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Published on: December 28, 2017
Deciphering Antifungal Drug Resistance in Pneumocystis jirovecii DHFR with Molecular Dynamics and Machine Learning
Florian Leidner1, Nese Kurt Yilmaz1, Celia A Schiffer1
1Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, Massachusetts 01605, United States.
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.
Insights
Drug resistance in Pneumocystis jirovecii dihydrofolate reductase to trimethoprim is driven by specific molecular features. These key interactions, even in non-varying residues, predict therapeutic resistance.
Area of Science:
- Biochemistry
- Computational Biology
- Drug Discovery
Background:
- Drug resistance compromises therapeutic efficacy.
- Identifying resistance-driving mutations, especially distant ones, is difficult.
Purpose of the Study:
- To identify key molecular interactions conferring trimethoprim resistance in Pneumocystis jirovecii dihydrofolate reductase variants.
- To understand the mechanisms underlying drug resistance.
Main Methods:
- Homology modeling
- Molecular dynamics simulations
- Machine learning analysis
Main Results:
- Six molecular features were identified as the strongest indicators of trimethoprim resistance.
- These key features primarily involved residues that did not undergo mutation.
Conclusions:
- Specific molecular interactions, not necessarily in the active site, are critical for trimethoprim resistance.
- Computational and machine learning approaches can elucidate resistance mechanisms.
