A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers.
A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers.
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DOI：
10.1016/j.cpc.2013.10.028
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发表时间：
2014-03
影响因子：
6.3
通讯作者：
Barba, L. A.
中科院分区：
文献类型：
--
作者：
Cooper, Christopher D.;Bardhan, Jaydeep P.;Barba, L. A.
关键词：
将连续统理论应用于生物分子静电学，得到了一个由泊松-玻尔兹曼方程控制的隐式溶剂模型。依靠边界积分表示的求解器通常不考虑溶剂填充腔或离子排除（斯特恩）层等特征，因为处理多个边界表面增加了难度。这阻碍了与基于体积的方法进行有意义的比较，并且包括这些特征对准确性的影响仍然未知。这项工作提出了一个名为PyGBe的求解器，它使用边界元素公式，可以处理多个相互作用的表面。研究了溶剂填充空腔和Stern层对蛋白质溶剂化能和结合能计算精度的影响，采用了著名的apbs有限差分代码进行比较。结果表明，如果应用要求的精度允许溶剂化能误差大于约2%，则可以使用更简单的单表面模型。在计算结合能时，对多表面模型的需求是与问题相关的，当配体和受体的大小相当时，这一点变得更加重要。与apbs求解器相比，边界元求解器在精度要求较高时速度更快。PyGBe代码的交叉点在一个gpu卡（nvidia Tesla C2075）上运行时，与在六个Intel Xeon cpu内核上运行的apb相比，误差为1-2%。PyGBe使用treecode实现边界元素方法的算法加速，并通过PyCuda使用gpu从用户可见的全部Python代码实现硬件加速。该代码在MIT许可下是开源的。
The continuum theory applied to biomolecular electrostatics leads to an implicit-solvent model governed by the Poisson-Boltzmann equation. Solvers relying on a boundary integral representation typically do not consider features like solvent-filled cavities or ion-exclusion (Stern) layers, due to the added difficulty of treating multiple boundary surfaces. This has hindered meaningful comparisons with volume-based methods, and the effects on accuracy of including these features has remained unknown. This work presents a solver called PyGBe that uses a boundary-element formulation and can handle multiple interacting surfaces. It was used to study the effects of solvent-filled cavities and Stern layers on the accuracy of calculating solvation energy and binding energy of proteins, using the well-known apbs finite-difference code for comparison. The results suggest that if required accuracy for an application allows errors larger than about 2% in solvation energy, then the simpler, single-surface model can be used. When calculating binding energies, the need for a multi-surface model is problem-dependent, becoming more critical when ligand and receptor are of comparable size. Comparing with the apbs solver, the boundary-element solver is faster when the accuracy requirements are higher. The cross-over point for the PyGBe code is in the order of 1–2% error, when running on one gpu card (nvidia Tesla C2075), compared with apbs running on six Intel Xeon cpu cores. PyGBe achieves algorithmic acceleration of the boundary element method using a treecode, and hardware acceleration using gpus via PyCuda from a user-visible code that is all Python. The code is open-source under MIT license.
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