@article{Ito_J-2025_211995,
title = {A protein language model for exploring viral fitness landscapes},
author = {Ito, J. and Strange, A. and Liu, W. and Joas, G. and Lytras, S. and Sato, K. and Genotype to Phenotype Japan (G2P-Japan) Consortium},
month = {may},
year = {2025},
abstract = {Successively emerging SARS-CoV-2 variants lead to repeated epidemic surges through escalated fitness (i.e., relative effective reproduction number between variants). Modeling the genotype-fitness relationship enables us to pinpoint the mutations boosting viral fitness and flag high-risk variants immediately after their detection. Here, we present CoVFit, a protein language model adapted from ESM-2, designed to predict variant fitness based solely on spike protein sequences. CoVFit was trained on genotype-fitness data derived from viral genome surveillance and functional mutation assays related to immune evasion. CoVFit successively ranked the fitness of unknown future variants harboring nearly 15 mutations with informative accuracy. CoVFit identified 959 fitness elevation events throughout SARS-CoV-2 evolution until late 2023. Furthermore, we show that CoVFit is applicable for predicting viral evolution through single amino acid mutations. Our study gives insight into the SARS-CoV-2 fitness landscape and provides a tool for efficiently identifying SARS-CoV-2 variants with higher epidemic risk.},
volume = {16},
journal = {Nature Communications},
publisher = {Springer Nature},
url = {https://doi.org/10.1038/s41467-025-59422-w},
}
