eSIG-Net: an interaction language model that decodes the protein code of single mutations

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.