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

Protein-protein Interfaces

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

Ligand Binding Sites

Ligand Binding Sites

Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...

You might also read

Related Articles

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

Sort by

Same author

An Explainable Deep Learning Framework Integrating DNA Sequence and Transcription Initiation Signals for Gene Expression Prediction.

ACS synthetic biology·2026

Same author

Steroid Hormone Changes after Short-Term High-Dose Exposure to Cannabidiol in Healthy Subjects: An Exploratory Study.

Medical cannabis and cannabinoids·2026

Same author

LysePred: A Multiscale Convolutional Neural Network for Predicting Hemolytic Activity of Antimicrobial Peptides.

ACS synthetic biology·2026

Same author

MMTF-DTI: Drug-target interaction prediction via multimodal feature extraction and dynamic fusion.

Journal of biomedical informatics·2026

Same author

GenOT: generative optimal transport enables spatiotemporal interpolation and generation in cross-platform spatial transcriptomics.

Genome biology·2026

Same author

An Interpretable Deep Learning Framework Leveraging RNA Foundation Model and Capsule Networks for Accurate Prediction of RNA 2'-O-Methylation Sites.

Journal of chemical information and modeling·2026

Same journal

An interpretable framework for cancer drug response prediction using integrated drug and multi-omics data with a hybrid Bi-LSTM-GRU network.

Computational biology and chemistry·2026

Same journal

SegMWB: A lightweight deep learning framework for microscopic image classification.

Computational biology and chemistry·2026

Same journal

Protein dynamic simulations: From early inception to clinical translation over half a century.

Computational biology and chemistry·2026

Same journal

Integrated omics and virtual screening predict Tabularin as a dual inhibitor of the prognostic microRNAs mir-19a and mir-32 in colorectal cancer.

Computational biology and chemistry·2026

Same journal

In silico characterization of acetyl-CoA carboxylase from Staphylococcus aureus and Escherichia coli: A comparative analysis.

Computational biology and chemistry·2026

Same journal

An optimized cascaded transformer with progressive attention for lung and colon cancer diagnosis from histopathological images.

Computational biology and chemistry·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 15, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

PLM-IL4: Enhancing IL-4-inducing peptide prediction with protein language model.

Ruiqi Liu¹, Shankai Yan¹, Zilong Zhang¹

¹School of Computer Science and Technology, Hainan University, Haikou 570228, China.

Computational Biology and Chemistry

|April 9, 2025

Summary

This summary is machine-generated.

This study enhances the prediction of Interleukin-4 (IL-4) inducing peptides using advanced machine learning. The novel approach improves accuracy for potential immunotherapy and vaccine development.

Keywords:

ENN ESM-2 model IL-4 inducing peptides Protein language model SMOTE

More Related Videos

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Related Experiment Videos

Last Updated: May 15, 2025

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Published on: March 25, 2014

Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Interleukin-4 (IL-4) plays a critical role in immune regulation and allergic responses.
Predicting IL-4 inducing peptides is vital for advancing immunotherapy and vaccine development.
Existing prediction methods face challenges with data imbalance and feature extraction.

Purpose of the Study:

To improve the predictive accuracy of IL-4-inducing peptides.
To address data imbalance issues in peptide datasets.
To enhance deep feature extraction for better peptide prediction.

Main Methods:

Utilized SMOTE (Synthetic Minority Over-sampling Technique) and ENN (Edited Nearest Neighbors) for dataset balancing.
Employed a 30-layer ESM-2 model for deep feature extraction from peptide sequences.
Applied a hyperparameter-tuned Gated Recurrent Unit (GRU) model for classification.

Main Results:

Achieved a high Area Under the Curve (AUC) of 0.98.
Reached a prediction accuracy of 93.1%.
Demonstrated significant improvements in IL-4 inducing peptide prediction.

Conclusions:

The developed method offers enhanced predictive accuracy for IL-4-inducing peptides.
This approach holds significant potential for future immunotherapy and vaccine design.
The PLM-IL4 web server and datasets are publicly available for research.