Alignment of helical membrane protein sequences using AlignMe

Few sequence alignment methods have been designed specifically for integral membrane proteins, even though these important proteins have distinct evolutionary and structural properties that might affect their alignments.
Few sequence alignment methods have been designed specifically for integral membrane proteins, even though these important proteins have distinct evolutionary and structural properties that might affect their alignments. Existing approaches typically consider membrane-related information either by using membrane-specific substitution matrices or by assigning distinct penalties for gap creation in transmembrane and non-transmembrane regions. Here, we ask whether favoring matching of predicted transmembrane segments within a standard dynamic programming algorithm can improve the accuracy of pairwise membrane protein sequence alignments. We tested various strategies using a specifically designed program called AlignMe. An updated set of homologous membrane protein structures, called HOMEP2, was used as a reference for optimizing the gap penalties. The best of the membrane-protein optimized approaches were then tested on an independent reference set of membrane protein sequence alignments from the BAliBASE collection. When secondary structure (S) matching was combined with evolutionary information (using a position-specific substitution matrix (P)), in an approach we called AlignMePS, the resultant pairwise alignments were typically among the most accurate over a broad range of sequence similarities when compared to available methods. Matching transmembrane predictions (T), in addition to evolutionary information, and secondary-structure predictions, in an approach called AlignMePST, generally reduces the accuracy of the alignments of closely-related proteins in the BAliBASE set relative to AlignMePS, but may be useful in cases of extremely distantly related proteins for which sequence information is less informative. The open source AlignMe code is available at https://sourceforge.net/projects/alignme​/, and at http://www.forrestlab.org, along with an online server and the HOMEP2 data set.
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Metadaten
Author:Marcus Stamm, René Staritzbichler, Kamil Khafizov, Lucy R. Forrest
URN:urn:nbn:de:hebis:30:3-259451
DOI:http://dx.doi.org/10.1371/journal.pone.0057731
ISSN:1932-6203
Pubmed Id:http://www.ncbi.nlm.nih.gov/pubmed?term=23469223
Parent Title (English):PLoS One
Publisher:PLoS
Place of publication:Lawrence, Kan.
Document Type:Article
Language:English
Date of Publication (online):2013/03/04
Date of first Publication:2013/03/04
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2013/03/13
Volume:8
Issue:(3): e57731
Pagenumber:15
First Page:1
Last Page:15
Note:
Copyright: © 2013 Stamm et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
HeBIS PPN:448150492
Institutes:Medizin
Dewey Decimal Classification:610 Medizin und Gesundheit
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 3.0

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