Pretrained models may fail to capture immunological sequences

Area of Science:

• Immunoinformatics
• Computational Biology
• Machine Learning in Immunology

Background:

• Pretrained models, common in vision and text, are increasingly applied to biological sequences.
• Immunological sequence analysis, particularly for predicting T cell receptor (TCR) interactions and immunogenicity, benefits from advanced computational tools.
• Existing models like pMTnet utilize pretrained modules, but their efficacy with diverse immunological data requires scrutiny.

Purpose of the Study:

• To investigate the effectiveness of pretrained models in representing complex immunological sequences.
• To evaluate the impact of specific pretrained modules, such as the T cell receptor (TCR) autoencoder in pMTnet, on immunogenicity prediction accuracy.
• To identify potential limitations arising from data heterogeneity and distribution discrepancies in immunological sequence pretraining.

Main Methods:

• Ablation study on the pMTnet model, specifically removing the pretrained TCR autoencoder.
• Comparative analysis of prediction accuracy between the original pMTnet and the modified model.
• Examination of the TCR pretraining dataset used by pMTnet against a broader TCR repertoire.

Main Results:

• Removing the pretrained TCR autoencoder from pMTnet led to improved immunogenicity prediction accuracy.
• The TCR pretraining data utilized by pMTnet was found to significantly deviate from a more comprehensive and representative TCR repertoire.
• Heterogeneous representations and distribution discrepancies in immunological sequence data were identified as critical factors.

Conclusions:

• Pretrained models are not universally optimal for all immunological tasks and may require task-specific adaptations.
• The quality and representativeness of pretraining data are crucial for the performance of immunological sequence models.
• Careful consideration of data distribution and model component contributions is necessary for developing robust immunoinformatics tools.