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一种估计病原体耐药性的定量模型。

A Quantitative Model to Estimate Drug Resistance in Pathogens.

作者信息

Baker Frazier N, Cushion Melanie T, Porollo Aleksey

机构信息

Department of Electrical Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, USA 45221; Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA 45229.

Department of Internal Medicine University of Cincinnati College of Medicine, Cincinnati, OH, USA 45267; melanie; The Veterans Affairs Medical Center, Cincinnati, OH, USA 45220.

出版信息

J Fungi (Basel). 2016 Dec;2(4). doi: 10.3390/jof2040030. Epub 2016 Dec 5.

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 (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 (, ) 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 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 ( 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.

摘要

肺孢子菌肺炎(PCP)是一种发生于人类和其他免疫系统衰弱的哺乳动物的机会性感染。这些感染由肺孢子菌属真菌引起,它们对标准抗真菌药物不敏感。尽管经过数十年的研究和药物开发,PCP的主要治疗和预防方法仍然是甲氧苄啶(TMP)和磺胺甲恶唑(SMX)的联合使用,它们分别作用于叶酸生物合成中的两种酶,即二氢叶酸还原酶(DHFR)和二氢蝶酸合酶(DHPS)。越来越多的证据表明,感染人类的该物种对与靶向酶突变相关的TMP-SMX出现了耐药性。在本研究中,我们报告了一种准确的定量模型的开发,用于预测抑制剂( )与突变蛋白结合亲和力的变化。该模型基于进化信息和氨基酸协方差分析。突变后预测的结合亲和力变化与实验测量数据高度相关。虽然该模型是基于DHFR/TMP数据训练的,但在针对另一种药物/靶点对(PjDHPS/SMX)和另一种生物体( DHFR/TMP)的耐药数据进行评估时,该模型表现出相似或更好的性能。因此,我们预计所开发的预测模型将有助于评估病原体新测序变体的可能耐药性,并可扩展到其他药物靶点和生物体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1271/5715933/c82500e0ec08/jof-02-00030-g001.jpg

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