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首页> 外文期刊>PLoS Computational Biology >Correlated Electrostatic Mutations Provide a Reservoir of Stability in HIV Protease
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Correlated Electrostatic Mutations Provide a Reservoir of Stability in HIV Protease

机译:相关的静电突变为HIV蛋白酶提供了稳定的储存库

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摘要

HIV protease, an aspartyl protease crucial to the life cycle of HIV, is the target of many drug development programs. Though many protease inhibitors are on the market, protease eventually evades these drugs by mutating at a rapid pace and building drug resistance. The drug resistance mutations, called primary mutations, are often destabilizing to the enzyme and this loss of stability has to be compensated for. Using a coarse-grained biophysical energy model together with statistical inference methods, we observe that accessory mutations of charged residues increase protein stability, playing a key role in compensating for destabilizing primary drug resistance mutations. Increased stability is intimately related to correlations between electrostatic mutations – uncorrelated mutations would strongly destabilize the enzyme. Additionally, statistical modeling indicates that the network of correlated electrostatic mutations has a simple topology and has evolved to minimize frustrated interactions. The model's statistical coupling parameters reflect this lack of frustration and strongly distinguish like-charge electrostatic interactions from unlike-charge interactions for of the most significantly correlated double mutants. Finally, we demonstrate that our model has considerable predictive power and can be used to predict complex mutation patterns, that have not yet been observed due to finite sample size effects, and which are likely to exist within the larger patient population whose virus has not yet been sequenced.
机译:HIV蛋白酶是一种对HIV生命周期至关重要的天冬氨酰蛋白酶,是许多药物开发计划的目标。尽管市场上有许多蛋白酶抑制剂,但是蛋白酶最终通过快速突变并建立耐药性而逃避了这些药物。耐药性突变(称为原发突变)通常会使酶不稳定,因此必须弥补这种稳定性的损失。使用粗粒生物物理能模型和统计推断方法,我们观察到带电残基的辅助突变增加了蛋白质的稳定性,在补偿不稳定的原发性耐药性突变中起关键作用。稳定性的提高与静电突变之间的相关性密切相关-不相关的突变会严重破坏酶的稳定性。此外,统计建模表明,相关的静电突变网络具有简单的拓扑结构,并且已经发展到可以最大程度地减少受挫的相互作用。该模型的统计耦合参数反映了这种缺乏的挫败感,并强烈区分了相关性最高的双突变体的相同电荷的静电相互作用与不同电荷的相互作用。最后,我们证明了我们的模型具有相当大的预测能力,可以用于预测复杂的突变模式,这些模式由于有限的样本量效应尚未被观察到,并且可能存在于尚未感染病毒的较大患者群中已排序。

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