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用于预测青霉素结合蛋白抑制作用的蛋白质化学计量模型

Proteochemometric model for predicting the inhibition of penicillin-binding proteins.

作者信息

Nabu Sunanta, Nantasenamat Chanin, Owasirikul Wiwat, Lawung Ratana, Isarankura-Na-Ayudhya Chartchalerm, Lapins Maris, Wikberg Jarl E S, Prachayasittikul Virapong

机构信息

Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.

出版信息

J Comput Aided Mol Des. 2015 Feb;29(2):127-41. doi: 10.1007/s10822-014-9809-0. Epub 2014 Oct 26.

Abstract

Neisseria gonorrhoeae infection threatens to become an untreatable sexually transmitted disease in the near future owing to the increasing emergence of N. gonorrhoeae strains with reduced susceptibility and resistance to the extended-spectrum cephalosporins (ESCs), i.e. ceftriaxone and cefixime, which are the last remaining option for first-line treatment of gonorrhea. Alteration of the penA gene, encoding penicillin-binding protein 2 (PBP2), is the main mechanism conferring penicillin resistance including reduced susceptibility and resistance to ESCs. To predict and investigate putative amino acid mutations causing β-lactam resistance particularly for ESCs, we applied proteochemometric modeling to generalize N. gonorrhoeae susceptibility data for predicting the interaction of PBP2 with therapeutic β-lactam antibiotics. This was afforded by correlating publicly available data on antimicrobial susceptibility of wild-type and mutant N. gonorrhoeae strains for penicillin-G, cefixime and ceftriaxone with 50 PBP2 protein sequence data using partial least-squares projections to latent structures. The generated model revealed excellent predictability (R2=0.91, Q2=0.77, QExt2=0.78). Moreover, our model identified amino acid mutations in PBP2 with the highest impact on antimicrobial susceptibility and provided information on physicochemical properties of amino acid mutations affecting antimicrobial susceptibility. Our model thus provided insight into the physicochemical basis for resistance development in PBP2 suggesting its use for predicting and monitoring novel PBP2 mutations that may emerge in the future.

摘要

由于对扩展谱头孢菌素(ESCs),即头孢曲松和头孢克肟敏感性降低及耐药的淋病奈瑟菌菌株不断出现,淋病奈瑟菌感染在不久的将来可能会成为一种无法治疗的性传播疾病,而ESCs是淋病一线治疗的最后选择。编码青霉素结合蛋白2(PBP2)的penA基因的改变是导致青霉素耐药的主要机制,包括对ESCs敏感性降低和耐药。为了预测和研究导致β-内酰胺耐药尤其是对ESCs耐药的假定氨基酸突变,我们应用蛋白质化学计量学建模来概括淋病奈瑟菌的敏感性数据,以预测PBP2与治疗性β-内酰胺抗生素的相互作用。这是通过使用偏最小二乘投影到潜在结构,将野生型和突变型淋病奈瑟菌菌株对青霉素G、头孢克肟和头孢曲松的抗菌药敏公开数据与50个PBP2蛋白序列数据相关联来实现的。生成的模型显示出优异的预测能力(R2 = 0.91,Q2 = 0.77,QExt2 = 0.78)。此外,我们的模型确定了PBP2中对抗菌药敏影响最大的氨基酸突变,并提供了影响抗菌药敏的氨基酸突变的物理化学性质信息。因此,我们的模型深入了解了PBP2耐药性产生的物理化学基础,表明其可用于预测和监测未来可能出现的新型PBP2突变。

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