteins: From Transcript Synthesis to Protein Degradation. Science. 2008;322:1365-1368.
Not in our library10.1126/science.1163581
303.
Uversky V.N., Oldfield C.J., Dunker A.K. Intrinsically disordered proteins in human diseases: Introducing the D2 concept. Annu. Rev. Biophys. 2008;37:215-246.
Not in our library10.1146/annurev.biophys.37.032807.125924
304.
Langella E., Buonanno M., De Simone G., Monti S.M. Intrinsically disordered features of carbonic anhydrase IX proteoglycan-like domain. Cell. Mol. Life Sci. 2021;78:2059-2067.
Not in our library10.1007/s00018-020-03697-3
305.
Chang B.S., Minn A.J., Muchmore S.W., Fesik S.W., Thompson C.B. Identification of a novel regulatory domain in Bcl-X(L) and Bcl-2. EMBO J. 1997;16:968-977.
Not in our library10.1093/emboj/16.5.968
306.
Lee H., Mok K.H., Muhandiram R., Park K., Suk J., Kim D., Chang J., Sung Y.C., Choi K.Y., Han K. Local Structural Elements in the Mostly Unstructured Transcriptional Activation Domain of Human p53. J. Biol. Chem. 2000;275:29426-29432.
Not in our library10.1074/jbc.m003107200
307.
Garg N., Kumar P., Gadhave K., Giri R. The dark proteome of cancer: Intrinsic disorderedness and functionality of HIF-1α along with its interacting proteins. Prog. Mol. Biol. Transl. Sci. 2019;166:371-403.
Not in our library10.1016/bs.pmbts.2019.05.006
308.
Glenner G.G., Wong C.W. Alzheimer’s disease and Down’s syndrome: Sharing of a unique cerebrovascular amyloid fibril protein. Biochem. Biophys. Res. Commun. 1984;122:1131-1135.
Not in our library10.1016/0006-291x(84)91209-9
309.
Lee V., Balin B., Otvos L.J., Trojanowski J. A68: A major subunit of paired helical filaments and derivatized forms of normal Tau. Science. 1991;251:675-678.
Not in our library10.1126/science.1899488
310.
Ueda K., Fukushima H., Masliah E., Xia Y., Iwai A., Yoshimoto M., Otero D., Kondo J., Ihara Y., Saitoh T. Molecular cloning of cDNA encoding an unrecognized component of amyloid in Alzheimer disease. Proc. Natl. Acad. Sci. USA. 1993;90:11282-11286.
Not in our library10.1073/pnas.90.23.11282
311.
Prusiner S. Shattuck lecture: Neurodegenerative diseases and prions. N. Engl. J. Med. 2001;344:1516-1526.
Not in our library10.1056/nejm200105173442006
312.
Eftekharzadeh BHyman B.T., Wegmann S. Structural studies on the mechanism of protein aggregation in age related neurodegenerative diseases. Mech. Ageing Dev. 2016;156:1-13.
Not in our library10.1016/j.mad.2016.03.001
313.
Kovacs G.G. Concepts and classification of neurodegenerative diseases. Handb. Clin. Neurol. 2017;145:301-307.
Not in our library10.1016/b978-0-12-802395-2.00021-3
314.
Martinelli A.H.S., Lopes F.C., John E.B.O., Carlini C.R., Ligabue-Braun R. Modulation of Disordered Proteins with a Focus on Neurodegenerative Diseases and Other Pathologies. Int. J. Mol. Sci. 2019;20:1322.
Not in our library10.3390/ijms20061322
315.
Kulkarni P., Uversky V.N. Intrinsically Disordered Proteins in Chronic Diseases. Biomolecules. 2019;9:147.
Not in our library10.3390/biom9040147
316.
Monti S.M., De Simone G., Langella E. The Amazing World of IDPs in Human Diseases. Biomolecules. 2021;11:333.
Not in our library10.3390/biom11020333
317.
Monti S.M., De Simone G., Langella E. The Amazing World of IDPs in Human Diseases II. Biomolecules. 2022;12:369.
