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Fast Rescoring Protocols to Improve the Performance of Structure-Based Virtual Screening Performed on Protein-Protein Interfaces.
Singh, Natesh; Chaput, Ludovic; Villoutreix, Bruno O.
  • Singh N; Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France.
  • Chaput L; Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France.
  • Villoutreix BO; Université de Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, F-59000 Lille, France.
J Chem Inf Model ; 60(8): 3910-3934, 2020 08 24.
Article in English | MEDLINE | ID: covidwho-714258
ABSTRACT
Protein-protein interactions (PPIs) are attractive targets for drug design because of their essential role in numerous cellular processes and disease pathways. However, in general, PPIs display exposed binding pockets at the interface, and as such, have been largely unexploited for therapeutic interventions with low-molecular weight compounds. Here, we used docking and various rescoring strategies in an attempt to recover PPI inhibitors from a set of active and inactive molecules for 11 targets collected in ChEMBL and PubChem. Our focus is on the screening power of the various developed protocols and on using fast approaches so as to be able to apply such a strategy to the screening of ultralarge libraries in the future. First, we docked compounds into each target using the fast "pscreen" mode of the structure-based virtual screening (VS) package Surflex. Subsequently, the docking poses were postprocessed to derive a set of 3D topological descriptors (i) shape similarity and (ii) interaction fingerprint similarity with a co-crystallized inhibitor, (iii) solvent-accessible surface area, and (iv) extent of deviation from the geometric center of a reference inhibitor. The derivatized descriptors, together with descriptor-scaled scoring functions, were utilized to investigate possible impacts on VS performance metrics. Moreover, four standalone scoring functions, RF-Score-VS (machine-learning), DLIGAND2 (knowledge-based), Vinardo (empirical), and X-SCORE (empirical), were employed to rescore the PPI compounds. Collectively, the results indicate that the topological scoring algorithms could be valuable both at a global level, with up to 79% increase in areas under the receiver operating characteristic curve for some targets, and in early stages, with up to a 4-fold increase in enrichment factors at 1% of the screened collections. Outstandingly, DLIGAND2 emerged as the best scoring function on this data set, outperforming all rescoring techniques in terms of VS metrics. The described methodology could help in the rational design of small-molecule PPI inhibitors and has direct applications in many therapeutic areas, including cancer, CNS, and infectious diseases such as COVID-19.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Design / Small Molecule Libraries / Drug Discovery / Protein Interaction Maps Type of study: Prognostic study Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2020 Document Type: Article Affiliation country: Acs.jcim.0c00545

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Drug Design / Small Molecule Libraries / Drug Discovery / Protein Interaction Maps Type of study: Prognostic study Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2020 Document Type: Article Affiliation country: Acs.jcim.0c00545