Hoque Md Tamjidul, Yang Yuedong, Mishra Avdesh, Zhou Yaoqi
Computer Science, University of New Orleans, New Orleans, Louisiana, 70148.
Institute for Glycomics and School of Informatics and Communication Technology, Griffith University, Queensland, 4222, Australia.
J Comput Chem. 2016 May 5;37(12):1119-24. doi: 10.1002/jcc.24298. Epub 2016 Feb 5.
An important unsolved problem in molecular and structural biology is the protein folding and structure prediction problem. One major bottleneck for solving this is the lack of an accurate energy to discriminate near-native conformations against other possible conformations. Here we have developed sDFIRE energy function, which is an optimized linear combination of DFIRE (the Distance-scaled Finite Ideal gas Reference state based Energy), the orientation dependent (polar-polar and polar-nonpolar) statistical potentials, and the matching scores between predicted and model structural properties including predicted main-chain torsion angles and solvent accessible surface area. The weights for these scoring terms are optimized by three widely used decoy sets consisting of a total of 134 proteins. Independent tests on CASP8 and CASP9 decoy sets indicate that sDFIRE outperforms other state-of-the-art energy functions in selecting near native structures and in the Pearson's correlation coefficient between the energy score and structural accuracy of the model (measured by TM-score).
分子与结构生物学中一个重要的未解决问题是蛋白质折叠与结构预测问题。解决此问题的一个主要瓶颈是缺乏一种准确的能量来区分近天然构象与其他可能构象。在此，我们开发了sDFIRE能量函数，它是DFIRE（基于距离缩放的有限理想气体参考态能量）、方向依赖（极性 - 极性和极性 - 非极性）统计势以及预测与模型结构属性（包括预测的主链扭转角和溶剂可及表面积）之间匹配分数的优化线性组合。这些评分项的权重通过由总共134种蛋白质组成的三个广泛使用的诱饵集进行优化。对CASP8和CASP9诱饵集的独立测试表明，在选择近天然结构以及能量得分与模型结构准确性（通过TM分数衡量）之间的皮尔逊相关系数方面，sDFIRE优于其他现有最先进的能量函数。
