Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Bioinformatics, School of Computer Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
Proteins. 2014 Mar;82(3):415-23. doi: 10.1002/prot.24407. Epub 2013 Oct 17.
This study is aimed at showing that considering only nonlocal interactions (interactions of two atoms with a sequence separation larger than five amino acids) extracted using Delaunay tessellation is sufficient and accurate for protein fold recognition. An atomic knowledge-based potential was extracted based on a Delaunay tessellation with 167 atom types from a sample of the native structures and the normalized energy was calculated for only nonlocal interactions in each structure. The performance of this method was tested on several decoy sets and compared to a method considering all interactions extracted by Delaunay tessellation and three other popular scoring functions. Features such as the contents of different types of interactions and atoms with the highest number of interactions were also studied. The results suggest that considering only nonlocal interactions in a Delaunay tessellation of protein structure is a discrete structure catching deep properties of the three-dimensional protein data.
本研究旨在表明，仅考虑使用 Delaunay 三角剖分提取的非局部相互作用（两个原子之间的相互作用，序列间隔大于五个氨基酸）对于蛋白质折叠识别就足够且准确。从天然结构样本中提取了基于原子知识的势能，基于具有 167 种原子类型的 Delaunay 三角剖分，并仅针对每个结构中的非局部相互作用计算归一化能量。该方法在几个诱饵集上进行了测试，并与考虑 Delaunay 三角剖分提取的所有相互作用以及其他三种流行评分函数的方法进行了比较。还研究了不同类型的相互作用的含量以及具有最高相互作用数的原子等特征。结果表明，仅考虑蛋白质结构的 Delaunay 三角剖分中的非局部相互作用是捕捉三维蛋白质数据深层特性的离散结构。
