Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking

Methods

Volumn 93, Issue , 2016, Pages 72-83

  Blaszczyk, Maciej ^a   Kurcinski, Mateusz ^a   Kouza, Maksim ^a   Wieteska, Lukasz ^a   Debinski, Aleksander ^a   Kolinski, Andrzej ^a   Kmiecik, Sebastian ^a  

^a UNIVERSITY OF WARSAW   (Poland)

Author keywords

CABS dock; Flexible docking; Molecular docking; Peptide binding; Peptide folding; Protein peptide docking

Indexed keywords

AMINO ACID SEQUENCE; ANALYTIC METHOD; ARTICLE; BINDING SITE; CALCULATION; CARBON ALPHA CARBON BETA SIDE CHAIN DOCK METHOD; COMPUTER PROGRAM; MOLECULAR DOCKING; MOLECULAR DYNAMICS; MOLECULAR MODEL; PREDICTION; PRIORITY JOURNAL; PROTEIN ANALYSIS; PROTEIN BINDING; PROTEIN CONFORMATION; PROTEIN FOLDING; PROTEIN PROTEIN INTERACTION; PROTEIN STRUCTURE; SIMULATION; STATIC ELECTRICITY; WEB BROWSER; CHEMISTRY; METABOLISM; PHYSIOLOGY; PROCEDURES; PROTEIN SECONDARY STRUCTURE; PROTEIN TERTIARY STRUCTURE;

PEPTIDE; PROTEIN;

BINDING SITES; MODELS, MOLECULAR; MOLECULAR DOCKING SIMULATION; PEPTIDES; PROTEIN STRUCTURE, SECONDARY; PROTEIN STRUCTURE, TERTIARY; PROTEINS; WEB BROWSER;

EID: 84953219575     PISSN: 10462023     EISSN: 10959130     Source Type: Journal    
DOI: 10.1016/j.ymeth.2015.07.004     Document Type: Article

References (93)

