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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

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Published on: September 25, 2021

Deep-Learning Driven Identification of Novel Antimicrobial Peptides.

Silvia Arino¹, Gianmattia Sgueglia¹, Linda Leone¹

¹Department of Chemical Sciences, University of Napoli Federico II, via Cintia 26, Napoli, 80126, Italy.

Chemistry (Weinheim an Der Bergstrasse, Germany)

|August 13, 2025

Summary

We identified novel antimicrobial peptides (AMPs) using machine learning. One peptide, AMP3, showed broad-spectrum antibacterial activity and disrupted bacterial membranes, highlighting the potential of deep learning for drug discovery.

Keywords:

antimicrobial peptides biological assays biophysical study computational peptide design deep learning

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Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids

Antimicrobial Peptides Produced by Selective Pressure Incorporation of Non-canonical Amino Acids

Published on: May 4, 2018

Area of Science:

Biochemistry
Computational Biology
Microbiology

Background:

Antimicrobial resistance necessitates the discovery of novel antimicrobial peptides (AMPs).
Machine learning (ML) and deep learning (DL) offer powerful tools for accelerating peptide discovery.
Traditional methods for identifying AMPs are often time-consuming and resource-intensive.

Purpose of the Study:

To identify novel antimicrobial peptides (AMPs) using a machine learning-driven pipeline.
To experimentally validate the antimicrobial activity and characterize the mechanism of action of candidate AMPs.
To assess the potential of deep learning approaches for rapid AMP discovery.

Main Methods:

Utilized HydrAMP deep learning (DL) algorithm to generate short Trp-rich peptide sequences.
Employed AMPlify DL model for in silico screening of antimicrobial activity.
Synthesized and experimentally validated three candidate peptides (AMP1, AMP2, AMP3) in vitro.
Performed biophysical analyses to investigate the membrane interaction mechanism of the most potent peptide.

Main Results:

Identified three novel antimicrobial peptide candidates (AMP1, AMP2, AMP3).
AMP3 exhibited the broadest antibacterial spectrum against Gram-positive and Gram-negative bacteria.
Biophysical analyses confirmed AMP3's ability to perturb bacterial membrane bilayer stability.
Demonstrated a membrane-targeting mechanism of action for AMP3.

Conclusions:

Deep learning approaches can significantly accelerate the discovery of novel antimicrobial peptides.
AMP3 represents a promising candidate for further development as an antibacterial agent.
The study validates the utility of ML/DL pipelines for both discovery and mechanistic characterization of AMPs.