ArticleNature communications2025
PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Homology-Based Variant-Effect Predictors Break Down on Cytochrome P450 Pharmacogenes.bioRxiv : the preprint server for biology · 2026Article
- The GPCRDiabetologia · 2026Article
- Unsupervised discovery of functional sequence patterns from protein language model with MotifAE.Nature communications · 2026Article
- Molecular dynamics simulations of intrinsically disordered protein regions enable biophysical interpretation of variant-effect predictors.HGG advances · 2026Article
- Defining the molecular tolerance-to-damage landscape of SMARCA4 helicase genetic alterations.bioRxiv : the preprint server for biology · 2026Article
- Actually, what is a gain-of-function mutation?Genetics · 2026Article
- Disease- and gene-specific deep learning for pathogenicity prediction of rare missense variants in cancer predisposition genes.BioData mining · 2026Article
- Protein language models trained on biophysical dynamics inform mutation effects.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Low frequency and rare coding variants affect susceptibility and progression of childhood acute lymphoblastic leukemia.Cancer cell international · 2026Article
- A Transformer-Based Deep Learning Approach to Predicting Air Organic Pollutant-Human Protein Interactions.Environmental science & technology · 2025Article
- Language Modelling Techniques for Analysing the Impact of Human Genetic Variation.Bioinformatics and biology insights · 2025Review
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5 authors.
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Abstract
Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We develop a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we show that PreMode reaches state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode's prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and can be expanded to fitness optimization of non-human proteins with active learning.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.