  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.
