Deciphering General Characteristics of Residues Constituting Allosteric Communication Paths

Allostery is one of most important processes in molecular biology by which proteins transmit the information from one functional site to another, frequently distant site. The information on ligand binding or on posttranslational modification at one site is transmitted along allosteric communication path to another functional site allowing for regulation of protein activity. The detailed analysis of the general character of allosteric communication paths is therefore extremely important. It enables to better understand the mechanism of allostery and can be used in for the design of new generations of drugs.

[1]  D. Leitner,et al.  Mass fractal dimension and the compactness of proteins. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  A. Atilgan,et al.  Manipulation of conformational change in proteins by single-residue perturbations. , 2010, Biophysical journal.

[3]  Lila M Gierasch,et al.  The changing landscape of protein allostery. , 2006, Current opinion in structural biology.

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

[5]  R. Nussinov,et al.  The Role of Protein Loops and Linkers in Conformational Dynamics and Allostery. , 2016, Chemical reviews.

[6]  M. Friedman A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings , 1940 .

[7]  J. Changeux,et al.  ON THE NATURE OF ALLOSTERIC TRANSITIONS: A PLAUSIBLE MODEL. , 1965, Journal of molecular biology.

[8]  Eugene I. Shakhnovich Protein Folding Thermodynamics and Dynamics: Where Physics, Chemistry, and Biology Meet , 2006 .

[9]  D. Koshland,et al.  Comparison of experimental binding data and theoretical models in proteins containing subunits. , 1966, Biochemistry.

[10]  L. Freeman,et al.  Centrality in valued graphs: A measure of betweenness based on network flow , 1991 .

[11]  R. Ranganathan,et al.  Evolutionarily conserved pathways of energetic connectivity in protein families. , 1999, Science.

[12]  Ruth Nussinov,et al.  A Unified View of “How Allostery Works” , 2014, PLoS Comput. Biol..

[13]  Lila M. Gierasch,et al.  Sending Signals Dynamically , 2009, Science.

[14]  R. Nussinov,et al.  Allostery and population shift in drug discovery. , 2010, Current opinion in pharmacology.

[15]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[16]  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.

[17]  K. Gurney,et al.  Network ‘Small-World-Ness’: A Quantitative Method for Determining Canonical Network Equivalence , 2008, PloS one.

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

[19]  F. Wilcoxon Individual Comparisons by Ranking Methods , 1945 .

[20]  Adam Liwo,et al.  Coarse-grained force field: general folding theory. , 2011, Physical chemistry chemical physics : PCCP.

[21]  Ruth Nussinov,et al.  Introduction to Protein Ensembles and Allostery. , 2016, Chemical reviews.

[22]  Mateusz Kurcinski,et al.  Modeling of protein-peptide interactions using the CABS-dock web server for binding site search and flexible docking. , 2015, Methods.

[23]  R. Nussinov,et al.  The origin of allosteric functional modulation: multiple pre-existing pathways. , 2009, Structure.

[24]  Guang Song,et al.  An enhanced elastic network model to represent the motions of domain‐swapped proteins , 2006, Proteins.

[25]  S. Solla,et al.  Self-sustained activity in a small-world network of excitable neurons. , 2003, Physical review letters.

[26]  Ina Koch,et al.  Enumerating all connected maximal common subgraphs in two graphs , 2001, Theor. Comput. Sci..

[27]  Nikolay V Dokholyan,et al.  Controlling Allosteric Networks in Proteins. , 2013, Chemical reviews.

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

[29]  I. Ghosh,et al.  Revisiting the Myths of Protein Interior: Studying Proteins with Mass-Fractal Hydrophobicity-Fractal and Polarizability-Fractal Dimensions , 2009, PloS one.

[30]  V. Traag,et al.  Community detection in networks with positive and negative links. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Peter Willett,et al.  Maximum common subgraph isomorphism algorithms for the matching of chemical structures , 2002, J. Comput. Aided Mol. Des..

[32]  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..

[33]  A Kolinski,et al.  Oligomerization of FVFLM peptides and their ability to inhibit beta amyloid peptides aggregation: consideration as a possible model. , 2017, Physical chemistry chemical physics : PCCP.

[34]  M. Cieplak,et al.  Proteins at air-water and oil-water interfaces in an all-atom model. , 2017, Physical chemistry chemical physics : PCCP.

[35]  Indira Ghosh,et al.  Fractal symmetry of protein interior: what have we learned? , 2011, Cellular and Molecular Life Sciences.

[36]  P. Bonacich Power and Centrality: A Family of Measures , 1987, American Journal of Sociology.

[37]  Shlomi Reuveni,et al.  Anomalies in the vibrational dynamics of proteins are a consequence of fractal-like structure , 2010, Proceedings of the National Academy of Sciences.

