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首页> 外文期刊>PLoS Computational Biology >A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the Prediction of the Effect of Mutations
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A Coarse-Grained Elastic Network Atom Contact Model and Its Use in the Simulation of Protein Dynamics and the Prediction of the Effect of Mutations

机译：粗粒弹性网络原子接触模型及其在蛋白质动力学模拟和突变效应预测中的应用

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Normal mode analysis (NMA) methods are widely used to study dynamic aspects of protein structures. Two critical components of NMA methods are coarse-graining in the level of simplification used to represent protein structures and the choice of potential energy functional form. There is a trade-off between speed and accuracy in different choices. In one extreme one finds accurate but slow molecular-dynamics based methods with all-atom representations and detailed atom potentials. On the other extreme, fast elastic network model (ENM) methods with Cα?only representations and simplified potentials that based on geometry alone, thus oblivious to protein sequence. Here we present ENCoM, an Elastic Network Contact Model that employs a potential energy function that includes a pairwise atom-type non-bonded interaction term and thus makes it possible to consider the effect of the specific nature of amino-acids on dynamics within the context of NMA. ENCoM is as fast as existing ENM methods and outperforms such methods in the generation of conformational ensembles. Here we introduce a new application for NMA methods with the use of ENCoM in the prediction of the effect of mutations on protein stability. While existing methods are based on machine learning or enthalpic considerations, the use of ENCoM, based on vibrational normal modes, is based on entropic considerations. This represents a novel area of application for NMA methods and a novel approach for the prediction of the effect of mutations. We compare ENCoM to a large number of methods in terms of accuracy and self-consistency. We show that the accuracy of ENCoM is comparable to that of the best existing methods. We show that existing methods are biased towards the prediction of destabilizing mutations and that ENCoM is less biased at predicting stabilizing mutations.
机译：正态模式分析（NMA）方法被广泛用于研究蛋白质结构的动态方面。 NMA方法的两个关键组成部分是用于表示蛋白质结构的简化程度的粗粒度以及对势能功能形式的选择。在不同的选择中，速度和精度之间需要权衡。在一个极端中，人们发现了一种精确但缓慢的基于分子动力学的方法，该方法具有全原子表示和详细的原子势。在另一种极端的情况下，仅具有Cα？表示和简化的电势的快速弹性网络模型（ENM）方法仅基于几何，因此不考虑蛋白质序列。在这里，我们介绍ENCoM，这是一种弹性网络接触模型，该模型采用势能函数，该函数包括成对的原子型非键相互作用项，因此可以在上下文中考虑氨基酸的特定性质对动力学的影响NMA。 ENCoM与现有的ENM方法一样快，并且在构象集合的生成方面胜过此类方法。在这里，我们介绍了使用ENCoM预测NMA方法的新应用，以预测突变对蛋白质稳定性的影响。尽管现有方法是基于机器学习或焓的考虑因素，但基于振动法线模式的ENCoM的使用是基于熵的考虑因素。这代表了NMA方法的新应用领域和预测突变效果的新方法。在准确性和自洽性方面，我们将ENCoM与大量方法进行了比较。我们表明，ENCoM的准确性可与现有最佳方法相媲美。我们表明，现有方法偏向去稳定突变的预测，而ENCoM偏向去预测稳定突变的偏向。

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