Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction
<|reference_start|>Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction: Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants, but they still lag experimental accuracy. Here, we present a novel fine-tuning approach to improve the performance of PLMs with experimental maps of variant effects from Deep Mutational Scanning (DMS) assays using a Normalised Log-odds Ratio (NLR) head. We find consistent improvements in a held-out protein test set, and on independent DMS and clinical variant annotation benchmarks from ProteinGym and ClinVar. These findings demonstrate that DMS is a promising source of sequence diversity and supervised training data for improving the performance of PLMs for variant effect prediction.<|reference_end|>
@article{lafita2024fine-tuning,
    title={Fine-tuning Protein Language Models with Deep Mutational Scanning
  improves Variant Effect Prediction},
    author={Aleix Lafita, Ferran Gonzalez, Mahmoud Hossam, Paul Smyth, Jacob
  Deasy, Ari Allyn-Feuer, Daniel Seaton, Stephen Young},
    journal={arXiv preprint arXiv:2405.06729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06729},
    primaryClass={q-bio.GN cs.LG}
}