Predicting allostery and microbial drug resistance with molecular simulations

被引:11
|
作者
Cortina, George A. [1 ,2 ]
Kassont, Peter M. [1 ,2 ,3 ]
机构
[1] Univ Virginia, Dept Mol Physiol, Charlottesville, VA 22908 USA
[2] Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA
[3] Uppsala Univ, Dept Cell & Mol Biol, Sci Life Lab, S-75146 Uppsala, Sweden
基金
美国国家卫生研究院;
关键词
MARKOV STATE MODELS; A BETA-LACTAMASE; CONFORMATIONAL FLUCTUATIONS; LIGAND-BINDING; EVOLUTION; DYNAMICS; MUTATIONS; MECHANISM; TEM-1; REVEALS;
D O I
10.1016/j.sbi.2018.09.001
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Beta-lactamase enzymes mediate the most common forms of gram-negative antibiotic resistance affecting clinical treatment. They also constitute an excellent model system for the difficult problem of understanding how allosteric mutations can augment catalytic activity of already-competent enzymes. Multiple allosteric mutations have been identified that alter catalytic activity or drug-resistance spectrum in class A beta lactamases, but predicting these in advance continues to be challenging. Here, we review computational techniques based on structure and/or molecular simulation to predict such mutations. Structure-based techniques have been particularly helpful in developing graph algorithms for analyzing critical residues in beta-lactamase function, while classical molecular simulation has recently shown the ability to prospectively predict allosteric mutations increasing beta-lactamase activity and drug resistance. These will ultimately achieve the greatest power when combined with simulation methods that model reactive chemistry to calculate activation free energies directly.
引用
收藏
页码:80 / 86
页数:7
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