[38]  Andrzej Kloczkowski,et al.  Orientational distributions of contact clusters in proteins closely resemble those of an icosahedron , 2008, Proteins.

[39]  D. Kern,et al.  The role of dynamics in allosteric regulation. , 2003, Current opinion in structural biology.

[40]  Jeffrey J. Gray,et al.  Contact rearrangements form coupled networks from local motions in allosteric proteins , 2008, Proteins.

[41]  Zheng Yang,et al.  Allosteric Transitions of Supramolecular Systems Explored by Network Models: Application to Chaperonin GroEL , 2009, PLoS Comput. Biol..

[42]  Phillip Bonacich,et al.  Simultaneous group and individual centralities , 1991 .

[43]  M. Babu,et al.  Molecular signatures of G-protein-coupled receptors , 2013, Nature.

[44]  Andrzej Kolinski,et al.  Switch from thermal to force-driven pathways of protein refolding. , 2017, The Journal of chemical physics.

[45]  S. Takada,et al.  Frustration, specific sequence dependence, and nonlinearity in large-amplitude fluctuations of allosteric proteins , 2011, Proceedings of the National Academy of Sciences.

[46]  Andrzej Kolinski,et al.  Protocols for efficient simulations of long-time protein dynamics using coarse-grained CABS model. , 2014, Methods in molecular biology.

[47]  Adilson E Motter,et al.  Heterogeneity in oscillator networks: are smaller worlds easier to synchronize? , 2003, Physical review letters.

[48]  S. Wuchty Scale-free behavior in protein domain networks. , 2001, Molecular biology and evolution.

[49]  Andrej Sali,et al.  Structure-based model of allostery predicts coupling between distant sites , 2012, Proceedings of the National Academy of Sciences.

[50]  J. Reichardt,et al.  Statistical mechanics of community detection. , 2006, Physical review. E, Statistical, nonlinear, and soft matter physics.

[51]  Z. Nevin Gerek,et al.  Change in Allosteric Network Affects Binding Affinities of PDZ Domains: Analysis through Perturbation Response Scanning , 2011, PLoS Comput. Biol..

[52]  Mauricio Barahona,et al.  Synchronization in small-world systems. , 2002, Physical review letters.

[53]  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.

[54]  Pietro Liò,et al.  Prediction by Graph Theoretic Measures of Structural Effects in Proteins Arising from Non-Synonymous Single Nucleotide Polymorphisms , 2008, PLoS Comput. Biol..

[55]  M. Cieplak,et al.  Non-local effects of point mutations on the stability of a protein module. , 2017, The Journal of chemical physics.

[56]  Andrzej Kloczkowski,et al.  Kinetics and mechanical stability of the fibril state control fibril formation time of polypeptide chains: A computational study. , 2018, The Journal of chemical physics.

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

[58]  Adam Liwo,et al.  Protein-folding dynamics: overview of molecular simulation techniques. , 2007, Annual review of physical chemistry.

[59]  Weitao Sun,et al.  From Isotropic to Anisotropic Side Chain Representations: Comparison of Three Models for Residue Contact Estimation , 2011, PloS one.

[60]  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.

[61]  Daisuke Kihara,et al.  Threading without optimizing weighting factors for scoring function , 2008, Proteins.

[62]  R. Nussinov,et al.  Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell. , 2016, Chemical reviews.

[63]  Huan‐Xiang Zhou,et al.  Protein Allostery and Conformational Dynamics. , 2016, Chemical reviews.

[64]  M Karplus,et al.  Small-world view of the amino acids that play a key role in protein folding. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[65]  N. Buchete,et al.  Amyloid β Protein and Alzheimer's Disease: When Computer Simulations Complement Experimental Studies. , 2015, Chemical reviews.

[66]  J. Schellman,et al.  The Factors Affecting the Stability of Hydrogen-bonded Polypeptide Structures in Solution , 1958 .

[67]  Andrzej Kloczkowski,et al.  A global machine learning based scoring function for protein structure prediction , 2014, Proteins.

[68]  Andrzej Kloczkowski,et al.  Four‐body contact potentials derived from two protein datasets to discriminate native structures from decoys , 2007, Proteins.

[69]  A. del Sol,et al.  Small‐world network approach to identify key residues in protein–protein interaction , 2004, Proteins.

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

[71]  J. Changeux,et al.  Allosteric Mechanisms of Signal Transduction , 2005, Science.

[72]  A. Kloczkowski,et al.  A topological order parameter for describing folding free energy landscapes of proteins. , 2018, The Journal of chemical physics.

[73]  P Willett,et al.  Identification of tertiary structure resemblance in proteins using a maximal common subgraph isomorphism algorithm. , 1993, Journal of molecular biology.

