In silico Druggability Assessment of the NUDIX Hydrolase Protein Family as a Workflow for Target Prioritization.

Autor:	Michel M; Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden., Homan EJ; Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden., Wiita E; Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden., Pedersen K; Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden., Almlöf I; Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden., Gustavsson AL; Chemical Biology Consortium Sweden (CBCS), Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden., Lundbäck T; Chemical Biology Consortium Sweden (CBCS), Science for Life Laboratory, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.; Mechanistic Biology and Profiling, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden., Helleday T; Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.; Department of Oncology and Metabolism, Sheffield Cancer Centre, University of Sheffield, Sheffield, United Kingdom., Warpman Berglund U; Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Jazyk:	angličtina
Zdroj:	Frontiers in chemistry [Front Chem] 2020 May 29; Vol. 8, pp. 443. Date of Electronic Publication: 2020 May 29 (Print Publication: 2020).
DOI:	10.3389/fchem.2020.00443
Abstrakt:	Computational chemistry has now been widely accepted as a useful tool for shortening lead times in early drug discovery. When selecting new potential drug targets, it is important to assess the likelihood of finding suitable starting points for lead generation before pursuing costly high-throughput screening campaigns. By exploiting available high-resolution crystal structures, an in silico druggability assessment can facilitate the decision of whether, and in cases where several protein family members exist, which of these to pursue experimentally. Many of the algorithms and software suites commonly applied for in silico druggability assessment are complex, technically challenging and not always user-friendly. Here we applied the intuitive open access servers of DoGSite, FTMap and CryptoSite to comprehensively predict ligand binding pockets, druggability scores and conformationally active regions of the NUDIX protein family. In parallel we analyzed potential ligand binding sites, their druggability and pocket parameter using Schrödinger's SiteMap. Then an in silico docking cascade of a subset of the ZINC FragNow library using the Glide docking program was performed to assess identified pockets for large-scale small-molecule binding. Subsequently, this initial dual ranking of druggable sites within the NUDIX protein family was benchmarked against experimental hit rates obtained both in-house and by others from traditional biochemical and fragment screening campaigns. The observed correlation suggests that the presented user-friendly workflow of a dual parallel in silico druggability assessment is applicable as a standalone method for decision on target prioritization and exclusion in future screening campaigns. (Copyright © 2020 Michel, Homan, Wiita, Pedersen, Almlöf, Gustavsson, Lundbäck, Helleday and Warpman Berglund.)
Databáze:	MEDLINE
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