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本文引用的文献

1
Critical assessment of methods of protein structure prediction (CASP)--round IX.蛋白质结构预测方法的关键评估(CASP)——第九轮。
Proteins. 2011;79 Suppl 10(0 10):1-5. doi: 10.1002/prot.23200. Epub 2011 Oct 14.
2
CASP9 target classification.CASP9 靶标分类。
Proteins. 2011;79 Suppl 10(Suppl 10):21-36. doi: 10.1002/prot.23190. Epub 2011 Oct 14.
3
A conformation ensemble approach to protein residue-residue contact.一种用于蛋白质残基-残基接触的构象系综方法。
BMC Struct Biol. 2011 Oct 12;11:38. doi: 10.1186/1472-6807-11-38.
4
APOLLO: a quality assessment service for single and multiple protein models.APOLLO:用于单蛋白模型和多蛋白模型的质量评估服务。
Bioinformatics. 2011 Jun 15;27(12):1715-6. doi: 10.1093/bioinformatics/btr268. Epub 2011 May 5.
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Methods Enzymol. 2011;487:545-74. doi: 10.1016/B978-0-12-381270-4.00019-6.
6
Protein loop modeling by using fragment assembly and analytical loop closure.利用片段组装和分析环闭合进行蛋白质环建模。
Proteins. 2010 Dec;78(16):3428-36. doi: 10.1002/prot.22849. Epub 2010 Sep 24.
7
I-TASSER: a unified platform for automated protein structure and function prediction.I-TASSER:一个用于自动化蛋白质结构和功能预测的统一平台。
Nat Protoc. 2010 Apr;5(4):725-38. doi: 10.1038/nprot.2010.5. Epub 2010 Mar 25.
8
MULTICOM: a multi-level combination approach to protein structure prediction and its assessments in CASP8.MULTICOM:一种多层次组合方法,用于蛋白质结构预测及其在 CASP8 中的评估。
Bioinformatics. 2010 Apr 1;26(7):882-8. doi: 10.1093/bioinformatics/btq058. Epub 2010 Feb 11.
9
A self-organizing algorithm for modeling protein loops.一种用于蛋白质环建模的自组织算法。
PLoS Comput Biol. 2009 Aug;5(8):e1000478. doi: 10.1371/journal.pcbi.1000478. Epub 2009 Aug 21.
10
Target domain definition and classification in CASP8.目标域在 CASP8 中的定义和分类。
Proteins. 2009;77 Suppl 9(Suppl 9):10-7. doi: 10.1002/prot.22497.

递归蛋白质建模:一种用于蛋白质结构预测的分而治之策略及其在CASP9中的案例研究

Recursive protein modeling: a divide and conquer strategy for Protein Structure Prediction and its case study in CASP9.

作者信息

Cheng Jianlin, Eickholt Jesse, Wang Zheng, Deng Xin

机构信息

Department of Computer Science, University of Missouri, Columbia, MO 65211, USA.

出版信息

J Bioinform Comput Biol. 2012 Jun;10(3):1242003. doi: 10.1142/S0219720012420036.

DOI:10.1142/S0219720012420036
PMID:22809379
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3622867/
Abstract

After decades of research, protein structure prediction remains a very challenging problem. In order to address the different levels of complexity of structural modeling, two types of modeling techniques--template-based modeling and template-free modeling--have been developed. Template-based modeling can often generate a moderate- to high-resolution model when a similar, homologous template structure is found for a query protein but fails if no template or only incorrect templates are found. Template-free modeling, such as fragment-based assembly, may generate models of moderate resolution for small proteins of low topological complexity. Seldom have the two techniques been integrated together to improve protein modeling. Here we develop a recursive protein modeling approach to selectively and collaboratively apply template-based and template-free modeling methods to model template-covered (i.e. certain) and template-free (i.e. uncertain) regions of a protein. A preliminary implementation of the approach was tested on a number of hard modeling cases during the 9th Critical Assessment of Techniques for Protein Structure Prediction (CASP9) and successfully improved the quality of modeling in most of these cases. Recursive modeling can significantly reduce the complexity of protein structure modeling and integrate template-based and template-free modeling to improve the quality and efficiency of protein structure prediction.

摘要

经过数十年的研究,蛋白质结构预测仍然是一个极具挑战性的问题。为了解决结构建模中不同层次的复杂性,人们开发了两种建模技术——基于模板的建模和无模板建模。当为查询蛋白找到相似的同源模板结构时,基于模板的建模通常可以生成中等至高分辨率的模型,但如果未找到模板或仅找到错误的模板,则该方法会失效。无模板建模,例如基于片段的组装,对于拓扑复杂性较低的小蛋白可能会生成中等分辨率的模型。这两种技术很少被整合在一起以改进蛋白质建模。在此,我们开发了一种递归蛋白质建模方法,以选择性地、协同地应用基于模板和无模板的建模方法,对蛋白质的模板覆盖区域(即确定区域)和无模板区域(即不确定区域)进行建模。在第九届蛋白质结构预测技术关键评估(CASP9)期间,该方法的初步实现被应用于一些困难的建模案例测试中,并且在大多数案例中成功提高了建模质量。递归建模可以显著降低蛋白质结构建模的复杂性,并整合基于模板和无模板的建模,以提高蛋白质结构预测的质量和效率。