Department of Mathematics, Rose-Hulman Institute of Technology, 5500 Wabash Avenue, Terre Haute, IN 47803, USA.
IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul-Aug;8(4):867-75. doi: 10.1109/TCBB.2011.24.
A new intrinsic geometry based on a spectral analysis is used to motivate methods for aligning protein folds. The geometry is induced by the fact that a distance matrix can be scaled so that its eigenvalues are positive. We provide a mathematically rigorous development of the intrinsic geometry underlying our spectral approach and use it to motivate two alignment algorithms. The first uses eigenvalues alone and dynamic programming to quickly compute a fold alignment. Family identification results are reported for the Skolnick40 and Proteus300 data sets. The second algorithm extends our spectral method by iterating between our intrinsic geometry and the 3D geometry of a fold to make high-quality alignments. Results and comparisons are reported for several difficult fold alignments. The second algorithm's ability to correctly identify fold families in the Skolnick40 and Proteus300 data sets is also established.
一种新的基于谱分析的内在几何结构被用来激发用于对齐蛋白质折叠的方法。这种几何结构是由这样一个事实引起的，即距离矩阵可以缩放，使得它的特征值是正的。我们为我们的谱方法提供了内在几何结构的数学严格发展，并利用它来激发两种对齐算法。第一种方法仅使用特征值和动态规划来快速计算折叠对齐。为 Skolnick40 和 Proteus300 数据集报告了家族识别结果。第二种算法通过在我们的内在几何结构和折叠的 3D 几何结构之间迭代来扩展我们的谱方法，以进行高质量的对齐。报告了几个困难的折叠对齐的结果和比较。还确定了第二种算法在 Skolnick40 和 Proteus300 数据集正确识别折叠家族的能力。
