Generalized Born model with a simple, robust molecular volume correction
Generalized Born model with a simple, robust molecular volume correction
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DOI：
10.1021/ct600085e
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发表时间：
2007-01-01
影响因子：
5.5
通讯作者：
Onufriev, Alexey
中科院分区：
文献类型：
--
作者：
Mongan, John;Simmerling, Carlos;Onufriev, Alexey
广义Born(GB)模型为描述溶剂的静电效应提供了一种有效的计算手段，在分子动力学中得到了广泛的应用。一类特别快速的GB模型是基于内部体积上的积分，该内部体积被近似为原子球的两两并集，有效地，内部由van der Waals而不是Lee-Richards分子表面定义。该近似在计算上是有效的，但如果不进行修正，则允许原子之间的高介电性(水)区域小于水分子，从而导致精度降低。在这里，早期的成对GB模型被一个简单的解析修正项扩展，通过正确地描述每对原子的溶剂排除体积，在很大程度上缓解了这个问题。修正项为非键原子的分离引入了自由能垒。这种自由能垒在显式溶剂和Lee-Richards分子表面隐式溶剂计算中可以看到，但在早期的成对GB模型中没有。当用于MD时，修正项得到了蛋白质氢键长度分布和多肽构象系综，与早期的成对模型相比，这些结果与显式溶剂结果更符合。修正的稳健性和简单性保持了成对GB模型的效率，同时使它们更接近现实。
Generalized Born (GB) models provide a computationally efficient means of representing the electrostatic effects of solvent and are widely used, especially in molecular dynamics (MD). A class of particularly fast GB models is based on integration over an interior volume approximated as a pairwise union of atom sphereseffectively, the interior is defined by a van der Waals rather than Lee-Richards molecular surface. The approximation is computationally efficient but, if uncorrected, allows for high dielectric (water) regions smaller than a water molecule between atoms, leading to decreased accuracy. Here, an earlier pairwise GB model is extended by a simple analytic correction term that largely alleviates the problem by correctly describing the solvent-excluded volume of each pair of atoms. The correction term introduces a free energy barrier to the separation of nonbonded atoms. This free energy barrier is seen in explicit solvent and Lee-Richards molecular surface implicit solvent calculations but has been absent from earlier pairwise GB models. When used in MD, the correction term yields protein hydrogen bond length distributions and polypeptide conformational ensembles that are in better agreement with explicit solvent results than earlier pairwise models. The robustness and simplicity of the correction preserves the efficiency of the pairwise GB models while making them a better approximation to reality.