Cai Yu-Dong, Feng Kai-Yan, Li Yi-Xue, Chou Kuo-Chen
Shanghai Research Centre of Biotechnology, Chinese Academy of Sciences, Shanghai, China.
Peptides. 2003 Apr;24(4):629-30. doi: 10.1016/s0196-9781(03)00100-1.
Tight turns play an important role in globular proteins from both the structural and functional points of view. Of tight turns, beta-turns and gamma-turns have been extensively studied, but alpha-turns were little investigated. Recently, a systematic search for alpha-turns classified alpha-turns into nine different types according to their backbone trajectory features. In this paper, Support Vector Machines (SVMs), a new machine learning method, is proposed for predicting the alpha-turn types in proteins. The high rates of correct prediction imply that that the formation of different alpha-turn types is evidently correlated with the sequence of a pentapeptide, and hence can be approximately predicted based on the sequence information of the pentapeptide alone, although the incorporation of its interaction with the other part of a protein, the so-called "long distance interaction", will further improve the prediction quality.
从结构和功能的角度来看，紧密转角在球状蛋白质中起着重要作用。在紧密转角中，β-转角和γ-转角已得到广泛研究，但α-转角的研究较少。最近，一项对α-转角的系统搜索根据其主链轨迹特征将α-转角分为九种不同类型。本文提出了一种新的机器学习方法——支持向量机（SVM），用于预测蛋白质中的α-转角类型。高预测正确率表明，不同α-转角类型的形成显然与一个五肽的序列相关，因此仅基于该五肽的序列信息就可以进行近似预测，尽管考虑其与蛋白质其他部分的相互作用（即所谓的“长距离相互作用”）将进一步提高预测质量。
