Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach

Bioinformatics

Volumn 29, Issue 1, 2013, Pages 8-14

  Andreatta, Massimo ^a   Lund, Ole ^a   Nielsen, Morten ^a,b  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b INSTITUTO DE INVESTIGACIONES BIOTECNOLÓGICAS   (Argentina)

Author keywords

[No Author keywords available]

Indexed keywords

HLA ANTIGEN; LIGAND; PEPTIDE;

ALGORITHM; ALLELE; ARTICLE; CHEMISTRY; CLUSTER ANALYSIS; GENE; GENETICS; HUMAN; METABOLISM; POSITION WEIGHT MATRIX; PROTEIN BINDING; PROTEIN DOMAIN; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS; SRC HOMOLOGY DOMAIN;

ALGORITHMS; ALLELES; CLUSTER ANALYSIS; GENES, MHC CLASS I; GENES, MHC CLASS II; HLA ANTIGENS; HUMANS; LIGANDS; PEPTIDES; POSITION-SPECIFIC SCORING MATRICES; PROTEIN BINDING; PROTEIN INTERACTION DOMAINS AND MOTIFS; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, PROTEIN; SRC HOMOLOGY DOMAINS;

EID: 84871811905     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/bts621     Document Type: Article

References (33)

1. Andreatta, M. et al. (2011) NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data. PLoS One, 6, e26781.
2. Assarsson, E. et al. (2007) A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection. J. Immunol., 178, 7890-7901. (Pubitemid 46898061)
3. Bailey, T.L. and Elkan, C. (1995) Unsupervised learning of multiple motifs in bio-polymers using expectation maximization. Mach. Learn., 21, 51-80.
4. Bailey, T.L. et al. (2006) MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res., 34, W369-W373. (Pubitemid 44529795)
5. Bratkovic, T. (2010) Progress in phage display: evolution of the technique and its applications. Cell. Mol. Life Sci., 67, 749-767.
6. Busch, D.H. and Pamer, E.G. (1998) MHC class I/peptide stability: implications for immunodominance, in vitro proliferation, and diversity of responding CTL. J. Immunol., 160, 4441-4448. (Pubitemid 28217302)
7. Carducci, M. et al. (2012) The protein interaction network mediated by human SH3 domains. Biotechnol. Adv., 30, 4-15.
8. Geironson, L. et al. (2012) Stability of peptide-HLA-I complexes and tapasin folding facilitation-tools to define immunogenic peptides. FEBS Lett., 586, 1336-1343.
9. Gfeller, D. (2012) Uncovering new aspects of protein interactions through analysis of specificity landscapes in peptide recognition domains. FEBS Lett., 586, 2764-2772.
10. Gfeller, D. et al. (2011) The multiple-specificity landscape of molecular peptide recognition domains. Mol. Syst. Biol., 7, 484.
11. Halperin, R.F. et al. (2011) Exploring antibody recognition of sequence space through random-sequence peptide microarrays. Mol. Cell. Proteomics, 10, M110.000786.
12. Harndahl, M. et al. (2012) Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. Eur. J. Immunol., 42, 1405-1416.
13. Hubert, L. and Arabie, P. (1985) Comparing partitions. J. Classif., 2, 193-218.
14. Karosiene, E. et al. (2012) NetMHCcons: a consensus method for the major histo-compatibility complex class I predictions. Immunogenetics, 64, 177-186.
15. Kim, T. et al. (2012) MUSI: an integrated system for identifying multiple specificity from large peptide or nucleic acid data sets. Nucleic Acids Res., 40, e47.
16. Koivunen, E. et al. (1999) Identification of receptor ligands with phage display pep-tide libraries. J. Nucl. Med., 40, 883-888. (Pubitemid 29210767)
17. Lawrence, C.E. et al. (1993) Detecting subtle sequence signals: a gibbs sampling strategy for multiple alignment. Science, 262, 208-214.
18. Lund, O. et al. (2004) Definition of supertypes for HLA molecules using clustering of specificity matrices. Immunogenetics, 55, 797-810. (Pubitemid 38650985)
19. Mayer, B.J. (2001) SH3 domains: complexity in moderation. J. Cell Sci., 114, 1253-1263. (Pubitemid 32409462)
20. Nielsen, M. and Lund, O. (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics, 10, 296.
21. Nielsen, M. et al. (2004) Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics, 20, 1388-1397. (Pubitemid 38937025)
22. Noguchi, H. et al. (2002) Hidden Markov model-based prediction of antigenic pep-tides that interact with MHC class II molecules. J. Biosci. Bioeng., 94, 264-270. (Pubitemid 35192075)
23. Rammensee, H. et al. (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 50, 213-219. (Pubitemid 30099526)
24. Rand, W.M. (1971) Objective criteria for the evaluation of clustering methods. J. Am. Stat. Assoc., 66, 846-850.
25. Rao, X. et al. (2009) A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses. J. Immunol., 182, 1526-1532.
26. Saksela, K. and Permi, P. (2012) SH3 domain ligand specificity: what's the consensus and where's the specificity. FEBS Lett., 586, 2609-2614.
27. Schutkowski, M et al. (2005) Peptide arrays for kinase profiling. ChemBioChem, 6, 513-521. (Pubitemid 40868139)
28. Soen, Y. et al. (2003) Detection and characterization of cellular immune responses using peptide-MHC microarrays. PLoS Biol., 1, e65.
29. Thomsen, M.C. and Nielsen, M. (2012) Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion. Nucleic Acids Res., 40, 1-7.
30. Uttamchandani, M. and Yao, S.Q. (2008) Peptide microarrays: next generation bio-chips for detection, diagnostics and high-throughput screening. Curr. Pharm. Des., 14, 2428-2438.
31. Wang, P. et al. (2010) Peptide binding predictions for HLA DR, DP and DQ molecules. BMC Bioinformatics, 11, 568.
32. Yewdell, J.W. and Bennink, J.R. (1999) Mechanisms of viral interference with MHC class I antigen processing and presentation. Annu. Rev. Cell Dev. Biol., 15, 579-606. (Pubitemid 32250387)
33. Yu, H. et al. (1994) Structural basis for the binding of proline-rich peptides to SH3 domains. Cell, 76, 933-945.