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. AVAILABILITY AND IMPLEMENTATION: Hammock is published under GNU GPL v. 3 license and is freely available as a standalone program (from http://www.recamo.cz/en/software/hammock-cluster-peptides/) or as a tool for the Galaxy toolbox (from https://toolshed.g2.bx.psu.edu/view/hammock/hammock). The source code can be downloaded from https://github.com/hammock-dev/hammock/releases. CONTACT: muller@mou.cz SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Faculty of Informatics Masaryk University Botanicka 68a 60200 Brno Czech Republic

RECAMO Masaryk Memorial Cancer Institute Zluty kopec 7 65653 Brno Czech Republic

University of Edinburgh Institute of Genetics and Molecular Medicine Cancer Research Centre Edinburgh EH4 2XR UK and

000      

00000naa a2200000 a 4500

001      

bmc16028246

003      

CZ-PrNML

005      

20161020095559.0

007      

ta

008      

161005s2016 enk f 000 0|eng||

009      

AR

024    7_

$a 10.1093/bioinformatics/btv522 $2 doi

024    7_

$a 10.1093/bioinformatics/btv522 $2 doi

035    __

$a (PubMed)26342231

040    __

$a ABA008 $b cze $d ABA008 $e AACR2

041    0_

$a eng

044    __

$a enk

100    1_

$a Krejci, Adam $u RECAMO, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653, Brno, Czech Republic.

245    10

$a Hammock: a hidden Markov model-based peptide clustering algorithm to identify protein-interaction consensus motifs in large datasets / $c A. Krejci, TR. Hupp, M. Lexa, B. Vojtesek, P. Muller,

520    9_

$a 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. AVAILABILITY AND IMPLEMENTATION: Hammock is published under GNU GPL v. 3 license and is freely available as a standalone program (from http://www.recamo.cz/en/software/hammock-cluster-peptides/) or as a tool for the Galaxy toolbox (from https://toolshed.g2.bx.psu.edu/view/hammock/hammock). The source code can be downloaded from https://github.com/hammock-dev/hammock/releases. CONTACT: muller@mou.cz SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

650    12

$a algoritmy $7 D000465

650    _2

$a sekvence aminokyselin $7 D000595

650    _2

$a monoklonální protilátky $x chemie $7 D000911

650    _2

$a shluková analýza $7 D016000

650    12

$a databáze proteinů $7 D030562

650    _2

$a epitopy $x chemie $7 D000939

650    _2

$a lidé $7 D006801

650    _2

$a Markovovy řetězce $7 D008390

650    _2

$a molekulární sekvence - údaje $7 D008969

650    _2

$a peptidy $x chemie $7 D010455

650    12

$a interakční proteinové domény a motivy $7 D054730

650    _2

$a sekvenční seřazení $7 D016415

650    _2

$a software $7 D012984

650    _2

$a src homologní domény $7 D018909

655    _2

$a časopisecké články $7 D016428

655    _2

$a práce podpořená grantem $7 D013485

700    1_

$a Hupp, Ted R $u University of Edinburgh, Institute of Genetics and Molecular Medicine, Cancer Research Centre, Edinburgh EH4 2XR, UK and.

700    1_

$a Lexa, Matej $u Faculty of Informatics, Masaryk University, Botanicka 68a, 60200 Brno, Czech Republic.

700    1_

$a Vojtesek, Borivoj $u RECAMO, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653, Brno, Czech Republic.

700    1_

$a Muller, Petr $u RECAMO, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653, Brno, Czech Republic.

773    0_

$w MED00008115 $t Bioinformatics (Oxford, England) $x 1367-4811 $g Roč. 32, č. 1 (2016), s. 9-16

856    41

$u https://pubmed.ncbi.nlm.nih.gov/26342231 $y Pubmed

910    __

$a ABA008 $b sig $c sign $y a $z 0

990    __

$a 20161005 $b ABA008

991    __

$a 20161020100004 $b ABA008

999    __

$a ok $b bmc $g 1166560 $s 952876

BAS    __

$a 3

BAS    __

$a PreBMC

BMC    __

$a 2016 $b 32 $c 1 $d 9-16 $e 20150905 $i 1367-4811 $m Bioinformatics $n Bioinformatics $x MED00008115

LZP    __

$a Pubmed-20161005