Journal Article

PreMode predicts mode-of-action of missense variants by deep graph representation learning of protein sequence and structural context

Zhao, Yige, Zhuang, Demi, Chung, Wendy K., Shen, Yufeng, Zhong, Guojie

2025

Overview

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. Accurate prediction of the functional impact of missense variants is important in clinical genetic diagnostics and therapeutic strategies. Here the authors introduce a largest-to-date dataset of human missense variants labeled with their mode-of-action and a deep learning method to predict mode-of-action effects with state-of-the-art performance.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 45/70 / 631/114/2397 / 631/208/2489/144 / Amino Acid Sequence / Computational Biology - methods / Datasets / Deep Learning / Experiments / Gain-Loss / Genes / Genomes / Graph neural networks / Graph representations / Graphical representations / Humanities and Social Sciences / Humans / Kinases / Machine learning / multidisciplinary / Mutation / Mutation, Missense - genetics / Neural networks / Neural Networks, Computer / Pathogenicity / Predictions / Protein Conformation / Protein engineering / Protein folding / Proteins / Proteins - chemistry / Proteins - genetics / Representation learning / Science / Science (multidisciplinary) / State of the art / Transfer learning