CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site

Abstract

Protein-peptide interactions play a key role in cell functions. Their structural characterization, though challenging, is important for the discovery of new drugs. The CABS-dock web server provides an interface for modeling protein-peptide interactions using a highly efficient protocol for the flexible docking of peptides to proteins. While other docking algorithms require pre-defined localization of the binding site, CABS-dock does not require such knowledge. Given a protein receptor structure and a peptide sequence (and starting from random conformations and positions of the peptide), CABS-dock performs simulation search for the binding site allowing for full flexibility of the peptide and small fluctuations of the receptor backbone. This protocol was extensively tested over the largest dataset of non-redundant protein-peptide interactions available to date (including bound and unbound docking cases). For over 80% of bound and unbound dataset cases, we obtained models with high or medium accuracy (sufficient for practical applications). Additionally, as optional features, CABS-dock can exclude user-selected binding modes from docking search or to increase the level of flexibility for chosen receptor fragments. CABS-dock is freely available as a web server at http://biocomp.chem.uw.edu.pl/CABSdock.

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References 24

1. 1.

   Trellet M., Melquiond A.J., Bonvin A.J.J., Zhou P, Huang J. Information-Driven Modeling of Protein-Peptide Complexes. Computational Peptidology. 2015;1268:221-239.

   Not in our library10.1007/978-1-4939-2285-7_10

2. 2.

   Rubinstein M., Niv M.Y. Peptidic modulators of protein-protein interactions: progress and challenges in computational design. Biopolymers. 2009;91:505-513.

   Not in our library10.1002/bip.21164

3. 3.

   Raveh B., London N., Schueler-Furman O. Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins. 2010;78:2029-2040.

   Not in our library10.1002/prot.22716

4. 4.

   Hetényi C., van der Spoel D. Efficient docking of peptides to proteins without prior knowledge of the binding site. Protein Sci. 2002;11:1729-1737.

   Not in our library10.1110/ps.0202302

5. 5.

   Kurcinski M., Kolinski A., Kmiecik S. Mechanism of folding and binding of an intrinsically disordered protein as revealed by ab initio simulations. Journal of Chemical Theory and Computation. 2014;10:2224-2231.

   Not in our library10.1021/ct500287c

6. 6.

   Horwacik I., Kurcinski M., Bzowska M., Kowalczyk A.K., Czaplicki D., Kolinski A., Rokita H. Analysis and optimization of interactions between peptides mimicking the GD2 ganglioside and the monoclonal antibody 14G2a. Int. J. Mol. Med. 2011;28:47-57.

   Not in our library10.3892/ijmm.2011.655

7. 7.

   Kurcinski M., Kolinski A. Theoretical study of molecular mechanism of binding TRAP220 coactivator to Retinoid X Receptor alpha, activated by 9-cis retinoic acid. J. Steroid. Biochem. Mol. Biol. 2010;121:124-129.

   Not in our library10.1016/j.jsbmb.2010.03.086

8. 8.

   Kurcinski M., Kolinski A. Steps towards flexible docking: modeling of three-dimensional structures of the nuclear receptors bound with peptide ligands mimicking co-activators’ sequences. J. Steroid. Biochem. Mol. Biol. 2007;103:357-360.

   Not in our library10.1016/j.jsbmb.2006.12.059

9. 9.

   Kolinski A. Protein modeling and structure prediction with a reduced representation. Acta Biochim. Pol. 2004;51:349-371.

   Not in our library

10. 10.

    Kmiecik S., Kolinski A. Simulation of chaperonin effect on protein folding: a shift from nucleation-condensation to framework mechanism. J. Am. Chem. Soc. 2011;133:10283-10289.

    Not in our library10.1021/ja203275f

11. 11.

    Kmiecik S., Kolinski A. Characterization of protein-folding pathways by reduced-space modeling. Proc. Natl. Acad. Sci. U.S.A. 2007;104:12330-12335.

    Not in our library10.1073/pnas.0702265104

12. 12.

    Jamroz M., Kolinski A., Kmiecik S. CABS-flex predictions of protein flexibility compared with NMR ensembles. Bioinformatics. 2014;30:2150-2154.

    Not in our library10.1093/bioinformatics/btu184

13. 13.
    CABS-flex: server for fast simulation of protein structure fluctuations · 2013

    No notice
14. 14.

    Blaszczyk M., Jamroz M., Kmiecik S., Kolinski A. CABS-fold: server for the de novo and consensus-based prediction of protein structure. Nucleic Acids Res. 2013;41:W406-W411.

    Not in our library10.1093/nar/gkt462

15. 15.

    Kolinski A., Bujnicki J.M. Generalized protein structure prediction based on combination of fold-recognition with de novo folding and evaluation of models. Proteins. 2005;61(Suppl. 7):84-90.

    Not in our library10.1002/prot.20723

16. 16.

    Kmiecik S., Jamroz M., Kolinski M. Structure prediction of the second extracellular loop in G-protein-coupled receptors. Biophys. J. 2014;106:2408-2416.

    Not in our library10.1016/j.bpj.2014.04.022

17. 17.

    Eswar N., Webb B., Marti-Renom M.A., Madhusudhan M.S., Eramian D., Shen M.Y., Pieper U., Sali A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Protein Sci. 2007.

    Not in our library10.1002/0471140864.ps0209s50

18. 18.

    Shen M.Y., Sali A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 2006;15:2507-2524.

    Not in our library10.1110/ps.062416606

19. 19.
    A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking · 2013

    No notice
20. 20.

    Altschul S.F., Madden T.L., Schaffer A.A., Zhang J., Zhang Z., Miller W., Lipman D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25:3389-3402.

    Not in our library10.1093/nar/25.17.3389

21. 21.
    3Dmol.js: molecular visualization with WebGL · 2014

    No notice
22. 22.

    Hanson R.M., Prilusky J., Renjian Z., Nakane T., Sussman J.L. JSmol and the next-generation web-based representation of 3D molecular structure as applied to proteopedia. Isr. J. Chem. 2013;53:207-216.

    Not in our library

23. 23.

    London N., Raveh B., Cohen E., Fathi G., Schueler-Furman O. Rosetta FlexPepDock web server–high resolution modeling of peptide-protein interactions. Nucleic Acids Res. 2011;39:W249-W253.

    Not in our library10.1093/nar/gkr431

24. 24.

    Ortiz A.R., Kolinski A., Skolnick J. Nativelike topology assembly of small proteins using predicted restraints in Monte Carlo folding simulations. Proc. Natl. Acad. Sci. U.S.A. 1998;95:1020-1025.

    Not in our library10.1073/pnas.95.3.1020

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