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

Protein-peptide interactions play essential functional roles in living organisms and their structural characterization is a hot subject of current experimental and theoretical research. Computational modeling of the structure of protein-peptide interactions is usually divided into two stages: prediction of the binding site at a protein receptor surface, and then docking (and modeling) the peptide structure into the known binding site. This paper presents a comprehensive CABS-dock method for the simultaneous search of binding sites and flexible protein-peptide docking, available as a user's friendly web server. We present example CABS-dock results obtained in the default CABS-dock mode and using its advanced options that enable the user to increase the range of flexibility for chosen receptor fragments or to exclude user-selected binding modes from docking search. Furthermore, we demonstrate a strategy to improve CABS-dock performance by assessing the quality of models with classical molecular dynamics. Finally, we discuss the promising extensions and applications of the CABS-dock method and provide a tutorial appendix for the convenient analysis and visualization of CABS-dock results. The CABS-dock web server is freely available at http://biocomp.chem.uw.edu.pl/CABSdock/.

[1]  Mohammad Tabrizi,et al.  Translational Strategies for Development of Antibody-Based Therapeutics: An Overview , 2012 .

[2]  J. Bajorath,et al.  Docking and scoring in virtual screening for drug discovery: methods and applications , 2004, Nature Reviews Drug Discovery.

[3]  Robert B. Russell,et al.  PepSite: prediction of peptide-binding sites from protein surfaces , 2012, Nucleic Acids Res..

[4]  Mateusz Kurcinski,et al.  Theoretical study of molecular mechanism of binding TRAP220 coactivator to Retinoid X Receptor alpha, activated by 9-cis retinoic acid , 2010, The Journal of Steroid Biochemistry and Molecular Biology.

[5]  B. Katz Binding of biotin to streptavidin stabilizes intersubunit salt bridges between Asp61 and His87 at low pH. , 1997, Journal of molecular biology.

[6]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[7]  Andrzej Kolinski,et al.  Folding pathway of the b1 domain of protein G explored by multiscale modeling. , 2007, Biophysical journal.

[8]  James W. Murray,et al.  High–quality protein backbone reconstruction from alpha carbons using Gaussian mixture models , 2013, J. Comput. Chem..

[9]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[10]  Marc F Lensink,et al.  Docking, scoring, and affinity prediction in CAPRI , 2013, Proteins.

[11]  Nir London,et al.  Sub‐angstrom modeling of complexes between flexible peptides and globular proteins , 2010, Proteins.

[12]  Mateusz Kurcinski,et al.  CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site , 2015, Nucleic Acids Res..

[13]  Mai Suan Li,et al.  Relationship between population of the fibril-prone conformation in the monomeric state and oligomer formation times of peptides: insights from all-atom simulations. , 2010, The Journal of chemical physics.

[14]  Nir London,et al.  Druggable protein-protein interactions--from hot spots to hot segments. , 2013, Current opinion in chemical biology.

[15]  H. Berendsen,et al.  Interaction Models for Water in Relation to Protein Hydration , 1981 .

[16]  Aleksandar Poleksic,et al.  STRUCTFAST: Protein sequence remote homology detection and alignment using novel dynamic programming and profile–profile scoring , 2006, Proteins.

[17]  Dominik Gront,et al.  Optimization of protein models , 2012 .

[18]  R. Friesner,et al.  Evaluation and Reparametrization of the OPLS-AA Force Field for Proteins via Comparison with Accurate Quantum Chemical Calculations on Peptides† , 2001 .

[19]  Alexander D. MacKerell,et al.  All-atom empirical potential for molecular modeling and dynamics studies of proteins. , 1998, The journal of physical chemistry. B.

[20]  Dominik Czaplicki,et al.  Analysis and optimization of interactions between peptides mimicking the GD2 ganglioside and the monoclonal antibody 14G2a. , 2011, International journal of molecular medicine.

[21]  H. Berendsen,et al.  Molecular dynamics with coupling to an external bath , 1984 .

[22]  Rafael Zambrano,et al.  AGGRESCAN3D (A3D): server for prediction of aggregation properties of protein structures , 2015, Nucleic Acids Res..

[23]  Ora Schueler-Furman,et al.  Rosetta FlexPepDock web server—high resolution modeling of peptide–protein interactions , 2011, Nucleic Acids Res..

[24]  Dominik Gront,et al.  Denatured proteins and early folding intermediates simulated in a reduced conformational space. , 2005, Acta biochimica Polonica.

[25]  Nir London,et al.  Can self‐inhibitory peptides be derived from the interfaces of globular protein–protein interactions? , 2010, Proteins.

[26]  Kevin M. D'Auria,et al.  Structural and dynamic determinants of protein-peptide recognition. , 2011, Structure.

[27]  Mateusz Kurcinski,et al.  Steps towards flexible docking: Modeling of three-dimensional structures of the nuclear receptors bound with peptide ligands mimicking co-activators’ sequences , 2007, The Journal of Steroid Biochemistry and Molecular Biology.

