Abstract
Peptides are molecules of varying complexity, with different functions in the organism and with remarkable therapeutic interest. Predicting peptide activity by computational means can help us to understand their mechanism of action and deliver powerful drug-screening methodologies. In this chapter, we describe how to apply artificial neural networks to predict antimicrobial peptide activity.
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References
Nelson DL, Cox MM (2005) Lehninger principles of biochemistry. 4th edn. p 1100
Liu F, Baggerman G, Schoofs L et al (2008) The construction of a bioactive peptide database in Metazoa. J Proteome Res 7:4119–4131
Zhang H, Forman HJ (2012) Glutathione synthesis and its role in redox signaling. Semin Cell Dev Biol 23:722–728
Patel BM, Mehta AA (2012) Aldosterone and angiotensin: role in diabetes and cardiovascular diseases. Eur J Pharmacol 697:1–12
Amblard M, Fehrentz J-A, Martinez J et al (2005) Fundamentals of modern peptide synthesis. Methods Mol Biol 298:3–24
Coin I, Beyermann M, Bienert M (2007) Solid-phase peptide synthesis: from standard procedures to the synthesis of difficult sequences. Nat Protoc 2:3247–3256
Hilpert K, Winkler DFH, Hancock REW (2007) Peptide arrays on cellulose support: SPOT synthesis, a time and cost efficient method for synthesis of large numbers of peptides in a parallel and addressable fashion. Nat Protoc 2:1333–1349
Winkler DFH, Campbell WD (2008) The spot technique: synthesis and screening of peptide macroarrays on cellulose membranes. Methods Mol Biol 494:47–70
Reddy AS, Pati SP, Kumar PP et al (2007) Virtual screening in drug discovery—a computational perspective. Curr Protein Pept Sci 8:329–351
Boix E, Nogues VM, Torrent M (2012) Discovering new in silico tools for antimicrobial peptide prediction. Curr Drug Targets 13:1148–1157
Torrent M, Andreu D, Nogués VM et al (2011) Connecting peptide physicochemical and antimicrobial properties by a rational prediction model. PLoS One 6:e16968
Wang G, Li X, Wang Z (2009) APD2: the updated antimicrobial peptide database and its application in peptide design. Nucleic Acids Res 37:D933–D937
Hammami R, Zouhir A, Le Lay C et al (2010) BACTIBASE second release: a database and tool platform for bacteriocin characterization. BMC Microbiol 10:22
Waghu FH, Gopi L, Barai RS et al (2014) CAMP: collection of sequences and structures of antimicrobial peptides. Nucleic Acids Res 42:D1154–D1158
Seebah S, Suresh A, Zhuo S et al (2007) Defensins knowledgebase: a manually curated database and information source focused on the defensins family of antimicrobial peptides. Nucleic Acids Res 35:D265–D268
Gueguen Y, Garnier J, Robert L et al (2006) PenBase, the shrimp antimicrobial peptide penaeidin database: sequence-based classification and recommended nomenclature. Dev Comp Immunol 30:283–288
Hammami R, Ben HJ, Vergoten G et al (2009) PhytAMP: a database dedicated to antimicrobial plant peptides. Nucleic Acids Res 37:D963–D968
Li Y, Chen Z (2008) RAPD: a database of recombinantly-produced antimicrobial peptides. FEMS Microbiol Lett 289:126–129
Huang Y, Niu B, Gao Y et al (2010) CD-HIT Suite: a web server for clustering and comparing biological sequences. Bioinformatics 26:680–682
Jenssen H (2011) Descriptors for antimicrobial peptides. Expert Opin Drug Discov 6:171–184
Kawashima S, Pokarowski P, Pokarowska M et al (2008) AAindex: amino acid index database, progress report 2008. Nucleic Acids Res 36:D202–D205
Kawashima S, Ogata H, Kanehisa M (1999) AAindex: amino acid index database. Nucleic Acids Res 27:368–369
Li ZR, Lin HH, Han LY et al (2006) PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 34:W32–W37
Rao HB, Zhu F, Yang GB et al (2011) Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 39:W385–W390
Krogh A (2008) What are artificial neural networks? Nat Biotechnol 26:195–197
Heaton J. (2012) Introduction to the Math of Neural Networks. Heaton Research Inc. 119 pages.
Günther F, Fritsch S (2010) neuralnet: training of neural networks. R J 2:30–38
Bergmeir C, Benitez JM (2012) Neural Networks in R using the Stuttgart Neural Network Simulator: RSNNS. J Stat Software 46:1–26
Kuhn M (2008) Building predictive models in R using the caret package. J Stat Software 28:1–26
Acknowledgements
This work was supported by the European Union [grant number HEALTH-F3-2008-223414 to D.A. and FP7-PEOPLE-2012-IEF-330352 to M.T.], the Ministerio de Economía y Competitividad [grant number SAF2011-24899 to D.A.], and the Generalitat de Catalunya [grant number SGR2009-494 to D.A.]. M.T. would also like to acknowledge support from the Ramón y Cajal Program (Spain).
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Andreu, D., Torrent, M. (2015). Prediction of Bioactive Peptides Using Artificial Neural Networks. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_7
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DOI: https://doi.org/10.1007/978-1-4939-2239-0_7
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