Computational Modeling of Small Molecule Ligand Binding Interactions and Affinities.

Understanding and controlling biological phenomena via structure-based drug screening efforts often critically rely on accurate description of protein-ligand interactions. However, most of the currently available computational techniques are affected by severe deficiencies in both protein and ligand conformational sampling as well as in the scoring of the obtained docking solutions. To overcome these limitations, we have recently developed MedusaDock, a novel docking methodology, which simultaneously models ligand and receptor flexibility. Coupled with MedusaScore, a physical force field-based scoring function that accounts for the protein-ligand interaction energy, MedusaDock, has reported the highest success rate in the CSAR 2011 exercise. Here, we present a standard computational protocol to evaluate the binding properties of the two enantiomers of the non-selective β-blocker propanolol in the β2 adrenergic receptor's binding site. We describe details of our protocol, which have been successfully applied to several other targets.

[1]  J A McCammon,et al.  Accommodating protein flexibility in computational drug design. , 2000, Molecular pharmacology.

[2]  Stefan Wallin,et al.  Exploring Protein-Peptide Binding Specificity through Computational Peptide Screening , 2013, PLoS Comput. Biol..

[3]  Pradeep Kota,et al.  Automated minimization of steric clashes in protein structures , 2011, Proteins.

[4]  T. Halgren Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[5]  Ruben Abagyan,et al.  Consistent Improvement of Cross-Docking Results Using Binding Site Ensembles Generated with Elastic Network Normal Modes , 2009, J. Chem. Inf. Model..

[6]  X. Barril,et al.  Unveiling the full potential of flexible receptor docking using multiple crystallographic structures. , 2005, Journal of medicinal chemistry.

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

[8]  Nikolay V. Dokholyan,et al.  MedusaScore: An Accurate Force Field-Based Scoring Function for Virtual Drug Screening , 2008, J. Chem. Inf. Model..

[9]  Joseph Audie,et al.  Recent work in the development and application of protein-peptide docking. , 2012, Future medicinal chemistry.

[10]  Xiaoqin Zou,et al.  Challenges, Applications, and Recent Advances of Protein-Ligand Docking in Structure-Based Drug Design , 2014, Molecules.

[11]  Pedro Alexandrino Fernandes,et al.  Protein–ligand docking: Current status and future challenges , 2006, Proteins.

[12]  Kevin M. D'Auria,et al.  Structural and dynamic determinants of protein-peptide recognition. , 2011, Structure.

[13]  Heather A Carlson,et al.  Exploring experimental sources of multiple protein conformations in structure-based drug design. , 2007, Journal of the American Chemical Society.

[14]  W. L. Jorgensen,et al.  The OPLS [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin. , 1988, Journal of the American Chemical Society.

[15]  S. Teague Implications of protein flexibility for drug discovery , 2003, Nature Reviews Drug Discovery.

[16]  Martin Karplus,et al.  Molecular dynamics of biological macromolecules: A brief history and perspective , 2003, Biopolymers.

[17]  C. C. Heyde,et al.  Central Limit Theorem , 2006 .

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

[19]  Feng Ding,et al.  Incorporating Backbone Flexibility in MedusaDock Improves Ligand-Binding Pose Prediction in the CSAR2011 Docking Benchmark , 2013, J. Chem. Inf. Model..

[20]  Brian K Shoichet,et al.  Prediction of protein-ligand interactions. Docking and scoring: successes and gaps. , 2006, Journal of medicinal chemistry.

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

[22]  Adrian W. R. Serohijos,et al.  Structural basis for μ-opioid receptor binding and activation. , 2011, Structure.

[23]  T. A. Jones,et al.  The Uppsala Electron-Density Server. , 2004, Acta crystallographica. Section D, Biological crystallography.

[24]  A. Tropsha,et al.  Beware of q 2 , 2002 .

[25]  Pradeep Kota,et al.  Gaia: automated quality assessment of protein structure models , 2011, Bioinform..

[26]  Thomas A. Halgren Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94 , 1996, J. Comput. Chem..

[27]  R. Abagyan,et al.  Conserved binding mode of human beta2 adrenergic receptor inverse agonists and antagonist revealed by X-ray crystallography. , 2010, Journal of the American Chemical Society.

[28]  B. X. Carlson,et al.  A single glycine residue at the entrance to the first membrane-spanning domain of the gamma-aminobutyric acid type A receptor beta(2) subunit affects allosteric sensitivity to GABA and anesthetics. , 2000, Molecular pharmacology.

[29]  M L Teodoro,et al.  Conformational flexibility models for the receptor in structure based drug design. , 2003, Current pharmaceutical design.

[30]  A. Tropsha,et al.  Beware of q2! , 2002, Journal of molecular graphics & modelling.

[31]  W. L. Jorgensen,et al.  Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids , 1996 .

[32]  L. Dardenne,et al.  Receptor–ligand molecular docking , 2013, Biophysical Reviews.

[33]  L. Kelley,et al.  An automated approach for clustering an ensemble of NMR-derived protein structures into conformationally related subfamilies. , 1996, Protein engineering.

[34]  G. Klebe,et al.  Statistical potentials and scoring functions applied to protein-ligand binding. , 2001, Current opinion in structural biology.