Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Michel M

PROVIDER: S-EPMC7274155 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

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

Michel Maurice M Homan Evert J EJ Wiita Elisée E Pedersen Kia K Almlöf Ingrid I Gustavsson Anna-Lena AL Lundbäck Thomas T Helleday Thomas T Warpman Berglund Ulrika U

Frontiers in chemistry 20200529

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 <i>in silico</i> druggability assessment can facilitate the decision of whether, and in cases where several protein ...[more]

PMID: 32548091

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Project description:The featureless interface formed by protein-protein interactions (PPIs) is notorious for being considered a difficult and poorly druggable target. However, recent advances have shown PPIs to be druggable, with the discovery of potent inhibitors and stabilizers, some of which are currently being clinically tested and approved for medical use. In this study, we assess the druggability of 12 commonly targeted PPIs using the computational tool, SiteMap. After evaluating 320 crystal structures, we find that the PPI binding sites have a wide range of druggability scores. This can be attributed to the unique structural and physiochemical features that influence their ligand binding and concomitantly, their druggability predictions. We then use these features to propose a specific classification system suitable for assessing PPI targets based on their druggability scores and measured binding-affinity. Interestingly, this system was able to distinguish between different PPIs and correctly categorize them into four classes (i.e. very druggable, druggable, moderately druggable, and difficult). We also studied the effects of protein flexibility on the computed druggability scores and found that protein conformational changes accompanying ligand binding in ligand-bound structures result in higher protein druggability scores due to more favorable structural features. Finally, the drug-likeness of many published PPI inhibitors was studied where it was found that the vast majority of the 221 ligands considered here, including orally tested/marketed drugs, violate the currently acceptable limits of compound size and hydrophobicity parameters. This outcome, combined with the lack of correlation observed between druggability and drug-likeness, reinforces the need to redefine drug-likeness for PPI drugs. This work proposes a PPI-specific classification scheme that will assist researchers in assessing the druggability and identifying inhibitors of the PPI interface.

Dataset Information

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

Publications

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

Similar Datasets

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