[28]  R. Best,et al.  Force-field dependence of chignolin folding and misfolding: comparison with experiment and redesign. , 2012, Biophysical journal.

[29]  M. Parrinello,et al.  Canonical sampling through velocity rescaling. , 2007, The Journal of chemical physics.

[30]  C. Sander,et al.  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. , 1991, Journal of molecular biology.

[31]  Alexandre M. J. J. Bonvin,et al.  A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking , 2013, PloS one.

[32]  T. Smani,et al.  Homer proteins mediate the interaction between STIM1 and Cav1.2 channels. , 2015, Biochimica et biophysica acta.

[33]  Yang Zhang,et al.  REMO: A new protocol to refine full atomic protein models from C‐alpha traces by optimizing hydrogen‐bonding networks , 2009, Proteins.

[34]  David Baker,et al.  Scoring functions for protein-protein interactions. , 2013, Current opinion in structural biology.

[35]  Andrew E. Torda,et al.  The GROMOS biomolecular simulation program package , 1999 .

[36]  Ora Schueler-Furman,et al.  Modeling Peptide-Protein Interactions , 2017, Methods in Molecular Biology.

[37]  Dominik Gront,et al.  Combining Coarse-Grained Protein Models with Replica-Exchange All-Atom Molecular Dynamics , 2013, International journal of molecular sciences.

[38]  Adrian A Canutescu,et al.  SCWRL and MolIDE: computer programs for side-chain conformation prediction and homology modeling , 2008, Nature Protocols.

[39]  Ray Luo,et al.  Physical scoring function based on AMBER force field and Poisson–Boltzmann implicit solvent for protein structure prediction , 2004, Proteins.

[40]  Andrzej Kolinski,et al.  CABS-flex: server for fast simulation of protein structure fluctuations , 2013, Nucleic Acids Res..

[41]  Modesto Orozco,et al.  Consistent View of Protein Fluctuations from All-Atom Molecular Dynamics and Coarse-Grained Dynamics with Knowledge-Based Force-Field. , 2013, Journal of chemical theory and computation.

[43]  Xiaoqin Zou,et al.  Electrostatics of ligand binding: parametrization of the generalized Born model and comparison with the Poisson-Boltzmann approach. , 2006, The journal of physical chemistry. B.

[44]  Iris Antes,et al.  DynaDock: A new molecular dynamics‐based algorithm for protein–peptide docking including receptor flexibility , 2010, Proteins.

[45]  Roded Sharan,et al.  BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm493 Structural bioinformatics Pepitope: epitope mapping from affinity-selected peptides , 2022 .

[46]  K Schulten,et al.  VMD: visual molecular dynamics. , 1996, Journal of molecular graphics.

[47]  Elif Ozkirimli Olmez,et al.  Protein-Peptide Interactions Revolutionize Drug Development , 2012 .

[48]  Joost Schymkowitz,et al.  Protein-peptide complex prediction through fragment interaction patterns. , 2013, Structure.

[49]  Berk Hess,et al.  LINCS: A linear constraint solver for molecular simulations , 1997, J. Comput. Chem..

[50]  Todd J. A. Ewing,et al.  DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases , 2001, J. Comput. Aided Mol. Des..

[51]  Nir London,et al.  Rosetta FlexPepDock ab-initio: Simultaneous Folding, Docking and Refinement of Peptides onto Their Receptors , 2011, PloS one.

[52]  A. Kolinski Protein modeling and structure prediction with a reduced representation. , 2004, Acta biochimica Polonica.

[53]  G. Klebe,et al.  Knowledge-based scoring function to predict protein-ligand interactions. , 2000, Journal of molecular biology.

[54]  R. Abagyan,et al.  Ab initio prediction of peptide‐MHC binding geometry for diverse class I MHC allotypes , 2006, Proteins.

[55]  Carsten Kutzner,et al.  GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. , 2008, Journal of chemical theory and computation.

[56]  Andrej Sali,et al.  Comparative Protein Structure Modeling Using MODELLER , 2014, Current protocols in bioinformatics.

[57]  Mateusz Kurcinski,et al.  Mechanism of Folding and Binding of an Intrinsically Disordered Protein As Revealed by ab Initio Simulations. , 2014, Journal of chemical theory and computation.

[58]  Joseph Audie,et al.  Recent work in the development and application of protein-peptide docking. , 2012, Future medicinal chemistry.

[59]  Christopher L. McClendon,et al.  Reaching for high-hanging fruit in drug discovery at protein–protein interfaces , 2007, Nature.

[60]  Dominik Gront,et al.  From coarse-grained to atomic-level characterization of protein dynamics: transition state for the folding of B domain of protein A. , 2012, The journal of physical chemistry. B.

[61]  Dominik Gront,et al.  Backbone building from quadrilaterals: A fast and accurate algorithm for protein backbone reconstruction from alpha carbon coordinates , 2007, J. Comput. Chem..

