Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/79276
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dc.contributor.authorWong, Wing-Cheongen
dc.contributor.authorYap, Choon-Kongen
dc.contributor.authorEisenhaber, Birgiten
dc.contributor.authorEisenhaber, Franken
dc.date.accessioned2015-09-18T08:36:35Zen
dc.date.accessioned2019-12-06T13:21:26Z-
dc.date.available2015-09-18T08:36:35Zen
dc.date.available2019-12-06T13:21:26Z-
dc.date.copyright2015en
dc.date.issued2015en
dc.identifier.citationWong, W.-C., Yap, C.-K., Eisenhaber, B., & Eisenhaber, F. (2015). dissectHMMER: a HMMER-based score dissection framework that statistically evaluates fold-critical sequence segments for domain fold similarity. Biology Direct, 10(39).en
dc.identifier.issn1745-6150en
dc.identifier.urihttps://hdl.handle.net/10356/79276-
dc.description.abstractBackground: Annotation transfer for function and structure within the sequence homology concept essentially requires protein sequence similarity for the secondary structural blocks forming the fold of a protein. A simplistic similarity approach in the case of non-globular segments (coiled coils, low complexity regions, transmembrane regions, long loops, etc.) is not justified and a pertinent source for mistaken homologies. The latter is either due to positional sequence conservation as a result of a very simple, physically induced pattern or integral sequence properties that are critical for function. Furthermore, against the backdrop that the number of well-studied proteins continues to grow at a slow rate, it necessitates for a search methodology to dive deeper into the sequence similarity space to connect the unknown sequences to the well-studied ones, albeit more distant, for biological function postulations. Results: Based on our previous work of dissecting the hidden markov model (HMMER) based similarity score into fold-critical and the non-globular contributions to improve homology inference, we propose a framework-dissectHMMER, that identifies more fold-related domain hits from standard HMMER searches. Subsequent statistical stratification of the fold-related hits into cohorts of functionally-related domains allows for the function postulation of the query sequence. Briefly, the technical problems as to how to recognize non-globular parts in the domain model, resolve contradictory HMMER2/HMMER3 results and evaluate fold-related domain hits for homology, are addressed in this work. The framework is benchmarked against a set of SCOP-to-Pfam domain models. Despite being a sequence-to-profile method, dissectHMMER performs favorably against a profile-to-profile based method-HHsuite/HHsearch. Examples of function annotation using dissectHMMER, including the function discovery of an uncharacterized membrane protein Q9K8K1_BACHD (WP_010899149.1) as a lactose/H+ symporter, are presented. Finally, dissectHMMER webserver is made publicly available at http://dissecthmmer.bii.a-star.edu.sg. Conclusions: The proposed framework-dissectHMMER, is faithful to the original inception of the sequence homology concept while improving upon the existing HMMER search tool through the rescue of statistically evaluated false-negative yet fold-related domain hits to the query sequence. Overall, this translates into an opportunity for any novel protein sequence to be functionally characterized. Reviewers: This article was reviewed by Masanori Arita, Shamil Sunyaev and L. Aravind.en
dc.description.sponsorshipASTAR (Agency for Sci., Tech. and Research, S’pore)en
dc.language.isoenen
dc.relation.ispartofseriesBiology Directen
dc.rights© 2015 Wong et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.en
dc.titledissectHMMER: a HMMER-based score dissection framework that statistically evaluates fold-critical sequence segments for domain fold similarityen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Engineeringen
dc.identifier.doi10.1186/s13062-015-0068-3en
dc.description.versionPublished versionen
item.grantfulltextopen-
item.fulltextWith Fulltext-
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