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

• Main Authors: Ko Junsu, Park Hahnbeom, Seok Chaok
• Format: Article
• Language: English
• Published: BMC 2012-08-01
• Series: BMC Bioinformatics
• Subjects: Protein structure prediction; Model refinement; Loop modeling; Terminus modeling
• Online Access: http://www.biomedcentral.com/1471-2105/13/198

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