Modeling Binding with Large Conformational Changes: Key Points in Ensemble-Docking Approaches

Protein dynamics play a critical role in ligand binding, and different models have been proposed to explain the relationships between protein motion and molecular recognition. Here, we present a study of ligand-binding processes associated with large conformational changes of a protein to elucidate the critical choices in ensemble-docking approaches for effective prediction of the binding geometry. Two study cases were selected in which binding involves different protein motions and intermolecular interactions and, accordingly, conformational selection and induced-fit mechanisms play different roles: binding of multiple ligands to the acetylcholine binding protein and highly specific binding of D-allose to the allose binding protein. Our results indicated that the ensemble-docking technique can provide reliable predictions of the structure of ligand-protein complexes, starting from simulations of the apo systems, when suitable methodological choices are made according to the different mechanistic scenarios. In particular, accelerated molecular dynamics simulations are suitable for conformational sampling when the unbound and bound states are separated by high energy barriers, provided that the acceleration parameters are carefully set to extensively sample the relevant conformations. A strategy specifically developed for geometric clustering of the binding site proved to be effective for selecting a set of conformations relevant to binding from the MD trajectory. Specific strategies have to be selected to incorporate different degrees of ligand-induced protein flexibility into the docking or pose-refinement steps.

[1]  Ruth Nussinov,et al.  Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics , 2016, PLoS Comput. Biol..

[2]  R. Nussinov,et al.  The role of dynamic conformational ensembles in biomolecular recognition. , 2009, Nature chemical biology.

[3]  Alessandro Pandini,et al.  Predicting the accuracy of protein–ligand docking on homology models , 2011, J. Comput. Chem..

[4]  Leo S. D. Caves,et al.  Bio3d: An R Package , 2022 .

[5]  C. Simmerling,et al.  ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB. , 2015, Journal of chemical theory and computation.

[6]  Junmei Wang,et al.  Development and testing of a general amber force field , 2004, J. Comput. Chem..

[7]  M. Lill Efficient incorporation of protein flexibility and dynamics into molecular docking simulations. , 2011, Biochemistry.

[8]  R. Nussinov,et al.  Induced Fit, Conformational Selection and Independent Dynamic Segments: an Extended View of Binding Events Opinion , 2022 .

[9]  Robert J. Doerksen,et al.  Docking Challenge: Protein Sampling and Molecular Docking Performance , 2013, J. Chem. Inf. Model..

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

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

[12]  Matthew P. Repasky,et al.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes. , 2006, Journal of medicinal chemistry.

[13]  Jin Li,et al.  On Evaluating Molecular-Docking Methods for Pose Prediction and Enrichment Factors , 2006, J. Chem. Inf. Model..

[14]  M H Saier,et al.  Structural, functional, and evolutionary relationships among extracellular solute-binding receptors of bacteria , 1993, Microbiological reviews.

[15]  Hege S. Beard,et al.  Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. , 2004, Journal of medicinal chemistry.

[16]  R. Abagyan,et al.  Flexible ligand docking to multiple receptor conformations: a practical alternative. , 2008, Current opinion in structural biology.

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

[18]  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.

[19]  Donald Hamelberg,et al.  Towards fast, rigorous and efficient conformational sampling of biomolecules: Advances in accelerated molecular dynamics. , 2015, Biochimica et biophysica acta.

[20]  J. Changeux,et al.  Conformational selection or induced fit? 50 years of debate resolved , 2011, F1000 biology reports.

[21]  D. Bertrand,et al.  Crystal structure of nicotinic acetylcholine receptor homolog AChBP in complex with an α-conotoxin PnIA variant , 2005, Nature Structural &Molecular Biology.

[22]  Wolfgang Sadee,et al.  The venus flytrap of periplasmic binding proteins: An ancient protein module present in multiple drug receptors , 1999, AAPS PharmSci.

[23]  Ross C. Walker,et al.  An overview of the Amber biomolecular simulation package , 2013 .

[24]  T. Darden,et al.  Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems , 1993 .

[25]  S. Teichmann,et al.  Probing the diverse landscape of protein flexibility and binding. , 2012, Current opinion in structural biology.

[26]  G. Clore,et al.  Open-to-closed transition in apo maltose-binding protein observed by paramagnetic NMR , 2007, Nature.

[27]  Araz Jakalian,et al.  Fast, efficient generation of high‐quality atomic charges. AM1‐BCC model: I. Method , 2000 .

