Template-based protein structure modeling using TASSERVMT
Article first published online: 22 NOV 2011
Copyright © 2011 Wiley Periodicals, Inc.
Proteins: Structure, Function, and Bioinformatics
Volume 80, Issue 2, pages 352–361, February 2012
How to Cite
Zhou, H. and Skolnick, J. (2012), Template-based protein structure modeling using TASSERVMT. Proteins, 80: 352–361. doi: 10.1002/prot.23183
- Issue published online: 10 JAN 2012
- Article first published online: 22 NOV 2011
- Accepted manuscript online: 14 SEP 2011 03:57PM EST
- Manuscript Accepted: 4 SEP 2011
- Manuscript Revised: 25 AUG 2011
- Manuscript Received: 28 JUN 2011
- NIH. Grant Numbers: GM-48835, GM-37408.
- template-based modeling;
Template-based protein structure modeling is commonly used for protein structure prediction. Based on the observation that multiple template-based methods often perform better than single template-based methods, we further explore the use of a variable number of multiple templates for a given target in the latest variant of TASSER, TASSERVMT. We first develop an algorithm that improves the target-template alignment for a given template. The improved alignment, called the SP3 alternative alignment, is generated by a parametric alignment method coupled with short TASSER refinement on models selected using knowledge-based scores. The refined top model is then structurally aligned to the template to produce the SP3 alternative alignment. Templates identified using SP3 threading are combined with the SP3 alternative and HHEARCH alignments to provide target alignments to each template. These template models are then grouped into sets containing a variable number of template/alignment combinations. For each set, we run short TASSER simulations to build full-length models. Then, the models from all sets of templates are pooled, and the top 20–50 models selected using FTCOM ranking method. These models are then subjected to a single longer TASSER refinement run for final prediction. We benchmarked our method by comparison with our previously developed approach, pro-sp3-TASSER, on a set with 874 easy and 318 hard targets. The average GDT-TS score improvements for the first model are 3.5 and 4.3% for easy and hard targets, respectively. When tested on the 112 CASP9 targets, our method improves the average GDT-TS scores as compared to pro-sp3-TASSER by 8.2 and 9.3% for the 80 easy and 32 hard targets, respectively. It also shows slightly better results than the top ranked CASP9 Zhang-Server, QUARK and HHpredA methods. The program is available for download at http://cssb.biology.gatech.edu/. © 2011 Wiley Periodicals, Inc.