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Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries

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Abstract

Microbial diseases in fish, plant, animal and human are rising constantly; thus, discovery of their antidote is imperative. The use of antibiotic in aquaculture further compounds the problem by development of resistance and consequent consumer health risk by bio-magnification. Antimicrobial peptides (AMPs) have been highly promising as natural alternative to chemical antibiotics. Though AMPs are molecules of innate immune defense of all advance eukaryotic organisms, fish being heavily dependent on their innate immune defense has been a good source of AMPs with much wider applicability. Machine learning-based prediction method using wet laboratory-validated fish AMP can accelerate the AMP discovery using available fish genomic and proteomic data. Earlier AMP prediction servers are based on multi-phyla/species data, and we report here the world’s first AMP prediction server in fishes. It is freely accessible at http://webapp.cabgrid.res.in/fishamp/. A total of 151 AMPs related to fish collected from various databases and published literature were taken for this study. For model development and prediction, N-terminus residues, C-terminus residues and full sequences were considered. Best models were with kernels polynomial-2, linear and radial basis function with accuracy of 97, 99 and 97 %, respectively. We found that performance of support vector machine-based models is superior to artificial neural network. This in silico approach can drastically reduce the time and cost of AMP discovery. This accelerated discovery of lead AMP molecules having potential wider applications in diverse area like fish and human health as substitute of antibiotics, immunomodulator, antitumor, vaccine adjuvant and inactivator, and also for packaged food can be of much importance for industries.

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References

  1. Davies J, Davies D (2010) Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev 74(3):417–433

    Article  CAS  Google Scholar 

  2. Cabello FC (2006) Heavy use of prophylactic antibiotics in aquaculture: a growing problem for human and animal health and for the environment. Environ Microbiol 8:1137–1144

    Article  CAS  Google Scholar 

  3. Silva NC, Sarmento B, Pintado M (2013) The importance of antimicrobial peptides and their potential for therapeutic use in ophthalmology. Int J Antimicrob Agents 41(1):5–10

    Article  CAS  Google Scholar 

  4. Bradshaw J (2003) Cationic antimicrobial peptides: issues for potential clinical use. BioDrugs 17(4):233–240

    Article  CAS  Google Scholar 

  5. Rakers S, Niklasson L, Steinhagen D, Kruse C, Schauber J, Sundell K et al (2013) Antimicrobial peptides (AMPs) from fish epidermis: perspectives for investigative dermatology. J Invest Dermatol 133:1140–1149

    Article  CAS  Google Scholar 

  6. Alderman DJ, Hastings TS (2003) Antibiotic use in aquaculture: development of antibiotic resistance-potential for consumer health risks. Int J Food Sci Technol 33:139–155

    Article  Google Scholar 

  7. Pridgeon JW, Klesius PH (2012) Major bacterial diseases in aquaculture and their vaccine development. Cab Rev 7(48):1–16

    Article  Google Scholar 

  8. FAO (2009) Fishstat Plus. Food and Agricultural Organisation of the United Nations, Rome

    Google Scholar 

  9. FAO (2012) Food and Agriculture Organization of the United Nations. Fisheries Department. The state of world fisheries and aquaculture. Food and Agriculture Organization of the United Nations, Rome

  10. Lafferty KD, Harvell CD, Conrad JM, Friedman CS, Kent ML, Kuris AM et al (2015) Infectious diseases affect marine fisheries and aquaculture economics. Annu Rev Mar Sci 7:471–496

    Article  Google Scholar 

  11. Cederlund A, Gudmundsson GH, Agerberth B (2011) Antimicrobial peptides important in innate immunity. FEBS J 278(20):3942–3951

    Article  CAS  Google Scholar 

  12. Sarika IM, Rai A (2012) Biotic stress resistance in agriculture through antimicrobial peptides. Peptides 36:322–330

    Article  CAS  Google Scholar 

  13. Diamond G, Beckloff N, Weinberg A, Kisich KO (2009) The roles of antimicrobial peptides in innate host defense. Curr Pharm Des 15(21):2377–2392

    Article  CAS  Google Scholar 

  14. Rajanbabu V, Chen JY (2011) Applications of antimicrobial peptides from fish and perspectives for the future. Peptides 32(2):415–420

    Article  CAS  Google Scholar 

  15. Beisswenger C, Bals R (2005) Functions of antimicrobial peptides in host defense and immunity. Curr Protein Pept Sci 6(3):255–264

    Article  CAS  Google Scholar 

  16. Izadpanah A, Gallo RL (2005) Antimicrobial peptides. J Am Acad Dermatol 52:381–390

    Article  Google Scholar 

  17. Gordon YJ, Romanowski EG, McDermott AM (2005) A review of antimicrobial peptides and their therapeutic potential as anti-infective drugs. Curr Eye Res 30(7):505–515

    Article  CAS  Google Scholar 

  18. Austin B (2012) Infectious disease in aquaculture: prevention and control. Woodhead Pub. Ltd, Oxford

    Book  Google Scholar 

  19. Karen EB, Dunman PM, McAleese F (2004) Global gene expression in Staphylococcus aureus biofilms. J Bacteriol 186(14):4665–4684

    Article  Google Scholar 

  20. Snehlata SB, Raghava GPS (2007) Analysis and prediction of antibacterial peptides. BMC Bioinform 8:263

    Article  Google Scholar 

  21. Thomas S, Karnik S, Barai RS, Jayaraman VK, Thomas SI (2010) CAMP: a useful resource for research on antimicrobial peptides. Nucleic Acids Res 38:D774–D780

    Article  CAS  Google Scholar 

  22. Sarika IM, Arora V, Rai A, Kumar D (2015) Species specific approach to the development of web-based antimicrobial peptides prediction tool for cattle. Comput Electron Agric 111:55–61

