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.
journal_name
Probiotics Antimicrob Proteinsjournal_title
Probiotics and antimicrobial proteinsauthors
Gautam A,Sharma A,Jaiswal S,Fatma S,Arora V,Iquebal MA,Nandi S,Sundaray JK,Jayasankar P,Rai A,Kumar Ddoi
10.1007/s12602-016-9215-0subject
Has Abstractpub_date
2016-09-01 00:00:00pages
141-9issue
3eissn
1867-1306issn
1867-1314pii
10.1007/s12602-016-9215-0journal_volume
8pub_type
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journal_title:Probiotics and antimicrobial proteins
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journal_title:Probiotics and antimicrobial proteins
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abstract::The original version of this article contained mistakes, and the authors would like to correct them. The correct details are given below. ...
journal_title:Probiotics and antimicrobial proteins
pub_type: 已发布勘误
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