Zhou Jianhong, Yan Wenying, Hu Guang, Shen Bairong
Center for Systems Biology, Soochow University, Suzhou, Jiangsu, 215006, China.
Proteins. 2014 Apr;82(4):556-64. doi: 10.1002/prot.24421. Epub 2013 Oct 17.
An accurate score function for detecting the most native-like models among a huge number of decoy sets is essential to the protein structure prediction. In this work, we developed a novel integrated score function (SVR_CAF) to discriminate native structures from decoys, as well as to rank near-native structures and select best decoys when native structures are absent. SVR_CAF is a machine learning score, which incorporates the contact energy based score (CE_score), amino acid network based score (AAN_score), and the fast Fourier transform based score (FFT_score). The score function was evaluated with four decoy sets for its discriminative ability and it shows higher overall performance than the state-of-the-art score functions.
在大量诱饵集中检测最接近天然结构的模型时，一个准确的评分函数对于蛋白质结构预测至关重要。在这项工作中，我们开发了一种新颖的综合评分函数（SVR_CAF），用于区分天然结构和诱饵结构，以及在没有天然结构时对接近天然的结构进行排名并选择最佳诱饵。SVR_CAF是一种机器学习评分，它结合了基于接触能量的评分（CE_score）、基于氨基酸网络的评分（AAN_score）和基于快速傅里叶变换的评分（FFT_score）。该评分函数针对四个诱饵集评估了其判别能力，结果表明它比现有最先进的评分函数具有更高的整体性能。