1. Petsalaki E., Russell R.B. Peptide-mediated interactions in biological systems: new discoveries and applications. Curr. Opin. Biotechnol. 2008, 19:344-350.
2. London N., Raveh B., Schueler-Furman O. Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. Curr. Opin. Struct. Biol. 2013, 23:894-902.
3. London N., Raveh B., Schueler-Furman O. Modeling peptide-protein interactions. Methods Mol. Biol. 2012, 857:375-398.
4. Yoon J., Feng X., Kim Y.S., Shin D.M., Hatzi K., Wang H., Morse H.C. Interferon regulatory factor 8 (IRF8) interacts with the B cell lymphoma 6 (BCL6) corepressor BCOR. J. Biol. Chem. 2014, 289:34250-34257.
5. Dionisio N., Smani T., Woodard G.E., Castellano A., Salido G.M., Rosado J.A. Homer proteins mediate the interaction between STIM1 and Ca1.2 channels. Biochim. Biophys. Acta 1853, 2015:1145-1153.
6. Tovar C., Rosinski J., Filipovic Z., Higgins B., Kolinsky K., Hilton H., Zhao X., Vu B.T., Qing W., Packman K., Myklebost O., Heimbrook D.C., Vassilev L.T. Small-molecule MDM2 antagonists reveal aberrant p53 signaling in cancer: implications for therapy. Proc. Natl. Acad. Sci. U.S.A. 2006, 103:1888-1893.
7. Penna G., Amuchastegui S., Cossetti C., Aquilano F., Mariani R., Giarratana N., De Carli E., Fibbi B., Adorini L. Spontaneous and prostatic steroid binding protein peptide-induced autoimmune prostatitis in the nonobese diabetic mouse. J. Immunol. 2007, 179:1559-1567.
8. Audie J., Swanson J. Recent work in the development and application of protein-peptide docking. Future Med. Chem. 2012, 4:1619-1644.
9. Joughin B.A., Tidor B., Yaffe M.B. A computational method for the analysis and prediction of protein:phosphopeptide-binding sites. Protein Sci. 2005, 14:131-139.
10. Betel D., Breitkreuz K.E., Isserlin R., Dewar-Darch D., Tyers M., Hogue C.W. Structure-templated predictions of novel protein interactions from sequence information. PLoS Comput. Biol. 2007, 3:1783-1789.
11. Nielsen M., Lundegaard C., Blicher T., Lamberth K., Harndahl M., Justesen S., Roder G., Peters B., Sette A., Lund O., Buus S. NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS ONE 2007, 2:e796.
12. Sanchez I.E., Beltrao P., Stricher F., Schymkowitz J., Ferkinghoff-Borg J., Rousseau F., Serrano L. Genome-wide prediction of SH2 domain targets using structural information and the FoldX algorithm. PLoS Comput. Biol. 2008, 4:e1000052.
13. Verschueren E., Vanhee P., Rousseau F., Schymkowitz J., Serrano L. Protein-peptide complex prediction through fragment interaction patterns. Structure 2013, 21:789-797.
14. Trabuco L.G., Lise S., Petsalaki E., Russell R.B. PepSite: prediction of peptide-binding sites from protein surfaces. Nucleic Acids Res. 2012, 40:W423-W427.
15. Lavi A., Ngan C.H., Movshovitz-Attias D., Bohnuud T., Yueh C., Beglov D., Schueler-Furman O., Kozakov D. Detection of peptide-binding sites on protein surfaces: the first step toward the modeling and targeting of peptide-mediated interactions. Proteins 2013, 81:2096-2105.
16. Antes I. DynaDock: a new molecular dynamics-based algorithm for protein-peptide docking including receptor flexibility. Proteins 2010, 78:1084-1104.
17. 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.
18. Raveh B., London N., Zimmerman L., Schueler-Furman O. Rosetta FlexPepDock ab initio: simultaneous folding, docking and refinement of peptides onto their receptors. PLoS ONE 2011, 6:e18934.
19. Trellet M., Melquiond A.S., Bonvin A.M. A unified conformational selection and induced fit approach to protein-peptide docking. PLoS ONE 2013, 8:e58769.
20. Trellet M., Melquiond A.S., Bonvin A.M. Information-driven modeling of protein-peptide complexes. Methods Mol. Biol. 2015, 1268:221-239.
21. Donsky E., Wolfson H.J. PepCrawler: a fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide inhibitors. Bioinformatics 2011, 27:2836-2842.
22. Dagliyan O., Proctor E.A., D'Auria K.M., Ding F., Dokholyan N.V. Structural and dynamic determinants of protein-peptide recognition. Structure 2011, 19:1837-1845.
23. Petsalaki E., Stark A., Garcia-Urdiales E., Russell R.B. Accurate prediction of peptide binding sites on protein surfaces. PLoS Comput. Biol. 2009, 5:e1000335.
24. Kurcinski M., Jamroz M., Blaszczyk M., Kolinski A., Kmiecik S. CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site. Nucleic Acids Res. 2015, 43(W1):W419-W424.
25. Humphrey W., Dalke A., Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. 1996, 14(33-38):27-38.
26. Kolinski A. Protein modeling and structure prediction with a reduced representation. Acta Biochim. Pol. 2004, 51:349-371.
27. 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.
28. Debe D.A., Danzer J.F., Goddard W.A., Poleksic A. STRUCTFAST: protein sequence remote homology detection and alignment using novel dynamic programming and profile-profile scoring. Proteins 2006, 64:960-967.
29. 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.
30. Kmiecik S., Kurcinski M., Rutkowska A., Gront D., Kolinski A. Denatured proteins and early folding intermediates simulated in a reduced conformational space. Acta Biochim. Pol. 2006, 53:131-144.
