Harrison Reed E S, Mohan Rohith R, Gorham Ronald D, Kieslich Chris A, Morikis Dimitrios
Department of Bioengineering, Bourns College of Engineering, University of California, Riverside, California.
Department of Bioengineering, Bourns College of Engineering, University of California, Riverside, California.
Biophys J. 2017 May 9;112(9):1761-1766. doi: 10.1016/j.bpj.2017.04.005.
Electric fields often play a role in guiding the association of protein complexes. Such interactions can be further engineered to accelerate complex association, resulting in protein systems with increased productivity. This is especially true for enzymes where reaction rates are typically diffusion limited. To facilitate quantitative comparisons of electrostatics in protein families and to describe electrostatic contributions of individual amino acids, we previously developed a computational framework called AESOP. We now implement this computational tool in Python with increased usability and the capability of performing calculations in parallel. AESOP utilizes PDB2PQR and Adaptive Poisson-Boltzmann Solver to generate grid-based electrostatic potential files for protein structures provided by the end user. There are methods within AESOP for quantitatively comparing sets of grid-based electrostatic potentials in terms of similarity or generating ensembles of electrostatic potential files for a library of mutants to quantify the effects of perturbations in protein structure and protein-protein association.
电场常常在引导蛋白质复合物的缔合过程中发挥作用。这种相互作用可以进一步设计以加速复合物的缔合，从而产生具有更高生产力的蛋白质系统。对于反应速率通常受扩散限制的酶来说尤其如此。为了便于对蛋白质家族中的静电作用进行定量比较，并描述单个氨基酸的静电贡献，我们之前开发了一个名为AESOP的计算框架。我们现在用Python实现了这个计算工具，提高了其可用性，并具备并行执行计算的能力。AESOP利用PDB2PQR和自适应泊松-玻尔兹曼求解器为终端用户提供的蛋白质结构生成基于网格的静电势文件。AESOP中有一些方法可用于根据相似性对基于网格的静电势集进行定量比较，或者为一组突变体生成静电势文件集合，以量化蛋白质结构和蛋白质-蛋白质缔合中扰动的影响。
