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AMAP : Hierarchical multi-label prediction of biologically active and antimicrobial peptides

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Gull, Sadaf, Shamim, Nauman and Minhas, Fayyaz ul Amir Afsar (2019) AMAP : Hierarchical multi-label prediction of biologically active and antimicrobial peptides. Computers in Biology and Medicine, 107 . pp. 172-181. doi:10.1016/j.compbiomed.2019.02.018 ISSN 0010-4825.

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Official URL: http://dx.doi.org/10.1016/j.compbiomed.2019.02.018

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Abstract

Due to increase in antibiotic resistance in recent years, development of efficient and accurate techniques for discovery and design of biologically active peptides such as antimicrobial peptides (AMPs) has become essential. The screening of natural and synthetic AMPs in the wet lab is a challenge due to time and cost involved in such experiments. Bioinformatics methods can be used to speed up discovery and design of antimicrobial peptides by limiting the wet-lab search to promising peptide sequences. However, most such tools are typically limited to the prediction of whether a peptide exhibits antimicrobial activity or not and they do not identify the exact type of the biological activities of these peptides. In this work, we have designed a machine learning based model called AMAP for predicting biological activity of peptides with a specialized focus on antimicrobial activity prediction. AMAP used multi-label classification to predict 14 different types of biological functions of a given peptide sequence with improved accuracy in comparison to existing state of the art techniques. We have performed stringent performance analyses of the proposed method. In addition to cross-validation and performance comparison with existing AMP predictors, AMAP has also been benchmarked on recently published experimentally verified peptides that were not a part of our training set. We have also analyzed features used in this work and our analysis shows that the proposed predictor can generalize well in predicting biological activity of novel peptide sequences. A webserver of the proposed method is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#AMAP

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QP Physiology
Q Science > QR Microbiology
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Peptides -- Biotechnology, Drug resistance in microorganisms, Peptide antibiotics -- Research, Machine learning
Journal or Publication Title: Computers in Biology and Medicine
Publisher: Pergamon
ISSN: 0010-4825
Official Date: April 2019
Dates:
DateEvent
April 2019Published
25 February 2019Available
20 February 2019Accepted
Volume: 107
Page Range: pp. 172-181
DOI: 10.1016/j.compbiomed.2019.02.018
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 4 November 2019
Date of first compliant Open Access: 25 February 2020
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
Indigenous 5000 Ph.D. fellowship schemeHigher Education Commission, Pakistanhttp://dx.doi.org/10.13039/501100004681

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