| KREJČÍ, Adam, TR HUPP, Matej LEXA, Bořivoj VOJTĚŠEK and Petr MÜLLER. Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets. Bioinformatics. Oxford: Oxford University Press, 2016, vol. 32, No 1, p. 9-16. ISSN 1367-4803. Available from: https://dx.doi.org/10.1093/bioinformatics/btv522. |
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@article{1335191,
author = {Krejčí, Adam and Hupp, TR and Lexa, Matej and Vojtěšek, Bořivoj and Müller, Petr},
article_location = {Oxford},
article_number = {1},
doi = {http://dx.doi.org/10.1093/bioinformatics/btv522},
keywords = {phage display; sequence logo; clustering;},
language = {eng},
issn = {1367-4803},
journal = {Bioinformatics},
title = {Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets},
volume = {32},
year = {2016}
}
TY - JOUR
ID - 1335191
AU - Krejčí, Adam - Hupp, TR - Lexa, Matej - Vojtěšek, Bořivoj - Müller, Petr
PY - 2016
TI - Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets
JF - Bioinformatics
VL - 32
IS - 1
SP - 9-16
EP - 9-16
PB - Oxford University Press
SN - 13674803
KW - phage display
KW - sequence logo
KW - clustering;
N2 - Motivation: Proteins often recognize their interaction partners on the basis of short linear motifs located in disordered regions on proteins' surface. Experimental techniques that study such motifs use short peptides to mimic the structural properties of interacting proteins. Continued development of these methods allows for large-scale screening, resulting in vast amounts of peptide sequences, potentially containing information on multiple protein-protein interactions. Processing of such datasets is a complex but essential task for large-scale studies investigating protein-protein interactions. Results: The software tool presented in this article is able to rapidly identify multiple clusters of sequences carrying shared specificity motifs in massive datasets from various sources and generate multiple sequence alignments of identified clusters. The method was applied on a previously published smaller dataset containing distinct classes of ligands for SH3 domains, as well as on a new, an order of magnitude larger dataset containing epitopes for several monoclonal antibodies. The software successfully identified clusters of sequences mimicking epitopes of antibody targets, as well as secondary clusters revealing that the antibodies accept some deviations from original epitope sequences. Another test indicates that processing of even much larger datasets is computationally feasible.
ER -
KREJČÍ, Adam, TR HUPP, Matej LEXA, Bořivoj VOJTĚŠEK and Petr MÜLLER. Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets. \textit{Bioinformatics}. Oxford: Oxford University Press, 2016, vol.~32, No~1, p.~9-16. ISSN~1367-4803. Available from: https://dx.doi.org/10.1093/bioinformatics/btv522.
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