Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning
| dc.contributor.author | Leidner, Florian | |
| dc.contributor.author | Yilmaz, Nese Kurt | |
| dc.contributor.author | Schiffer, Celia A. | |
| dc.date | 2022-08-11T08:08:28.000 | |
| dc.date.accessioned | 2022-08-23T15:56:16Z | |
| dc.date.available | 2022-08-23T15:56:16Z | |
| dc.date.issued | 2021-06-28 | |
| dc.date.submitted | 2022-01-02 | |
| dc.identifier.citation | <p>Leidner F, Kurt Yilmaz N, Schiffer CA. Deciphering Antifungal Drug Resistance in <em>Pneumocystis jirovecii</em> 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. <a href="https://doi.org/10.1021/acs.jcim.1c00403">Link to article on publisher's site</a></p> | |
| dc.identifier.issn | 1549-9596 (Linking) | |
| dc.identifier.doi | 10.1021/acs.jcim.1c00403 | |
| dc.identifier.pmid | 34138546 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14038/29931 | |
| dc.description.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. | |
| dc.language.iso | en_US | |
| dc.relation | <p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=34138546&dopt=Abstract">Link to Article in PubMed</a></p> | |
| dc.relation.url | https://doi.org/10.1021/acs.jcim.1c00403 | |
| dc.subject | Drug resistance | |
| dc.subject | Peptides and proteins | |
| dc.subject | Genetics | |
| dc.subject | Antifungal activit | |
| dc.subject | Biochemistry, Biophysics, and Structural Biology | |
| dc.subject | Medicinal and Pharmaceutical Chemistry | |
| dc.subject | Medicinal Chemistry and Pharmaceutics | |
| dc.subject | Medicinal-Pharmaceutical Chemistry | |
| dc.title | Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning | |
| dc.type | Journal Article | |
| dc.source.journaltitle | Journal of chemical information and modeling | |
| dc.source.volume | 61 | |
| dc.source.issue | 6 | |
| dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/faculty_pubs/2134 | |
| dc.identifier.contextkey | 27074125 | |
| html.description.abstract | <p>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.</p> | |
| dc.identifier.submissionpath | faculty_pubs/2134 | |
| dc.contributor.department | Schiffer Lab | |
| dc.contributor.department | Department of Biochemistry and Molecular Pharmacology | |
| dc.source.pages | 2537-2541 |