TL;DR
RGTBind, a graph transformer that combines multi-scale radial basis function distance encoding with a learnable threshold-gating mechanism to model spatially informative residue interactions, achieved the best F1, AUC, and MCC among the compared methods.
DeepPNI is a deep learning regression model that integrates sequence- and structure-based features to estimate mutation-induced changes in binding free energy in protein–nucleic acid complexes, developed using a comprehensive dataset of 1754 mutations spanning protein–DNA and protein–RNA complexes.
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.
An innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions and gives a better understanding of the structural processes that control PPI.
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...
This work introduces DNAreader, the first predictor specifically designed to predict DBRs in the structured and disordered sequence regions, and develops the DNAreaderDBIDR module, which accurately predicts DNA-binding IDRs, providing flexibility to identify DBRs within IDRs or to predict entire disordered DNA-binding...
This work proposes ARF-GNN, an adaptive receptive field graph neural network tailored for protein function prediction, which dynamically models structural context via hierarchical multi-hop neighborhood aggregation and introduces a dual-branch meta-learning framework.
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