Not in our library10.3390/biom12030369
318.
Brundin P., Melki R. Prying into the Prion Hypothesis for Parkinson’s Disease. J. Neurosci. 2017;37:9808-9818.
Not in our library10.1523/jneurosci.1788-16.2017
319.
Current understanding of Alzheimer’s disease diagnosis and treatment · 2018
No notice
320.
Dos Santos Picanco L.C., Ozela P.F., de Fatima de Brito Brito M., Pinheiro A.A., Padilha E.C., Braga F.S., de Paula da Silva C.H.T., Dos Santos C.B.R., Rosa J.M.C., da Silva Hage-Melim L.I. Alzheimer’s Disease: A Review from the Pathophysiology to Diagnosis, New Perspectives for Pharmacological Treatment. Curr. Med. Chem. 2018;25:3141-3159.
Not in our library10.2174/0929867323666161213101126
321.
Ledeen R.W., Wu G. Gangliosides, α-Synuclein, and Parkinson’s Disease. Prog. Mol. Biol. Transl. Sci. 2018;156:435-454.
Not in our library10.1016/bs.pmbts.2017.12.009
322.
The neuropathological diagnosis of Alzheimer’s disease · 2019
No notice
323.
Coskuner O., Uversky V.N. Intrinsically disordered proteins in various hypotheses on the pathogenesis of Alzheimer’s and Parkinson’s diseases. Prog. Mol. Biol. Transl. Sci. 2019;166:145-223.
Not in our library10.1016/bs.pmbts.2019.05.007
324.
Clusterin in Alzheimer’s Disease: Mechanisms, Genetics, and Lessons From Other Pathologies · 2019
No notice
325.
Coskuner-Weber O., Mirzanli O., Uversky V.N. Intrinsically disordered proteins and proteins with intrinsically disordered regions in neurodegenerative diseases. Biophys. Rev. 2022;14:679-707.
Not in our library10.1007/s12551-022-00968-0
326.
Akbayrak I.Y., Caglayan S.I., Ozcan Z., Uversky V.N., Coskuner-Weber O. Current Challenges and Limitations in the Studies of Intrinsically Disordered Proteins in Neurodegenerative Diseases by Computer Simulations. Curr. Alzheimer Res. 2020;17:805-818.
Not in our library10.2174/1567205017666201109094908
327.
Roterman I., Stapor K., Fabian P., Konieczny L. In Silico Modeling of the Influence of Environment on Amyloid Folding Using FOD-M Model. Int. J. Mol. Sci. 2021;19:10587.
Not in our library10.3390/ijms221910587
328.
Tsvetkov P., Myers N., Adler J., Shaul Y. Degradation of Intrinsically Disordered Proteins by the NADH 26S Proteasome. Biomolecules. 2020;10:1642.
Not in our library10.3390/biom10121642
329.
Gadhave K., Kumar P., Kapuganti S.K., Uversky V.N., Giri R. Unstructured Biology of Proteins from Ubiquitin-Proteasome System: Roles in Cancer and Neurodegenerative Diseases. Biomolecules. 2020;10:796.
Not in our library10.3390/biom10050796
330.
Ortega-Alarcon D., Claveria-Gimeno R., Vega S., Jorge-Torres O.C., Esteller M., Abian O., Velazquez-Campoy A. Molecular Context-Dependent Effects Induced by Rett Syndrome-Associated Mutations in MeCP2. Biomolecules. 2020;10:1533.
Not in our library10.3390/biom10111533
331.
Ortega-Alarcon D., Claveria-Gimeno R., Vega S., Jorge-Torres O.C., Esteller M., Abian O., Velazquez-Campoy A. Stabilization Effect of Intrinsically Disordered Regions on Multidomain Proteins: The Case of the Methyl-CpG Protein 2, MeCP2. Biomolecules. 2021;11:1216.
Not in our library10.3390/biom11081216
332.
