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Updated: Aug 16, 2025

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

PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder.

Pengfei Zhang¹, Seojin Bang, Heewook Lee

¹School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 21, 2022

Summary

This summary is machine-generated.

Predicting T cell receptor (TCR) binding affinity is crucial for immunotherapy. Our PiTE pipeline uses advanced sequence encoders with pre-trained amino acid embeddings to achieve state-of-the-art TCR-epitope binding affinity prediction.

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Last Updated: Aug 16, 2025

A Protocol for Computer-Based Protein Structure and Function Prediction

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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

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Published on: February 10, 2023

Area of Science:

Immunoinformatics
Computational Biology
Machine Learning

Background:

Accurate prediction of T cell receptor (TCR) binding affinity to target antigens is vital for advancing immunotherapy strategies.
Current computational methods often rely on evolutionary-based matrices like BLOSUM for sequence embedding, with less focus on summarizing amino-acid-wise embeddings into sequence-wise representations.

Purpose of the Study:

To introduce PiTE, a novel two-step pipeline designed for enhanced TCR-epitope binding affinity prediction.
To explore and evaluate various neural network architectures for sequence encoding within the prediction pipeline.

Main Methods:

Utilized a pre-trained amino acid embedding model on extensive unlabeled TCR sequences to generate real-valued representations.
Developed a binding affinity prediction model incorporating two sequence encoders and linear layers to predict TCR-epitope affinity scores.
Investigated diverse neural network architectures, including Transformer-like models, for sequence encoding.

Main Results:

The Transformer-like sequence encoder demonstrated state-of-the-art performance in TCR-epitope binding affinity prediction.
The proposed pipeline significantly outperformed existing methods, highlighting the efficacy of advanced sequence encoders.
The ability of the Transformer model to capture contextual amino acid information was identified as a key factor in its superior performance.

Conclusions:

The PiTE pipeline, leveraging pre-trained representations and advanced sequence encoders, significantly improves TCR-epitope binding affinity prediction.
Advanced sequence encoders, particularly Transformer-based architectures, are crucial for capturing sequence context and enhancing predictive accuracy.
This work underscores the potential of sophisticated deep learning approaches in immunoinformatics for developing effective immunotherapies.