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Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods
Open accessAbstract
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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Cites
Some remarks on protein attribute prediction and pseudo amino acid composition
Chou, K.-C. (2011) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2010.12.024 · Cited by 1,225
Self-citation
Cited by
Designing antimicrobial peptides: form follows function
Fjell, C. D., Hiss, J. A., Hancock, R. E. W., & Schneider, G. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3591 · Cited by 2,035
Mooney, C., Haslam, N. J., Pollastri, G., & Shields, D. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045012 · Cited by 615
Meher, P. K., Sahu, T. K., Saini, V., & Rao, A. R. (2017) · Scientific Reports · doi:10.1038/srep42362 · Cited by 516
Antimicrobial Peptides: An Update on Classifications and Databases
Bin Hafeez, A., Jiang, X., Bergen, P. J., & Zhu, Y. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms222111691 · Cited by 341
Bhadra, P., Yan, J., Li, J., Fong, S., & Siu, S. W. I. (2018) · Scientific Reports · doi:10.1038/s41598-018-19752-w · Cited by 339
Metadata
- Authors
- Wang, P., Hu, L., Liu, G., Jiang, N., Chen, X., Xu, J., Zheng, W., Li, L., Tan, M., Chen, Z., Song, H., Cai, Y.-D., & Chou, K.-C.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2011-04-13
- Volume
- 6
- Issue
- 4
- Pages
- e18476
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2027731040
- Cited by
- 201
- References
- 1
- FWCI
- 15.17
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wang, P., Hu, L., Liu, G., Jiang, N., Chen, X., Xu, J., Zheng, W., Li, L., Tan, M., Chen, Z., Song, H., Cai, Y.-D., & Chou, K.-C. (2011). Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods. PLoS ONE. https://doi.org/10.1371/journal.pone.0018476
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