[28]  P. Taylor,et al.  Galanthamine and non-competitive inhibitor binding to ACh-binding protein: evidence for a binding site on non-alpha-subunit interfaces of heteromeric neuronal nicotinic receptors. , 2007, Journal of molecular biology.

[29]  A. Cavalli,et al.  Role of Molecular Dynamics and Related Methods in Drug Discovery. , 2016, Journal of medicinal chemistry.

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

[31]  W. L. Jorgensen,et al.  Comparison of simple potential functions for simulating liquid water , 1983 .

[32]  Ruben Abagyan,et al.  ALiBERO: Evolving a Team of Complementary Pocket Conformations Rather than a Single Leader , 2012, J. Chem. Inf. Model..

[33]  James Andrew McCammon,et al.  Accessing a Hidden Conformation of the Maltose Binding Protein Using Accelerated Molecular Dynamics , 2011, PLoS Comput. Biol..

[34]  T A Jones,et al.  Structure of D-allose binding protein from Escherichia coli bound to D-allose at 1.8 A resolution. , 1999, Journal of molecular biology.

[35]  R. Nussinov,et al.  Folding funnels and binding mechanisms. , 1999, Protein engineering.

[36]  Jacob D. Durrant,et al.  POVME: an algorithm for measuring binding-pocket volumes. , 2011, Journal of molecular graphics & modelling.

[37]  H. Wolfson,et al.  Multiple diverse ligands binding at a single protein site : A matter of pre-existing populations , 2001 .

[38]  William Sinko,et al.  Improved Reweighting of Accelerated Molecular Dynamics Simulations for Free Energy Calculation , 2014, Journal of chemical theory and computation.

[39]  J. Mccammon,et al.  Accounting for Receptor Flexibility and Enhanced Sampling Methods in Computer‐Aided Drug Design , 2013, Chemical biology & drug design.

[40]  Wilfred F van Gunsteren,et al.  Comparing geometric and kinetic cluster algorithms for molecular simulation data. , 2010, The Journal of chemical physics.

[41]  M. Karplus,et al.  Molecular dynamics and protein function. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[42]  P. Taylor,et al.  Structures of Aplysia AChBP complexes with nicotinic agonists and antagonists reveal distinctive binding interfaces and conformations , 2005, The EMBO journal.

[43]  Sherry L. Mowbray,et al.  Hinge-bending Motion of d-Allose-binding Protein from Escherichia coli , 2002, The Journal of Biological Chemistry.

[44]  Bing Xie,et al.  Efficiency of Stratification for Ensemble Docking Using Reduced Ensembles , 2018, J. Chem. Inf. Model..

[45]  Ruth Nussinov,et al.  A second molecular biology revolution? The energy landscapes of biomolecular function. , 2014, Physical chemistry chemical physics : PCCP.

[46]  S. Takada,et al.  Dynamic energy landscape view of coupled binding and protein conformational change: Induced-fit versus population-shift mechanisms , 2008, Proceedings of the National Academy of Sciences.

[47]  R. Friesner,et al.  Novel procedure for modeling ligand/receptor induced fit effects. , 2006, Journal of medicinal chemistry.

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

[49]  Christian Tyrchan,et al.  Binding Mode and Induced Fit Predictions for Prospective Computational Drug Design , 2016, J. Chem. Inf. Model..

[50]  C. E. Peishoff,et al.  A critical assessment of docking programs and scoring functions. , 2006, Journal of medicinal chemistry.

[51]  Matthew P. Repasky,et al.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. , 2004, Journal of medicinal chemistry.

[52]  Jung-Hsin Lin,et al.  The relaxed complex method: Accommodating receptor flexibility for drug design with an improved scoring scheme. , 2003, Biopolymers.

[53]  Rommie E. Amaro,et al.  An improved relaxed complex scheme for receptor flexibility in computer-aided drug design , 2008, J. Comput. Aided Mol. Des..

[54]  Wilfred F van Gunsteren,et al.  Biomolecular modeling: Goals, problems, perspectives. , 2006, Angewandte Chemie.

[55]  Siti Azma Jusoh,et al.  Knowledge-Based Methods To Train and Optimize Virtual Screening Ensembles , 2016, J. Chem. Inf. Model..

[56]  Jianyin Shao,et al.  Clustering Molecular Dynamics Trajectories: 1. Characterizing the Performance of Different Clustering Algorithms. , 2007, Journal of chemical theory and computation.

