Centrality of drug targets in protein networks
Abstract 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 function...
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| Format: | Article |
| Language: | English |
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BMC
2021-10-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-021-04342-x |
| _version_ | 1818730069021949952 |
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| author | Ariele Viacava Follis |
| author_facet | Ariele Viacava Follis |
| author_sort | Ariele Viacava Follis |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-12-17T22:55:55Z |
| format | Article |
| id | doaj.art-56061ea6ff5e4ffb9f3785777af8b3a8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-17T22:55:55Z |
| publishDate | 2021-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-56061ea6ff5e4ffb9f3785777af8b3a82022-12-21T21:29:32ZengBMCBMC Bioinformatics1471-21052021-10-0122112910.1186/s12859-021-04342-xCentrality of drug targets in protein networksAriele Viacava Follis0EMD Serono Research and Development Inc.Abstract 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.https://doi.org/10.1186/s12859-021-04342-xDrug targetProtein networkGraph analysis |
| spellingShingle | Ariele Viacava Follis Centrality of drug targets in protein networks BMC Bioinformatics Drug target Protein network Graph analysis |
| title | Centrality of drug targets in protein networks |
| title_full | Centrality of drug targets in protein networks |
| title_fullStr | Centrality of drug targets in protein networks |
| title_full_unstemmed | Centrality of drug targets in protein networks |
| title_short | Centrality of drug targets in protein networks |
| title_sort | centrality of drug targets in protein networks |
| topic | Drug target Protein network Graph analysis |
| url | https://doi.org/10.1186/s12859-021-04342-x |
| work_keys_str_mv | AT arieleviacavafollis centralityofdrugtargetsinproteinnetworks |