RECURSIVE PROTEIN MODELING: A DIVIDE AND CONQUER STRATEGY FOR PROTEIN STRUCTURE PREDICTION AND ITS CASE STUDY IN CASP9

被引:5
作者
Cheng, Jianlin [1 ,2 ]
Eickholt, Jesse [1 ]
Wang, Zheng [1 ,3 ]
Deng, Xin [1 ]
机构
[1] Univ Missouri, Dept Comp Sci, Columbia, MO 65211 USA
[2] Univ Missouri, Inst Informat, Columbia, MO 65211 USA
[3] Univ Missouri, Bond Life Sci Ctr, Columbia, MO 65211 USA
关键词
Protein structure prediction; recursive protein modeling; template-free modeling; template-based modeling; CASP; HIDDEN MARKOV-MODELS; HOMOLOGY DETECTION; QUALITY ASSESSMENT; LOOP CLOSURE; FOLD RECOGNITION; GENERATION; FRAGMENTS; CLASSIFICATION; ALGORITHM; GENOMICS;
D O I
10.1142/S0219720012420036
中图分类号
Q5 [生物化学];
学科分类号
070307 [化学生物学];
摘要
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.
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页数:18
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