A Dual- Task Hierarchical Graph Attention Network for Protein-Protein interaction sites Prediction

Hierarchical Graph Representation Learning for Protein-Protein Interaction Site Prediction.

Predictions of protein–protein interactions: Learning sequences and structures

A neural network-based pipeline that integrates amino acid sequences with structural features is developed and provides a modular prototype for follow-up, more extensive protein modeling, including larger proteins and sequence of variable sizes.

Dual Cross-Attention Network for Hierarchical Feature Fusion in Protein-Protein Interaction Prediction

Characterizing protein-protein interactions (PPIs) is essential for deciphering core biological processes, including signal transduction, metabolic pathway regulation, immune recognition, and cell cycle control. However, experimental PPI determination remains time-consuming and expensive, driving the adoption of deep learning as an efficient and accurate computational approach. Current deep-learning-based PPI prediction models typically process both intra- and inter-protein as isolated units in feature extraction, thereby ignoring mutual information transfer within a single protein and the interacting pair. To address this limitation, we propose DCAPPI (Dual Cross-Attention network for Protein-Protein Interaction prediction), a novel framework leveraging dual cross-attention modules for hierarchical feature fusion at both intra- and inter-protein levels. First, the Channel Cross-Attention module processes protein sequence and structure as distinct input channels. It generates deep intra-protein representations by performing cross-attention between sequence-derived and structure-derived tokens, achieving multimodal feature integration. Second, the Partner Cross-Attention module models the target protein and its interacting partner as a pair of correlative units. By performing cross-attention operations across these units, it enables collaborative feature fusion and constructs context-aware inter-protein interaction features. Evaluation results indicate that DCAPPI achieves superior performance over state-of-the-art methods on benchmark datasets.

A map of human protein-protein interaction embeddings for functional discovery

A protein’s function depends not just on its own structure and localization, but also on the interactions with its partners. Many proteins are therefore better described by a set of partner-dependent roles than by a single annotation. Yet most approaches to the functional interpretation of protein-protein interactions (PPIs) remain protein or set-centric. They rely on pre-existing annotations, and perform worst where knowledge is sparse. Here, we present MAPPIE (Map of Protein-Protein Interaction Embeddings), a method that treats each PPI, rather than each protein, as a unit of representation. From 199,137 human interactions spanning 15,503 proteins, we build a two-dimensional map of the human PPI landscape for functional discovery. Protein language model embeddings for two protein interaction partners are combined and compressed into a latent space, with model selection guided by domain-domain interactions used as a structural proxy for interaction similarity. The resulting geometry separates domain defined interaction classes, organizes disorder associated interactions spatially, and splits interactions involving the same protein by partner. A query PPI’s latent neighbourhood recovers its own annotated functions across molecular, complex, pathway, and biological processes. MAPPIE contributes most where existing functional evidence is weakest, outperforming interactome and sequence identity baselines for sparsely connected interactions. MAPPIE neighbours of query PPIs are enriched for partners in independent protein networks, recovering curated complex-level function even when subunits are spread across the map. Applied to a human dark interactome, MAPPIE assigns specific, experimentally supported functions to dark hub proteins.

A Structure-Aware Multimodal Framework for Drug–Target Interaction Prediction via Heterogeneous Graph Learning

Predicting drug–target interactions is critical for drug discovery, yet many deep learning methods overlook atom–residue–level relationships, so PHGDTI is proposed, a multimodal framework that integrates sequence and structural cues for binding prediction.

GAMT-GINE: A Graph Isomorphism Network Integrating Continuous Spatial Awareness and Multi-Task Learning for Protein–Ligand Binding Affinity Prediction

Comprehensive evaluations indicate that GAMT-GINE can effectively utilize continuous spatial information and heterogeneous affinity labels, achieving good predictive accuracy and cross-dataset generalization capability.