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首页> 外文期刊>Physical chemistry chemical physics: PCCP >Predicting stabilizing mutations in proteins using Poisson-Boltzmann based models: study of unfolded state ensemble models and development of a successful binary classifier based on residue interaction energies
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Predicting stabilizing mutations in proteins using Poisson-Boltzmann based models: study of unfolded state ensemble models and development of a successful binary classifier based on residue interaction energies

机译：使用基于Poisson-Boltzmann的模型预测蛋白质中的稳定突变：未折叠状态集成模型的研究以及基于残基相互作用能的成功二元分类器的开发

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摘要

In many cases the stability of a protein has to be increased to permit its biotechnological use. Rational methods of protein stabilization based on optimizing electrostatic interactions have provided some fine successful predictions. However, the precise calculation of stabilization energies remains challenging, one reason being that the electrostatic effects on the unfolded state are often neglected. We have explored here the feasibility of incorporating Poisson-Boltzmann model electrostatic calculations performed on representations of the unfolded state as large ensembles of geometrically optimized conformations calculated using the ProtSA server. Using a data set of 80 electrostatic mutations experimentally tested in two-state proteins, the predictive performance of several such models has been compared to that of a simple one that considers an unfolded structure of non-interacting residues. The unfolded ensemble models, while showing correlation between the predicted stabilization values and the experimental ones, are worse than the simple model, suggesting that the ensembles do not capture well the energetics of the unfolded state. A more attainable goal is classifying potential mutations as either stabilizing or non-stabilizing, rather than accurately calculating their stabilization energies. To implement a fast classification method that can assist in selecting stabilizing mutations, we have used a much simpler electrostatic model based only on the native structure and have determined its precision using different stabilizing energy thresholds. The binary classifier developed finds 7 true stabilizing mutants out of every 10 proposed candidates and can be used as a robust tool to propose stabilizing mutations.
机译：在许多情况下，必须增加蛋白质的稳定性以允许其生物技术用途。基于优化静电相互作用的合理的蛋白质稳定方法已经提供了一些很好的成功预测。然而，稳定能的精确计算仍然具有挑战性，原因之一是经常忽略了对展开状态的静电影响。我们在这里探讨了合并Poisson-Boltzmann模型静电计算的可行性，该静电计算是根据使用ProtSA服务器计算出的几何优化构型的大集合进行的展开状态表示而进行的。使用在两个状态的蛋白质中实验测试的80个静电突变的数据集，已将几种此类模型的预测性能与考虑非相互作用残基的未折叠结构的简单模型的预测性能进行了比较。展开的集成模型虽然显示了预测的稳定值与实验值之间的相关性，但比简单的模型差，这表明这些集成无法很好地捕获展开状态的能量。一个更可实现的目标是将潜在突变分为稳定突变或非稳定突变，而不是准确计算其稳定能量。为了实现可以帮助选择稳定突变的快速分类方法，我们仅基于天然结构使用了一种简单得多的静电模型，并使用不同的稳定能量阈值确定了其精确度。开发的二元分类器在每10个建议的候选物中找到7个真正的稳定突变体，可以用作提出稳定突变的强大工具。

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