Predicting protein targets for drug-like compounds using transcriptomics

• Autores: Pabon, Nicolas; Xia, Yan; Estabrooks, Samuel K.; Ye, Zhaofeng; Herbrand, Amanda K.; Süß, Evelyn; Biondi, Ricardo Miguel; Assimon, Victoria A.; Gestwicki, Jason E.; Brodsky, Jeffrey L.; Camacho, Carlos; Bar Joseph, Ziv
• Año de publicación: 2018
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: small compound; target prediction; transcriptomics
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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.
Fil: Pabon, Nicolas. University of Pittsburgh; Estados Unidos
Fil: Xia, Yan. University of Carnegie Mellon; Estados Unidos
Fil: Estabrooks, Samuel K.. University of Pittsburgh; Estados Unidos
Fil: Ye, Zhaofeng. Tsinghua University; China
Fil: Herbrand, Amanda K.. Goethe Universitat Frankfurt; Alemania
Fil: Süß, Evelyn. Goethe Universitat Frankfurt; Alemania
Fil: Biondi, Ricardo Miguel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigación en Biomedicina de Buenos Aires - Instituto Partner de la Sociedad Max Planck; Argentina. Goethe Universitat Frankfurt; Alemania
Fil: Assimon, Victoria A.. University of California; Estados Unidos
Fil: Gestwicki, Jason E.. University of California; Estados Unidos
Fil: Brodsky, Jeffrey L.. University of Pittsburgh; Estados Unidos
Fil: Camacho, Carlos. University of Pittsburgh; Estados Unidos
Fil: Bar Joseph, Ziv. University of Carnegie Mellon; Estados Unidos