eSIG-Net: an interaction language model that decodes the protein code of single mutations
Abstract
Most proteins act through interactions with other molecules, yet predicting how single mutations perturb these interactions-defined as 'protein codes'-remains a central challenge in computational biology. Here we introduce eSIG-Net, the edgetic mutation sequence-based interaction grammar network, a language model that integrates protein sequence embeddings with syntax-aware and evolution-aware mutation encoding and contrastive learning to predict mutation-driven interaction changes. eSIG-Net outperforms state-of-the-art sequence-based and structure-based methods, nominates causal variants and provides mechanistic insights. Together, eSIG-Net is a mutation-centric interaction language model that accurately predicts interaction-specific network rewiring from sequence information alone and generalizes across biological contexts.
Fields of Study & Subjects
Genomics and Rare Diseases
Bioinformatics and Genomic Networks
Biomedical Text Mining and Ontologies
Publication details
- Journal / source
- Nature Methods
- Publisher
- Springer Science and Business Media LLC
- Volume
- 23
- Issue
- 6
- Pages
- 1115–1120
- Publication date
- 29 April 2026
- Language
- EN