title: Profile-QSAR and Surrogate AutoShim protein-family modeling of proteases.
abstract: The 2D Profile-QSAR and 3D Surrogate AutoShim protein-family virtual screening methods were originally developed for kinases. They are the key components of an iterative medium-throughput screening alternative to expensive and time-consuming experimental high-throughput screening. Encouraged by the success with kinases, the S1-serine proteases were selected as a second protein family to tackle, based on the structural and SAR similarity among them, availability of structural and bioactivity data, and the current and future small-molecule drug discovery interest. A validation study on 24 S1-serine protease assay data sets from 16 unique proteases gave very promising results. Profile-QSAR gave a median R(ext)² = 0.60 for 24 assay data sets, and pairwise selectivity modeling on 60 protease pairs gave a median R(ext)² = 0.64, comparable to the performance for kinases. A 17-structure universal ensemble S1-serine protease surrogate receptor for Autoshim was developed from a collection of ~1500 X-ray structures. The predictive performance on 24 S1-serine protease assays was good, with a median R(ext)² = 0.41, but lower than had been obtained for kinases. Analysis suggested that the higher structural diversity of the protease structures, as well as smaller assay data sets and fewer potent compounds, both contributed to the decreased predictive power. In a prospective virtual screening application, 32 compounds were ordered from a 1.5 million archive and tested in a biochemical assay. Thirteen of the 32 compounds were active at IC₅₀ ≤ 10 μM, a 41% hit-rate. Three new scaffolds were identified which are being followed up with testing of additional analogues. A SAR similarity analysis for this target against 13 other proteases also indicated two potential protease targets which were positively and negatively correlated with the activity of the target protease.
authors.affiliation: Oncology and Exploratory Chemistry, Global Discovery Chemistry, Novartis Institutes for Biomedical Research, 4560 Horton Street, Emeryville, California 94608, USA. prasenjit.mukherjee@novartis.com
authors.forename: Prasenjit
authors.initials: P
authors.lastname: Mukherjee
authors.forename: Eric
authors.initials: E
authors.lastname: Martin
mesh_terms: D008958:Models, Molecular; D010447:Peptide Hydrolases / Q000378:metabolism*; D021281:Quantitative Structure-Activity Relationship; D013329:Structure-Activity Relationship
chemical_list: D010447:Peptide Hydrolases