Fine-tuning protein language models to understand the functional impact of missense variants

2025/01/01 by Ali Saadat, Jacques Fellay · 1 voice
Biochemistry, Genetics and Molecular Biology · #Genomics and Rare Diseases #Genomics and Phylogenetic Studies #RNA and protein synthesis mechanisms

paper · pdf · doi:10.1016/j.csbj.2025.05.022

openalex publication_date 2025/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

Elucidating the functional effects of missense variants is crucial yet challenging. To investigate their impact, we fine-tuned protein language models, including ESM2 and ProtT5, to classify 20 protein features at amino acid resolution. In addition, we trained a fully connected neural network classifier on frozen embeddings and compared its performance to fine-tuning in order to quantify the added value of task-specific adaptation. We then used the fine-tuned models to: 1) identify protein features enriched in either pathogenic or benign missense variants, and 2) compare the predicted feature profiles of proteins with reference and alternate alleles to understand how missense variants affect protein functionality. We show that our models can be used to reclassify variants of uncertain significance and provide mechanistic insights into the functional consequences of missense mutations.

Citations

• REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants
• ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
• Evolutionary-scale prediction of atomic-level protein structure with a language model
• Accurate proteome-wide missense variant effect prediction with AlphaMissense
• A genomic mutational constraint map using variation in 76,156 human genomes

Discussions

• 🤖 Are protein language models the next revolution in functional genomics? 🔗 Fine-tuning protein language models to understand the functional impact of missense variants. Computational and Structura [bsky, 2 points, 0 comments]

Related

• Fine-tuning the ESM2 protein language model to understand the functional impact of missense variants
• Protein language model rescue mutations highlight variant effects and structure in clinically relevant genes
• Deciphering the Language of Nature: A transformer-based language model for deleterious mutations in proteins
• Decoding molecular mechanisms for loss-of-function variants in the human proteome
• Genome-wide prediction of disease variant effects with a deep protein language model