Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints

Abstract

The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict inter-atomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25% of these so-called dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16% of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.

Full text

Its licence does not allow us to show the full text. Read it at PubMed Central:

References 49

1. 1.

   de Juan D, Pazos F, Valencia A. Emerging methods in protein co-evolution. Nat. Rev. Genet. 2013;14:249-261.

   Not in our library10.1038/nrg3414

2. 2.

   Monastyrskyy B, D’Andrea D, Fidelis K, Tramontano A, Kryshtafovych A. New encouraging developments in contact prediction: Assessment of the CASP11 results. Protein. Struct. Funct. Bioinf. 2015;84:131-144.

   Not in our library10.1002/prot.24943

3. 3.

   Michel M, et al. PconsFold: improved contact predictions improve protein models. Bioinformatics. 2014;30:i482-i488.

   Not in our library10.1093/bioinformatics/btu458

4. 4.

   Bender BJ, et al. Protocols for molecular modeling with Rosetta3 and RosettaScripts. Biochemistry. 2016;55:4748-4763.

   Not in our library10.1021/acs.biochem.6b00444

5. 5.

   Kosciolek T, Jones DT. De novo structure prediction of globular proteins aided by sequence variation-derived contacts. PLoS ONE. 2014;9:e92197.

   Not in our library10.1371/journal.pone.0092197

6. 6.

   Ovchinnikov S, Park H, Kim DE, DiMaio F, Baker D. Protein structure prediction using Rosetta in CASP12. Protein. Struct. Funct. Bioinf. 2018;86:113-121.

   Not in our library10.1002/prot.25390

7. 7.

   Kim DE, Chivian D, Malmström L, Baker D. Automated prediction of domain boundaries in CASP6 targets using Ginzu and RosettaDOM. Protein. Struct. Funct. Bioinf. 2005;61:193-200.

   Not in our library10.1002/prot.20737

8. 8.

   Aszódi A, Gradwell MJ, Taylor WR. Global fold determination from a small number of distance restraints. J. Mol. Biol. 1995;251:308-326.

   Not in our library10.1006/jmbi.1995.0436

9. 9.
   CONFOLD2: improved contact-driven ab initio protein structure modeling · 2018

   Not checked
10. 10.
    Protein 3D Structure Computed from Evolutionary Sequence Variation · 2011

    No notice
11. 11.

    Brunger AT. Version 1.2 of the crystallography and NMR system. Nat. Protoc. 2007;2:2728-2733.

    Not in our library10.1038/nprot.2007.406

12. 12.

    Pastore A, Atkinson RA, Saudek V, Williams RJ. Topological mirror images in protein structure computation: an underestimated problem. Protein. Struct. Funct. Bioinf. 1991;10:22-32.

    Not in our library10.1002/prot.340100104

13. 13.

    Michel M, Menéndez Hurtado D, Uziela K, Elofsson A. Large-scale structure prediction by improved contact predictions and model quality assessment. Bioinformatics. 2017;33:i23-i29.

    Not in our library10.1093/bioinformatics/btx239

14. 14.

    Xu Jinbo. Distance-based protein folding powered by deep learning. Proceedings of the National Academy of Sciences. 2019;116(34):16856-16865.

    Not in our library10.1073/pnas.1821309116

15. 15.

    AlQuraishi M. End-to-end differentiable learning of protein structure. Cell Syst. 2019;8:292-301.

    Not in our library10.1016/j.cels.2019.03.006

16. 16.

    Anonymous. Learning protein structure with a differentiable simulator. ICLR 2019 Conference Blind Submission. (2018).

    Not in our library

17. 17.

    Kandathil S. M., Greener J. G., Jones D. T. Prediction of interresidue contacts with DeepMetaPSICOV in CASP13. Proteins Struct. Funct. Bioinf. (2019).

    Not in our library10.1002/prot.25779

18. 18.
    The Pfam protein families database in 2019 · 2018

    No notice
19. 19.

    Ovchinnikov S, et al. Protein structure determination using metagenome sequence data. Science. 2017;355:294-298.

    Not in our library10.1126/science.aah4043

20. 20.

    Lamb John, Jarmolinska Aleksandra I., Michel Mirco, Menéndez-Hurtado David, Sulkowska Joanna I., Elofsson Arne. PconsFam: An Interactive Database of Structure Predictions of Pfam Families. Journal of Molecular Biology. 2019;431(13):2442-2448.

    Not in our library10.1016/j.jmb.2019.01.047

21. 21.