    Article  Google Scholar 

  23. Zhao X, Wu H, Lu H, Li G, Huang Q (2013) LAMP: a database linking antimicrobial peptides. PLoS ONE 8(6):e66557

    Article  CAS  Google Scholar 

  24. Waghu FH, Gopi L, Barai RS, Ramteke P, Nizami B, Thomas SI (2014) CAMP: collection of sequences and structures of antimicrobial peptides. Nucleic Acids Res 42:D1154–D1158

    Article  CAS  Google Scholar 

  25. Gueguen Y, Garnier J, Robert L, Lefranc MP, Mougenot I, de Lorgeril J et al (2006) PenBase, the shrimp antimicrobial peptide penaeidin database: sequence-based classification and recommended nomenclature. Dev Comp Immunol 30(3):283–288

    Article  CAS  Google Scholar 

  26. Zamyatnin AA, Borchikov AS, Vladimirov MG, Voronina OL (2006) The EROP-Moscow oligopeptide database. Nucleic Acids Res 34:D261–D266

    Article  CAS  Google Scholar 

  27. 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

    Article  CAS  Google Scholar 

  28. Li W, Godzik A (2006) Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22:1658–1659

    Article  CAS  Google Scholar 

  29. Kumar M, Verma R, Raghava GPS (2006) Prediction of mitochondrial proteins using support vector. J Biol Chem 281(9):5357–5363

    Article  CAS  Google Scholar 

  30. Martin W, Mentel M (2010) The origin of mitochondria. Nat Educ 3(9):58

    Google Scholar 

  31. StatSoft, Inc. (2001) STATISTICA (Data Analysis Software System). Version 6.0. www.statsoft.com

  32. Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD et al (2005) Protein identification and analysis tools on the ExPASy server. In: Walker JM (ed) The proteomics protocols handbook. Humana Press, New York, pp 571–607

    Chapter  Google Scholar 

  33. Joachims T (1999) Making large-Scale SVM learning practical. In: Schölkopf B, Burges C, Smola A (eds) Advances in Kernel methods—support vector learning. MIT-Press, Cambridge, pp 1–22

    Google Scholar 

  34. Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan Publishing, New York

    Google Scholar 

  35. Cheng B, Titterington DM (1994) Neural networks: a review from a statistical perspective. Stat Sci 9(1):2–30

    Article  Google Scholar 

  36. Vapnik V (2000) The nature of statistical learning theory. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  37. Efron B (1983) Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc 78:316–631

    Article  Google Scholar 

  38. Bhasin M, Raghava GPS (2004) Prediction of CTL epitopes using QM, SVM and ANN techniques. Vaccine 22(23–24):3195–3204

    Article  CAS  Google Scholar 

  39. Pathan FK, Venkata DA, Panguluri SK (2010) Recent patents on antimicrobial peptides. Recent Pat DNA Gene Seq 4(1):10–16

    Article  CAS  Google Scholar 

  40. Kindrachuk J, Napper S (2010) Structure-activity relationships of multifunctional host defence peptides. Mini Rev Med Chem 10:596–614

    Article  CAS  Google Scholar 

  41. Chen JY, Lin WJ, Lin TL (2009) A fish antimicrobial peptide, tilapia hepcidin TH2-3, shows potent antitumor activity against human fibrosarcoma cells. Peptides 30:1636–1642

    Article  CAS  Google Scholar 

  42. Chen JY, Lin WJ, Wu JL, Her GM, Hui CF (2009) Epinecidin-1 peptide induces apoptosis which enhances antitumor effects in human leukemia U937 cells. Peptides 30:2365–2373

    Article  CAS  Google Scholar 

  43. Lin WJ, Chien YL, Pan CY, Lin TL, Chen JY et al (2009) Epinecidin-1, an antimicrobial peptide from fish (Epinephelus coioides) which has an antitumor effect like lytic peptides in human fibrosarcoma cells. Peptides 30:283–290

    Article  CAS  Google Scholar 

  44. Chiou PP, Khoo J, Bols NC, Douglas S, Chen TT (2006) Effects of linear cationic alpha-helical antimicrobial peptides on immune-relevant genes in trout macrophages. Dev Comp Immunol 30:797–806

    Article  CAS  Google Scholar 

  45. Wang YD, Kung CW, Chi SC, Chen JY (2010) Inactivation of nervous necrosis virus infecting grouper (Epinephelus coioides) by epinecidin-1 and hepcidin 1-5 antimicrobial peptides, and downregulation of Mx2 and Mx3 gene expressions. Fish Shellfish Immunol 28:113–120

    Article  CAS  Google Scholar 

  46. Fulmer PA, Lundin JG, Wynne JH (2010) Development of antimicrobial peptides (AMPs) for use in self-decontaminating coatings. ACS Appl Mater Interfaces 2:1266–1270

    Article  CAS  Google Scholar 

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    Authors and Affiliations

    1. Jaypee University of Information Technology, Solan, Himachal Pradesh, India

      Aditi Gautam & Asuda Sharma

    2. Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India

      Sarika Jaiswal, Samar Fatma, Vasu Arora, M. A. Iquebal, Anil Rai & Dinesh Kumar

    3. Division of Fish Genetics and Biotechnology, ICAR-Central Institute of Freshwater Aquaculture, Bhubaneswar, Odisha, India

      S. Nandi, J. K. Sundaray & P. Jayasankar

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    1. Aditi Gautam
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    Correspondence to Dinesh Kumar.

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    Aditi Gautam, Asuda Sharma and Sarika Jaiswal have contributed equally to this work.

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    Gautam, A., Sharma, A., Jaiswal, S. et al. Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries. Probiotics & Antimicro. Prot. 8, 141–149 (2016). https://doi.org/10.1007/s12602-016-9215-0

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