Neira J.L., Rizzuti B., Jimenez-Alesanco A., Palomino-Schätzlein M., Abian O., Velazquez-Campoy A., Iovanna J.L. A Phosphorylation-Induced Switch in the Nuclear Localization Sequence of the Intrinsically Disordered NUPR1 Hampers Binding to Importin. Biomolecule. 2020;10:1313.
Not in our library10.3390/biom10091313
333.
Wong E.T.C., So V., Guron M., Kuechler E.R., Malhis N., Bui J.M., Gsponer J. Protein-Protein Interactions Mediated by Intrinsically Disordered Protein Regions Are Enriched in Missense Mutations. Biomolecules. 2020;10:1097.
Not in our library10.3390/biom10081097
334.
Raut K.K., Ponniah K., Pascal S.M. Structural Analysis of the cl-Par-4 Tumor Suppressor as a Function of Ionic Environment. Biomolecules. 2021;11:3386.
Not in our library10.3390/biom11030386
335.
Wang J., Cao Z., Zhao L., Li S. Novel strategies for drug discovery based on Intrinsically Disordered Proteins (IDPs). Int. J. Mol. Sci. 2011;12:3205-3219.
Not in our library10.3390/ijms12053205
336.
Melo A.M., Coraor J., Alpha-Cobb G., Elbaum-Garfinkle S., Nath A., Rhoades E. A functional role for intrinsic disorder in the tau-tubulin complex. Proc. Natl. Acad. Sci. USA. 2016;113:14336-14341.
Not in our library10.1073/pnas.1610137113
337.
Hu G., Wu Z., Uversky V.N., Kurgan L. Functional Analysis of Human Hub Proteins and Their Interactors Involved in the Intrinsic Disorder-Enriched Interactions. Int. J. Mol. Sci. 2017;18:2761.
Not in our library10.3390/ijms18122761
338.
Critical assessment of protein intrinsic disorder prediction · 2021
No notice
339.
Necci M., Piovesan D., Tosatto S.C. Large-scale analysis of intrinsic disorder flavors and associated functions in the protein sequence universe. Protein Sci. 2016;25:2164-2174.
Not in our library10.1002/pro.3041
340.
A Review on Applications of Computational Methods in Drug Screening and Design · 2020
No notice
341.
Optimization of cell viability assays to improve replicability and reproducibility of cancer drug sensitivity screens · 2020
No notice
342.
Kitaeva K.V., Rutland C.S., Rizvanov A.A., Solovyeva V.V. Cell Culture Based in vitro Test Systems for Anticancer Drug Screening. Front. Bioeng. Biotechnol. 2020;8:322.
Not in our library10.3389/fbioe.2020.00322
343.
Rothenaigner I., Hadian K. Brief Guide: Experimental Strategies for High-Quality Hit Selection from Small-Molecule Screening Campaigns. SLAS Discov. 2021;7:851-854.
Not in our library10.1177/24725552211008862
344.
Tanoli Z., Aldahdooh J., Alam F., Wang Y., Seemab U., Fratelli M., Pavlis P., Hajduch M., Bietrix F., Gribbon P., et al. Minimal information for chemosensitivity assays (MICHA): A next-generation pipeline to enable the FAIRification of drug screening experiments. Brief Bioinform. 2022;23:bbab350.
Not in our library10.1093/bib/bbab350
345.
Rizzuti B., Lan W., Santofimia-Castaño P., Zhou Z., Velázquez-Campoy A., Abián O., Peng L., Neira J.L., Xia Y., Iovanna J.L. Design of Inhibitors of the Intrinsically Disordered Protein NUPR1: Balance between Drug Affinity and Target Function. Biomolecules. 2021;11:1453.
Not in our library10.3390/biom11101453
346.
Choudhary S., Lopus M., Hosur R.V. Targeting disorders in unstructured and structured proteins in various diseases. Biophys. Chem. 2022;281:106742.
Not in our library10.1016/j.bpc.2021.106742
347.
