{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T22:21:36Z","timestamp":1750026096631},"reference-count":17,"publisher":"Oxford University Press (OUP)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009,6,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Summary: 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\u2013residue 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.<\/jats:p>\n               <jats:p>Availability: Freely available at http:\/\/www.bioinfo.tsinghua.edu.cn\/\u223cxiaxf\/micalign for both web server and source code.<\/jats:p>\n               <jats:p>Contact: \u00a0sunzhr@mail.tsinghua.edu.cn<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btp251","type":"journal-article","created":{"date-parts":[[2009,4,10]],"date-time":"2009-04-10T01:10:02Z","timestamp":1239325802000},"page":"1433-1434","source":"Crossref","is-referenced-by-count":7,"title":["MICAlign: a sequence-to-structure alignment tool integrating multiple sources of information in conditional random fields"],"prefix":"10.1093","volume":"25","author":[{"given":"Xuefeng","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing 100084, China"}]},{"given":"Song","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing 100084, China"}]},{"given":"Yu","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing 100084, China"}]},{"given":"Zhirong","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Biological Sciences and Biotechnology, MOE Key Laboratory of Bioinformatics, State Key Laboratory of Biomembrane and Membrane Biotechnology, Tsinghua University, Beijing 100084, China"}]}],"member":"286","published-online":{"date-parts":[[2009,4,9]]},"reference":[{"key":"2023013111495979000_B1","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/11732990_15","article-title":"CONTRAlign: discriminative training for protein sequence alignment","volume":"3909","author":"Do","year":"2006","journal-title":"Lecture Notes in Computer Science"},{"key":"2023013111495979000_B2","first-page":"282","article-title":"Conditional random fields: probabilistic models for segmenting and labeling sequence data","author":"Lafferty","year":"2001","journal-title":"Proc. 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