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.
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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DOI：
10.1371/journal.pcbi.1003569
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发表时间：
2014-04
影响因子：
4.3
通讯作者：
Najmanovich RJ
中科院分区：
文献类型：
--
作者：
Frappier V;Najmanovich RJ
正态分析（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水平的粗粒度和势能功能形式的选择。在不同的选择中，速度和准确性之间存在权衡。在一个极端中，人们发现基于全原子表示和详细原子势的精确但缓慢的分子动力学方法。另一方面，快速弹性网络模型（ENM）方法仅使用Cα−表示，简化了仅基于几何结构的电位，因此忽略了蛋白质序列。在这里，我们提出了ENCoM，一个弹性网络接触模型，它采用了一个势能函数，其中包括一个成对的原子型非键相互作用项，因此可以考虑氨基酸的特定性质对NMA背景下动力学的影响。ENCoM与现有的ENM方法一样快，并且在生成构象集成方面优于其他方法。本文介绍了NMA方法在预测突变对蛋白质稳定性影响方面的新应用。虽然现有的方法是基于机器学习或焓的考虑，但基于振动正模态的ENCoM的使用是基于熵的考虑。这代表了NMA方法的新应用领域和预测突变效应的新方法。我们在准确性和自一致性方面将ENCoM与许多方法进行了比较。结果表明，该方法的精度可与现有的最佳方法相媲美。我们表明，现有的方法偏向于预测不稳定突变，而ENCoM在预测稳定突变方面的偏差较小。正态模态分析（NMA）方法可以通过计算与不同正态模态相关的特征向量和特征值来探索平衡构象周围的潜在运动。每个正常模式代表了整个蛋白质的整体集体、相关和复杂的运动形式。任何平衡附近的构象都可以表示为正规模式的加权组合。两个结构之间特征值集合的大小差异可以用来计算熵的差异。我们引入了ENCoM，这是第一个考虑原子特异性侧链相互作用的粗粒度NMA方法，从而解释了突变对特征向量和特征值的影响。在NMA方法的传统应用方面，ENCoM比现有的NMA方法表现得更好，但它是第一个预测突变对蛋白质稳定性和功能影响的方法。将ENCoM与大量用于预测突变对蛋白质稳定性影响的专用方法进行比较，表明ENCoM比现有方法表现更好，特别是在稳定突变方面。ENCoM是第一个基于熵的方法，用于预测突变对蛋白质稳定性的影响。
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. Normal mode analysis (NMA) methods can be used to explore potential movements around an equilibrium conformation by mean of calculating the eigenvectors and eigenvalues associated to different normal modes. Each normal mode represents a global collective, correlated and complex, form of motion of the entire protein. Any conformation around equilibrium can be represented as a weighted combination of normal modes. Differences in the magnitudes of the set of eigenvalues between two structures can be used to calculate differences in entropy. We introduce ENCoM the first coarse-grained NMA method to consider atom-specific side-chain interactions and thus account for the effect of mutations on eigenvectors and eigenvalues. ENCoM performs better than existing NMA methods with respect to traditional applications of NMA methods but is the first to predict the effect of mutations on protein stability and function. Comparing ENCoM to a large set of dedicated methods for the prediction of the effect of mutations on protein stability shows that ENCoM performs better than existing methods particularly on stabilizing mutations. ENCoM is the first entropy-based method developed to predict the effect of mutations on protein stability.
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影响因子：
5.6
作者：
Eyal, E;Gerzon, S;Sobolev, V
通讯作者：
Sobolev, V
影响因子：
2.9
作者：
Garbuzynskiy, SO;Melnik, BS;Galzitskaya, OV
通讯作者：
Galzitskaya, OV
影响因子：
2.7
作者：
GEKKO, K;YAMAGAMI, K;IWAKURA, M
通讯作者：
IWAKURA, M
DOI：
10.1073/pnas.80.12.3696
发表时间：
1983-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
GO, N;NOGUTI, T;NISHIKAWA, T
通讯作者：
NISHIKAWA, T
DOI：
10.1016/s1359-0278(97)00024-2
发表时间：
1997-01-01
期刊：
FOLDING & DESIGN
影响因子：
--
作者：
Bahar, I;Atilgan, AR;Erman, B
通讯作者：
Erman, B