A Coarse-Grained Methodology Identifies Intrinsic Mechanisms That Dissociate Interacting Protein Pairs

We address the problem of triggering dissociation events between proteins that have formed a complex. We have collected a set of 25 non-redundant, functionally diverse protein complexes having high-resolution three-dimensional structures in both the unbound and bound forms. We unify elastic network models with perturbation response scanning (PRS) methodology as an efficient approach for predicting residues that have the propensity to trigger dissociation of an interacting protein pair, using the three-dimensional structures of the bound and unbound proteins as input. PRS reveals that while for a group of protein pairs, residues involved in the conformational shifts are confined to regions with large motions, there are others where they originate from parts of the protein unaffected structurally by binding. Strikingly, only a few of the complexes have interface residues responsible for dissociation. We find two main modes of response: In one mode, remote control of disassociation in which disruption of the electrostatic potential distribution along protein surfaces play the major role; in the alternative mode, mechanical control of dissociation by remote residues prevail. In the former, dissociation is triggered by changes in the local environment of the protein e.g. pH or ionic strength, while in the latter, specific perturbations arriving at the controlling residues, e.g. via binding to a third interacting partner is required for decomplexation. We resolve the observations by relying on an electromechanical coupling model which reduces to the usual elastic network result in the limit of the lack of coupling. We validate the approach by illustrating the biological significance of top residues selected by PRS on select cases where we show that the residues whose perturbation leads to the observed conformational changes correspond to either functionally important or highly conserved residues in the complex.

[1]  Alan R. Fersht,et al.  Tailoring the pH dependence of enzyme catalysis using protein engineering , 1985, Nature.

[2]  ben-Avraham Vibrational normal-mode spectrum of globular proteins. , 1993, Physical review. B, Condensed matter.

[3]  Y. Sanejouand,et al.  A new approach for determining low‐frequency normal modes in macromolecules , 1994 .

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

[5]  Tirion,et al.  Large Amplitude Elastic Motions in Proteins from a Single-Parameter, Atomic Analysis. , 1996, Physical review letters.

[6]  A. Atilgan,et al.  Direct evaluation of thermal fluctuations in proteins using a single-parameter harmonic potential. , 1997, Folding & design.

[7]  R. Bourret,et al.  Catalytic mechanism of phosphorylation and dephosphorylation of CheY: kinetic characterization of imidazole phosphates as phosphodonors and the role of acid catalysis. , 1997, Biochemistry.

[8]  K. Hinsen Analysis of domain motions by approximate normal mode calculations , 1998, Proteins.

[9]  A. Warshel Electrostatic Origin of the Catalytic Power of Enzymes and the Role of Preorganized Active Sites* , 1998, The Journal of Biological Chemistry.

[10]  R. Huber,et al.  Specific inhibition of insect α-amylases: yellow meal worm α-amylase in complex with the Amaranth α-amylase inhibitor at 2.0 Å resolution , 1999 .

[11]  J. Koepke,et al.  Crystal structure of cancer chemopreventive Bowman-Birk inhibitor in ternary complex with bovine trypsin at 2.3 A resolution. Structural basis of Janus-faced serine protease inhibitor specificity. , 2000, Journal of molecular biology.

[12]  Ali Rana Atilgan,et al.  Identifying the adaptive mechanism in globular proteins: Fluctuations in densely packed regions manipulate flexible parts , 2000 .

[13]  M Welch,et al.  Further insights into the mechanism of function of the response regulator CheY from crystallographic studies of the CheY--CheA(124--257) complex. , 2001, Acta crystallographica. Section D, Biological crystallography.

[14]  A. Atilgan,et al.  Elucidating the structural mechanisms for biological activity of the chemokine family , 2001, Proteins.

[15]  Y. Sanejouand,et al.  Conformational change of proteins arising from normal mode calculations. , 2001, Protein engineering.

[16]  R. Jernigan,et al.  Anisotropy of fluctuation dynamics of proteins with an elastic network model. , 2001, Biophysical journal.

[17]  Nathan A. Baker,et al.  Electrostatics of nanosystems: Application to microtubules and the ribosome , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[18]  U. Baumann,et al.  Crystal Structure of a Complex between Pseudomonas aeruginosa Alkaline Protease and Its Cognate Inhibitor , 2001, The Journal of Biological Chemistry.

[19]  A. Atilgan,et al.  Coordination topology and stability for the native and binding conformers of chymotrypsin inhibitor 2 , 2001, Proteins.

[20]  Mark Gerstein,et al.  Normal mode analysis of macromolecular motions in a database framework: Developing mode concentration as a useful classifying statistic , 2002, Proteins.

[21]  Zhiping Weng,et al.  Docking unbound proteins using shape complementarity, desolvation, and electrostatics , 2002, Proteins.

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

[23]  O. Lichtarge,et al.  Evolutionary predictions of binding surfaces and interactions. , 2002, Current opinion in structural biology.

[24]  Ruth Nussinov,et al.  Close‐Range Electrostatic Interactions in Proteins , 2002, Chembiochem : a European journal of chemical biology.

