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On-the-Fly Numerical Surface Integration for Finite-Difference Poisson-Boltzmann Methods

机译:有限差分Poisson-Boltzmann方法的实时数值表面积分

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Most implicit solvation models require the definition of a molecular surface as the interface that separates the solute in atomic detail from the solvent approximated as a continuous medium. Commonly used surface definitions include the solvent accessible surface (SAS), the solvent excluded surface (SES), and the van der Waals surface. In this study, we present an efficient numerical algorithm to compute the SES and SAS areas to facilitate the applications of finite-difference Poisson-Boltzmann methods in biomolecular simulations. Different from previous numerical approaches, our algorithm is physics-inspired and intimately coupled to the finite-difference Poisson-Boltzmann methods to fully take advantage of its existing data structures. Our analysis shows that the algorithm can achieve very good agreement with the analytical method in the calculation of the SES and SAS areas. Specifically, in our comprehensive test of 155S molecules, the average unsigned relative error is 0.27% in the SES area calculations and 1.0596 in the SAS area calculations at a grid spacing of 1/2 A. In addition, a linear correlation analysis was found to improve the accuracy of the coarse-grid SES areas, with the average unsigned relative error reduced to 0.13%. These validation studies indicate fhat the proposed algorithm can be applied to biomolecules over a broad range of sizes and structures. Finally, the numerical algorithm can also be adapted to evaluate the surface integral of either a vector field or a scalar field defined on the molecular surface for additional solvation energetics and force calculations.
机译:大多数隐式溶剂化模型都需要定义分子表面作为将原子细节中的溶质与近似连续介质的溶剂分开的界面。常用的表面定义包括溶剂可及表面(SAS),溶剂排除表面(SES)和范德华表面。在这项研究中,我们提出了一种有效的数值算法来计算SES和SAS面积,以促进有限差分Poisson-Boltzmann方法在生物分子模拟中的应用。与以前的数值方法不同,我们的算法受物理学启发,并与有限差分Poisson-Boltzmann方法紧密耦合,以充分利用其现有数据结构。我们的分析表明,该算法在SES和SAS区域的计算中可以与解析方法取得很好的一致性。具体来说,在我们对155S分子的全面测试中,在1/2 A的网格间距下,SES面积计算的平均无符号相对误差为0.27%,SAS面积计算的平均无符号相对误差为1.0596。此外,还发现了线性相关分析提高了粗网格SES区域的精度,平均无符号相对误差降低到0.13%。这些验证研究表明,所提出的算法可以广泛应用于各种大小和结构的生物分子。最后,数值算法还可适用于评估在分子表面上定义的矢量场或标量场的表面积分,以进行额外的溶剂化能量学和力计算。

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