Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Antimicrobial Proteins

Antimicrobial Proteins

Antimicrobial proteins are important components of the immune system. They aid the body in combating pathogens by either killing them directly or hindering their replication processes. Four main types of antimicrobial substances are interferons, the complement system, iron-binding proteins, and antimicrobial proteins.
Interferons
Interferons (IFNs) are proteins produced by lymphocytes, macrophages, and fibroblasts infected with viruses. While IFNs cannot prevent viruses from entering and...

Microorganisms in Medicine and Therapeutics

Microorganisms in Medicine and Therapeutics

Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

IEEE transactions on neural networks and learning systems·2026

Same author

MEGCAM: MEta-Graph and Causal Attention Method for Drug Repurposing on Heterogeneous Drug-Target-Disease Knowledge Network.

Journal of chemical information and modeling·2026

Same author

BERT-T6: Toward High-Accuracy T6SS Bacterial Toxin Identification Using a Protein Language Model.

Journal of chemical information and modeling·2026

Same author

MURM-A: An Improved A Within Comprehensive Path-Planning Scheme for Cellular-Connected Multi-UAVs Based on Radio Map and Complex Network.

Sensors (Basel, Switzerland)·2026

Same author

Multi-AOP: a lightweight multi-view deep learning framework for antioxidant peptide discovery.

Bioresources and bioprocessing·2026

Same author

Latent obstacles in older adults' digital health participation: a community-based hybrid cluster analysis with natural language processing.

BMC public health·2025

Search research articles

Related Experiment Video

Updated: Aug 24, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep

Jielu Yan¹, Jianxiu Cai^2,3, Bob Zhang¹

¹PAMI Research Group, Department of Computer and Information Science, University of Macau, Taipa, Macau, China.

Antibiotics (Basel, Switzerland)

|October 27, 2022

Summary

Antimicrobial peptides (AMPs) show promise as next-generation antibiotics due to their unique membrane-disrupting action, which hinders resistance development. This review surveys deep learning methods for predicting and designing AMPs, addressing limitations in experimental discovery.

Keywords:

antimicrobial peptide classification deep learning machine learning medicine regression therapeutic peptide

More Related Videos

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

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

Published on: September 25, 2021

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

Related Experiment Videos

Last Updated: Aug 24, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

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

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

Published on: September 25, 2021

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
Drug Discovery

Background:

Antimicrobial resistance (AMR) is a critical global health threat, driven by antibiotic overuse and multi-drug-resistant microbes.
Antimicrobial peptides (AMPs) offer a promising alternative due to their broad activity, low host toxicity, and novel membrane-targeting mechanisms that impede resistance.
Traditional experimental methods for AMP discovery are costly and time-consuming.

Purpose of the Study:

To provide a comprehensive review of deep learning (DL) approaches for antimicrobial peptide (AMP) prediction and design.
To highlight the latest advancements in computational methods for identifying and developing novel AMPs.
To discuss the current limitations and future challenges in the field of DL-driven AMP discovery.

Main Methods:

Review of existing literature on deep learning techniques applied to antimicrobial peptides.
Explanation of feature encoding methods for peptide sequence representation.
Discussion of popular DL architectures (e.g., CNNs, RNNs, Transformers) and their application in AMP classification and de novo design.

Main Results:

Deep learning models demonstrate significant potential in accurately predicting AMP activity and facilitating the design of novel peptide sequences.
Various feature encoding strategies effectively represent peptide characteristics for DL model input.
Recent DL applications show promise in accelerating the discovery pipeline for next-generation antibiotics.

Conclusions:

Deep learning offers a powerful in silico approach to overcome the limitations of experimental AMP discovery.
Further research is needed to address challenges in DL model interpretability, data scarcity, and generalization for robust AMP prediction and design.
DL-driven strategies are crucial for developing novel antimicrobial agents to combat the growing threat of antimicrobial resistance.