Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 61, Issue 6, Pages 2537-2541Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.1c00403
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- NIGMS [R01 GM135919]
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Using a combination of homology modeling, molecular dynamics, and machine learning, the study identified six molecular features involving mainly residues that did not vary as the best indicators of resistance to trimethoprim in Pneumocystis jirovecii dihydrofolate reductase variants.
Drug resistance impacts the effectiveness of many new therapeutics. Mutations in the therapeutic target confer resistance; however, deciphering which mutations, often remote from the enzyme active site, drive resistance is challenging. In a series of Pneumocystis jirovecii dihydrofolate reductase variants, we elucidate which interactions are key bellwethers to confer resistance to trimethoprim using homology modeling, molecular dynamics, and machine learning. Six molecular features involving mainly residues that did not vary were the best indicators of resistance.
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