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In silico analysis of alternative splicing on drug-target gene interactions

Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present co...

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Autores principales: Ji, Yanrong, Mishra, Rama K., Davuluri, Ramana V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954184/
https://www.ncbi.nlm.nih.gov/pubmed/31924844
http://dx.doi.org/10.1038/s41598-019-56894-x
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author Ji, Yanrong
Mishra, Rama K.
Davuluri, Ramana V.
author_facet Ji, Yanrong
Mishra, Rama K.
Davuluri, Ramana V.
author_sort Ji, Yanrong
collection PubMed
description Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research.
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spelling pubmed-69541842020-01-15 In silico analysis of alternative splicing on drug-target gene interactions Ji, Yanrong Mishra, Rama K. Davuluri, Ramana V. Sci Rep Article Identifying and evaluating the right target are the most important factors in early drug discovery phase. Most studies focus on one protein ignoring the multiple splice-variant or protein-isoforms, which might contribute to unexpected therapeutic activity or adverse side effects. Here, we present computational analysis of cancer drug-target interactions affected by alternative splicing. By integrating information from publicly available databases, we curated 883 FDA approved or investigational stage small molecule cancer drugs that target 1,434 different genes, with an average of 5.22 protein isoforms per gene. Of these, 618 genes have ≥5 annotated protein-isoforms. By analyzing the interactions with binding pocket information, we found that 76% of drugs either miss a potential target isoform or target other isoforms with varied expression in multiple normal tissues. We present sequence and structure level alignments at isoform-level and make this information publicly available for all the curated drugs. Structure-level analysis showed ligand binding pocket architectures differences in size, shape and electrostatic parameters between isoforms. Our results emphasize how potentially important isoform-level interactions could be missed by solely focusing on the canonical isoform, and suggest that on- and off-target effects at isoform-level should be investigated to enhance the productivity of drug-discovery research. Nature Publishing Group UK 2020-01-10 /pmc/articles/PMC6954184/ /pubmed/31924844 http://dx.doi.org/10.1038/s41598-019-56894-x Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ji, Yanrong
Mishra, Rama K.
Davuluri, Ramana V.
In silico analysis of alternative splicing on drug-target gene interactions
title In silico analysis of alternative splicing on drug-target gene interactions
title_full In silico analysis of alternative splicing on drug-target gene interactions
title_fullStr In silico analysis of alternative splicing on drug-target gene interactions
title_full_unstemmed In silico analysis of alternative splicing on drug-target gene interactions
title_short In silico analysis of alternative splicing on drug-target gene interactions
title_sort in silico analysis of alternative splicing on drug-target gene interactions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6954184/
https://www.ncbi.nlm.nih.gov/pubmed/31924844
http://dx.doi.org/10.1038/s41598-019-56894-x
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