GeoSeq-EGNN: A geometry-sequence fused equivariant graph neural network for predicting short linear motifs within...

Short linear motifs (SLiMs) located within intrinsically disordered regions (IDRs) are essential for mediating dynamic protein-protein interactions. However, their short length, sequence degeneracy, and the conformational heterogeneity of IDRs pose significant challenges for accurate computational identification. Traditional methods that rely exclusively on one-dimensional sequences or static three-dimensional structures may not adequately capture the intricate interplay between structure and sequence, while severe class imbalance often results in unacceptably high false-positive rates. To address these challenges, we introduce GeoSeq-EGNN, a Geometry-Sequence Fused Equivariant Graph Neural Network designed for dual-modality SLiMs prediction. This architecture effectively integrates Global-Local Hybrid Equivariant Message Passing Layers (GLH-EMPL) to model the three-dimensional spatial folding dynamics of IDRs, alongside Multi-Scale Geometry-Sequence Fusion Network Layers (MSGS-FNL) that utilize Bidirectional LSTMs to extract critical one-dimensional primary sequence topologies. We also incorporate an adaptive coordinate update mechanism to improve geometric stability in deeper layers and dynamic gate feature fusion to reduce representation over-smoothing. To address class imbalance, we use a confidence-aware focal loss that penalizes overconfident erroneous predictions. On the evaluated SLiMs dataset, GeoSeq-EGNN achieved an accuracy of 92.94% and an AUPRC of 82.17% under the final F6 feature configuration and outperformed the evaluated baseline models in the controlled one-hot comparison, with higher Precision and Matthews Correlation Coefficient (MCC). These results support GeoSeq-EGNN as a computational framework for SLiM prediction and provide a basis for future studies of SLiM-mediated regulatory networks.