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.
Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing, China.
Xuefeng
X
Xia
Song
S
Zhang
Yu
Y
Su
Zhirong
Z
Sun
D030562:Databases, Protein; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein; D012984:Software
D011506:Proteins