Application of Enhanced Sampling Monte Carlo Methods for High-Resolution Protein-Protein Docking in Rosetta

The high-resolution refinement of docked protein-protein complexes can provide valuable structural and mechanistic insight into protein complex formation complementing experiment. Monte Carlo (MC) based approaches are frequently applied to sample putative interaction geometries of proteins including also possible conformational changes of the binding partners. In order to explore efficiency improvements of the MC sampling, several enhanced sampling techniques, including temperature or Hamiltonian replica exchange and well-tempered ensemble approaches, have been combined with the MC method and were evaluated on 20 protein complexes using unbound partner structures. The well-tempered ensemble method combined with a 2-dimensional temperature and Hamiltonian replica exchange scheme (WTE-H-REMC) was identified as the most efficient search strategy. Comparison with prolonged MC searches indicates that the WTE-H-REMC approach requires approximately 5 times fewer MC steps to identify near native docking geometries compared to conventional MC searches.

[1]  Z. Weng,et al.  Protein–protein docking benchmark version 3.0 , 2008, Proteins.

[2]  U. Hansmann.  Parallel tempering algorithm for conformational studies of biological molecules , 1997, physics/9710041.

[3]  Yang Zhang,et al.  Monte Carlo refinement of rigid‐body protein docking structures with backbone displacement and side‐chain optimization , 2007, Protein science : a publication of the Protein Society.

[4]  J. Pablo,et al.  Hyper-parallel tempering Monte Carlo: Application to the Lennard-Jones fluid and the restricted primitive model , 1999 .

[5]  Jeffrey J. Gray,et al.  Protein-protein docking with simultaneous optimization of rigid-body displacement and side-chain conformations. , 2003, Journal of molecular biology.

[6]  A. Laio,et al.  Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science , 2008 .

[7]  Roland L. Dunbrack,et al.  A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions. , 2011, Structure.

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

[9]  Dima Kozakov,et al.  Convergence and combination of methods in protein-protein docking. , 2009, Current opinion in structural biology.

[10]  Oliver F. Lange,et al.  Replica Exchange Improves Sampling in Low-Resolution Docking Stage of RosettaDock , 2013, PloS one.

[11]  E. Katchalski‐Katzir,et al.  Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques. , 1992, Proceedings of the National Academy of Sciences of the United States of America.

[12]  David Baker,et al.  Protein Structure Prediction Using Rosetta , 2004, Numerical Computer Methods, Part D.

[13]  J. D. de Pablo,et al.  Optimal allocation of replicas in parallel tempering simulations. , 2005, The Journal of chemical physics.

[14]  D. Baker,et al.  High-resolution Structural and Thermodynamic Analysis of Extreme Stabilization of Human Procarboxypeptidase by Computational Protein Design , 2007, Journal of molecular biology.

[15]  J. Pablo,et al.  Hyperparallel tempering Monte Carlo simulation of polymeric systems , 2000 .

[16]  D. Baker,et al.  Computational redesign of protein-protein interaction specificity , 2004, Nature Structural &Molecular Biology.

[17]  M. Parrinello,et al.  Enhanced sampling in the well-tempered ensemble. , 2009, Physical review letters.

[18]  David Baker,et al.  Protein–protein docking predictions for the CAPRI experiment , 2003, Proteins.

[19]  Jozef Hritz,et al.  Hamiltonian replica exchange molecular dynamics using soft-core interactions. , 2008, The Journal of chemical physics.

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

[21]  S. Takada,et al.  On the Hamiltonian replica exchange method for efficient sampling of biomolecular systems: Application to protein structure prediction , 2002 .

[22]  K. Dill,et al.  From Levinthal to pathways to funnels , 1997, Nature Structural Biology.

[23]  Marc F Lensink,et al.  Docking and scoring protein interactions: CAPRI 2009 , 2010, Proteins.

[24]  David Baker,et al.  A general computational approach for repeat protein design. , 2015, Journal of molecular biology.

[25]  Y. Sugita,et al.  Replica-exchange molecular dynamics method for protein folding , 1999 .

[26]  Hyeong Jun An,et al.  Estimating the size of the human interactome , 2008, Proceedings of the National Academy of Sciences.

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

[28]  S Vajda,et al.  Free energy landscapes of encounter complexes in protein-protein association. , 1999, Biophysical journal.

[29]  Nir London,et al.  Funnel hunting in a rough terrain: learning and discriminating native energy funnels. , 2008, Structure.

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

[31]  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.

[32]  Pralay Mitra,et al.  How Many Protein-Protein Interactions Types Exist in Nature? , 2012, PloS one.

[33]  Sheng-You Huang,et al.  Search strategies and evaluation in protein-protein docking: principles, advances and challenges. , 2014, Drug discovery today.

[34]  Matthias Troyer,et al.  Feedback-optimized parallel tempering Monte Carlo , 2006, cond-mat/0602085.

[35]  Nir London,et al.  An accurate binding interaction model in de novo computational protein design of interactions: if you build it, they will bind. , 2014, Journal of structural biology.

[36]  Zhiping Weng,et al.  Protein–protein docking benchmark version 4.0 , 2010, Proteins.

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

[38]  Martin Zacharias,et al.  Protein-Ligand Docking Using Hamiltonian Replica Exchange Simulations with Soft Core Potentials , 2014, J. Chem. Inf. Model..

[39]  Yair Weiss,et al.  Minimizing and Learning Energy Functions for Side-Chain Prediction , 2007, RECOMB.

[40]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[41]  Brian D. Weitzner,et al.  Benchmarking and Analysis of Protein Docking Performance in Rosetta v3.2 , 2011, PloS one.

[42]  Jeffrey J. Gray,et al.  High-resolution protein-protein docking. , 2006, Current opinion in structural biology.

[43]  Massimiliano Bonomi,et al.  Efficient Simulation of Explicitly Solvated Proteins in the Well-Tempered Ensemble. , 2012, Journal of chemical theory and computation.

[44]  H. Wolfson,et al.  Shape complementarity at protein–protein interfaces , 1994, Biopolymers.

[45]  Wang,et al.  Replica Monte Carlo simulation of spin glasses. , 1986, Physical review letters.

[46]  Michael W Deem,et al.  Parallel tempering: theory, applications, and new perspectives. , 2005, Physical chemistry chemical physics : PCCP.

[47]  M. Deem,et al.  A biased Monte Carlo scheme for zeolite structure solution , 1998, cond-mat/9809085.

[48]  D. Baker,et al.  Prediction and design of macromolecular structures and interactions , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[49]  Martin Zacharias,et al.  Advanced replica-exchange sampling to study the flexibility and plasticity of peptides and proteins. , 2013, Biochimica et biophysica acta.

[50]  David Baker,et al.  Centenary Award and Sir Frederick Gowland Hopkins Memorial Lecture. Protein folding, structure prediction and design. , 2014, Biochemical Society transactions.