Xu Gang, Ma Tianqi, Zang Tianwu, Sun Weitao, Wang Qinghua, Ma Jianpeng
School of Life Sciences, Tsinghua University, Beijing 100084, China.
Applied Physics Program, Rice University, Houston, TX 77005, United States; Department of Bioengineering, Rice University, Houston, TX 77005, United States.
J Mol Biol. 2017 Oct 13;429(20):3113-3120. doi: 10.1016/j.jmb.2017.08.013. Epub 2017 Aug 31.
We report a new distance- and orientation-dependent, all-atom statistical potential derived from side-chain packing, named OPUS-DOSP, for protein structure modeling. The framework of OPUS-DOSP is based on OPUS-PSP, previously developed by us [JMB (2008), 376, 288-301], with refinement and new features. In particular, distance or orientation contribution is considered depending on the range of contact distance. A new auxiliary function in energy function is also introduced, in addition to the traditional Boltzmann term, in order to adjust the contributions of extreme cases. OPUS-DOSP was tested on 11 decoy sets commonly used for statistical potential benchmarking. Among 278 native structures, 239 and 249 native structures were recognized by OPUS-DOSP without and with the auxiliary function, respectively. The results show that OPUS-DOSP has an increased decoy recognition capability comparing with those of other relevant potentials to date.
我们报道了一种新的基于侧链堆积的、与距离和方向相关的全原子统计势，命名为OPUS-DOSP，用于蛋白质结构建模。OPUS-DOSP的框架基于我们之前开发的OPUS-PSP[《分子生物学杂志》(2008年)，376卷，288 - 301页]，并进行了改进和添加了新特性。特别是，根据接触距离的范围考虑距离或方向贡献。除了传统的玻尔兹曼项外，还在能量函数中引入了一个新的辅助函数，以调整极端情况的贡献。OPUS-DOSP在常用于统计势基准测试的11个诱饵集上进行了测试。在278个天然结构中，OPUS-DOSP在没有辅助函数和有辅助函数的情况下分别识别出239个和249个天然结构。结果表明，与迄今为止其他相关势相比，OPUS-DOSP具有更强的诱饵识别能力。
