MICAlign: a sequence-to-structure alignment tool integrating multiple sources of information in conditional random fields.
CONCLUSIONS
Sequence-to-structure alignment in template-based protein structure modeling for remote homologs remains a difficult problem even following the correct recognition of folds. Here we present MICAlign, a sequence-to-structure alignment tool that incorporates multiple sources of information from local structural contexts of template, sequence profiles, predicted secondary structures, solvent accessibilities, potential-like terms (including residue-residue contacts and solvent exposures) and pre-aligned structures and sequences. These features, together with a position-specific gap scheme, were integrated into conditional random fields through which the optimal parameters were automatically learned. MICAlign showed improved alignment accuracy over several other state-of-the-art alignment tools based on comparisons by using independent datasets.


BACKGROUND
Freely available at (http://www.bioinfo.tsinghua.edu.cn/~xiaxf/micalign) for both web server and source code.
Xia
Xuefeng
X
Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.
Zhang
Song
S
Su
Yu
Y
Sun
Zhirong
Z
D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein; D012984:Software
D011506:Proteins