title: Development of Antimicrobial Peptide Prediction Tool for Aquaculture Industries.
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.
authors.affiliation: Jaypee University of Information Technology, Solan, Himachal Pradesh, India.
authors.forename: Aditi
authors.initials: A
authors.lastname: Gautam
authors.affiliation: Jaypee University of Information Technology, Solan, Himachal Pradesh, India.
authors.forename: Asuda
authors.initials: A
authors.lastname: Sharma
authors.affiliation: Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
authors.forename: Sarika
authors.initials: S
authors.lastname: Jaiswal
authors.affiliation: Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
authors.forename: Samar
authors.initials: S
authors.lastname: Fatma
authors.affiliation: Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
authors.forename: Vasu
authors.initials: V
authors.lastname: Arora
authors.affiliation: Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
authors.forename: M A
authors.initials: MA
authors.lastname: Iquebal
authors.affiliation: Division of Fish Genetics and Biotechnology, ICAR-Central Institute of Freshwater Aquaculture, Bhubaneswar, Odisha, India.
authors.forename: S
authors.initials: S
authors.lastname: Nandi
authors.affiliation: Division of Fish Genetics and Biotechnology, ICAR-Central Institute of Freshwater Aquaculture, Bhubaneswar, Odisha, India.
authors.forename: J K
authors.initials: JK
authors.lastname: Sundaray
authors.affiliation: Division of Fish Genetics and Biotechnology, ICAR-Central Institute of Freshwater Aquaculture, Bhubaneswar, Odisha, India.
authors.forename: P
authors.initials: P
authors.lastname: Jayasankar
authors.affiliation: Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
authors.forename: Anil
authors.initials: A
authors.lastname: Rai
authors.affiliation: Centre for Agricultural Bioinformatics, ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India. dineshkumarbhu@gmail.com.
authors.forename: Dinesh
authors.initials: D
authors.lastname: Kumar
mesh_terms: D000465:Algorithms; D000818:Animals; D000890:Anti-Infective Agents / Q000032:analysis; D017756:Aquaculture / Q000295:instrumentation* / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D010455:Peptides; D040901:Proteomics
chemical_list: D000890:Anti-Infective Agents; D010455:Peptides
keywords: Antimicrobial peptides; Fish; Prediction; Support vector machine; Web-server