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A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules

ABSTRACT: Cells expressing mutant huntingtin were treated in triplicate with serum-free DMEM with vehicle (Q111SST) or serum-free DMEM with one of 4 protective compounds (Cyproheptadine, Loxapine, Diacylglycerol Kinase Inhibitor II, Meclizine) for 24 hours. Wild type cells were also treated with serum-free DMEM with vehicle (Q7SST) as an additional control for 24 hours. We examined the compounds' proteomic and some phosphoproteomic effects on the cells using shotgun proteomics.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Mus Musculus (ncbitaxon:10090)

SUBMITTER: Ernest Fraenkel

PROVIDER: MSV000084607 | MassIVE | Wed Nov 20 19:46:00 GMT 2019

REPOSITORIES: MassIVE

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A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

Patel-Murray Natasha L NL Adam Miriam M Huynh Nhan N Wassie Brook T BT Milani Pamela P Fraenkel Ernest E

Scientific reports 20200122 1

High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncovering MoAs through an interpretable machine learning model of transcriptomics, epigenomics, metabolomics, and proteomics. Examining compounds with beneficial effects in models of Huntington's Disease, we found common MoAs for compounds with unrelated s ...[more]

PMID: 31969612

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