School of Physics , Huazhong University of Science and Technology , Wuhan , Hubei 430074 , P. R. China.
J Chem Inf Model. 2018 Mar 26;58(3):724-732. doi: 10.1021/acs.jcim.7b00601. Epub 2018 Feb 22.
Accurate prediction of a protein's structure requires a reliable free energy function that consists of both enthalpic and entropic contributions. Although considerable progresses have been made in the calculation of potential energies in protein structure prediction, the computation for entropies of protein has lagged far behind, due to the challenge that estimation of entropies often requires expensive conformational sampling. In this study, we have used a knowledge-based approach to estimate the backbone conformational entropies from experimentally determined structures. Instead of conducting computationally expensive MD/MC simulations, we obtained the entropies of protein structures based on the normalized probability distributions of back dihedral angles observed in the native structures. Our new knowledge-based scoring function with inclusion of the backbone entropies, which is referred to as ITScoreDA or ITDA, was extensively evaluated on 16 commonly used decoy sets and compared with 50 other published scoring functions. It was shown that ITDA is significantly superior to the other tested scoring functions in selecting native structures from decoys. The present study suggests the role of backbone conformational entropies in protein structures and provides a way for fast estimation of the entropic effect.
准确预测蛋白质的结构需要一个可靠的自由能函数，该函数由焓和熵贡献组成。尽管在蛋白质结构预测的势能计算方面已经取得了相当大的进展，但由于熵的估计通常需要昂贵的构象采样，因此蛋白质熵的计算远远落后。在这项研究中，我们使用基于知识的方法从实验确定的结构中估计主链构象熵。我们没有进行计算成本高昂的 MD/MC 模拟，而是基于在天然结构中观察到的后角的归一化概率分布来获得蛋白质结构的熵。我们的新的基于知识的打分函数包括主链熵，称为 ITScoreDA 或 ITDA，在 16 个常用的诱饵集上进行了广泛评估，并与其他 50 个已发表的打分函数进行了比较。结果表明，ITDA 在从诱饵中选择天然结构方面明显优于其他测试打分函数。本研究表明了主链构象熵在蛋白质结构中的作用，并提供了一种快速估计熵效应的方法。