    Schaeffer RD, Liao Y, Cheng H, Grishin NV. ECOD: new developments in the evolutionary classification of domains. Nucleic Acids Res. 2017;45:D296-D302.

    Not in our library10.1093/nar/gkw1137

22. 22.

    Zhang Y, Skolnick J. Scoring function for automated assessment of protein structure template quality. Protein. Struct. Funct. Bioinf. 2004;57:702-710.

    Not in our library10.1002/prot.20264

23. 23.

    Kabsch, W. & Sander, C. Dictionary of protein secondary structure: Pattern recognition of hydrogen-bonded and geometrical features. Biopolymers22, 2577–2637 (1983).

    Not in our library10.1002/bip.360221211

24. 24.

    Hopf TA, et al. Three-dimensional structures of membrane proteins from genomic sequencing. Cell. 2012;149:1607-1621.

    Not in our library10.1016/j.cell.2012.04.012

25. 25.

    Nugent T, Jones DT. Accurate de novo structure prediction of large transmembrane protein domains using fragment-assembly and correlated mutation analysis. Proc. Natl Acad. Sci. USA. 2012;109:E1540-E1547.

    Not in our library10.1073/pnas.1120036109

26. 26.

    Perdigão N, et al. Unexpected features of the dark proteome. Proc. Natl Acad. Sci. USA. 2015;112:15898-15903.

    Not in our library10.1073/pnas.1508380112

27. 27.

    Dessailly BH, et al. PSI-2: structural genomics to cover protein domain family space. Structure. 2009;17:869-881.

    Not in our library10.1016/j.str.2009.03.015

28. 28.

    Jones DT, Buchan DWA, Cozzetto D, Pontil M. PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments. Bioinformatics. 2012;28:184-190.

    Not in our library10.1093/bioinformatics/btr638

29. 29.

    Seemayer S, Gruber M, Söding J. CCMpred–fast and precise prediction of protein residue-residue contacts from correlated mutations. Bioinformatics. 2014;30:3128-3130.

    Not in our library10.1093/bioinformatics/btu500

30. 30.

    Lewis TE, et al. Genome3D: exploiting structure to help users understand their sequences. Nucleic Acids Res. 2015;43:D382-D386.

    Not in our library10.1093/nar/gku973

31. 31.
    UniProt: the universal protein knowledgebase · 2018

    No notice
32. 32.

    Somody JC, MacKinnon SS, Windemuth A. Structural coverage of the proteome for pharmaceutical applications. Drug Disco. Today. 2017;22:1792-1799.

    Not in our library10.1016/j.drudis.2017.08.004

33. 33.

    Jumper, J. De novo protein folding using statistical potentials from deep learning. CASP13 presentation (2018).

    Not in our library

34. 34.

    Jones DT, Kandathil SM. High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features. Bioinformatics. 2018;34:3308-3315.

    Not in our library10.1093/bioinformatics/bty341

35. 35.

    Goodfellow I, Warde-Farley D, Mirza M, Courville A, Bengio Y. Maxout networks. PMLR. 2013;28:1319-1327.

    Not in our library

36. 36.

    Kingma, D. P., Ba, J. Adam: a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR) (2015).

    Not in our library

37. 37.

    Remmert M, Biegert A, Hauser A, Söding J. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment. Nat. Methods. 2011;9:173-175.

    Not in our library10.1038/nmeth.1818

38. 38.

    Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9:1735-1780.

    Not in our library10.1162/neco.1997.9.8.1735

39. 39.

    Heffernan R, Yang Y, Paliwal K, Zhou Y. Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility. Bioinformatics. 2017;33:2842-2849.

    Not in our library10.1093/bioinformatics/btx218

40. 40.

    Li, H., Hou, J., Adhikari, B., Lyu, Q., Cheng, J. Deep learning methods for protein torsion angle prediction. BMC Bioinform. 18, 417 (2017).

    Not in our library10.1186/s12859-017-1834-2

Cited by papers we hold 6

• Modeling protein conformational ensembles by guiding AlphaFold2 with Double Electron Electron Resonance (DEER) distance distributions · 2025
  Not checked
• Protein remote homology detection and structural alignment using deep learning · 2023
  No notice
• Improved Protein Structure Prediction Using a New Multi‐Scale Network and Homologous Templates · 2021
  Not checked
• Structure-based protein function prediction using graph convolutional networks · 2021
  No notice
• Highly accurate protein structure prediction for the human proteome · 2021
  No notice
• Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks · 2020
  No notice

Reads

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.