Aug 2026· Interdisciplinary Sciences Computational Life Sciences· 0 citations· 31 references
Medicine
TL;DR
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.
Oras A. Hussein, E. Al-Shamery· Journal of Intelligent Infor...· 0 citations
This work introduces a scalable TDA framework for extracting information on PPIs directly from localized protein surface patches that leverages multiscale topological descriptors, evaluated from patch-wise point cloud representations of protein mesh surfaces, combined with supervised machine learning models for interfa...
Angan Mukherjee, ByungUk Park, Adam Malmstrom et al.· bioRxiv· 0 citations
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...
Bin Lu, Fujun Xiang, Hai-Long Wang et al.· Applied Sciences· 0 citations
By combining protein language model embeddings with topology-adaptive geometric reasoning, DiConSite offers a reusable framework for residue-level protein interaction analysis and achieves consistently strong and often best-performing results, while improving robustness to structural uncertainty and cross-modal variati...
Shou-Zhi Chen, Zhen-Chao Tang, Lin-Lin You et al.· IEEE Transactions on Pattern...· 1 citation
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.
Hua Qian, Deng Pan, Liangpeng Nie et al.· Journal of Computational Bio...· 0 citations
The results demonstrate the effectiveness of integrating multi-scale and multi-modal representations with cross-scale alignment for protein–RNA affinity prediction, and suggest that M2-PRNet can highlight relevant RNA-binding regions and support preliminary discrimination between strong and weak binders when plausible...
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
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