Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics
Principal component analysis of native ensembles of biomolecular structures (PCA_NEST): insights into functional dynamics
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DOI：
10.1093/bioinformatics/btp023
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发表时间：
2009-03
期刊：
影响因子：
5.8
通讯作者：
Lee-Wei Yang;E. Eyal;I. Bahar;A. Kitao
中科院分区：
文献类型：
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作者：
Lee-Wei Yang;E. Eyal;I. Bahar;A. Kitao
为了有效地分析接近其折叠状态的蛋白质的“天然构象系综”，并从观察到的构象分布中提取必要的信息，需要可靠的数学方法和计算工具。结果NMR和X-射线测定的24对结构的检查表明，通过这两种技术解决的相同蛋白质的动力学的差异可以跟踪到最强大的低频模式阐明的NMR模型的主成分分析（PCA）。发现酶的活性位点在这些PCA模式中受到高度限制。此外，预测为高度不动的残基被证明是进化保守的，贷款支持PCA为基础的识别潜在的功能位点。在线工具PCA_NEST旨在从实验解析或计算生成的结构系综中推导出构象变化的主要模式。http://ignm.ccbb.pitt.edu/oPCA_Online.htm
MOTIVATION To efficiently analyze the 'native ensemble of conformations' accessible to proteins near their folded state and to extract essential information from observed distributions of conformations, reliable mathematical methods and computational tools are needed. RESULT Examination of 24 pairs of structures determined by both NMR and X-ray reveals that the differences in the dynamics of the same protein resolved by the two techniques can be tracked to the most robust low frequency modes elucidated by principal component analysis (PCA) of NMR models. The active sites of enzymes are found to be highly constrained in these PCA modes. Furthermore, the residues predicted to be highly immobile are shown to be evolutionarily conserved, lending support to a PCA-based identification of potential functional sites. An online tool, PCA_NEST, is designed to derive the principal modes of conformational changes from structural ensembles resolved by experiments or generated by computations. AVAILABILITY http://ignm.ccbb.pitt.edu/oPCA_Online.htm