31. Kmiecik S., Kolinski A. Characterization of protein-folding pathways by reduced-space modeling. Proc. Natl. Acad. Sci. U.S.A. 2007, 104:12330-12335.
32. Kmiecik S., Kolinski A. Folding pathway of the b1 domain of protein G explored by multiscale modeling. Biophys. J. 2008, 94:726-736.
33. Kmiecik S., Jamroz M., Kolinski A. Multiscale Approaches to Protein Modeling 2011, 281-293. Springer, New York. A. Kolinski (Ed.).
34. Kmiecik S., Gront D., Kouza M., Kolinski A. From coarse-grained to atomic-level characterization of protein dynamics: transition state for the folding of B domain of protein A. J. Phys. Chem. B 2012, 116:7026-7032.
35. 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.
36. Wabik J., Kmiecik S., Gront D., Kouza M., Kolinski A. Combining coarse-grained protein models with replica-exchange all-atom molecular dynamics. Int. J. Mol. Sci. 2013, 14:9893-9905.
37. Jamroz M., Kolinski A., Kmiecik S. CABS-flex: server for fast simulation of protein structure fluctuations. Nucleic Acids Res. 2013, 41:W427-W431.
38. Jamroz M., Orozco M., Kolinski A., Kmiecik S. Consistent view of protein fluctuations from all-atom molecular dynamics and coarse-grained dynamics with knowledge-based force-field. J. Chem. Theory Comput. 2013, 9:119-125.
39. Jamroz M., Kolinski A., Kmiecik S. CABS-flex predictions of protein flexibility compared with NMR ensembles. Bioinformatics 2014, 30:2150-2154.
40. Zambrano R., Jamroz M., Szczasiuk A., Pujols J., Kmiecik S., Ventura S. AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures. Nucleic Acids Res. 2015, 43(W1):W306-W313.
41. Steczkiewicz K., Zimmermann M.T., Kurcinski M., Lewis B.A., Dobbs D., Kloczkowski A., Jernigan R.L., Kolinski A., Ginalski K. Human telomerase model shows the role of the TEN domain in advancing the double helix for the next polymerization step. Proc. Natl. Acad. Sci. U.S.A. 2011, 108:9443-9448.
42. Kurcinski M., Kolinski A. Hierarchical modeling of protein interactions. J. Mol. Model. 2007, 13:691-698.
43. 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.
44. 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.
45. Horwacik I., Kurciński M., Bzowska M., Kowalczyk A.K., Czaplicki D., Koliński 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.
46. Kurcinski M., Kolinski A., Kmiecik S. Mechanism of Folding and Binding of an Intrinsically Disordered Protein As Revealed by ab Initio Simulations. J. Chem. Theory Comput. 2014, 10:2224-2231.
47. 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, 2:1-31.
48. Shen M.Y., Sali A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 2006, 15:2507-2524.
49. Katz B.A. Binding of biotin to streptavidin stabilizes intersubunit salt bridges between Asp61 and His87 at low pH. J. Mol. Biol. 1997, 274:776-800.
50. Kmiecik S., Gront D., Kolinski A. Towards the high-resolution protein structure prediction. Fast refinement of reduced models with all-atom force field. BMC Struct. Biol. 2007, 7:43.
51. Gront D., Kmiecik S., Kolinski A. Backbone building from quadrilaterals: a fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates. J. Comput. Chem. 2007, 28:1593-1597.
52. Adcock S.A. Peptide backbone reconstruction using dead-end elimination and a knowledge-based forcefield. J. Comput. Chem. 2004, 25:16-27.
53. Claessens M., Van Cutsem E., Lasters I., Wodak S. Modelling the polypeptide backbone with 'spare parts' from known protein structures. Protein Eng. 1989, 2:335-345.
54. Feig M., Rotkiewicz P., Kolinski A., Skolnick J., Brooks C.L. Accurate reconstruction of all-atom protein representations from side-chain-based low-resolution models. Proteins 2000, 41:86-97.
55. Holm L., Sander C. Database algorithm for generating protein backbone and side-chain co-ordinates from a C alpha trace application to model building and detection of co-ordinate errors. J. Mol. Biol. 1991, 218:183-194.
56. Levitt M. Accurate modeling of protein conformation by automatic segment matching. J. Mol. Biol. 1992, 226:507-533.
57. Maupetit J., Gautier R., Tuffery P. SABBAC: online structural alphabet-based protein backbone reconstruction from alpha-carbon trace. Nucleic Acids Res. 2006, 34:W147-W151.
58. Moore B.L., Kelley L.A., Barber J., Murray J.W., MacDonald J.T. High-quality protein backbone reconstruction from alpha carbons using gaussian mixture models. J. Comput. Chem. 2013, 34:1881-1889.
59. Xu D., Zhang Y. Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. Biophys. J. 2011, 101:2525-2534.
60. Wang Q., Canutescu A.A., Dunbrack R.L. SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling. Nat. Protoc. 2008, 3:1832-1847.
61. Gront D., Kmiecik S., Blaszczyk M., Ekonomiuk D., Koliński A. Optimization of protein models. WIREs Comput. Mol. Sci. 2012, 2:479-493.
62. Li Y., Zhang Y. REMO: a new protocol to refine full atomic protein models from C-alpha traces by optimizing hydrogen-bonding networks. Proteins 2009, 76:665-676.
63. Moal I.H., Moretti R., Baker D., Fernandez-Recio J. Scoring functions for protein-protein interactions. Curr. Opin. Struct. Biol. 2013, 23:862-867.
