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J. Fungi 2016, 2(4), 30; doi:10.3390/jof2040030

A Quantitative Model to Estimate Drug Resistance in Pathogens

1
Department of Electrical Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH 45221, USA
2
Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
3
Department of Internal Medicine University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
4
The Veterans Affairs Medical Center, Cincinnati, OH 45220, USA
5
Department of Biomedical Informatics, University of Cincinnati College of Medicine and Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45267, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Maurizio Del Poeta
Received: 13 October 2016 / Revised: 30 November 2016 / Accepted: 1 December 2016 / Published: 5 December 2016
(This article belongs to the Special Issue Novel Antifungal Drug Discovery)
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Abstract

Pneumocystis pneumonia (PCP) is an opportunistic infection that occurs in humans and other mammals with debilitated immune systems. These infections are caused by fungi in the genus Pneumocystis, which are not susceptible to standard antifungal agents. Despite decades of research and drug development, the primary treatment and prophylaxis for PCP remains a combination of trimethoprim (TMP) and sulfamethoxazole (SMX) that targets two enzymes in folic acid biosynthesis, dihydrofolate reductase (DHFR) and dihydropteroate synthase (DHPS), respectively. There is growing evidence of emerging resistance by Pneumocystis jirovecii (the species that infects humans) to TMP-SMX associated with mutations in the targeted enzymes. In the present study, we report the development of an accurate quantitative model to predict changes in the binding affinity of inhibitors (Ki, IC50) to the mutated proteins. The model is based on evolutionary information and amino acid covariance analysis. Predicted changes in binding affinity upon mutations highly correlate with the experimentally measured data. While trained on Pneumocystis jirovecii DHFR/TMP data, the model shows similar or better performance when evaluated on the resistance data for a different inhibitor of PjDFHR, another drug/target pair (PjDHPS/SMX) and another organism (Staphylococcus aureus DHFR/TMP). Therefore, we anticipate that the developed prediction model will be useful in the evaluation of possible resistance of the newly sequenced variants of the pathogen and can be extended to other drug targets and organisms. View Full-Text
Keywords: Pneumocystis pneumonia; Pneumocystis jirovecii; folate biosynthesis; drug resistance; QSAR; amino acid covariance; amino acid coevolution Pneumocystis pneumonia; Pneumocystis jirovecii; folate biosynthesis; drug resistance; QSAR; amino acid covariance; amino acid coevolution
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Baker, F.N.; Cushion, M.T.; Porollo, A. A Quantitative Model to Estimate Drug Resistance in Pathogens. J. Fungi 2016, 2, 30.

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