{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T23:04:57Z","timestamp":1780527897646,"version":"3.54.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,3,1]]},"abstract":"<jats:p>Motivation: High-quality protein sequence alignments are essential for a number of downstream applications such as template-based protein structure prediction. In addition to the similarity score between sequence profile columns, many current profile\u2013profile alignment tools use extra terms that compare 1D-structural properties such as secondary structure and solvent accessibility, which are predicted from short profile windows around each sequence position. Such scores add non-redundant information by evaluating the conservation of local patterns of hydrophobicity and other amino acid properties and thus exploiting correlations between profile columns.<\/jats:p><jats:p>Results: Here, instead of predicting and comparing known 1D properties, we follow an agnostic approach. We learn in an unsupervised fashion a set of maximally conserved patterns represented by 13-residue sequence profiles, without the need to know the cause of the conservation of these patterns. We use a maximum likelihood approach to train a set of 32 such profiles that can best represent patterns conserved within pairs of remotely homologs, structurally aligned training profiles. We include the new context score into our Hmm-Hmm alignment tool hhsearch and improve especially the quality of difficult alignments significantly.<\/jats:p><jats:p>Conclusion: The context similarity score improves the quality of homology models and other methods that depend on accurate pairwise alignments.<\/jats:p><jats:p>Contact: \u00a0soeding@mpibpc.mpg.de<\/jats:p><jats:p>Supplementary information: \u00a0Supplementary Data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btu697","type":"journal-article","created":{"date-parts":[[2014,10,23]],"date-time":"2014-10-23T00:08:46Z","timestamp":1414022926000},"page":"674-681","source":"Crossref","is-referenced-by-count":12,"title":["Context similarity scoring improves protein sequence alignments in the midnight zone"],"prefix":"10.1093","volume":"31","author":[{"given":"Armin","family":"Meier","sequence":"first","affiliation":[{"name":"1 \u00a01Gene Center, LMU Munich, 81377 Munich and 2Max Planck Institute for Biophysical Chemistry, 37077 G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"S\u00f6ding","sequence":"additional","affiliation":[{"name":"1 \u00a01Gene Center, LMU Munich, 81377 Munich and 2Max Planck Institute for Biophysical Chemistry, 37077 G\u00f6ttingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2014,11,22]]},"reference":[{"key":"2023020116165446300_btu697-B1","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1093\/bioinformatics\/bts622","article-title":"Discriminative modelling of context-specific amino acid substitution probabilities","volume":"28","author":"Angerm\u00fcller","year":"2012","journal-title":"Bioinformatics"},{"key":"2023020116165446300_btu697-B2","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1073\/pnas.0810767106","article-title":"Sequence context-specific profiles for homology searching","volume":"106","author":"Biegert","year":"2009","journal-title":"Proc. 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