Refinement of protein‐protein complexes in contact map space with metadynamics simulations

Accurate protein‐protein complex prediction, to atomic detail, is a challenging problem. For flexible docking cases, current state‐of‐the‐art docking methods are limited in their ability to exhaustively search the high dimensionality of the problem space. In this study, to obtain more accurate models, an investigation into the local optimization of initial docked solutions is presented with respect to a reference crystal structure. We show how physics‐based refinement of protein‐protein complexes in contact map space (CMS), within a metadynamics protocol, can be performed. The method uses 5 times replicated 10 ns simulations for sampling and ranks the generated conformational snapshots with ZRANK to identify an ensemble of n snapshots for final model building. Furthermore, we investigated whether the reconstructed free energy surface (FES), or a combination of both FES and ZRANK, referred to as CSα, can help to reduce snapshot ranking error.

[1]  T. Straatsma,et al.  THE MISSING TERM IN EFFECTIVE PAIR POTENTIALS , 1987 .

[2]  Jeffrey J. Gray,et al.  Pushing the Backbone in Protein-Protein Docking. , 2016, Structure.

[3]  Miriam Eisenstein,et al.  On proteins, grids, correlations, and docking. , 2004, Comptes rendus biologies.

[4]  D. Baker,et al.  Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information , 2014, eLife.

[5]  Marc F. Lensink,et al.  Score_set: A CAPRI benchmark for scoring protein complexes , 2014, Proteins.

[6]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[7]  Zhiping Weng,et al.  IRaPPA: information retrieval based integration of biophysical models for protein assembly selection , 2017, Bioinform..

[8]  Massimiliano Bonomi,et al.  PLUMED 2: New feathers for an old bird , 2013, Comput. Phys. Commun..

[9]  Andrey Tovchigrechko,et al.  GRAMM-X public web server for protein–protein docking , 2006, Nucleic Acids Res..

[10]  S. Teichmann,et al.  Structure, dynamics, assembly, and evolution of protein complexes. , 2015, Annual review of biochemistry.

[11]  R. Abagyan,et al.  ICM‐DISCO docking by global energy optimization with fully flexible side‐chains , 2003, Proteins.

[12]  Cinque S. Soto,et al.  Evaluating conformational free energies: The colony energy and its application to the problem of loop prediction , 2002, Proceedings of the National Academy of Sciences of the United States of America.

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

[14]  Roland L. Dunbrack,et al.  proteins STRUCTURE O FUNCTION O BIOINFORMATICS Improved prediction of protein side-chain conformations with SCWRL4 , 2022 .

[15]  Z. Weng,et al.  ZDOCK: An initial‐stage protein‐docking algorithm , 2003, Proteins.

[16]  Ilya A Vakser,et al.  Chasing funnels on protein-protein energy landscapes at different resolutions. , 2008, Biophysical journal.

[17]  Vahid Mirjalili,et al.  Physics‐based protein structure refinement through multiple molecular dynamics trajectories and structure averaging , 2014, Proteins.

[18]  S. Wodak,et al.  Assessment of blind predictions of protein–protein interactions: Current status of docking methods , 2003, Proteins.

[19]  Kaori H. Yamada,et al.  Phosphorylation-independent dual-site binding of the FHA domain of KIF13 mediates phosphoinositide transport via centaurin α1 , 2010, Proceedings of the National Academy of Sciences.

[20]  S. Wodak,et al.  Assessment of CAPRI predictions in rounds 3–5 shows progress in docking procedures , 2005, Proteins.

[21]  Genki Terashi,et al.  The SKE‐DOCK server and human teams based on a combined method of shape complementarity and free energy estimation , 2007, Proteins.

[22]  Paul A Bates,et al.  A machine learning approach for ranking clusters of docked protein‐protein complexes by pairwise cluster comparison , 2017, Proteins.

[23]  Fei-Fei Li,et al.  Visualizing and Understanding Recurrent Networks , 2015, ArXiv.

[24]  Martin Zacharias,et al.  Protein–protein docking with a reduced protein model accounting for side‐chain flexibility , 2003, Protein science : a publication of the Protein Society.

[25]  J. Thornton,et al.  Structural characterisation and functional significance of transient protein-protein interactions. , 2003, Journal of molecular biology.

[26]  Ruth Nussinov,et al.  PatchDock and SymmDock: servers for rigid and symmetric docking , 2005, Nucleic Acids Res..

[27]  Paul A. Bates,et al.  Predicting improved protein conformations with a temporal deep recurrent neural network , 2018, bioRxiv.

[28]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

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

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

[31]  Sandor Vajda,et al.  CAPRI: A Critical Assessment of PRedicted Interactions , 2003, Proteins.

[32]  Zhiping Weng,et al.  ZRANK: Reranking protein docking predictions with an optimized energy function , 2007, Proteins.

[33]  Francesco Luigi Gervasio,et al.  Effects of oncogenic mutations on the conformational free-energy landscape of EGFR kinase , 2013, Proceedings of the National Academy of Sciences.

[34]  Ioannis Ch. Paschalidis,et al.  Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes , 2008, PLoS Comput. Biol..

[35]  P. Bates,et al.  SwarmDock and the Use of Normal Modes in Protein-Protein Docking , 2010, International journal of molecular sciences.

[36]  Stefano Piana,et al.  Refinement of protein structure homology models via long, all‐atom molecular dynamics simulations , 2012, Proteins.

[37]  Marcin Król,et al.  Flexible relaxation of rigid‐body docking solutions , 2007, Proteins.

[38]  Sandor Vajda,et al.  ClusPro: an automated docking and discrimination method for the prediction of protein complexes , 2004, Bioinform..

[39]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[40]  Massimiliano Bonomi,et al.  Metadynamics , 2019, ioChem-BD Computational Chemistry Datasets.

[41]  L. T. Ten Eyck,et al.  Protein docking using continuum electrostatics and geometric fit. , 2001, Protein engineering.

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

[43]  Joël Janin,et al.  Protein-protein docking tested in blind predictions: the CAPRI experiment. , 2010, Molecular bioSystems.

[44]  Sergey Lyskov,et al.  The RosettaDock server for local protein–protein docking , 2008, Nucleic Acids Res..

[45]  M. Parrinello,et al.  Well-tempered metadynamics: a smoothly converging and tunable free-energy method. , 2008, Physical review letters.

[46]  D. Baker,et al.  The structural and energetic basis for high selectivity in a high-affinity protein-protein interaction , 2010, Proceedings of the National Academy of Sciences.

[47]  Mohammad Moghadasi,et al.  Protein docking refinement by convex underestimation in the low-dimensional subspace of encounter complexes , 2018, Scientific Reports.

[48]  David W. Ritchie,et al.  Ultra-fast FFT protein docking on graphics processors , 2010, Bioinform..

[49]  S. Jones,et al.  Principles of protein-protein interactions. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[50]  P. Aloy,et al.  Interactome3D: adding structural details to protein networks , 2013, Nature Methods.

[51]  C. Dominguez,et al.  HADDOCK: a protein-protein docking approach based on biochemical or biophysical information. , 2003, Journal of the American Chemical Society.