Publication: Toward insights on determining factors for high activity in antimicrobial peptides via machine learning
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Issued Date
2019-01-01
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ISSN
21678359
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2-s2.0-85076828798
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Mahidol University
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SCOPUS
Bibliographic Citation
PeerJ. Vol.2019, No.12 (2019)
Suggested Citation
Hao Li, Chanin Nantasenamat Toward insights on determining factors for high activity in antimicrobial peptides via machine learning. PeerJ. Vol.2019, No.12 (2019). doi:10.7717/peerj.8265 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/49842
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Title
Toward insights on determining factors for high activity in antimicrobial peptides via machine learning
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Abstract
Copyright 2019 Li and Nantasenamat. The continued and general rise of antibiotic resistance in pathogenic microbes is a well-recognized global threat. Host defense peptides (HDPs), a component of the innate immune system have demonstrated promising potential to become a next generation antibiotic effective against a plethora of pathogens. While the effectiveness of antimicrobial HDPs has been extensively demonstrated in experimental studies, theoretical insights on the mechanism by which these peptides function is comparably limited. In particular, experimental studies of AMP mechanisms are limited in the number of different peptides investigated and the type of peptide parameters considered. This study makes use of the random forest algorithm for classifying the antimicrobial activity as well for identifying molecular descriptors underpinning the antimicrobial activity of investigated peptides. Subsequent manual interpretation of the identified important descriptors revealed that polarity-solubility are necessary for the membrane lytic antimicrobial activity of HDPs.