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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

APTAnet: an atom-level peptide-TCR interaction affinity prediction model.

Peng Xiong¹, Anyi Liang¹, Xunhui Cai²

¹School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China.

Biophysics Reports

|May 13, 2024

Summary

This summary is machine-generated.

We developed APTAnet, an advanced model for predicting peptide-TCR interactions, crucial for tumor-infiltrating lymphocyte (TIL) immunotherapy. This method effectively identifies tumor peptides and screens tumor-specific T-cell receptors (TCRs).

Keywords:

Antigen Immunotherapy Natural language processing TCR Transfer learning

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Published on: February 6, 2017

Area of Science:

Computational Biology
Immunoinformatics
Bioinformatics

Background:

Predicting T-cell receptor (TCR) and peptide affinity is vital for advancing tumor-infiltrating lymphocyte (TIL) immunotherapy.
Existing methods for drug-protein interaction (DPI) research inspired novel approaches in peptide-TCR interaction (PTI) analysis.

Purpose of the Study:

To propose APTAnet, an atom-level model for predicting peptide-TCR interaction (PTI) affinity.
To leverage natural language processing (NLP) techniques for enhanced affinity prediction.

Main Methods:

Developed APTAnet, an atom-level model utilizing NLP methods for peptide-TCR interaction (PTI) affinity prediction.
Performed ten-fold cross-validation on 25,675 PTI data pairs.
Validated the model on an independent test set from the McPAS database and real tumor patient data.

Main Results:

APTAnet achieved an average ROC-AUC of 0.893 and PR-AUC of 0.877 in cross-validation.
The model demonstrated superior performance compared to current mainstream models on the McPAS database.
APTAnet successfully identified tumor peptides and screened tumor-specific TCRs in 11 real tumor patient cases.

Conclusions:

APTAnet offers a robust and effective computational approach for predicting peptide-TCR affinity.
The model holds significant potential for improving the development and application of TIL immunotherapy.
APTAnet can aid in identifying tumor-specific targets and screening relevant TCRs for personalized cancer treatment.