Hierarchical Enzyme Function Prediction Based on Structural Confidence and Active-Site-Aware Attention

A novel benchmark dataset for enzyme function prediction reveals the limitations of state-of-the-art models

It is demonstrated that modern EC predictors largely fail to distinguish catalytically incompetent variants from functional enzymes, and it is proposed that integrating structure-aware negative examples into both training and benchmarking is critical for developing functionally robust models in computational enzymology...

Interpreting Protein Language Models: high attention sites predict functional regions

The utility of HA sites for suggesting candidate binding sites and the biological interpretability of PLM representations is explored, demonstrating the biological interpretability of PLM representations and offers a valuable method to prioritize functionally relevant protein residues for targeted biomedical research.

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

This work presents HGRL-PPIS, a novel hierarchical graph representation learning approach for predicting protein-protein interaction sites that achieves superior performance over competing methods on multiple benchmark datasets, enabling more reliable detection of protein-protein binding residues.

DHST: A Deep Hybrid Structure–Topology Framework for Accurate Protein Function Prediction

DHST is proposed, a deep hybrid structure–topology framework that integrates sequence semantics from a pretrained protein language model with local structural information learned by a residual graph convolutional network and introduces site-specific persistent homology to encode multi-scale topological invariants and a...

Computational structural insights into alternative proteins: The impact of AlphaFold and AI-based prediction.

Retrieval-Augmented Residual Graph Neural Network for Protein-Protein Interaction Site Prediction.

RGLLA-PPIS, a novel multimodal prediction model that integrates retrieval-augmented learning and residual GNNs for PPIS identification, outperforms several state-of-the-art baselines in both accuracy and robustness and demonstrates its potential to guide real-world protein engineering tasks.