Grid convergence of PyGBe with immunoglobulin G near a charged surface

Reproducibility package containing data, running script, plotting script and final plot of grid-convergence study for immunoglobulin G near a charged surface.The running script invokes the open-source bioelectrostatics solver PyGBe with the given input data, computes the extrapolated value of solvation plus surface energy (with Richardson extrapolation), and plots the final result of convergence. This result is part of the publication:—<strong>"Probing protein orientation near charged nanosurfaces for simulation-assisted biosensor design,"</strong> Christopher D. Cooper, Natalia C. Clementi, Lorena A. Barba; <i>J. Chem. Phys</i>., Vol. 143: 124709 (2015) PyGBe solves biomolecular electrostatics problems using an implicit-solvent model (Poisson-Boltzmann) and it uses GPU hardware for fast execution. It is written in Python, PyCUDA and CUDA. More information about the PyGBe code in: <em>—"Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods,"</em> Christopher Cooper, Lorena A. Barba. figshare.<br>http://dx.doi.org/10.6084/m9.figshare.154331 — —"A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers", Christopher D. Cooper, Jaydeep P. Bardhan, L. A. Barba. <em>Comput. Phys. Comm.</em>, <strong>185(</strong>3):720–729 (March 2014). 10.1016/j.cpc.2013.10.028 // Preprint arXiv:1309.4018 <strong>Acknowledgement:</strong><br>This research is made possible by support from the Office of Naval Research, Applied Computational Analysis Program, N00014-11-1-0356. LAB also acknowledges support from NSF CAREER award OCI-1149784.

本复现包包含带电表面附近免疫球蛋白G（immunoglobulin G）的网格收敛性研究相关数据、运行脚本、绘图脚本与最终生成图像。该运行脚本调用开源生物静电求解器PyGBe及给定输入数据，通过理查森外推法（Richardson extrapolation）计算溶剂化能与表面能的外推值，并绘制收敛性分析的最终结果。本成果隶属于以下学术论文：——《探究带电纳米表面附近的蛋白质取向以辅助仿真驱动的生物传感器设计》（原标题："Probing protein orientation near charged nanosurfaces for simulation-assisted biosensor design"），作者Christopher D. Cooper、Natalia C. Clementi、Lorena A. Barba；刊载于《J. Chem. Phys.》第143卷：124709（2015年）。
PyGBe采用隐式溶剂模型（泊松-玻尔兹曼模型，Poisson-Boltzmann）求解生物分子静电问题，并依托GPU硬件实现快速计算。该代码基于Python、PyCUDA与CUDA开发。PyGBe的更多相关信息可参考以下文献：
1. 《基于边界元法的泊松-玻尔兹曼方程PyGBe代码验证》（原标题："Validation of the PyGBe code for Poisson-Boltzmann equation with boundary element methods"），作者Christopher Cooper、Lorena A. Barba，发布于figshare平台，DOI：10.6084/m9.figshare.154331；
2. 《一种可处理溶剂填充空腔与斯特恩层的、基于Python、GPU与边界元的生物分子静电求解器》（原标题："A biomolecular electrostatics solver using Python, GPUs and boundary elements that can handle solvent-filled cavities and Stern layers"），作者Christopher D. Cooper、Jaydeep P. Bardhan、L. A. Barba，刊载于《Comput. Phys. Comm.》第185卷第3期：720–729（2014年3月），DOI：10.1016/j.cpc.2013.10.028；预印本发布于arXiv:1309.4018。
致谢：本研究得到美国海军研究办公室应用计算分析项目（项目编号N00014-11-1-0356）的资助。LAB同时感谢美国国家科学基金会（National Science Foundation, NSF）CAREER奖OCI-1149784的支持。