title: BioGPS: navigating biological space to predict polypharmacology, off-targeting, and selectivity.
abstract: The structural comparison of protein binding sites is increasingly important in drug design; identifying structurally similar sites can be useful for techniques such as drug repurposing, and also in a polypharmacological approach to deliberately affect multiple targets in a disease pathway, or to explain unwanted off-target effects. Once similar sites are identified, identifying local differences can aid in the design of selectivity. Such an approach moves away from the classical "one target one drug" approach and toward a wider systems biology paradigm. Here, we report a semiautomated approach, called BioGPS, that is based on the software FLAP which combines GRID Molecular Interactions Fields (MIFs) and pharmacophoric fingerprints. BioGPS comprises the automatic preparation of protein structure data, identification of binding sites, and subsequent comparison by aligning the sites and directly comparing the MIFs. Chemometric approaches are included to reduce the complexity of the resulting data on large datasets, enabling focus on the most relevant information. Individual site similarities can be analyzed in terms of their Pharmacophoric Interaction Field (PIF) similarity, and importantly the differences in their PIFs can be extracted. Here we describe the BioGPS approach, and demonstrate its applicability to rationalize off-target effects (ERα and SERCA), to classify protein families and explain polypharmacology (ABL1 kinase and NQO2), and to rationalize selectivity between subfamilies (MAP kinases p38α/ERK2 and PPARδ/PPARγ). The examples shown demonstrate a significant validation of the method and illustrate the effectiveness of the approach.
authors.affiliation: Laboratory for Chemometrics and Molecular Modeling, Department of Chemistry, Biology and Biotechnology, University of Perugia, Perugia, 06123, Italy.
authors.forename: Lydia
authors.initials: L
authors.lastname: Siragusa
authors.forename: Simon
authors.initials: S
authors.lastname: Cross
authors.forename: Massimo
authors.initials: M
authors.lastname: Baroni
authors.forename: Laura
authors.initials: L
authors.lastname: Goracci
authors.forename: Gabriele
authors.initials: G
authors.lastname: Cruciani
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D008958:Models, Molecular; D010600:Pharmacology; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
chemical_list: D011506:Proteins
keywords: clustering; molecular interaction fields; protein active sites; protein-pharmacophore; protein-protein comparison