[25]  Robert L. Jernigan,et al.  Dynamics of large proteins through hierarchical levels of coarse‐grained structures , 2002, J. Comput. Chem..

[26]  R L Jernigan,et al.  Molecular mechanisms of chaperonin GroEL-GroES function. , 2002, Biochemistry.

[27]  Jianpeng Ma,et al.  Conformational flexibility of pyruvate dehydrogenase complexes: a computational analysis by quantized elastic deformational model. , 2003, Journal of molecular biology.

[28]  Hui Lu,et al.  The mechanical stability of ubiquitin is linkage dependent , 2003, Nature Structural Biology.

[29]  J. Onuchic,et al.  Nonlinear elasticity, proteinquakes, and the energy landscapes of functional transitions in proteins , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[30]  M. Bochtler,et al.  The Staphostatin-Staphopain Complex , 2003, Journal of Biological Chemistry.

[31]  Sebastian Doniach,et al.  A comparative study of motor-protein motions by using a simple elastic-network model , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Jie Liang,et al.  Protein-protein interactions: hot spots and structurally conserved residues often locate in complemented pockets that pre-organized in the unbound states: implications for docking. , 2004, Journal of molecular biology.

[33]  M. Delarue,et al.  On the use of low-frequency normal modes to enforce collective movements in refining macromolecular structural models. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[34]  Luhua Lai,et al.  Structure-based method for analyzing protein–protein interfaces , 2004, Journal of molecular modeling.

[35]  Konrad Hinsen,et al.  Normal mode-based fitting of atomic structure into electron density maps: application to sarcoplasmic reticulum Ca-ATPase. , 2005, Biophysical journal.

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

[37]  A. Pautsch,et al.  Crystal structure of the C3bot–RalA complex reveals a novel type of action of a bacterial exoenzyme , 2005, The EMBO journal.

[38]  Ivet Bahar,et al.  Elastic network models for understanding biomolecular machinery: from enzymes to supramolecular assemblies , 2005, Physical biology.

[39]  M. Wall,et al.  Quantifying allosteric effects in proteins , 2005, Proteins.

[40]  A. Kidera,et al.  Protein structural change upon ligand binding: linear response theory. , 2005, Physical review letters.

[41]  H. Gohlke,et al.  Multiscale modeling of macromolecular conformational changes combining concepts from rigidity and elastic network theory , 2006, Proteins.

[42]  Stewart A. Adcock,et al.  Molecular dynamics: survey of methods for simulating the activity of proteins. , 2006, Chemical reviews.

[43]  Hendrik Dietz,et al.  Anisotropic deformation response of single protein molecules , 2006, Proceedings of the National Academy of Sciences.

[44]  A. Warshel,et al.  Electrostatic basis for enzyme catalysis. , 2006, Chemical reviews.

[45]  Pedro A Fernandes,et al.  Hot spots—A review of the protein–protein interface determinant amino‐acid residues , 2007, Proteins.

[46]  S. White,et al.  Structures of the dI2dIII1 complex of proton-translocating transhydrogenase with bound, inactive analogues of NADH and NADPH reveal active site geometries. , 2007, Biochemistry.

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

[48]  G. Hummer,et al.  Protein conformational transitions explored by mixed elastic network models , 2007, Proteins.

[49]  Nurit Haspel,et al.  Electrostatic contributions drive the interaction between Staphylococcus aureus protein Efb‐C and its complement target C3d , 2008, Protein science : a publication of the Protein Society.

[50]  Alejandra Leo-Macias,et al.  An efficient conformational sampling method for homology modeling , 2008, Proteins.

[51]  Robert L Jernigan,et al.  Focused functional dynamics of supramolecules by use of a mixed-resolution elastic network model. , 2009, Biophysical journal.

[52]  Ali Rana Atilgan,et al.  Perturbation-Response Scanning Reveals Ligand Entry-Exit Mechanisms of Ferric Binding Protein , 2009, PLoS Comput. Biol..

[53]  Philip E. Bourne,et al.  Drug Discovery Using Chemical Systems Biology: Identification of the Protein-Ligand Binding Network To Explain the Side Effects of CETP Inhibitors , 2009, PLoS Comput. Biol..

[54]  Canan Atilgan,et al.  How orientational order governs collectivity of folded proteins , 2010, Proteins.

[55]  Javier De Las Rivas,et al.  Protein–Protein Interactions Essentials: Key Concepts to Building and Analyzing Interactome Networks , 2010, PLoS Comput. Biol..

[56]  Valentina Tozzini,et al.  Multiscale modeling of proteins. , 2010, Accounts of chemical research.

[57]  Dennis R Livesay,et al.  Allosteric response is both conserved and variable across three CheY orthologs. , 2010, Biophysical journal.

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

[59]  I. Bahar,et al.  Global dynamics of proteins: bridging between structure and function. , 2010, Annual review of biophysics.