[62]  Yong-soo Kim,et al.  Interferon Regulatory Factor 8 (IRF8) Interacts with the B Cell Lymphoma 6 (BCL6) Corepressor BCOR* , 2014, The Journal of Biological Chemistry.

[63]  Christopher W. V. Hogue,et al.  Structure-Templated Predictions of Novel Protein Interactions from Sequence Information , 2007, PLoS Comput. Biol..

[64]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[65]  O. Lund,et al.  NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence , 2007, PloS one.

[66]  S. Wodak,et al.  Modelling the polypeptide backbone with 'spare parts' from known protein structures. , 1989, Protein engineering.

[67]  Andrzej Kolinski,et al.  CABS-flex predictions of protein flexibility compared with NMR ensembles , 2014, Bioinform..

[68]  Yang Zhang,et al.  Improving the physical realism and structural accuracy of protein models by a two-step atomic-level energy minimization. , 2011, Biophysical journal.

[69]  A. Sali,et al.  Statistical potential for assessment and prediction of protein structures , 2006, Protein science : a publication of the Protein Society.

[70]  Andrzej Kolinski,et al.  Multiscale Approaches to Protein Modeling , 2011 .

[71]  A. Kolinski,et al.  Characterization of protein-folding pathways by reduced-space modeling , 2007, Proceedings of the National Academy of Sciences.

[72]  B. Kay,et al.  Mapping Protein–Protein Interactions with Phage-Displayed Combinatorial Peptide Libraries and Alanine Scanning , 2014, Methods in molecular biology.

[73]  Andrzej Kolinski,et al.  CABS-fold: server for the de novo and consensus-based prediction of protein structure , 2013, Nucleic Acids Res..

[74]  M Feig,et al.  Accurate reconstruction of all‐atom protein representations from side‐chain‐based low‐resolution models , 2000, Proteins.

[75]  Luhua Lai,et al.  Protein ligand docking based on empirical method for binding affinity estimation , 2001, J. Comput. Aided Mol. Des..

[76]  Bruce Tidor,et al.  A computational method for the analysis and prediction of protein:phosphopeptide‐binding sites , 2005, Protein science : a publication of the Protein Society.

[77]  Dima Kozakov,et al.  Detection of peptide‐binding sites on protein surfaces: The first step toward the modeling and targeting of peptide‐mediated interactions , 2013, Proteins.

[78]  O. Myklebost,et al.  Small-molecule MDM2 antagonists reveal aberrant p53 signaling in cancer: implications for therapy. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[79]  Eduardo Garcia Urdiales,et al.  Accurate Prediction of Peptide Binding Sites on Protein Surfaces , 2009, PLoS Comput. Biol..

[80]  Andrzej Kolinski,et al.  Simulation of Chaperonin Effect on Protein Folding: A Shift from Nucleation–Condensation to Framework Mechanism , 2011, Journal of the American Chemical Society.

[81]  Stewart A. Adcock Peptide backbone reconstruction using dead‐end elimination and a knowledge‐based forcefield , 2004, J. Comput. Chem..

[82]  Dominik Gront,et al.  BMC Structural Biology BioMed Central , 2007 .

[83]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[84]  M. Levitt Accurate modeling of protein conformation by automatic segment matching. , 1992, Journal of molecular biology.

[85]  Benjamin A. Lewis,et al.  Human telomerase model shows the role of the TEN domain in advancing the double helix for the next polymerization step , 2011, Proceedings of the National Academy of Sciences.

[86]  Nir London,et al.  Peptide docking and structure-based characterization of peptide binding: from knowledge to know-how. , 2013, Current opinion in structural biology.

[87]  Alexandre M J J Bonvin,et al.  Information-driven modeling of protein-peptide complexes. , 2015, Methods in molecular biology.

[88]  Mateusz Kurcinski,et al.  Hierarchical modeling of protein interactions , 2007, Journal of molecular modeling.

[89]  R. Russell,et al.  Peptide-mediated interactions in biological systems: new discoveries and applications. , 2008, Current opinion in biotechnology.

[90]  Pierre Tufféry,et al.  SABBAC: online Structural Alphabet-based protein BackBone reconstruction from Alpha-Carbon trace , 2006, Nucleic Acids Res..

[91]  Haim J. Wolfson,et al.  PepCrawler: a fast RRT-based algorithm for high-resolution refinement and binding affinity estimation of peptide inhibitors , 2011, Bioinform..

[92]  Ignacio E. Sánchez,et al.  Genome-Wide Prediction of SH2 Domain Targets Using Structural Information and the FoldX Algorithm , 2008, PLoS Comput. Biol..

[93]  Giuseppe Penna,et al.  Spontaneous and Prostatic Steroid Binding Protein Peptide-Induced Autoimmune Prostatitis in the Nonobese Diabetic Mouse1 , 2007, The Journal of Immunology.

[94]  Janusz M Bujnicki,et al.  Generalized protein structure prediction based on combination of fold‐recognition with de novo folding and evaluation of models , 2005, Proteins.