Approximately 40–50 percent of all drugs targets are G protein-coupled receptors (GPCRs). Three-dimensional structure of GPCRs is important to probe their biophysical and biochemical functions and their pharmaceutical applications. Lacking reliable and high quality free function is one of the ugent problems of computational predicting the three-dimensional structure in this community. We proposed a GPCR-specified energy function composed of four novel empirical potential energy terms: a two-dimensional contact energy force field, knowledge-based helix pair connection distance energy term, knowledge-based helix pair angle restraint energy term and a disulfide bond energy term. To validate the energy function, we employed an ab initio GPCR three-dimensional structure predictor to test if the energy function improved the accuracy of prediction. We evaluated 28 solved GPCRs and found that 21(75 percent) targets were correctly folded (TM-score>0.5). Also, the average TM-score using the energy function was 0.54, which was improved 134 percent than the TM-score 0.23 for MODELLER energy function and 170 percent than the TM-score 0.20 for Rosetta membrane energy function. The results confirmed that our empirical potential energy function toward ab initio folding is competitive to state-of-the-art solutions for structural prediction of GPCRs.

中文翻译：

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从头开始折叠 G 蛋白偶联受体的经验势能函数

大约 40-50% 的药物靶标是 G 蛋白偶联受体 (GPCR)。GPCRs的三维结构对于探索其生物物理和生化功能及其药物应用具有重要意义。缺乏可靠和高质量的自由函数是该社区中计算预测三维结构的紧迫问题之一。我们提出了一个 GPCR 指定的能量函数，由四个新的经验势能项组成：二维接触能力场、基于知识的螺旋对连接距离能项、基于知识的螺旋对角约束能项和二硫键能学期。为了验证能量函数，我们采用了从头算 GPCR 三维结构预测器来测试能量函数是否提高了预测的准确性。我们评估了 28 个已解决的 GPCR，发现 21 个（75%）目标被正确折叠（TM 分数>0.5）。此外，使用能量函数的平均 TM 分数为 0.54，比 MODELLER 能量函数的 TM 分数 0.23 提高了 134%，比 Rosetta 膜能量函数的 TM 分数 0.20 提高了 170%。结果证实，我们对 ab initio 折叠的经验势能函数与用于 GPCR 结构预测的最新解决方案具有竞争力。