基于稳定性诱变的蛋白质结构模型选择贝叶斯误差优化。

Xiaoduan Ye, A. Friedman, C. Bailey-Kellogg
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引用次数: 3

摘要

定点诱变以依赖于突变残基的局部结构环境的方式影响蛋白质的稳定性;例如,疏水取代到极性取代在蛋白质的核心和表面表现不同。因此,基于预测和实验稳定性变化(DeltaDeltaGo值)之间的一致性,定点诱变和稳定性测量可以对预测的结构模型进行评估和选择。本文提出了一种规划一组单个位点定向突变用于蛋白质结构模型选择的方法,以最小化贝叶斯误差,即选择错误模型的概率。虽然通常很难精确计算由一组突变定义的多维贝叶斯误差,但我们利用“DeltaDeltaGo空间”的结构来开发严密的上界和下界。我们进一步开发了使用候选集合中固定数量的突变的任何计划的贝叶斯误差的下界。我们在分支定界规划算法中使用这个定界来寻找最优和近最优规划。我们证明了这种方法在计划突变以阐明来自噬菌体lambda的pTfa伴侣蛋白结构方面的重要性和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Bayes error for protein structure model selection by stability mutagenesis.
Site-directed mutagenesis affects protein stability in a manner dependent on the local structural environment of the mutated residue; e.g., a hydrophobic to polar substitution would behave differently in the core vs. on the surface of the protein. Thus site-directed mutagenesis followed by stability measurement enables evaluation of and selection among predicted structure models, based on consistency between predicted and experimental stability changes (DeltaDeltaGo values). This paper develops a method for planning a set of individual site-directed mutations for protein structure model selection, so as to minimize the Bayes error, i.e., the probability of choosing the wrong model. While in general it is hard to calculate exactly the multi-dimensional Bayes error defined by a set of mutations, we leverage the structure of "DeltaDeltaGo space" to develop tight upper and lower bounds. We further develop a lower bound on the Bayes error of any plan that uses a fixed number of mutations from a set of candidates. We use this bound in a branch-and-bound planning algorithm to find optimal and near-optimal plans. We demonstrate the significance and effectiveness of this approach in planning mutations for elucidating the structure of the pTfa chaperone protein from bacteriophage lambda.
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