University of Massachusetts Medical School Faculty Publications

Title

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

UMMS Affiliation

Department of Biochemistry and Molecular Pharmacology; Schiffer Lab

Publication Date

2021-06-28

Document Type

Article

Disciplines

Biochemistry, Biophysics, and Structural Biology | Medicinal and Pharmaceutical Chemistry | Medicinal Chemistry and Pharmaceutics | Medicinal-Pharmaceutical Chemistry

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.

Keywords

Drug resistance, Peptides and proteins, Genetics, Antifungal activit

DOI of Published Version

10.1021/acs.jcim.1c00403

Source

Leidner F, Kurt Yilmaz N, Schiffer CA. Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning. J Chem Inf Model. 2021 Jun 28;61(6):2537-2541. doi: 10.1021/acs.jcim.1c00403. Epub 2021 Jun 17. PMID: 34138546. Link to article on publisher's site

Related Resources

Link to Article in PubMed

Journal/Book/Conference Title

Journal of chemical information and modeling

PubMed ID

34138546

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