Dataset Information

Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning.

ABSTRACT: 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.

SUBMITTER: Leidner F

PROVIDER: S-EPMC9109225 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Deciphering Antifungal Drug Resistance in <i>Pneumocystis jirovecii</i> DHFR with Molecular Dynamics and Machine Learning.

Leidner Florian F Kurt Yilmaz Nese N Schiffer Celia A CA

Journal of chemical information and modeling 20210617 6

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 <i>Pneumocystis jirovecii</i> 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 involv ...[more]

PMID: 34138546

Dataset Information

Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning.

Publications

Deciphering Antifungal Drug Resistance in <i>Pneumocystis jirovecii</i> DHFR with Molecular Dynamics and Machine Learning.

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