A Contrastive Learning Framework for Efficient Viral Escape Prediction.

Area of Science:

• Virology
• Computational Biology
• Machine Learning

Background:

• Viral evolution necessitates understanding immune escape mechanisms for effective treatment development.
• Predicting mutations that allow viruses to evade the immune system remains a significant challenge.
• Protein language models offer promising in silico approaches for analyzing viral escape.

Purpose of the Study:

• To introduce CoV-SNN, a novel framework for unifying viral variant classification and immune escape prediction.
• To leverage contrastive learning and protein language models for efficient in silico surveillance of viral evolution.

Main Methods:

• Developed CoV-SNN, a Siamese neural network utilizing CoV-RoBERTa embeddings for sequence analysis.
• Employed an enhanced Constrained Semantic Change Search (CSCS) function to map antigenic variation and viral fitness.
• Trained and evaluated the model on diverse datasets including wet-lab-verified and computationally generated escape mutations.

Main Results:

• CoV-SNN achieved 98.8% accuracy in multi-class variant classification.
• Demonstrated an AUC of 0.909 in zero-shot variant classification.
• Reached 97.7% precision in identifying top escape mutations with a 125-fold inference speedup.

Conclusions:

• Contrastive learning provides a powerful approach for scalable in silico surveillance of viruses.
• CoV-SNN effectively prioritizes immune-evasive mutations, aiding in the development of preventive strategies.
• The framework supports rapid analysis of emerging viral variants and their potential for immune evasion.