Parameterizing elastic network models to capture the dynamics of proteins
Parameterizing elastic network models to capture the dynamics of proteins
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DOI：
10.1002/jcc.26701
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发表时间：
2021-06-11
影响因子：
3
通讯作者：
Delarue，Marc
中科院分区：
文献类型：
--
作者：
Koehl，Patrice;Orland，Henri;Delarue，Marc
蛋白质动力学的粗粒度正态分析依赖于蛋白质结构的几何结构包含足够的信息来计算其在平衡构象周围的波动。这种几何形状以弹性网络（EN）的形式捕获，即其残基之间的边缘网络。然后将蛋白质的正常模式与其EN的正常模式进行鉴定。已经提出了不同的方法来构建ENs，重点是选择由它们组成的边，以及通过与这些边相关的力常数来参数化它们。在这里，我们提出了新的工具来指导在这两个方面的选择。我们首先研究了不同的ENs几何模型。我们比较了基于截断的ENs（其边缘长度小于截断距离）和基于Delaunay的ENs，发现后者能更好地表示蛋白质结构的几何形状。然后，我们推导出了一种解析方法，用于参数化EN，使其动力学导致与实验B因子一致的原子涨落。为了限制过拟合，我们在EN中为每个原子附加一个称为柔性常数的参数，而不是每个边。参数化表示为一个非线性优化问题，其参数同时描述刚体和内部运动。我们发现，这种参数化导致了改进的ENs，其动力学模拟MD模拟比具有均匀力常数的ENs更好，并且减少了再现功能构象变化所需的正常模式的数量。
Coarse‐grained normal mode analyses of protein dynamics rely on the idea that the geometry of a protein structure contains enough information for computing its fluctuations around its equilibrium conformation. This geometry is captured in the form of an elastic network (EN), namely a network of edges between its residues. The normal modes of a protein are then identified with the normal modes of its EN. Different approaches have been proposed to construct ENs, focusing on the choice of the edges that they are comprised of, and on their parameterizations by the force constants associated with those edges. Here we propose new tools to guide choices on these two facets of EN. We study first different geometric models for ENs. We compare cutoff‐based ENs, whose edges have lengths that are smaller than a cutoff distance, with Delaunay‐based ENs and find that the latter provide better representations of the geometry of protein structures. We then derive an analytical method for the parameterization of the EN such that its dynamics leads to atomic fluctuations that agree with experimental B‐factors. To limit overfitting, we attach a parameter referred to as flexibility constant to each atom instead of to each edge in the EN. The parameterization is expressed as a non‐linear optimization problem whose parameters describe both rigid‐body and internal motions. We show that this parameterization leads to improved ENs, whose dynamics mimic MD simulations better than ENs with uniform force constants, and reduces the number of normal modes needed to reproduce functional conformational changes.