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 topology-guided residue-wise gated fusion module to modulate structure–semantics representations using local topological embeddings.
Abstract Accurate predictions of DNA-binding residues (DBRs) in protein sequences facilitate decoding molecular-level mechanisms underlying cellular functions that involve protein–DNA interactions. While dozens of these predictors have been released, they target either structured or intrinsically disordered regions (IDRs), and the latter were trained to predict less detailed DNA-binding IDRs rather than DBRs. Given this dichotomy, the structure-trained methods underperform on disordered proteins, and vice versa. Moreover, they suffer from high cross-prediction rates, incorrectly labeling many residues that interact with non-DNA ligands as DBRs. We address these issues by introducing DNAreader, the first predictor specifically designed to predict DBRs in the structured and disordered sequence regions. DNAreader relies on an innovative stacked transformer encoder network that combines batch training and contrastive learning, which substantially boosts predictive performance. Using two low-similarity test datasets, we demonstrate that DNAreader statistically outperforms existing tools, performs well for structured and disordered regions, and produces very few cross-predictions. We also developed the DNAreaderDBIDR module, which accurately predicts DNA-binding IDRs, providing flexibility to identify DBRs within IDRs or to predict entire disordered DNA-binding regions. We release DNAreader as a user-friendly web server at http://biomine.cs.vcu.edu/servers/DNAreader/, with the corresponding source code at https://github.com/jianzhang-xynu/DNAreader.
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.