Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning
Document Type
Journal ArticlePublication Date
2021-06-28Keywords
Drug resistancePeptides and proteins
Genetics
Antifungal activit
Biochemistry, Biophysics, and Structural Biology
Medicinal and Pharmaceutical Chemistry
Medicinal Chemistry and Pharmaceutics
Medicinal-Pharmaceutical Chemistry
Metadata
Show full item recordAbstract
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.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
DOI
10.1021/acs.jcim.1c00403Permanent Link to this Item
http://hdl.handle.net/20.500.14038/29931PubMed ID
34138546Related Resources
ae974a485f413a2113503eed53cd6c53
10.1021/acs.jcim.1c00403