GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions

BMC Bioinformatics

Junsu Ko¹  Hahnbeom Park¹  Chaok Seok¹ 

[1] Department of Chemistry, Seoul National University, Seoul, 151-747, Republic of Korea

关键词: Terminus modeling;    Loop modeling;    Model refinement;    Protein structure prediction;   

Others  :  1088172
DOI  :  10.1186/1471-2105-13-198

 received in 2012-02-23, accepted in 2012-08-07,  发布年份 2012

【 摘 要 】

Background

Protein structures can be reliably predicted by template-based modeling (TBM) when experimental structures of homologous proteins are available. However, it is challenging to obtain structures more accurate than the single best templates by either combining information from multiple templates or by modeling regions that vary among templates or are not covered by any templates.

Results

We introduce GalaxyTBM, a new TBM method in which the more reliable core region is modeled first from multiple templates and less reliable, variable local regions, such as loops or termini, are then detected and re-modeled by an ab initio method. This TBM method is based on “Seok-server,” which was tested in CASP9 and assessed to be amongst the top TBM servers. The accuracy of the initial core modeling is enhanced by focusing on more conserved regions in the multiple-template selection and multiple sequence alignment stages. Additional improvement is achieved by ab initio modeling of up to 3 unreliable local regions in the fixed framework of the core structure. Overall, GalaxyTBM reproduced the performance of Seok-server, with GalaxyTBM and Seok-server resulting in average GDT-TS of 68.1 and 68.4, respectively, when tested on 68 single-domain CASP9 TBM targets. For application to multi-domain proteins, GalaxyTBM must be combined with domain-splitting methods.

Conclusion

Application of GalaxyTBM to CASP9 targets demonstrates that accurate protein structure prediction is possible by use of a multiple-template-based approach, and ab initio modeling of variable regions can further enhance the model quality.

【 授权许可】

   
2012 Ko et al.; licensee BioMed Central Ltd.

【 图 表 】

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