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
Journal/Book/Conference Title
Journal of chemical information and modeling
PubMed ID
34138546
Repository Citation
Leidner F, Yilmaz NK, Schiffer CA. (2021). Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1021/acs.jcim.1c00403. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/2134