Protein pocket and ligand shape comparison and its application in virtual screening

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

[1]  Wolfgang H. B. Sauer,et al.  Molecular Shape Diversity of Combinatorial Libraries: A Prerequisite for Broad Bioactivity , 2003, J. Chem. Inf. Comput. Sci..

[2]  Matthias Wirth,et al.  Bioactive Molecules: Perfectly Shaped for Their Target? , 2011, Molecular informatics.

[3]  Sean Ekins,et al.  The importance of discerning shape in molecular pharmacology. , 2009, Trends in pharmacological sciences.

[4]  D. Blow,et al.  Rearrangement of Cruickshank's formulae for the diffraction-component precision index. , 2002, Acta crystallographica. Section D, Biological crystallography.

[5]  Pinak Chakrabarti,et al.  Cavities and Atomic Packing in Protein Structures and Interfaces , 2008, PLoS Comput. Biol..

[6]  J. Irwin,et al.  Benchmarking sets for molecular docking. , 2006, Journal of medicinal chemistry.

[7]  R. Sheridan,et al.  SQ: a program for rapidly producing pharmacophorically relevent molecular superpositions. , 1999, Journal of medicinal chemistry.

[8]  J. Thornton,et al.  Shape variation in protein binding pockets and their ligands. , 2007, Journal of molecular biology.

[9]  S. Bembenek,et al.  Ligand binding efficiency: trends, physical basis, and implications. , 2008, Journal of medicinal chemistry.

[10]  Sylvie Garneau-Tsodikova,et al.  Cover Picture: Exploring the Substrate Promiscuity of Drug-Modifying Enzymes for the Chemoenzymatic Generation of N-Acylated Aminoglycosides (ChemBioChem 1/2010) , 2009 .

[11]  Ajay N. Jain,et al.  Molecular Shape and Medicinal Chemistry: A Perspective , 2010, Journal of medicinal chemistry.

[12]  John P. Overington,et al.  Ligand efficiency indices for an effective mapping of chemico-biological space: the concept of an atlas-like representation. , 2010, Drug discovery today.

[13]  Ke Chen,et al.  Investigation of Atomic Level Patterns in Protein—Small Ligand Interactions , 2009, PloS one.

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

[15]  Stéphanie Pérot,et al.  Druggable pockets and binding site centric chemical space: a paradigm shift in drug discovery. , 2010, Drug discovery today.

[16]  Mark S. Johnson,et al.  ShaEP: Molecular Overlay Based on Shape and Electrostatic Potential , 2009, J. Chem. Inf. Model..

[17]  Santosh Putta,et al.  Shapes of things: computer modeling of molecular shape in drug discovery. , 2007, Current topics in medicinal chemistry.

[18]  Janet M. Thornton,et al.  Real spherical harmonic expansion coefficients as 3D shape descriptors for protein binding pocket and ligand comparisons , 2005, Bioinform..

[19]  Robert P. Sheridan,et al.  Comparison of Topological, Shape, and Docking Methods in Virtual Screening , 2007, J. Chem. Inf. Model..

[20]  Matthias Rarey,et al.  Analyzing the Topology of Active Sites: On the Prediction of Pockets and Subpockets , 2010, J. Chem. Inf. Model..

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

[22]  Benjamin A. Ellingson,et al.  Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database , 2010, J. Chem. Inf. Model..

[23]  I. Kuntz,et al.  The maximal affinity of ligands. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[24]  Yongbo Hu,et al.  Comparison of Several Molecular Docking Programs: Pose Prediction and Virtual Screening Accuracy , 2009, J. Chem. Inf. Model..

[25]  H. Edelsbrunner,et al.  Anatomy of protein pockets and cavities: Measurement of binding site geometry and implications for ligand design , 1998, Protein science : a publication of the Protein Society.

[26]  Daniel Kuhn,et al.  Combining Global and Local Measures for Structure-Based Druggability Predictions , 2012, J. Chem. Inf. Model..

[27]  W. Graham Richards,et al.  Ultrafast shape recognition to search compound databases for similar molecular shapes , 2007, J. Comput. Chem..

[28]  Paul Watson,et al.  Virtual Screening Using Protein-Ligand Docking: Avoiding Artificial Enrichment , 2004, J. Chem. Inf. Model..

[29]  John P. Overington,et al.  ChEMBL: a large-scale bioactivity database for drug discovery , 2011, Nucleic Acids Res..

[30]  Irini Akritopoulou-Zanze,et al.  Topography-biased compound library design: the shape of things to come? , 2007, Drug discovery today.

[31]  J. A. Grant,et al.  A shape-based 3-D scaffold hopping method and its application to a bacterial protein-protein interaction. , 2005, Journal of medicinal chemistry.

[32]  Jan M. Kriegl,et al.  Architectural Repertoire of Ligand‐Binding Pockets on Protein Surfaces , 2010, Chembiochem : a European journal of chemical biology.

[33]  Didier Rognan,et al.  sc-PDB: a database for identifying variations and multiplicity of 'druggable' binding sites in proteins , 2011, Bioinform..

[34]  G Náray-Szabó,et al.  Analysis of molecular recognition: Steric electrostatic and hydrophobic complementarity , 1993, Journal of molecular recognition : JMR.

[35]  Renxiao Wang,et al.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. , 2004, Journal of medicinal chemistry.

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

[37]  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.

[38]  Janet M Thornton,et al.  On the diversity of physicochemical environments experienced by identical ligands in binding pockets of unrelated proteins , 2010, Proteins.