[57]  Rommie E. Amaro,et al.  Emerging methods for ensemble-based virtual screening. , 2010, Current topics in medicinal chemistry.

[58]  J. Mccammon,et al.  Exploring the role of receptor flexibility in structure-based drug discovery. , 2014, Biophysical chemistry.

[59]  J. Mongan,et al.  Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. , 2004, The Journal of chemical physics.

[60]  Levi C. T. Pierce,et al.  Routine Access to Millisecond Time Scale Events with Accelerated Molecular Dynamics , 2012, Journal of chemical theory and computation.

[61]  Rommie E. Amaro,et al.  POVME 2.0: An Enhanced Tool for Determining Pocket Shape and Volume Characteristics , 2014, Journal of chemical theory and computation.

[62]  J. Mccammon,et al.  Induced Fit or Conformational Selection? The Role of the Semi-closed State in the Maltose Binding Protein , 2011, Biochemistry.

[63]  Hui Lu,et al.  Specialized Dynamical Properties of Promiscuous Residues Revealed by Simulated Conformational Ensembles , 2013, Journal of chemical theory and computation.

[64]  James Andrew McCammon,et al.  A virtual screening study of the acetylcholine binding protein using a relaxed-complex approach , 2009, Comput. Biol. Chem..

[65]  Feng Ding,et al.  Rapid Flexible Docking Using a Stochastic Rotamer Library of Ligands , 2010, J. Chem. Inf. Model..

[66]  J. Mccammon,et al.  Sampling of slow diffusive conformational transitions with accelerated molecular dynamics. , 2007, The Journal of chemical physics.

[67]  Matteo Masetti,et al.  Protein Flexibility in Drug Discovery: From Theory to Computation , 2015, ChemMedChem.

[68]  B. Zagrovic,et al.  Conformational selection and induced fit mechanism underlie specificity in noncovalent interactions with ubiquitin , 2009, Proceedings of the National Academy of Sciences.

[69]  P. Cozzini Target Flexibility: An Emerging Consideration in Drug Discovery and Design , 2009 .

[70]  P. Kollman,et al.  Automatic atom type and bond type perception in molecular mechanical calculations. , 2006, Journal of molecular graphics & modelling.

[71]  J. Mccammon,et al.  Computational drug design accommodating receptor flexibility: the relaxed complex scheme. , 2002, Journal of the American Chemical Society.

[72]  Alessandro Pandini,et al.  Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry. , 2013, Current topics in medicinal chemistry.

[73]  A. Davidson,et al.  Both maltose-binding protein and ATP are required for nucleotide-binding domain closure in the intact maltose ABC transporter , 2008, Proceedings of the National Academy of Sciences.

[74]  R. Nussinov,et al.  Folding funnels, binding funnels, and protein function , 1999, Protein science : a publication of the Protein Society.

[75]  Katrina W Lexa,et al.  Protein flexibility in docking and surface mapping , 2012, Quarterly Reviews of Biophysics.

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

[77]  Ruben Abagyan,et al.  Optimization of High Throughput Virtual Screening by Combining Shape‐Matching and Docking Methods. , 2008 .

[78]  D. Koshland Application of a Theory of Enzyme Specificity to Protein Synthesis. , 1958, Proceedings of the National Academy of Sciences of the United States of America.

[79]  Woody Sherman,et al.  Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments , 2013, Journal of Computer-Aided Molecular Design.

[80]  Kenneth M Merz,et al.  Molecular recognition and drug-lead identification: what can molecular simulations tell us? , 2010, Current medicinal chemistry.

[81]  G. Ciccotti,et al.  Numerical Integration of the Cartesian Equations of Motion of a System with Constraints: Molecular Dynamics of n-Alkanes , 1977 .

[82]  Jan H. Jensen,et al.  Very fast prediction and rationalization of pKa values for protein–ligand complexes , 2008, Proteins.

[83]  B. Brooks,et al.  Langevin dynamics of peptides: The frictional dependence of isomerization rates of N‐acetylalanyl‐N′‐methylamide , 1992, Biopolymers.

[84]  J A McCammon,et al.  Hinge-bending in L-arabinose-binding protein. The "Venus's-flytrap" model. , 1982, The Journal of biological chemistry.

[85]  Holger Gohlke,et al.  The Amber biomolecular simulation programs , 2005, J. Comput. Chem..