Residues Coevolution Guides the Systematic Identification of Alternative Functional Conformations in Proteins.

We present here a new approach for the systematic identification of functionally relevant conformations in proteins. Our fully automated pipeline, based on discrete molecular dynamics enriched with coevolutionary information, is able to capture alternative conformational states in 76% of the proteins studied, providing key atomic details for understanding their function and mechanism of action. We also demonstrate that, given its sampling speed, our method is well suited to explore structural transitions in a high-throughput manner, and can be used to determine functional conformational transitions at the entire proteome level.

[1]  Modesto Orozco,et al.  Approaching Elastic Network Models to Molecular Dynamics Flexibility. , 2010, Journal of chemical theory and computation.

[2]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

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

[4]  Modesto Orozco,et al.  Exploring the suitability of coarse-grained techniques for the representation of protein dynamics. , 2008, Biophysical journal.

[5]  G. Chirikjian,et al.  Efficient generation of feasible pathways for protein conformational transitions. , 2002, Biophysical journal.

[6]  T. Hwa,et al.  Identification of direct residue contacts in protein–protein interaction by message passing , 2009, Proceedings of the National Academy of Sciences.

[7]  N. Go,et al.  Studies on protein folding, unfolding, and fluctuations by computer simulation. II. A. Three‐dimensional lattice model of lysozyme , 1978 .

[8]  I. Bahar,et al.  Normal mode analysis of biomolecular structures: functional mechanisms of membrane proteins. , 2010, Chemical reviews.

[9]  C. Micheletti.  Comparing proteins by their internal dynamics: exploring structure-function relationships beyond static structural alignments. , 2012, Physics of life reviews.

[10]  J. Hub,et al.  Validating solution ensembles from molecular dynamics simulation by wide-angle X-ray scattering data. , 2014, Biophysical journal.

[11]  Ron Elber,et al.  Long-timescale simulation methods. , 2005, Current opinion in structural biology.

[12]  Modesto Orozco,et al.  FlexServ: an integrated tool for the analysis of protein flexibility , 2009, Bioinform..

[13]  R. Engh,et al.  Structural aspects of protein kinase control-role of conformational flexibility. , 2002, Pharmacology & therapeutics.

[14]  Dahlia R. Weiss,et al.  Can morphing methods predict intermediate structures? , 2009, Journal of molecular biology.

[15]  D. Zerbino,et al.  An analysis of core deformations in protein superfamilies. , 2005, Biophysical journal.

[16]  Elizabeth J. Denning,et al.  Zipping and unzipping of adenylate kinase: atomistic insights into the ensemble of open<-->closed transitions. , 2009, Journal of molecular biology.

[17]  N Go,et al.  The effect of amino acid substitution on protein‐folding and ‐unfolding transition studied by computer simulation , 1988, Biopolymers.

[18]  Bert L. de Groot,et al.  Conformational Transitions upon Ligand Binding: Holo-Structure Prediction from Apo Conformations , 2010, PLoS Comput. Biol..

[19]  Terence Hwa,et al.  Coevolutionary signals across protein lineages help capture multiple protein conformations , 2013, Proceedings of the National Academy of Sciences.

[20]  David T. Jones,et al.  MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins , 2014, Bioinform..

[21]  C. Sander,et al.  Direct-coupling analysis of residue coevolution captures native contacts across many protein families , 2011, Proceedings of the National Academy of Sciences.

[22]  Debora S. Marks,et al.  Amino acid coevolution reveals three-dimensional structure and functional domains of insect odorant receptors , 2015, Nature Communications.

[23]  R. Elber,et al.  Atomically detailed simulation of the recovery stroke in myosin by Milestoning , 2010, Proceedings of the National Academy of Sciences.

[24]  Mark A. Wilson,et al.  Intrinsic motions along an enzymatic reaction trajectory , 2007, Nature.

[25]  Modesto Orozco,et al.  Exploration of conformational transition pathways from coarse-grained simulations , 2013, Bioinform..

[26]  J. Onuchic,et al.  Topological and energetic factors: what determines the structural details of the transition state ensemble and "en-route" intermediates for protein folding? An investigation for small globular proteins. , 2000, Journal of molecular biology.

[27]  M. Orozco,et al.  Finding Conformational Transition Pathways from Discrete Molecular Dynamics Simulations. , 2012, Journal of chemical theory and computation.

[28]  S. Eddy,et al.  Pfam: the protein families database , 2013, Nucleic Acids Res..

[29]  J. P. Grossman,et al.  Biomolecular simulation: a computational microscope for molecular biology. , 2012, Annual review of biophysics.

