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Predicting protein targets for drug-like compounds using transcriptomics

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chem...

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Autores principales: Pabon, Nicolas A., Xia, Yan, Estabrooks, Samuel K., Ye, Zhaofeng, Herbrand, Amanda K., Süß, Evelyn, Biondi, Ricardo M., Assimon, Victoria A., Gestwicki, Jason E., Brodsky, Jeffrey L., Camacho, Carlos J., Bar-Joseph, Ziv
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300300/
https://www.ncbi.nlm.nih.gov/pubmed/30532261
http://dx.doi.org/10.1371/journal.pcbi.1006651
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author Pabon, Nicolas A.
Xia, Yan
Estabrooks, Samuel K.
Ye, Zhaofeng
Herbrand, Amanda K.
Süß, Evelyn
Biondi, Ricardo M.
Assimon, Victoria A.
Gestwicki, Jason E.
Brodsky, Jeffrey L.
Camacho, Carlos J.
Bar-Joseph, Ziv
author_facet Pabon, Nicolas A.
Xia, Yan
Estabrooks, Samuel K.
Ye, Zhaofeng
Herbrand, Amanda K.
Süß, Evelyn
Biondi, Ricardo M.
Assimon, Victoria A.
Gestwicki, Jason E.
Brodsky, Jeffrey L.
Camacho, Carlos J.
Bar-Joseph, Ziv
author_sort Pabon, Nicolas A.
collection PubMed
description An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.
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spelling pubmed-63003002018-12-28 Predicting protein targets for drug-like compounds using transcriptomics Pabon, Nicolas A. Xia, Yan Estabrooks, Samuel K. Ye, Zhaofeng Herbrand, Amanda K. Süß, Evelyn Biondi, Ricardo M. Assimon, Victoria A. Gestwicki, Jason E. Brodsky, Jeffrey L. Camacho, Carlos J. Bar-Joseph, Ziv PLoS Comput Biol Research Article An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions. Public Library of Science 2018-12-07 /pmc/articles/PMC6300300/ /pubmed/30532261 http://dx.doi.org/10.1371/journal.pcbi.1006651 Text en © 2018 Pabon et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Pabon, Nicolas A.
Xia, Yan
Estabrooks, Samuel K.
Ye, Zhaofeng
Herbrand, Amanda K.
Süß, Evelyn
Biondi, Ricardo M.
Assimon, Victoria A.
Gestwicki, Jason E.
Brodsky, Jeffrey L.
Camacho, Carlos J.
Bar-Joseph, Ziv
Predicting protein targets for drug-like compounds using transcriptomics
title Predicting protein targets for drug-like compounds using transcriptomics
title_full Predicting protein targets for drug-like compounds using transcriptomics
title_fullStr Predicting protein targets for drug-like compounds using transcriptomics
title_full_unstemmed Predicting protein targets for drug-like compounds using transcriptomics
title_short Predicting protein targets for drug-like compounds using transcriptomics
title_sort predicting protein targets for drug-like compounds using transcriptomics
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6300300/
https://www.ncbi.nlm.nih.gov/pubmed/30532261
http://dx.doi.org/10.1371/journal.pcbi.1006651
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