[74]  Anirban Banerji,et al.  An attempt to construct a (general) mathematical framework to model biological “context-dependence” , 2013, Systems and Synthetic Biology.

[75]  R. Nussinov,et al.  Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM , 2011, Nature Protocols.

[76]  L F Lago-Fernández,et al.  Fast response and temporal coherent oscillations in small-world networks. , 1999, Physical review letters.

[77]  M. Friedman The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance , 1937 .

[78]  Andrzej Kloczkowski,et al.  BioShell-Threading: versatile Monte Carlo package for protein 3D threading , 2014, BMC Bioinformatics.

[79]  A. Kolinski,et al.  Coarse-Grained Protein Models and Their Applications. , 2016, Chemical reviews.

[80]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[81]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[82]  H. Akaike A new look at the statistical model identification , 1974 .

[83]  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.

[84]  Generalized spring tensor models for protein fluctuation dynamics and conformation changes , 2009, BIBM 2009.

[85]  R. Jernigan,et al.  The energy profiles of atomic conformational transition intermediates of adenylate kinase , 2009, Proteins.

[86]  C. Chennubhotla,et al.  Intrinsic dynamics of enzymes in the unbound state and relation to allosteric regulation. , 2007, Current opinion in structural biology.

[87]  D. Leitner Energy flow in proteins. , 2008, Annual review of physical chemistry.

[88]  Thomas Lengauer,et al.  An Algorithm for Finding Maximal Common Subtopologies in a Set of Protein Structures , 1996, J. Comput. Biol..

[89]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[90]  Douglas L. Theobald,et al.  Optimal simultaneous superpositioning of multiple structures with missing data , 2012, Bioinform..

[91]  Andrzej Kloczkowski,et al.  Deciphering General Characteristics of Residues Constituting Allosteric Communication Paths , 2019, IWBBIO.

[92]  R. Nussinov,et al.  Conformational ensembles, signal transduction and residue hot spots: application to drug discovery. , 2010, Current opinion in drug discovery & development.

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

[94]  Chris Smith,et al.  Molecular Biology of the Cell (Fifth Edition) , 2008 .

[95]  André A. S. T. Ribeiro,et al.  A Chemical Perspective on Allostery. , 2016, Chemical reviews.

[96]  J. I. Sulkowska,et al.  Predicting the order in which contacts are broken during single molecule protein stretching experiments , 2008, Proteins.

[97]  A. Kolinski,et al.  Structural features that predict real‐value fluctuations of globular proteins , 2012, Proteins.

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

[99]  Andrzej Kloczkowski,et al.  Packing regularities in biological structures relate to their dynamics. , 2007, Methods in molecular biology.

[100]  Victoria A. Higman,et al.  Uncovering network systems within protein structures. , 2003, Journal of molecular biology.

[101]  Andrzej Kolinski,et al.  Preformed template fluctuations promote fibril formation: insights from lattice and all-atom models. , 2015, The Journal of chemical physics.

[102]  Aleksandra E. Badaczewska-Dawid,et al.  Modeling of Protein Structural Flexibility and Large-Scale Dynamics: Coarse-Grained Simulations and Elastic Network Models , 2018, International journal of molecular sciences.

[103]  A. Banerji,et al.  A new computational model to study mass inhomogeneity and hydrophobicity inhomogeneity in proteins , 2009, European Biophysics Journal.

[104]  Andrzej Kloczkowski,et al.  Role of Resultant Dipole Moment in Mechanical Dissociation of Biological Complexes , 2018, Molecules.

[105]  J. J. McGregor,et al.  Backtrack search algorithms and the maximal common subgraph problem , 1982, Softw. Pract. Exp..

[106]  Andrzej Kloczkowski,et al.  Classification of Allostery in Proteins: A Deep Learning Approach , 2018 .

[107]  Shuai Li,et al.  ASD v2.0: updated content and novel features focusing on allosteric regulation , 2013, Nucleic Acids Res..

[108]  A. Atilgan,et al.  Small-world communication of residues and significance for protein dynamics. , 2003, Biophysical journal.

[109]  Aron W Fenton,et al.  Allostery: an illustrated definition for the 'second secret of life'. , 2008, Trends in biochemical sciences.

[110]  D. Thirumalai,et al.  Determination of network of residues that regulate allostery in protein families using sequence analysis , 2006, Protein science : a publication of the Protein Society.

[111]  C. Frieden Kinetic Aspects of Regulation of Metabolic Processes , 2003 .

[112]  Dror Tobi,et al.  Allosteric changes in protein structure computed by a simple mechanical model: hemoglobin T<-->R2 transition. , 2003, Journal of molecular biology.