title: Computational tools for the analysis and visualization of multiple protein-ligand complexes.
abstract: Modern methods in genomics and high-throughput crystallography have ensured that we have access to a large and rapidly increasing, number of X-ray structures of protein-ligand complexes. A structure-based approach to drug design aims to exploit this information, but current methods are not suited to the examination of the large numbers of complexes available. We present computational tools that analyse and display multiple protein-ligand interactions and their properties in a simplified way. We illustrate how a novel binding-mode similarity metric is able to cluster 20 ligands complexed to HIV-1 reverse transcriptase into distinct groups. The properties of each cluster are then projected onto a group surface as a series of color gradients. Analysis of these surfaces reveals fundamental similarities and differences in the binding modes of these diverse compounds. In addition, the simplicity of the surface representations facilitates the transfer of information between the crystallographer, computational chemist and the chemist. We also show how two- and three-dimensional (2- and 3-D) similarities can be combined to provide enhanced understanding of 33 factor Xa inhibitor complexes. This methodology has enabled us to identify pharmaceutically relevant relationships between ligands and their binding modes that had previously been hidden in a wealth of data.
authors.affiliation: Department of Medicinal Informatics Structure and Design, Pfizer Global Research and Development, Sandwich, Kent, UK. sobrien@cylenepharma.com
authors.forename: Sean E
authors.initials: SE
authors.lastname: O'Brien
authors.forename: David G
authors.initials: DG
authors.lastname: Brown
authors.forename: James E
authors.initials: JE
authors.lastname: Mills
authors.forename: Chris
authors.initials: C
authors.lastname: Phillips
authors.forename: Gregg
authors.initials: G
authors.lastname: Morris
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D017076:Computer-Aided Design; D018360:Crystallography, X-Ray / Q000379:methods; D030562:Databases, Protein; D015195:Drug Design; D015951:Factor Xa / Q000737:chemistry; D065427:Factor Xa Inhibitors; D023281:Genomics; D054303:HIV Reverse Transcriptase / Q000037:antagonists & inhibitors / Q000737:chemistry; D008024:Ligands; D008958:Models, Molecular; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics; D018894:Reverse Transcriptase Inhibitors / Q000737:chemistry*; D015842:Serine Proteinase Inhibitors / Q000737:chemistry*; D012984:Software; D013329:Structure-Activity Relationship; D013499:Surface Properties
chemical_list: D065427:Factor Xa Inhibitors; D008024:Ligands; D011506:Proteins; D018894:Reverse Transcriptase Inhibitors; D015842:Serine Proteinase Inhibitors; D054303:HIV Reverse Transcriptase; D015951:Factor Xa