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.
Hao Li and Chanin 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 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.
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 |
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.
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.