Abstract
BackgroundAnalysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. Many previous binding site analyses only consider binding sites surrounding an experimentally observed bound ligand.ResultsTo encompass potential protein–ligand binding sites that do not have ligands known to bind, we have incorporated fpocket cavity detection software and assessed the impact of this inclusion on BSC performance. Using fpocket, we generated a database of ligand-independent potential binding sites and applied the BSC tool, SiteHopper, to analyze similarity relationships between protein binding sites. We developed a method for clustering potential binding sites using a curated dataset of structures for six therapeutically relevant proteins from diverse protein classes in the protein data bank. Two clustering methods were explored; hierarchical clustering and a density-based method adept at excluding noise and outliers from a dataset. We introduce circular plots to visualize binding site structure space. From the datasets analyzed in this study, we highlight a structural relationship between binding sites of cationic trypsin and prothrombin, protein targets known to bind structurally similar small molecules, exemplifying the potential utility of objectively and holistically mapping binding site space from the structural proteome.ConclusionsWe present a workflow for the objective mapping of potential protein–ligand binding sites derived from the currently available structural proteome. We show that ligand-independent binding site detection tools can be introduced without excessive penalty on BSC performance. Clustering combined with intuitive visualization tools can be applied to map relationships between the 3D structures of protein binding sites.Graphical abstractMapping binding site space. Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0180-0) contains supplementary material, which is available to authorized users.
Highlights
Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery
Incorporation of binding site detection tools removes bias associated with utilizing currently known liganded binding sites; it may introduce noise to the data through inclusion of cavities that are incapable of ligand binding
Fpocket is a well-established and freely available binding site detection tool capable of operating in highthroughput and applicable to large datasets of protein structure data [e.g. the sc-Protein Data Bank (PDB) (2013) contains 9283 structures]. fpocket was evaluated according to three criteria: Its ability to (1) detect cavities corresponding to functionally relevant binding sites starting from a global search of a protein structure; (2) detect similar cavities from an ensemble of structurally similar experimental structures of the same protein bound to the same ligand; and (3) rank and prioritize detected cavities according to their likelihood of binding small molecule ligands
Summary
Analysis of the 3D structures of protein–ligand binding sites can provide valuable insights for drug discovery. Binding site comparison (BSC) studies can be employed to elucidate the function of orphan proteins or to predict the potential for polypharmacology. The examination of functional binding sites is of importance in biological chemistry and drug discovery by rational design [2]. Evidence for the existence and location of a binding site can be built through experimental observation of protein–ligand binding events—often facilitated by protein X-ray crystallography and/or Nuclear Magnetic Resonance (NMR) spectroscopy. Prospective computational analysis to discover novel potential binding sites requires an objective and systematic cavity detection method, for which many tools exist [5,6,7]. Fpocket is a widely used and freely available software that employs geometric alpha shape theory to detect cavities in protein structure coordinates [8]
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