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SwiftTCR: efficient computational docking protocol of TCRpMHC-I complexes using restricted rotation matrices.

Farzaneh M Parizi1,2, Yannick J M Aarts1,3, Nils Smit4

  • 1Medical BioSciences, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, the Netherlands.

Briefings in Bioinformatics
|June 15, 2026
PubMed
Summary
This summary is machine-generated.

We developed a fast computational method to model T cell receptor-peptide-MHC (TCRpMHC) interactions. This approach accelerates structural analysis, aiding in designing immunotherapies and understanding T cell specificity.

Keywords:
T cell receptor (TCR)TCR binding anglecomputational immunologyintegrative modelingpeptide–MHC (pMHC)restricted docking

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Area of Science:

  • Immunology
  • Structural Biology
  • Computational Biology

Background:

  • T cell recognition of peptides presented by MHC molecules is crucial for immune responses.
  • The vast diversity of T cell receptors (TCRs) makes experimental and computational modeling of TCR-peptide-MHC (TCRpMHC) interactions challenging.
  • Accurate TCRpMHC structural data is vital for developing cancer immunotherapies, transplantation strategies, and treatments for autoimmune diseases.

Purpose of the Study:

  • To develop a rapid and accurate computational protocol for modeling TCRpMHC complex structures.
  • To overcome the limitations of experimental methods and general-purpose docking tools for analyzing diverse TCRpMHC interactions.
  • To provide structural insights into T cell recognition for therapeutic applications.

Main Methods:

  • Developed an integrative modeling protocol building upon the PIPER algorithm.
  • Leveraged unique docking patterns and polarized docking angles of TCRs at pMHC.
  • Integrated an ultra-fast structure superimposition tool, GradPose, for accelerated clustering.
  • Utilized Fast Fourier Transform (FFT) to reduce rotation sets.

Main Results:

  • The protocol models TCRpMHC complexes in 3-4 minutes on 12 CPUs, achieving a 25-40x speedup compared to ClusPro.
  • Outperformed state-of-the-art docking tools in model quality on a benchmark set of 38 TCRpMHC class I complexes.
  • Demonstrated high computational efficiency and accuracy in predicting TCRpMHC structures.

Conclusions:

  • The developed protocol offers a computationally efficient method for modeling TCRpMHC interactions.
  • This approach can provide structural information for specific peptide-targeting TCR repertoires.
  • Enables enrichment of TCR sequencing data and facilitates the development of structure-based deep learning algorithms for T cell recognition studies.