Tendulkar Ashish V, Ogunnaike Babatunde, Wangikar Pramod P
Kanwal Rekhi School of Information Technology, Indian Institute of Technology Bombay, Powai, Mumbai-400 076, India.
Pac Symp Biocomput. 2006:291-302.
Classification of helical structures and identification of class specific sequence features is of interest for protein structure modeling. We use geometric invariant based method to first select helix-like local conformations. These conformations are mapped in a principal component space and subjected to Gaussian mixture modeling. The largest Gaussian corresponds to the regular alpha-helix. Kinked helix and curved helix appear as a separate gaussians. Class conditional, position specific amino acid propensity analysis reveals striking difference among the three classes. In regular helix, proline propensity is significant only in the beginning and low in the rest of the region regardless of length of the helix. In kinked helix, the proline propensity has a sharp peak at the helix center, while in the curved helix, the proline propensity has a broad peak in the middle region.
螺旋结构的分类以及特定类别序列特征的识别对于蛋白质结构建模具有重要意义。我们使用基于几何不变量的方法首先选择类螺旋局部构象。这些构象被映射到主成分空间并进行高斯混合建模。最大的高斯分布对应于规则的α螺旋。扭结螺旋和弯曲螺旋表现为单独的高斯分布。类别条件下的位置特异性氨基酸倾向分析揭示了这三类之间的显著差异。在规则螺旋中，无论螺旋长度如何，脯氨酸倾向仅在起始处显著，而在其余区域较低。在扭结螺旋中，脯氨酸倾向在螺旋中心有一个尖锐的峰值，而在弯曲螺旋中，脯氨酸倾向在中间区域有一个宽峰。
