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Deep-AmPEP30: Improve short antimicrobial peptides prediction with deep learning

Publication Type : Journal Article

Thematic Areas : Center for Computational Engineering and Networking (CEN)

Source : Volume 20, 5 June 2020, Pages 882-894

Url :

Campus : Coimbatore

School : Computational Engineering and Networking

Center : Center for Computational Engineering and Networking, Computational Engineering and Networking

Department : Center for Computational Engineering and Networking (CEN)

Year : 2020

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 for both individual sequence prediction and genome screening for AMPs.

Cite this Research Publication : Jielu Yan; Pratiti Bhadra; Ang Li; Pooja Sethiya; Longguang Qin; Hio Kuan Tai; Koon Ho Wong; Shirley W. I. Siu., "Deep-AmPEP30: Improve Short Antimicrobial Peptides Prediction with Deep Learning" Volume 20, 5 June 2020, Pages 882-894

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