A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling.
A Stochastic Point Cloud Sampling Method for Multi-Template Protein Comparative Modeling.
复制标题
DOI：
10.1038/srep25687
复制
发表时间：
2016-05-10
影响因子：
4.6
通讯作者：
Cheng J
中科院分区：
文献类型：
--
作者：
Li J;Cheng J
从同源模板蛋白的已知结构及其成对序列比对生成目标蛋白的三级结构模型是蛋白质比较建模的关键步骤。在这里，我们开发了一种新的随机点云采样方法，称为MTMG，用于多模板蛋白质模型生成。该方法首先将模板结构的骨架叠加，叠加的模板的Cα原子形成靶蛋白每个位置的点云，其由三维多元正态分布表示。MTMG随机抽样法对分布中位置不确定的残基的Cα原子位置进行重新采样，并根据模拟退火协议接受或拒绝新的位置，有效地消除了多模板比较建模中常见的原子冲突.我们对MTMG分别针对CASP 9、CASP 10和CASP 11靶标生成的1，033个序列比对进行了基准测试。与使用单一模板相比，使用MTMG的多个模板在三个数据集上提高了结构模型的GDT-TS得分和TM得分2.96-6.37%和2.42-5.19%。MTMG的性能与Modeller的GDT-TS评分，TM评分，和GDT-HA评分，而平均RMSD提高了一个新的抽样方法。MTMG软件可在http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html上免费获得。
Generating tertiary structural models for a target protein from the known structure of its homologous template proteins and their pairwise sequence alignment is a key step in protein comparative modeling. Here, we developed a new stochastic point cloud sampling method, called MTMG, for multi-template protein model generation. The method first superposes the backbones of template structures, and the Cα atoms of the superposed templates form a point cloud for each position of a target protein, which are represented by a three-dimensional multivariate normal distribution. MTMG stochastically resamples the positions for Cα atoms of the residues whose positions are uncertain from the distribution, and accepts or rejects new position according to a simulated annealing protocol, which effectively removes atomic clashes commonly encountered in multi-template comparative modeling. We benchmarked MTMG on 1,033 sequence alignments generated for CASP9, CASP10 and CASP11 targets, respectively. Using multiple templates with MTMG improves the GDT-TS score and TM-score of structural models by 2.96–6.37% and 2.42–5.19% on the three datasets over using single templates. MTMG’s performance was comparable to Modeller in terms of GDT-TS score, TM-score, and GDT-HA score, while the average RMSD was improved by a new sampling approach. The MTMG software is freely available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/mtmg.html.