Reeve Stephanie M, Gainza Pablo, Frey Kathleen M, Georgiev Ivelin, Donald Bruce R, Anderson Amy C
Department of Pharmaceutical Sciences, University of Connecticut, Storrs, CT 06269; and.
Departments of Computer Science and.
Proc Natl Acad Sci U S A. 2015 Jan 20;112(3):749-54. doi: 10.1073/pnas.1411548112. Epub 2014 Dec 31.
Methods to accurately predict potential drug target mutations in response to early-stage leads could drive the design of more resilient first generation drug candidates. In this study, a structure-based protein design algorithm (K* in the OSPREY suite) was used to prospectively identify single-nucleotide polymorphisms that confer resistance to an experimental inhibitor effective against dihydrofolate reductase (DHFR) from Staphylococcus aureus. Four of the top-ranked mutations in DHFR were found to be catalytically competent and resistant to the inhibitor. Selection of resistant bacteria in vitro reveals that two of the predicted mutations arise in the background of a compensatory mutation. Using enzyme kinetics, microbiology, and crystal structures of the complexes, we determined the fitness of the mutant enzymes and strains, the structural basis of resistance, and the compensatory relationship of the mutations. To our knowledge, this work illustrates the first application of protein design algorithms to prospectively predict viable resistance mutations that arise in bacteria under antibiotic pressure.
准确预测针对早期先导化合物的潜在药物靶点突变的方法,可能会推动更具适应性的第一代候选药物的设计。在本研究中,一种基于结构的蛋白质设计算法(OSPREY套件中的K*)被用于前瞻性地识别赋予对一种有效对抗金黄色葡萄球菌二氢叶酸还原酶(DHFR)的实验性抑制剂抗性的单核苷酸多态性。在DHFR中排名靠前的四个突变被发现具有催化活性且对该抑制剂具有抗性。体外选择抗性细菌表明,两个预测的突变出现在一个补偿性突变的背景中。利用复合物的酶动力学、微生物学和晶体结构,我们确定了突变酶和菌株的适应性、抗性的结构基础以及突变的补偿关系。据我们所知,这项工作说明了蛋白质设计算法首次应用于前瞻性预测在抗生素压力下细菌中出现的可行抗性突变。