University of Freiburg, Freiburg.
IEEE/ACM Trans Comput Biol Bioinform. 2012 Jan-Feb;9(1):228-39. doi: 10.1109/TCBB.2011.52. Epub 2011 Mar 3.
Accurate identification of protein secondary structures is beneficial to understand three-dimensional structures of biological macromolecules. In this paper, a novel refined classification framework is proposed, which treats alpha-helix identification as a machine learning problem by representing each voxel in the density map with its Spherical Harmonic Descriptors (SHD). An energy function is defined to provide statistical analysis of its identification performance, which can be applied to all the α-helix identification approaches. Comparing with other existing α-helix identification methods for intermediate resolution electron density maps, the experimental results demonstrate that our approach gives the best identification accuracy and is more robust to the noise.
准确识别蛋白质二级结构有助于理解生物大分子的三维结构。在本文中，提出了一种新颖的精细化分类框架，通过用球面调和描述符（SHD）表示密度图中的每个体素来将α-螺旋识别视为机器学习问题。定义了一个能量函数来提供其识别性能的统计分析，该函数可应用于所有α-螺旋识别方法。与其他用于中间分辨率电子密度图的现有α-螺旋识别方法相比，实验结果表明，我们的方法具有最佳的识别精度，并且对噪声更鲁棒。
