title: ENRI: A tool for selecting structure-based virtual screening target conformations.
abstract: Finding pharmaceutically relevant target conformations from an arbitrary set of protein conformations remains a challenge in structure-based virtual screening (SBVS). The growth in the number of available conformations, either experimentally determined or computationally derived, obscures the situation further. While the inflated conformation space potentially contains viable druggable targets, the increase of conformational complexity, as a consequence, poses a selection problem. To address this challenge, we took advantage of machine learning methods, namely an over-sampling and a binary classification procedure, and present a novel method to select druggable receptor conformations. Specifically, we trained a binary classifier on a set of nuclear receptor conformations, wherein each conformation was labeled with an enrichment measure for a corresponding SBVS. The classifier enabled us to formulate suggestions and identify enriching SBVS targets for six of seven nuclear receptors. Further, the classifier can be extended to other proteins of interest simply by feeding new training data sets to the classifier. Our work, thus, provides a methodology to identify pharmaceutically interesting receptor conformations for nuclear receptors and other drug targets.
authors.affiliation: Center for Bioinformatics, Saarland University, Saarbruecken, Germany.
authors.forename: Rahmad
authors.initials: R
authors.lastname: Akbar
authors.affiliation: Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA.|Faculty of Pharmacy, Universiti Teknologi MARA Malaysia, Bandar Puncak Alam, Selangor, Malaysia.
authors.forename: Siti Azma
authors.identifier: 0000-0002-9028-5681
authors.initials: SA
authors.lastname: Jusoh
authors.affiliation: Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA, USA.
authors.forename: Rommie E
authors.initials: RE
authors.lastname: Amaro
authors.affiliation: Center for Bioinformatics, Saarland University, Saarbruecken, Germany.
authors.forename: Volkhard
authors.initials: V
authors.lastname: Helms
mesh_terms: D001665:Binding Sites; D030562:Databases, Protein; D016002:Discriminant Analysis; D062105:Molecular Docking Simulation; D056004:Molecular Dynamics Simulation; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism; D012984:Software; D060388:Support Vector Machine
chemical_list: D011506:Proteins
keywords: binding pocket; classification; conformational dynamics; molecular docking; molecular dynamics simulation; virtual screening