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

• Main Authors: Jielu Yan (Author), Pratiti Bhadra (Author), Ang Li (Author), Pooja Sethiya (Author), Longguang Qin (Author), Hio Kuan Tai (Author), Koon Ho Wong (Author), Shirley W.I. Siu (Author)
• Format: Book
• Published: Elsevier, 2020-06-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.

Summary

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.

Item Description

2162-2531
10.1016/j.omtn.2020.05.006