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Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach
BACKGROUND: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antim...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156846/ https://www.ncbi.nlm.nih.gov/pubmed/30255784 http://dx.doi.org/10.1186/s12864-018-5030-1 |
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author | Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. |
author_facet | Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. |
author_sort | Beltran, Jesus A. |
collection | PubMed |
description | BACKGROUND: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model’s performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. RESULTS: We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. CONCLUSIONS: The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5030-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6156846 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-61568462018-09-27 Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. BMC Genomics Research BACKGROUND: Antimicrobial peptides are a promising alternative for combating pathogens resistant to conventional antibiotics. Computer-assisted peptide discovery strategies are necessary to automatically assess a significant amount of data by generating models that efficiently classify what an antimicrobial peptide is, before its evaluation in the wet lab. Model’s performance depends on the selection of molecular descriptors for which an efficient and effective approach has recently been proposed. Unfortunately, how to adapt this method to the selection of molecular descriptors for the classification of antimicrobial peptides and the performance it can achieve, have only preliminary been explored. RESULTS: We propose an adaptation of this successful feature selection approach for the weighting of molecular descriptors and assess its performance. The evaluation is conducted on six high-quality benchmark datasets that have previously been used for the empirical evaluation of state-of-art antimicrobial prediction tools in an unbiased manner. The results indicate that our approach substantially reduces the number of required molecular descriptors, improving, at the same time, the performance of classification with respect to using all molecular descriptors. Our models also outperform state-of-art prediction tools for the classification of antimicrobial and antibacterial peptides. CONCLUSIONS: The proposed methodology is an efficient approach for the development of models to classify antimicrobial peptides. Particularly in the generation of models for discrimination against a specific antimicrobial activity, such as antibacterial. One of our future directions is aimed at using the obtained classifier to search for antimicrobial peptides in various transcriptomes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12864-018-5030-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-24 /pmc/articles/PMC6156846/ /pubmed/30255784 http://dx.doi.org/10.1186/s12864-018-5030-1 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Beltran, Jesus A. Aguilera-Mendoza, Longendri Brizuela, Carlos A. Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach |
title | Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach |
title_full | Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach |
title_fullStr | Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach |
title_full_unstemmed | Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach |
title_short | Optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach |
title_sort | optimal selection of molecular descriptors for antimicrobial peptides classification: an evolutionary feature weighting approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6156846/ https://www.ncbi.nlm.nih.gov/pubmed/30255784 http://dx.doi.org/10.1186/s12864-018-5030-1 |
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