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
Protein-protein interactions (PPIs) govern a wide range of cellular functions. The ability to predict PPI interfaces from protein molecular surfaces is important for understanding protein function and enabling therapeutic discovery. While recent advances in structure-based learning, particularly molecular-surface geometric deep learning frameworks, have demonstrated that protein surfaces encode rich geometric and physicochemical information, such approaches often remain computationally intensive and data-hungry. Alternatively, topological data analysis (TDA) has emerged as a mathematically rigorous framework for extracting robust, multiscale shape information from complex data. In this work, we introduce a scalable TDA framework for extracting information on PPIs directly from localized protein surface patches. Our approach leverages multiscale topological descriptors, evaluated from patch-wise point cloud representations of protein mesh surfaces, combined with supervised machine learning models for interface prediction. On a full dataset of 3,362 proteins, the proposed approach substantially reduced computational cost relative to an established geometric deep learning method, MaSIF-site, decreasing preprocessing time from approximately 27 s/protein to 5-8 s/protein and total training time from approximately 6 h to 1-1.3 h. Importantly, this computational reduction is achieved while maintaining mean test area under the receiver operating characteristic curve (AUC) values of 0.76 and 0.77 for patch radii of 9 Å and 12 Å, respectively, thus approaching the MaSIF-site test AUC of 0.84. Our results suggest that topology offers a scalable and computationally efficient approach for high-throughput extraction of information from complex biomolecular interfaces.
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 topology-guided residue-wise gated fusion module to modulate structure–semantics representations using local topological embeddings.
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 variation.
Shou-Zhi Chen, Zhenchao Tang, Linlin 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 complex structures are available.
Junkai Wang, G. Luo, Yun-Song Yang et al.· Bioinformatics· 0 citations
