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Toward insights on determining factors for high activity in antimicrobial peptides via machine learning

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Toward insights on determining factors for high activity in antimicrobial peptides via machine learning

NOTE: This GitHub repository page contains Supplementary Files for the manuscript entitled Toward insights on determining factors for high activity in antimicrobial peptides via machine learning that is submitted to the PeerJ journal.

Authors

Hao Li and Chanin Nantasenamat

Abstract

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 host defense peptides (AMPs) 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.

Supplementary files

In order to facilitate the reproducibility of this work, this repository includes the raw data and statistical analyses performed in the study.

File name Description
S1.xlsx Peptide sequence, activity and raw descriptors
S2.zip Multiple sequence alignment guide tree
S3.xlsx Raw prediction performance
S4.xlsx Important descriptor list
S5.xlsx Unique descriptor count

Acknowledgement

This work is supported by the Center of Excellence on Medical Biotechnology (CEMB), S&T Postgraduate Education and Research Development Office (PERDO), Office of Higher Education Commission (OHEC), Thailand.

Citing this work

If you find this work useful, please cite:

Li H, Nantasenamat C. Toward insights on determining factors for high activity in antimicrobial peptides via machine learning. PeerJ In Press.

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