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 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 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.
Metadaten
Author:Nicolas A. Pabon, Yan Xia, Samuel K. Estabrooks, Zhaofeng Ye, Amanda K. Herbrand, Evelyn Süß, Ricardo M. Biondi, Victoria A. Assimon, Jason E. Gestwicki, Jeffrey L. Brodsky, Carlos J. Camacho, Ziv Bar-Joseph
URN:urn:nbn:de:hebis:30:3-484597
DOI:https://doi.org/10.1371/journal.pcbi.1006651
ISSN:1553-7358
ISSN:1553-734X
Parent Title (English):PLoS Computational Biology
Publisher:Public Library of Science
Place of publication:San Francisco, Calif.
Contributor(s):Avner Schlessinger
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/12/07
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/12/20
Tag:Crystal structure; Drug discovery; Drug interactions; Drug therapy; Gene expression; Small molecules; Structural genomics; Ubiquitination
Volume:14
Issue:(12): e1006651
Page Number:24
First Page:1
Last Page:24
Note:
Copyright: © 2018 Pabon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
HeBIS-PPN:446350788
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0