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Conserved Binding Sites

Conserved Binding Sites

Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

TEINet: a deep learning framework for prediction of TCR-epitope binding specificity.

Yuepeng Jiang¹, Miaozhe Huo¹, Shuai Cheng Li¹

¹Department of Computer Science, City University of Hong Kong.

Briefings in Bioinformatics

|March 12, 2023

Summary

TEINet, a deep learning framework, accurately predicts T-cell receptor (TCR) and epitope binding specificities. This method offers novel insights into TCR-epitope interactions, outperforming existing approaches.

Keywords:

T cell receptor deep learning epitope specificity immunoinformatics transfer learning

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

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Area of Science:

Immunology
Bioinformatics
Machine Learning

Background:

Adaptive immunity relies on T-cell receptor (TCR) recognition of antigens.
Advances in TCR data generation enable machine learning for predicting TCR-epitope binding.
Predicting TCR binding specificity faces challenges, particularly in negative data sampling.

Purpose of the Study:

To develop TEINet, a deep learning framework for predicting TCR-epitope binding specificities.
To evaluate the effectiveness of transfer learning in TCR-epitope binding prediction.
To assess and recommend optimal negative sampling strategies for binding specificity prediction.

Main Methods:

TEINet utilizes separately pretrained encoders for TCR and epitope sequences.
Transformed sequences are processed by a fully connected neural network for prediction.
Comparative analysis with baseline methods and assessment of pretraining impact were performed.

Main Results:

TEINet achieved an average AUROC of 0.760, outperforming baseline methods by 6.4-26%.
The Unified Epitope approach was identified as the most suitable for negative data sampling.
Excessive pretraining was found to potentially reduce model transferability.

Conclusions:

TEINet accurately predicts TCR-epitope binding using only TCR (CDR3β) and epitope sequences.
The framework provides valuable insights into TCR-epitope interactions.
TEINet represents a significant advancement in predicting immune response specificity.