KREJČÍ, Adam, TR HUPP, Matej LEXA, Bořivoj VOJTĚŠEK a 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, roč. 32, č. 1, s. 9-16. ISSN 1367-4803. doi:10.1093/bioinformatics/btv522.
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Základní údaje
Originální název Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets
Autoři KREJČÍ, Adam (203 Česko), TR HUPP (826 Velká Británie), Matej LEXA (703 Slovensko, domácí), Bořivoj VOJTĚŠEK (203 Česko) a Petr MÜLLER (203 Česko).
Vydání Bioinformatics, Oxford, Oxford University Press, 2016, 1367-4803.
Další údaje
Originální jazyk angličtina
Typ výsledku Článek v odborném periodiku
Obor 10201 Computer sciences, information science, bioinformatics
Stát vydavatele Velká Británie
Utajení není předmětem státního či obchodního tajemství
Impakt faktor Impact factor: 7.307
Kód RIV RIV/00216224:14330/16:00089377
Organizační jednotka Fakulta informatiky
Doi http://dx.doi.org/10.1093/bioinformatics/btv522
UT WoS 000368357800002
Klíčová slova anglicky phage display; sequence logo; clustering;
Příznaky Mezinárodní význam, Recenzováno
Změnil Změnil: Ing. Matej Lexa, Ph.D., učo 31298. Změněno: 13. 3. 2018 14:02.
Anotace
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.
VytisknoutZobrazeno: 13. 7. 2020 05:16