Department of Computer Science, Informatics Institute and C. Bond Life Science Center, University of Missouri, Columbia, MO 65211, USA.
Bioinformatics. 2010 Apr 1;26(7):882-8. doi: 10.1093/bioinformatics/btq058. Epub 2010 Feb 11.
Protein structure prediction is one of the most important problems in structural bioinformatics. Here we describe MULTICOM, a multi-level combination approach to improve the various steps in protein structure prediction. In contrast to those methods which look for the best templates, alignments and models, our approach tries to combine complementary and alternative templates, alignments and models to achieve on average better accuracy.
The multi-level combination approach was implemented via five automated protein structure prediction servers and one human predictor which participated in the eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. The MULTICOM servers and human predictor were consistently ranked among the top predictors on the CASP8 benchmark. The methods can predict moderate- to high-resolution models for most template-based targets and low-resolution models for some template-free targets. The results show that the multi-level combination of complementary templates, alternative alignments and similar models aided by model quality assessment can systematically improve both template-based and template-free protein modeling.
The MULTICOM server is freely available at http://casp.rnet.missouri.edu/multicom_3d.html .
蛋白质结构预测是结构生物信息学中最重要的问题之一。在这里，我们描述了 MULTICOM，这是一种多层次组合方法，用于改进蛋白质结构预测的各个步骤。与那些寻找最佳模板、比对和模型的方法不同，我们的方法试图组合互补和替代的模板、比对和模型，以平均获得更好的准确性。
多层次组合方法通过五个自动化的蛋白质结构预测服务器和一个参加第八届蛋白质结构预测技术关键评估（CASP8）的人类预测者实现，2008 年。MULTICOM 服务器和人类预测者在 CASP8 基准测试中一直排名在前几位预测者之列。该方法可以预测大多数基于模板的目标的中等到高分辨率模型，以及一些无模板目标的低分辨率模型。结果表明，互补模板、替代比对和相似模型的多层次组合，辅以模型质量评估，可以系统地改进基于模板和无模板的蛋白质建模。
MULTICOM 服务器可免费在 http://casp.rnet.missouri.edu/multicom_3d.html 获得。