[30]  Modesto Orozco,et al.  United-Atom Discrete Molecular Dynamics of Proteins Using Physics-Based Potentials. , 2008, Journal of chemical theory and computation.

[31]  Thomas A. Hopf,et al.  Three-Dimensional Structures of Membrane Proteins from Genomic Sequencing , 2012, Cell.

[32]  Juan R Perilla,et al.  Computing ensembles of transitions from stable states: Dynamic importance sampling , 2011, J. Comput. Chem..

[33]  S. Iwata,et al.  Molecular Basis of Alternating Access Membrane Transport by the Sodium-Hydantoin Transporter Mhp1 , 2010, Science.

[34]  Joseph A. Bank,et al.  Supporting Online Material Materials and Methods Figs. S1 to S10 Table S1 References Movies S1 to S3 Atomic-level Characterization of the Structural Dynamics of Proteins , 2022 .

[35]  Tetsuya Hori,et al.  Structural Basis of the Substrate-specific Two-step Catalysis of Long Chain Fatty Acyl-CoA Synthetase Dimer* , 2004, Journal of Biological Chemistry.

[36]  Guang Song,et al.  How well can we understand large-scale protein motions using normal modes of elastic network models? , 2007, Biophysical journal.

[37]  X. Daura,et al.  Peptide Folding: When Simulation Meets Experiment , 1999 .

[38]  D. Kern,et al.  Dynamic personalities of proteins , 2007, Nature.

[39]  D. Baker,et al.  Assessing the utility of coevolution-based residue–residue contact predictions in a sequence- and structure-rich era , 2013, Proceedings of the National Academy of Sciences.

[40]  R. Elber.  A milestoning study of the kinetics of an allosteric transition: atomically detailed simulations of deoxy Scapharca hemoglobin. , 2007, Biophysical journal.

[41]  Thomas A. Hopf,et al.  Protein 3D Structure Computed from Evolutionary Sequence Variation , 2011, PloS one.

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

[43]  H. V. D. Bedem,et al.  Automated identification of functional dynamic contact networks from X-ray crystallography , 2013 .

[44]  Modesto Orozco,et al.  A theoretical view of protein dynamics. , 2014, Chemical Society reviews.

[45]  Thomas A. Hopf,et al.  Protein structure prediction from sequence variation , 2012, Nature Biotechnology.

[46]  Joseph J. Falke,et al.  A Moving Story , 2002, Science.

[47]  D. A. Bosco,et al.  Enzyme Dynamics During Catalysis , 2002, Science.

[48]  F. Ding,et al.  Discrete molecular dynamics , 2012 .

[49]  Marcin J. Skwark,et al.  PconsFold: improved contact predictions improve protein models , 2014, Bioinform..

[50]  A. Laio,et al.  Escaping free-energy minima , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[51]  Najeeb M. Halabi,et al.  Protein Sectors: Evolutionary Units of Three-Dimensional Structure , 2009, Cell.

[52]  A. Biegert,et al.  HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment , 2011, Nature Methods.

[53]  Valentina Tozzini,et al.  Coarse-grained models for proteins. , 2005, Current opinion in structural biology.

[54]  Alfonso Valencia,et al.  Emerging methods in protein co-evolution , 2013 .

[55]  Andras Fiser,et al.  Trends in structural coverage of the protein universe and the impact of the Protein Structure Initiative , 2014, Proceedings of the National Academy of Sciences.

[56]  P. Aloy,et al.  A systematic study of the energetics involved in structural changes upon association and connectivity in protein interaction networks. , 2011, Structure.

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

[58]  Modesto Orozco,et al.  Coarse-grained representation of protein flexibility. Foundations, successes, and shortcomings. , 2011, Advances in protein chemistry and structural biology.

[59]  Osamu Miyashita,et al.  Conformational transitions of adenylate kinase: switching by cracking. , 2007, Journal of molecular biology.

[60]  N. Go,et al.  Studies on protein folding, unfolding and fluctuations by computer simulation. I. The effect of specific amino acid sequence represented by specific inter-unit interactions. , 2009 .

[61]  Ivet Bahar,et al.  Exploring the Conformational Transitions of Biomolecular Systems Using a Simple Two-State Anisotropic Network Model , 2014, PLoS Comput. Biol..

[62]  Modesto Orozco,et al.  Comparison of molecular dynamics and superfamily spaces of protein domain deformation , 2009, BMC Structural Biology.

[63]  J. Falke,et al.  Enzymology. A moving story. , 2002, Science.

[64]  Feng Ding,et al.  Discrete molecular dynamics: an efficient and versatile simulation method for fine protein characterization. , 2012, The journal of physical chemistry. B.

[65]  M. Karplus,et al.  Dynamics of folded proteins , 1977, Nature.