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Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods

Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective...

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Detalles Bibliográficos
Autores principales: Wang, Ping, Hu, Lele, Liu, Guiyou, Jiang, Nan, Chen, Xiaoyun, Xu, Jianyong, Zheng, Wen, Li, Li, Tan, Ming, Chen, Zugen, Song, Hui, Cai, Yu-Dong, Chou, Kuo-Chen
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3076375/
https://www.ncbi.nlm.nih.gov/pubmed/21533231
http://dx.doi.org/10.1371/journal.pone.0018476
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author Wang, Ping
Hu, Lele
Liu, Guiyou
Jiang, Nan
Chen, Xiaoyun
Xu, Jianyong
Zheng, Wen
Li, Li
Tan, Ming
Chen, Zugen
Song, Hui
Cai, Yu-Dong
Chou, Kuo-Chen
author_facet Wang, Ping
Hu, Lele
Liu, Guiyou
Jiang, Nan
Chen, Xiaoyun
Xu, Jianyong
Zheng, Wen
Li, Li
Tan, Ming
Chen, Zugen
Song, Hui
Cai, Yu-Dong
Chou, Kuo-Chen
author_sort Wang, Ping
collection PubMed
description Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective computational method for accurately predicting novel AMPs because it can provide us with more candidates and useful insights for drug design. In this study, a new method for predicting AMPs was implemented by integrating the sequence alignment method and the feature selection method. It was observed that, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was over 80.23%, and the Mathews correlation coefficient is 0.73, indicating a good prediction. Moreover, it is indicated by an in-depth feature analysis that the results are quite consistent with the previously known knowledge that some amino acids are preferential in AMPs and that these amino acids do play an important role for the antimicrobial activity. For the convenience of most experimental scientists who want to use the prediction method without the interest to follow the mathematical details, a user-friendly web-server is provided at http://amp.biosino.org/.
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spelling pubmed-30763752011-04-29 Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods Wang, Ping Hu, Lele Liu, Guiyou Jiang, Nan Chen, Xiaoyun Xu, Jianyong Zheng, Wen Li, Li Tan, Ming Chen, Zugen Song, Hui Cai, Yu-Dong Chou, Kuo-Chen PLoS One Research Article Antimicrobial peptides (AMPs) represent a class of natural peptides that form a part of the innate immune system, and this kind of ‘nature's antibiotics’ is quite promising for solving the problem of increasing antibiotic resistance. In view of this, it is highly desired to develop an effective computational method for accurately predicting novel AMPs because it can provide us with more candidates and useful insights for drug design. In this study, a new method for predicting AMPs was implemented by integrating the sequence alignment method and the feature selection method. It was observed that, the overall jackknife success rate by the new predictor on a newly constructed benchmark dataset was over 80.23%, and the Mathews correlation coefficient is 0.73, indicating a good prediction. Moreover, it is indicated by an in-depth feature analysis that the results are quite consistent with the previously known knowledge that some amino acids are preferential in AMPs and that these amino acids do play an important role for the antimicrobial activity. For the convenience of most experimental scientists who want to use the prediction method without the interest to follow the mathematical details, a user-friendly web-server is provided at http://amp.biosino.org/. Public Library of Science 2011-04-13 /pmc/articles/PMC3076375/ /pubmed/21533231 http://dx.doi.org/10.1371/journal.pone.0018476 Text en Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Wang, Ping
Hu, Lele
Liu, Guiyou
Jiang, Nan
Chen, Xiaoyun
Xu, Jianyong
Zheng, Wen
Li, Li
Tan, Ming
Chen, Zugen
Song, Hui
Cai, Yu-Dong
Chou, Kuo-Chen
Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods
title Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods
title_full Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods
title_fullStr Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods
title_full_unstemmed Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods
title_short Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods
title_sort prediction of antimicrobial peptides based on sequence alignment and feature selection methods
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3076375/
https://www.ncbi.nlm.nih.gov/pubmed/21533231
http://dx.doi.org/10.1371/journal.pone.0018476
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