[60]  Richard T. Bradshaw,et al.  Comparing experimental and computational alanine scanning techniques for probing a prototypical protein-protein interaction. , 2011, Protein engineering, design & selection : PEDS.

[61]  A. Atilgan,et al.  Subtle pH differences trigger single residue motions for moderating conformations of calmodulin. , 2011, The Journal of chemical physics.

[62]  J. Meller,et al.  Computational Methods for Prediction of Protein-Protein Interaction Sites , 2012 .

[63]  Shuxing Zhang,et al.  Computational prediction of protein hot spot residues. , 2012, Current pharmaceutical design.

[64]  J. Morrow,et al.  Computational Prediction of Protein Hot Spot Residues , 2012 .

[65]  Dennis R Livesay,et al.  Ensemble properties of network rigidity reveal allosteric mechanisms. , 2012, Methods in molecular biology.

[66]  Ali Rana Atilgan,et al.  Network-based models as tools hinting at nonevident protein functionality. , 2012, Annual review of biophysics.

[67]  Mallur S. Madhusudhan,et al.  Depth: a web server to compute depth, cavity sizes, detect potential small-molecule ligand-binding cavities and predict the pKa of ionizable residues in proteins , 2013, Nucleic Acids Res..

[68]  Ali Rana Atilgan,et al.  Designing Molecular Dynamics Simulations to Shift Populations of the Conformational States of Calmodulin , 2013, PLoS Comput. Biol..

[69]  R. Immormino,et al.  Nonconserved active site residues modulate CheY autophosphorylation kinetics and phosphodonor preference. , 2013, Biochemistry.

[70]  Ashini Bolia,et al.  BP-Dock: A Flexible Docking Scheme for Exploring Protein-Ligand Interactions Based on Unbound Structures , 2014, J. Chem. Inf. Model..

[71]  A. Atilgan,et al.  Protonation states of remote residues affect binding-release dynamics of the ligand but not the conformation of apo ferric binding protein. , 2014, The journal of physical chemistry. B.

[72]  M. Orozco A theoretical view of protein dynamics. , 2014, Chemical Society reviews.

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

[74]  A. Atilgan,et al.  Detailed molecular dynamics simulations of human transferrin provide insights into iron release dynamics at serum and endosomal pH , 2015, JBIC Journal of Biological Inorganic Chemistry.

[75]  A. Atilgan,et al.  Perturbation response scanning specifies key regions in subtilisin serine protease for both function and stability , 2015, Journal of enzyme inhibition and medicinal chemistry.

[76]  Gabriele Cruciani,et al.  BioGPS: Navigating biological space to predict polypharmacology, off‐targeting, and selectivity , 2015, Proteins.

[77]  C. Atilgan,et al.  Predicting long term cooperativity and specific modulators of receptor interactions in human transferrin from dynamics within a single microstate. , 2016, Physical chemistry chemical physics : PCCP.

[78]  A. Atilgan,et al.  FbpA iron storage and release are governed by periplasmic microenvironments. , 2017, Physical chemistry chemical physics : PCCP.

[79]  Y. Ma,et al.  Crystal structure of complex , 2018 .

[80]  A. Atilgan,et al.  Unraveling the Motions behind Enterovirus 71 Uncoating. , 2018, Biophysical journal.

[81]  Michael Glenister,et al.  MODE-TASK: large-scale protein motion tools , 2017, bioRxiv.

[82]  C. Atilgan Computational Methods for Efficient Sampling of Protein Landscapes and Disclosing Allosteric Regions. , 2018, Advances in protein chemistry and structural biology.

[83]  Lei Deng,et al.  Machine Learning Approaches for Protein–Protein Interaction Hot Spot Prediction: Progress and Comparative Assessment , 2018, Molecules.

[84]  Yi Xiong,et al.  Protein-protein interface hot spots prediction based on a hybrid feature selection strategy , 2018, BMC Bioinformatics.

[85]  Jun Zhang,et al.  Hot spot prediction in protein-protein interactions by an ensemble system , 2018, BMC Systems Biology.

[86]  D. Ni,et al.  Allosteric Modulators of Protein-Protein Interactions (PPIs). , 2019, Advances in experimental medicine and biology.

[87]  Gail J. Bartlett,et al.  Predicting and Experimentally Validating Hot-Spot Residues at Protein–Protein Interfaces , 2019, ACS chemical biology.

[88]  Ozlem Keskin,et al.  Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing. , 2018, Methods in molecular biology.

[89]  Erik Lindahl,et al.  eBDIMS server: protein transition pathways with ensemble analysis in 2D-motion spaces , 2019, Bioinform..

[90]  M. Bronstein,et al.  Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning , 2019, Nature Methods.

[91]  Enrico Guarnera,et al.  Allosteric drugs and mutations: chances, challenges, and necessity. , 2020, Current opinion in structural biology.

[92]  Ozge Sensoy,et al.  Perturb-Scan-Pull: A Novel Method Facilitating Conformational Transitions in Proteins. , 2020, Journal of chemical theory and computation.