Protein Language Models Expose Viral Immune Mimicry | JoVE Visualize

Area of Science:

Computational virology
Immunoinformatics
Machine learning in biology

Background:

Viruses employ molecular mimicry to evade host immune responses, complicating immune recognition and therapeutic interventions.
Distinguishing viral from host proteins is crucial for understanding viral pathogenesis and developing effective countermeasures.
Existing methods face challenges in accurately identifying viral proteins, particularly those involved in immune evasion.

Purpose of the Study:

To develop and validate a machine learning model using pretrained protein language models (PLMs) to differentiate viral from human proteins.
To identify and characterize viral proteins that are frequently misclassified by the model, indicating potential immune evasion strategies.
To gain mechanistic insights into viral immune escape by integrating PLMs with explainable AI techniques.

Main Methods:

Leveraging pretrained protein language models (PLMs) for sequence-based classification of viral and human proteins.
Integrating PLMs with explainable AI models to interpret the features driving protein classification and misclassification.
Evaluating model performance using Receiver Operating Characteristic Area Under the Curve (ROC-AUC) and analyzing misclassified sequences.

Main Results:

The developed model achieved state-of-the-art performance with a ROC-AUC of 99.7%.
Misclassified viral proteins (3.9% of sequences) were characterized by low immunogenicity and belonged to human-specific viral families associated with chronic infections.
Analysis revealed overlapping biophysical signals confound both immune systems and machine learning models in identifying immune-evading viral proteins.

Conclusions:

Coupling PLMs with explainable AI provides a powerful approach for computational virology and understanding viral immune escape mechanisms.
The findings highlight the challenges posed by viral molecular mimicry and low-immunogenicity proteins for both natural immunity and artificial intelligence.
This research offers implications for the rational design of vaccines and strategies to combat persistent and pathogenic viral infections.