64. Lensink M.F., Wodak S.J. Docking, scoring, and affinity prediction in CAPRI. Proteins 2013, 81:2082-2095.
65. Kitchen D.B., Decornez H., Furr J.R., Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov. 2004, 3:935-949.
66. Tao P., Lai L.H. Protein ligand docking based on empirical method for binding affinity estimation. J. Comput. Aided Mol. Des. 2001, 15:429-446.
67. Gohlke H., Hendlich M., Klebe G. Knowledge-based scoring function to predict protein-ligand interactions. J. Mol. Biol. 2000, 295:337-356.
68. Hsieh M.J., Luo R. Physical scoring function based on AMBER force field and Poisson-Boltzmann implicit solvent for protein structure prediction. Proteins 2004, 56:475-486.
69. Liu H.Y., Zou X.Q. Electrostatics of ligand binding: Parametrization of the generalized born model and comparison with the Poisson-Boltzmann approach. J. Phys. Chem. B 2006, 110:9304-9313.
70. Ewing T.J.A., Makino S., Skillman A.G., Kuntz I.D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des. 2001, 15:411-428.
71. Kaminski G.A., Friesner R.A., Tirado-Rives J., Jorgensen W.L. Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J. Phys. Chem. B 2001, 105:6474-6487.
72. MacKerell A.D., Bashford D., Bellott M., Dunbrack R.L., Evanseck J.D., Field M.J., Fischer S., Gao J., Guo H., Ha S., Joseph-McCarthy D., Kuchnir L., Kuczera K., Lau F.T.K., Mattos C., Michnick S., Ngo T., Nguyen D.T., Prodhom B., Reiher W.E., Roux B., Schlenkrich M., Smith J.C., Stote R., Straub J., Watanabe M., Wiorkiewicz-Kuczera J., Yin D., Karplus M. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B 1998, 102:3586-3616.
73. Hornak V., Abel R., Okur A., Strockbine B., Roitberg A., Simmerling C. Comparison of multiple amber force fields and development of improved protein backbone parameters. Proteins 2006, 65:712-725.
74. Scott W.R.P., Hunenberger P.H., Tironi I.G., Mark A.E., Billeter S.R., Fennen J., Torda A.E., Huber T., Kruger P., van Gunsteren W.F. The GROMOS biomolecular simulation program package. J. Phys. Chem. A 1999, 103:3596-3607.
75. Berendsen H.J.C., Postma J.P.M., van Gunsteren W.F., Hermans J. Interaction models for water in relation to protein hydration. Intermolecular Forces 1981, 14:331-442.
76. Jorgensen W.L., Chandrasekhar J., Madura J.D., Impey R.W., Klein M.L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 1983, 79:926-935.
77. Nam H.B., Kouza M., Hoang Z., Li M.S. Relationship between population of the fibril-prone conformation in the monomeric state and oligomer formation times of peptides: insights from all-atom simulations. J. Chem. Phys. 2010, 132.
78. Kuhrova P., De Simone A., Otyepka M., Best R.B. Force-field dependence of chignolin folding and misfolding: comparison with experiment and redesign. Biophys. J. 2012, 102:1897-1906.
79. Hess B., Kutzner C., van der Spoel D., Lindahl E. GROMACS 4: algorithms for highly efficient, load-balanced, and scalable molecular simulation. J. Chem. Theory Comput. 2008, 4:435-447.
80. Bussi G., Donadio D., Parrinello M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007, 126.
81. Berendsen H.J.C., Postma J.P.M., Vangunsteren W.F., Dinola A., Haak J.R. Molecular-dynamics with coupling to an external bath. J. Chem. Phys. 1984, 81:3684-3690.
82. Darden T., York D., Pedersen L. Particle Mesh Ewald - an N. Log(N) method for Ewald Sums in large systems. J. Chem. Phys. 1993, 98:10089-10092.
83. Hess B., Bekker H., Berendsen H.J.C., Fraaije J.G.E.M. LINCS: a linear constraint solver for molecular simulations. J. Comput. Chem. 1997, 18:1463-1472.
84. Raveh B., London N., Schueler-Furman O. Sub-angstrom modeling of complexes between flexible peptides and globular proteins. Proteins 2010, 78:2029-2040.
85. Bordner A.J., Abagyan R. Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes. Proteins 2006, 63:512-526.
86. Kokoszka M.E., Kay B.K. Mapping protein-protein interactions with phage-displayed combinatorial peptide libraries and alanine scanning. Methods Mol. Biol. 2015, 1248:173-188.
87. London N., Raveh B., Schueler-Furman O. Druggable protein-protein interactions - from hot spots to hot segments. Curr. Opin. Chem. Biol. 2013, 17:952-959.
88. Wells J.A., McClendon C.L. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 2007, 450:1001-1009.
89. London N., Raveh B., Movshovitz-Attias D., Schueler-Furman O. Can self-inhibitory peptides be derived from the interfaces of globular protein-protein interactions?. Proteins 2010, 78:3140-3149.
90. E.O. Olmez, B.S. Akbulut, Protein-Peptide Interactions Revolutionize Drug Development, 2012.
91. Mayrose I., Penn O., Erez E., Rubinstein N.D., Shlomi T., Freund N.T., Bublil E.M., Ruppin E., Sharan R., Gershoni J.M., Martz E., Pupko T. Pepitope: epitope mapping from affinity-selected peptides. Bioinformatics 2007, 23:3244-3246.
92. Tabrizi M.A., Bornstein G.G., Klakamp S.L. Development of antibody-based therapeutics: translational considerations 2012, Springer, New York.
93. Gershoni J.M., Roitburd-Berman A., Siman-Tov D.D., Tarnovitski Freund N., Weiss Y. Epitope mapping: the first step in developing epitope-based vaccines. BioDrugs 2007, 21:145-156.