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Comprehensive prediction of drug-protein interactions and side effects for the human proteome

Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITE(comb), whose average precision is ~30% and recall ~27%...

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Autores principales: Zhou, Hongyi, Gao, Mu, Skolnick, Jeffrey
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603786/
https://www.ncbi.nlm.nih.gov/pubmed/26057345
http://dx.doi.org/10.1038/srep11090
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author Zhou, Hongyi
Gao, Mu
Skolnick, Jeffrey
author_facet Zhou, Hongyi
Gao, Mu
Skolnick, Jeffrey
author_sort Zhou, Hongyi
collection PubMed
description Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITE(comb), whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds.
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spelling pubmed-46037862015-10-23 Comprehensive prediction of drug-protein interactions and side effects for the human proteome Zhou, Hongyi Gao, Mu Skolnick, Jeffrey Sci Rep Article Identifying unexpected drug-protein interactions is crucial for drug repurposing. We develop a comprehensive proteome scale approach that predicts human protein targets and side effects of drugs. For drug-protein interaction prediction, FINDSITE(comb), whose average precision is ~30% and recall ~27%, is employed. For side effect prediction, a new method is developed with a precision of ~57% and a recall of ~24%. Our predictions show that drugs are quite promiscuous, with the average (median) number of human targets per drug of 329 (38), while a given protein interacts with 57 drugs. The result implies that drug side effects are inevitable and existing drugs may be useful for repurposing, with only ~1,000 human proteins likely causing serious side effects. A killing index derived from serious side effects has a strong correlation with FDA approved drugs being withdrawn. Therefore, it provides a pre-filter for new drug development. The methodology is free to the academic community on the DR. PRODIS (DRugome, PROteome, and DISeasome) webserver at http://cssb.biology.gatech.edu/dr.prodis/. DR. PRODIS provides protein targets of drugs, drugs for a given protein target, associated diseases and side effects of drugs, as well as an interface for the virtual target screening of new compounds. Nature Publishing Group 2015-06-09 /pmc/articles/PMC4603786/ /pubmed/26057345 http://dx.doi.org/10.1038/srep11090 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Zhou, Hongyi
Gao, Mu
Skolnick, Jeffrey
Comprehensive prediction of drug-protein interactions and side effects for the human proteome
title Comprehensive prediction of drug-protein interactions and side effects for the human proteome
title_full Comprehensive prediction of drug-protein interactions and side effects for the human proteome
title_fullStr Comprehensive prediction of drug-protein interactions and side effects for the human proteome
title_full_unstemmed Comprehensive prediction of drug-protein interactions and side effects for the human proteome
title_short Comprehensive prediction of drug-protein interactions and side effects for the human proteome
title_sort comprehensive prediction of drug-protein interactions and side effects for the human proteome
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4603786/
https://www.ncbi.nlm.nih.gov/pubmed/26057345
http://dx.doi.org/10.1038/srep11090
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