Centrality of drug targets in protein networks

Background: In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target’s ‘fitness’ based on its node characteristics within annotated protein functional netwo...

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Bibliographic Details
Main Author: Viacava Follis, A. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Online Access:View Fulltext in Publisher
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020 |a 14712105 (ISSN) 
245 1 0 |a Centrality of drug targets in protein networks 
260 0 |b BioMed Central Ltd  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1186/s12859-021-04342-x 
520 3 |a Background: In the pharmaceutical industry, competing for few validated drug targets there is a drive to identify new ways of therapeutic intervention. Here, we attempted to define guidelines to evaluate a target’s ‘fitness’ based on its node characteristics within annotated protein functional networks to complement contingent therapeutic hypotheses. Results: We observed that targets of approved, selective small molecule drugs exhibit high node centrality within protein networks relative to a broader set of investigational targets spanning various development stages. Targets of approved drugs also exhibit higher centrality than other proteins within their respective functional class. These findings expand on previous reports of drug targets’ network centrality by suggesting some centrality metrics such as low topological coefficient as inherent characteristics of a ‘good’ target, relative to other exploratory targets and regardless of its functional class. These centrality metrics could thus be indicators of an individual protein’s ‘fitness’ as potential drug target. Correlations between protein nodes’ network centrality and number of associated publications underscored the possibility of knowledge bias as an inherent limitation to such predictions. Conclusions: Despite some entanglement with knowledge bias, like structure-oriented ‘druggability’ assessments of new protein targets, centrality metrics could assist early pharmaceutical discovery teams in evaluating potential targets with limited experimental proof of concept and help allocate resources for an effective drug discovery pipeline. © 2021, The Author(s). 
650 0 4 |a Development stages 
650 0 4 |a drug 
650 0 4 |a drug development 
650 0 4 |a Drug Discovery 
650 0 4 |a Drug target 
650 0 4 |a Drug targets 
650 0 4 |a Functional class 
650 0 4 |a Functional network 
650 0 4 |a Graph analysis 
650 0 4 |a Graph analysis 
650 0 4 |a Network centralities 
650 0 4 |a Pharmaceutical industry 
650 0 4 |a Pharmaceutical Preparations 
650 0 4 |a protein 
650 0 4 |a Protein network 
650 0 4 |a Protein network 
650 0 4 |a Proteins 
650 0 4 |a Proteins 
650 0 4 |a Small-molecule drugs 
650 0 4 |a Therapeutic intervention 
700 1 |a Viacava Follis, A.  |e author 
773 |t BMC Bioinformatics