Klingler T M, Brutlag D L
Department of Biochemistry, Stanford University School of Medicine, California 94305-53075, USA.
Proc Int Conf Intell Syst Mol Biol. 1994;2:236-43.
Using a new representation for interactions in protein sequences based on correlations between pairs of amino acids, we have examined alpha-helical segments from known protein structures for important interactions. Traditional techniques for representing protein sequences usually make an explicit assumption of conditional independence of residues in the sequences. Protein structure analyses, however, have repeatedly demonstrated the importance of amino acid interactions for structural stability. We have developed an automated program for discovering sequence correlations in sets of aligned protein sequences using standard statistical tests and for representing them with Bayesian networks. In this paper, we demonstrate the power of our discovery program and representation by analyzing pairs of residues from alpha-helices. The sequence correlations we find represent physical and chemical interactions among amino-acid side chains in helical structures. Furthermore, these local interactions are likely to be important for stabilizing and packing alpha-helices. Lastly, we have also detect correlations in side-chain comformations that indicate important structural interactions but which don't appear as sequence correlations.
基于氨基酸对之间的相关性,我们采用一种新的蛋白质序列相互作用表示方法,研究了已知蛋白质结构中的α螺旋片段的重要相互作用。传统的蛋白质序列表示技术通常明确假设序列中残基的条件独立性。然而,蛋白质结构分析反复证明了氨基酸相互作用对结构稳定性的重要性。我们开发了一个自动化程序,用于使用标准统计测试在比对的蛋白质序列集中发现序列相关性,并用贝叶斯网络表示它们。在本文中,我们通过分析α螺旋中的残基对来展示我们的发现程序和表示方法的强大功能。我们发现的序列相关性代表了螺旋结构中氨基酸侧链之间的物理和化学相互作用。此外,这些局部相互作用可能对稳定和堆积α螺旋很重要。最后,我们还检测到侧链构象中的相关性,这些相关性表明了重要的结构相互作用,但未表现为序列相关性。