Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase.

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 Biol

journal_title

ACS chemical biology

authors

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 PS

doi

10.1021/acschembio.7b00468

subject

Has Abstract

pub_date

2017-09-15 00:00:00

pages

2448-2456

issue

9

eissn

1554-8929

issn

1554-8937

journal_volume

12

pub_type

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