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Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
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Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...

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TRAP: a contrastive learning-enhanced framework for robust TCR-pMHC binding prediction with improved

Jingxuan Ge1,2, Jike Wang1,2, Qing Ye1

  • 1College of Pharmaceutical Sciences, Zhejiang University Hangzhou 310058 Zhejiang China kimhsieh@zju.edu.cn tingjunhou@zju.edu.cn.

Chemical Science
|May 5, 2025
PubMed
Summary

TRAP, a new machine learning model, accurately predicts T cell receptor (TCR) and peptide-MHC (pMHC) binding. It improves immunotherapy development by outperforming existing models, especially with novel epitopes.

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Area of Science:

  • Immunology
  • Computational Biology
  • Machine Learning

Background:

  • T cell receptor (TCR) and peptide-MHC (pMHC) interactions are crucial for adaptive immunity.
  • Accurate prediction of TCR-pMHC binding is vital for advancing immunotherapies.
  • Current machine learning models struggle with predicting binding to unseen epitopes.

Purpose of the Study:

  • To develop a novel machine learning model, TRAP, for enhanced TCR-pMHC binding prediction.
  • To improve the performance of TCR-pMHC binding prediction models, particularly for novel epitopes.
  • To enable large-scale predictions for real-world immunotherapy applications.

Main Methods:

  • TRAP utilizes contrastive learning to integrate structural and sequence features of pMHC and TCR.
  • The model aligns pMHC structural/sequence data with TCR sequences for improved prediction.
  • Performance was evaluated on random and unseen epitope datasets.

Main Results:

  • TRAP significantly outperforms existing state-of-the-art models in both random (AUC 0.92) and unseen epitope (AUC 0.75) scenarios.
  • Achieved a 22% improvement in AUPR (0.84) in the random scenario.
  • Demonstrated capability in diagnosing TCR cross-reactivity and identifying potent TCRs.

Conclusions:

  • TRAP offers a robust and accurate tool for TCR-pMHC binding prediction.
  • The model shows significant potential for accelerating the development of TCR-based immunotherapies.
  • TRAP's performance supports its application in large-scale predictions and real-world settings.