EvoEF2: accurate and fast energy function for computational protein design
EvoEF2: accurate and fast energy function for computational protein design
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DOI：
10.1093/bioinformatics/btz740
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发表时间：
2020-02-15
期刊：
影响因子：
5.8
通讯作者：
Zhang, Yang
中科院分区：
文献类型：
--
作者：
Huang, Xiaoqiang;Pearce, Robin;Zhang, Yang
动机：从头蛋白质设计的准确性和成功率仍然有限，主要是因为当前能量函数的参数过度拟合，以及它们无法区分不正确的设计和正确的设计。结果：我们基于先前提出的物理能量函数EvoEF，开发了一种扩展的能量函数EvoEF2，用于高效的从头蛋白质序列设计。值得注意的是，EvoEF2对148个测试单体的核心和表面残基的回收率分别为32.5%、47.9%和22.3%，适用于蛋白质-蛋白质相互作用设计，因为它概括了88个测试二聚体的核心、界面和表面残基的30.9%、42.4%、31.3%和21.4%，在天然序列概括上明显优于EvoEF。我们进一步使用I-tasser来评估148个设计的单体序列的可折叠性，其中所有这些单体序列都被预测为与其相应的天然结构具有高度折叠和原子水平相似性的结构，87.8%的预测结构与其天然结构的均方根偏差小于2埃。研究还表明，物理能量函数的有用性与参数优化过程高度相关，使用序列重述优化参数的EvoEF2比基于热力学突变数据优化的EvoEF更适合于计算蛋白质序列设计。
Motivation: The accuracy and success rate of de novo protein design remain limited, mainly due to the parameter over-fitting of current energy functions and their inability to discriminate incorrect designs from correct designs.Results: We developed an extended energy function, EvoEF2, for efficient de novo protein sequence design, based on a previously proposed physical energy function, EvoEF. Remarkably, EvoEF2 recovered 32.5%, 47.9% and 22.3% of all, core and surface residues for 148 test monomers, and was generally applicable to protein-protein interaction design, as it recapitulated 30.9%, 42.4%, 31.3% and 21.4% of all, core, interface and surface residues for 88 test dimers, significantly outperforming EvoEF on the native sequence recapitulation. We further used I-TASSER to evaluate the foldability of the 148 designed monomer sequences, where all of them were predicted to fold into structures with high fold- and atomic-level similarity to their corresponding native structures, as demonstrated by the fact that 87.8% of the predicted structures shared a root-mean-square-deviation less than 2 angstrom to their native counterparts. The study also demonstrated that the usefulness of physical energy functions is highly correlated with the parameter optimization processes, and EvoEF2, with parameters optimized using sequence recapitulation, is more suitable for computational protein sequence design than EvoEF, which was optimized on thermodynamic mutation data.