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qKAT: Quantitative Semi-automated Typing of Killer-cell Immunoglobulin-like Receptor Genes

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TRain: T-cell receptor automated immunoinformatics.

Austin Seamann¹, Maia Bennett-Boehm¹, Ryan Ehrlich¹

¹School of Interdisciplinary Informatics, College of Information Science and Technology, University of Nebraska at Omaha, 1110 S 67TH, Omaha, NE, 68182, USA.

BMC Bioinformatics

|March 6, 2025

Summary

This summary is machine-generated.

A new Python tool, TRain, automates the complex process of predicting T-cell receptor (TCR) and peptide-MHC (pMHC) complex structures from sequence data. This tool aids in understanding adaptive immune responses by simplifying TCR-pMHC binding analysis.

Keywords:

Immunoinformatics TCR modeling protein docking

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T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

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Area of Science:

Immunology
Structural Biology
Computational Biology

Background:

Characterizing T-cell receptor (TCR) and peptide-Major Histocompatibility Complex (pMHC) binding is hindered by limited structural data.
Advances in sequencing provide abundant TCR sequence data, making protein structure modeling a promising approach.
Computational methods can predict TCR-pMHC interactions and 3D complex structures, but the process is complex and requires specialized expertise.

Purpose of the Study:

To develop an automated computational tool for predicting 3D TCR-pMHC complexes.
To streamline the workflow from TCR sequence data to predicted complex structures.
To facilitate the analysis of TCR-pMHC binding properties for insights into adaptive immunity.

Main Methods:

Developed TRain (T-cell Receptor Automated ImmunoiNformatics), a Python-based tool.
Automated conversion of sequencing data to TCR amino acid sequences.
Integrated existing TCR modeling pipelines and RosettaDock for automated TCR-pMHC structure prediction and analysis.
Enabled non-biased pairing of modeled TCRs with pMHC crystal structures prior to docking.

Main Results:

TRain successfully streamlines the prediction of 3D TCR-pMHC complexes from sequence data.
The tool automates multiple complex steps, including data conversion, model submission, and structure preparation for docking.
A case study demonstrated the basic functionality of TRain, with a manual available for further guidance.

Conclusions:

Introduced TRain, an open-source tool simplifying the prediction of 3D TCR-pMHC complexes.
The tool leverages established methods to provide predicted structures from sequence information.
Analyzing these predicted complexes offers deeper insights into TCR binding and adaptive immune responses.