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Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Conserved Binding Sites

Conserved Binding Sites

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Related Experiment Video

Updated: Jan 24, 2026

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

MHCSeqNet: a deep neural network model for universal MHC binding prediction.

Poomarin Phloyphisut¹, Natapol Pornputtapong^2,3, Sira Sriswasdi^4,5

¹Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, 254 Phayathai Road, Pathumwan, Bangkok, 10330, Thailand.

BMC Bioinformatics

|May 30, 2019

Summary

This summary is machine-generated.

Selecting effective neoepitopes for cancer vaccines is challenging. MHCSeqNet, a deep learning model, accurately predicts neoepitope immunogenicity by analyzing Major Histocompatibility Complex (MHC) binding affinity.

Keywords:

Deep learning MHC epitope prediction Recurrent neural networks

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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:

Computational biology
Immunology
Oncology

Background:

Cancer immunotherapy utilizes the host immune system to target cancer cells via neoepitopes.
Synthetic peptide vaccines are effective but require precise neoepitope selection.
Predicting neoepitope immunogenicity, particularly Major Histocompatibility Complex (MHC) binding affinity, is crucial for vaccine development.

Purpose of the Study:

To develop an advanced computational model for predicting neoepitope immunogenicity.
To improve the accuracy and flexibility of neoepitope screening for cancer vaccines.

Main Methods:

Developed MHCSeqNet, an open-source deep learning model.
Utilized natural language processing neural network architectures to represent MHC alleles and peptides as sequences.
Trained and validated the model on MHC binding affinity and MHC ligand peptidome datasets.

Main Results:

MHCSeqNet surpasses existing state-of-the-art predictors in MHC binding affinity and MHC ligand peptidome prediction.
The model demonstrates strong generalization capabilities to previously unseen MHC class I alleles.
MHCSeqNet accommodates peptides of variable lengths and new MHC alleles.

Conclusions:

MHCSeqNet offers enhanced performance and flexibility for neoepitope screening.
This tool is valuable for advancing the development of effective cancer vaccines.
The model's adaptability supports broader applications in immunoinformatics.