Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning

Jielu Yan; Pratiti Bhadra; Ang Li; Pooja Sethiya; Longguang Qin; Hio Kuan Tai; Koon Ho Wong; Shirley W I Siu

doi:10.1016/j.omtn.2020.05.006

Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning

Mol Ther Nucleic Acids. 2020 Jun 5:20:882-894. doi: 10.1016/j.omtn.2020.05.006. Epub 2020 May 12.

Authors

Jielu Yan¹, Pratiti Bhadra¹, Ang Li², Pooja Sethiya², Longguang Qin², Hio Kuan Tai¹, Koon Ho Wong³, Shirley W I Siu⁴

Affiliations

¹ Department of Computer and Information Science, University of Macau, Macau, China.
² Faculty of Health Sciences, University of Macau, Macau, China.
³ Faculty of Health Sciences, University of Macau, Macau, China; Institute of Translational Medicines, University of Macau, Macau, China.
⁴ Department of Computer and Information Science, University of Macau, Macau, China. Electronic address: shirleysiu@um.edu.mo.

Abstract

Antimicrobial peptides (AMPs) are a valuable source of antimicrobial agents and a potential solution to the multi-drug resistance problem. In particular, short-length AMPs have been shown to have enhanced antimicrobial activities, higher stability, and lower toxicity to human cells. We present a short-length (≤30 aa) AMP prediction method, Deep-AmPEP30, developed based on an optimal feature set of PseKRAAC reduced amino acids composition and convolutional neural network. On a balanced benchmark dataset of 188 samples, Deep-AmPEP30 yields an improved performance of 77% in accuracy, 85% in the area under the receiver operating characteristic curve (AUC-ROC), and 85% in area under the precision-recall curve (AUC-PR) over existing machine learning-based methods. To demonstrate its power, we screened the genome sequence of Candida glabrata-a gut commensal fungus expected to interact with and/or inhibit other microbes in the gut-for potential AMPs and identified a peptide of 20 aa (P3, FWELWKFLKSLWSIFPRRRP) with strong anti-bacteria activity against Bacillus subtilis and Vibrio parahaemolyticus. The potency of the peptide is remarkably comparable to that of ampicillin. Therefore, Deep-AmPEP30 is a promising prediction tool to identify short-length AMPs from genomic sequences for drug discovery. Our method is available at https://cbbio.cis.um.edu.mo/AxPEP for both individual sequence prediction and genome screening for AMPs.

Keywords: AmPEP; AxPEP; Candida glabrata; ampicillin; antimicrobial peptide; convolutional neural network; drug discovery; machine learning; reduced amino acid composition.