Gainza Pablo, Nisonoff Hunter M, Donald Bruce R
Department of Computer Science, Duke University, Durham, NC, United States.
Department of Computer Science, Duke University, Durham, NC, United States; Department of Biochemistry, Duke University Medical Center, Durham, NC, United States; Department of Chemistry, Duke University, Durham, NC, United States. Electronic address: brd+
Curr Opin Struct Biol. 2016 Aug;39:16-26. doi: 10.1016/j.sbi.2016.03.006. Epub 2016 Apr 14.
Computational structure-based protein design programs are becoming an increasingly important tool in molecular biology. These programs compute protein sequences that are predicted to fold to a target structure and perform a desired function. The success of a program's predictions largely relies on two components: first, the input biophysical model, and second, the algorithm that computes the best sequence(s) and structure(s) according to the biophysical model. Improving both the model and the algorithm in tandem is essential to improving the success rate of current programs, and here we review recent developments in algorithms for protein design, emphasizing how novel algorithms enable the use of more accurate biophysical models. We conclude with a list of algorithmic challenges in computational protein design that we believe will be especially important for the design of therapeutic proteins and protein assemblies.
基于计算结构的蛋白质设计程序正日益成为分子生物学中一种重要的工具。这些程序计算预计会折叠成目标结构并执行所需功能的蛋白质序列。程序预测的成功很大程度上依赖于两个组成部分:第一,输入的生物物理模型;第二,根据生物物理模型计算最佳序列和结构的算法。同时改进模型和算法对于提高当前程序的成功率至关重要,在此我们回顾蛋白质设计算法的最新进展,重点强调新颖算法如何能够使用更精确的生物物理模型。我们最后列出了计算蛋白质设计中的一系列算法挑战,我们认为这些挑战对于治疗性蛋白质和蛋白质组装体的设计尤为重要。