# Fine-tuning Protein Language Models with Deep Mutational Scanning improves Variant Effect Prediction

arXiv: https://arxiv.org/abs/2405.06729 · HTML: https://mlsanity.com/paper/2405.06729/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 10 May 2024.

## Builds on (most cited first, 2 of 2)

- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)

## Built on by (most cited first, 3 of 3)

- [Structure-Aware Masking for Protein Representation Learning](https://mlsanity.com/paper/2605.16581/) (2026)
- [Language modelling techniques for analysing the impact of human genetic variation](https://mlsanity.com/paper/2503.10655/) (2025)
- [Low-N Protein Activity Optimization with FolDE](https://mlsanity.com/paper/2510.24053/) (2025)

## Abstract (arXiv)

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.