Metallo S.J. Intrinsically disordered proteins are potential drug targets. Curr. Opin. Chem. Biol. 2010;14:481-488.
Not in our library10.1016/j.cbpa.2010.06.169
348.
Therapeutic Interventions of Cancers Using Intrinsically Disordered Proteins as Drug Targets: c-Myc as Model System · 2017
Not checked
349.
Targeting oncogenic Myc as a strategy for cancer treatment · 2018
No notice
350.
Na I., Choi S., Son S.H., Uversky V.N., Kim C.G. Drug Discovery Targeting the Disorder-To-Order Transition Regions through the Conformational Diversity Mimicking and Statistical Analysis. Int. J. Mol. Sci. 2020;21:5248.
Not in our library10.3390/ijms21155248
351.
Kim M.Y., Na I., Kim J.S., Son S.H., Choi S., Lee S.E., Kim J.H., Jang K., Alterovitz G., Chen Y., et al. Rational discovery of antimetastatic agents targeting the intrinsically disordered region of MBD2. Sci. Adv. 2019;5:eaav9810.
Not in our library10.1126/sciadv.aav9810
352.
Brabletz T., Kalluri R., Nieto M.A., Weinberg R.A. EMT in cancer. Nat. Rev. Cancer. 2018;18:128-134.
Not in our library10.1038/nrc.2017.118
353.
Cheng Y., LeGall T., Oldfield C.J., Mueller J.P., Van Y.Y., Romero P., Cortese M.S., Uversky V.N., Dunker A.K. Rational drug design via intrinsically disordered protein. Trends Biotechnol. 2006;24:435-442.
Not in our library10.1016/j.tibtech.2006.07.005
354.
Uversky V.N. Intrinsically disordered proteins and novel strategies for drug discovery. Expert Opin. Drug Discov. 2012;7:475-488.
Not in our library10.1517/17460441.2012.686489
355.
Uversky V.N. Intrinsic Disorder, Protein-Protein Interactions, and Disease. Adv. Protein Chem. Struct. Biol. 2018;110:85-121.
Not in our library10.1016/bs.apcsb.2017.06.005
356.
Vassilev L.T., Vu B.T., Graves B., Carvajal D., Podlaski F., Filipovic Z., Kong N., Kammlott U., Lukacs C., Klein C., et al. In Vivo Activation of the p53 Pathway by Small-Molecule Antagonists of MDM2. Science. 2004;303:844-848.
Not in our library10.1126/science.1092472
357.
Neira J.L., Bintz J., Arruebo M., Rizzuti B., Bonacci T., Vega S., Lanas A., Velázquez-Campoy A., Iovanna J.L., Abián O. Identification of a Drug Targeting an Intrinsically Disordered Protein Involved in Pancreatic Adenocarcinoma. Sci. Rep. 2017;7:39732.
Not in our library10.1038/srep39732
358.
Mishra P., Choudhary S., Mukherjee S., Sengupta D., Sharma S., Hosur R.V. Molten globule nature of Plasmodium falciparum P2 homo-tetramer. Biochem. Biophys. Rep. 2015;1:97-107.
Not in our library10.1016/j.bbrep.2015.03.010
359.
Santofimia-Castano P., Rizzuti B., Xia Y., Abian O., Peng L., Velazquez-Campoy A., Neira J.L., Iovanna J. Targeting intrinsically disordered proteins involved in cancer. Cell Mol Life Sci. 2020;77:1695-1707.
Not in our library10.1007/s00018-019-03347-3
360.
Pietrobono D., Giacomelli C., Trincavelli M.L., Daniele S., Martini C. Inhibitors of protein aggregates as novel drugs in neurodegenerative diseases. Glob. Drugs Ther. 2017;2:1-5.
Not in our library
361.
Save S.S., Rachineni K., Hosur R.V., Choudhary S. Natural compound safranal driven inhibition and dis-aggregation of α-synuclein fibrils. Int. J. Biol. Macromol. 2019;141:585-595.
Not in our library10.1016/j.ijbiomac.2019.09.053

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.