Abstract:
:Though phenotypic and target-based high-throughput screening approaches have been employed to discover new antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal antibacterial phenotypic screens and 636 bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.
journal_name
ACS Chem Bioljournal_title
ACS chemical biologyauthors
Santa Maria JP Jr,Park Y,Yang L,Murgolo N,Altman MD,Zuck P,Adam G,Chamberlin C,Saradjian P,Dandliker P,Boshoff HIM,Barry CE 3rd,Garlisi C,Olsen DB,Young K,Glick M,Nickbarg E,Kutchukian PSdoi
10.1021/acschembio.7b00468subject
Has Abstractpub_date
2017-09-15 00:00:00pages
2448-2456issue
9eissn
1554-8929issn
1554-8937journal_volume
12pub_type
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