Literature DB >> 22670676

Defensinpred: defensin and defensin types prediction server.

S Ramya Kumari1, Ritesh Badwaik, Vijayaraghavan Sundararajan, V K Jayaraman.   

Abstract

Defensins are considered to play an important role in the innate immune system of virtually all life forms, from insects and plants to amphibians and mammals. They are classified into alpha, beta and theta-defensins. Fast and accurate computational prediction of defensin and defensin types will help in annotating unidentified defensin novel peptides. Identified defensins, owing to their small length and potent antimicrobial activity can be used effectively for development of new clinically applicable antibiotics. Thus predicting the defensin candidates will aid in accurate identification of novel peptide drugs. Support vector machines prediction model accuracy was 99% for defensin and defensin types. The results indicate that it is most accurate and efficient prediction method for defensin peptides. User friendly defensin web server is provided at www.defensinpred.cdac.in for the benefit of scientific community.

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Year:  2012        PMID: 22670676     DOI: 10.2174/092986612803521594

Source DB:  PubMed          Journal:  Protein Pept Lett        ISSN: 0929-8665            Impact factor:   1.890


  6 in total

1.  Leveraging family-specific signatures for AMP discovery and high-throughput annotation.

Authors:  Faiza Hanif Waghu; Ram Shankar Barai; Susan Idicula-Thomas
Journal:  Sci Rep       Date:  2016-04-19       Impact factor: 4.379

2.  iDPF-PseRAAAC: A Web-Server for Identifying the Defensin Peptide Family and Subfamily Using Pseudo Reduced Amino Acid Alphabet Composition.

Authors:  Yongchun Zuo; Yang Lv; Zhuying Wei; Lei Yang; Guangpeng Li; Guoliang Fan
Journal:  PLoS One       Date:  2015-12-29       Impact factor: 3.240

3.  iDEF-PseRAAC: Identifying the Defensin Peptide by Using Reduced Amino Acid Composition Descriptor.

Authors:  Yongchun Zuo; Yu Chang; Shenghui Huang; Lei Zheng; Lei Yang; Guifang Cao
Journal:  Evol Bioinform Online       Date:  2019-07-31       Impact factor: 1.625

4.  In-Silico Tool for Predicting, Scanning, and Designing Defensins.

Authors:  Dilraj Kaur; Sumeet Patiyal; Chakit Arora; Ritesh Singh; Gaurav Lodhi; Gajendra P S Raghava
Journal:  Front Immunol       Date:  2021-11-22       Impact factor: 7.561

Review 5.  Recent development of machine learning-based methods for the prediction of defensin family and subfamily.

Authors:  Phasit Charoenkwan; Nalini Schaduangrat; S M Hasan Mahmud; Orawit Thinnukool; Watshara Shoombuatong
Journal:  EXCLI J       Date:  2022-05-05       Impact factor: 4.022

6.  Integrating "omics" Technologies to Conceptualize Dynamic Antimicrobial Peptide Responses.

Authors:  Jennifer K Plichta; Vanessa Nienhouse; Katherine A Radek
Journal:  Front Immunol       Date:  2012-09-17       Impact factor: 7.561

  6 in total

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