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      HHrep: de novo protein repeat detection and the origin of TIM barrels

      research-article
      Author(s): * , ,
      Publication date (Electronic):
      Journal: Nucleic Acids Research
      Publisher: Oxford University Press

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          Abstract

          HHrep is a web server for the de novo identification of repeats in protein sequences, which is based on the pairwise comparison of profile hidden Markov models (HMMs). Its main strength is its sensitivity, allowing it to detect highly divergent repeat units in protein sequences whose repeats could as yet only be detected from their structures. Examples include sequences with β-propellor fold, ferredoxin-like fold, double psi barrels or (βα) 8 (TIM) barrels. We illustrate this with proteins from four superfamilies of TIM barrels by revealing a clear 4- and 8-fold symmetry, which we detect solely from their sequences. This symmetry might be the trace of an ancient origin through duplication of a βαβα or βα unit. HHrep can be accessed at http://hhrep.tuebingen.mpg.de.

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          Protein homology detection by HMM-HMM comparison.

          Johannes Söding (2005)
          Protein homology detection and sequence alignment are at the basis of protein structure prediction, function prediction and evolution. We have generalized the alignment of protein sequences with a profile hidden Markov model (HMM) to the case of pairwise alignment of profile HMMs. We present a method for detecting distant homologous relationships between proteins based on this approach. The method (HHsearch) is benchmarked together with BLAST, PSI-BLAST, HMMER and the profile-profile comparison tools PROF_SIM and COMPASS, in an all-against-all comparison of a database of 3691 protein domains from SCOP 1.63 with pairwise sequence identities below 20%.Sensitivity: When the predicted secondary structure is included in the HMMs, HHsearch is able to detect between 2.7 and 4.2 times more homologs than PSI-BLAST or HMMER and between 1.44 and 1.9 times more than COMPASS or PROF_SIM for a rate of false positives of 10%. Approximately half of the improvement over the profile-profile comparison methods is attributable to the use of profile HMMs in place of simple profiles. Alignment quality: Higher sensitivity is mirrored by an increased alignment quality. HHsearch produced 1.2, 1.7 and 3.3 times more good alignments ('balanced' score >0.3) than the next best method (COMPASS), and 1.6, 2.9 and 9.4 times more than PSI-BLAST, at the family, superfamily and fold level, respectively.Speed: HHsearch scans a query of 200 residues against 3691 domains in 33 s on an AMD64 2GHz PC. This is 10 times faster than PROF_SIM and 17 times faster than COMPASS.
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            SCOP database in 2004: refinements integrate structure and sequence family data.

            Antonina Andreeva, Dave Howorth, Steven Brenner … (2004)
            The Structural Classification of Proteins (SCOP) database is a comprehensive ordering of all proteins of known structure, according to their evolutionary and structural relationships. Protein domains in SCOP are hierarchically classified into families, superfamilies, folds and classes. The continual accumulation of sequence and structural data allows more rigorous analysis and provides important information for understanding the protein world and its evolutionary repertoire. SCOP participates in a project that aims to rationalize and integrate the data on proteins held in several sequence and structure databases. As part of this project, starting with release 1.63, we have initiated a refinement of the SCOP classification, which introduces a number of changes mostly at the levels below superfamily. The pending SCOP reclassification will be carried out gradually through a number of future releases. In addition to the expanded set of static links to external resources, available at the level of domain entries, we have started modernization of the interface capabilities of SCOP allowing more dynamic links with other databases. SCOP can be accessed at http://scop.mrc-lmb.cam.ac.uk/scop.
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              Pfam: multiple sequence alignments and HMM-profiles of protein domains.

              E. Sonnhammer, S. Eddy, E. Birney … (1998)
              Pfam contains multiple alignments and hidden Markov model based profiles (HMM-profiles) of complete protein domains. The definition of domain boundaries, family members and alignment is done semi-automatically based on expert knowledge, sequence similarity, other protein family databases and the ability of HMM-profiles to correctly identify and align the members. Release 2.0 of Pfam contains 527 manually verified families which are available for browsing and on-line searching via the World Wide Web in the UK at http://www.sanger.ac.uk/Pfam/ and in the US at http://genome.wustl. edu/Pfam/ Pfam 2.0 matches one or more domains in 50% of Swissprot-34 sequences, and 25% of a large sample of predicted proteins from the Caenorhabditis elegans genome.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Nucleic Acids Res
                Journal ID (publisher-id): Nucleic Acids Research
                Title: Nucleic Acids Research
                Publisher: Oxford University Press
                ISSN (Print): 0305-1048
                ISSN (Electronic): 1362-4962
                Publication date Collection: 01 July 2006
                Publication date (Print): 01 July 2006
                Publication date (Electronic): 14 July 2006
                Volume: 34
                Issue: Web Server issue
                Pages: W137-W142
                Affiliations
                Department of Protein Evolution, Max-Planck-Institute for Developmental Biology Spemannstrasse 35, 72076 Tübingen, Germany
                Author notes
                *To whom correspondence should be addressed. Tel: +49 7071 601 451; Fax +49 7071 601 349; Email: johannes.soeding@ 123456tuebingen.mpg.de
                Article
                DOI: 10.1093/nar/gkl130
                PMC ID: 1538828
                PubMed ID: 16844977
                SO-VID: 0c65d36f-df7b-4cc5-acdc-fa63911ed448
                Copyright © © The Author 2006. Published by Oxford University Press. All rights reserved
                License:

                The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org

                History
                Date received : 13 February 2006
                Date revision received : 01 March 2006
                Date accepted : 14 March 2006
                Categories
                Subject: Article

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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