A hierarchical enzyme function prediction framework based on structural confidence and active-site-aware attention is proposed that outperforms representative sequence-based and structure-based baselines and enforces parent-child consistency among EC labels.
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...
João Sartori, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado· bioRxiv· 0 citations
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.
Sophia J. Pribus, Russ B. Altman, Gowri Nayar· bioRxiv· 0 citations
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 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
The application of AlphaFold provides significant opportunities for accelerating research in alternative proteins by enabling rapid structural characterization, enzyme engineering, and functional optimization of novel protein sources and its potential applications in alternative protein discovery, design, and processin...
Shiammala Periyasamy Natarajan, Bagavathi Lakshmi Ramarajan, Rani Sivakumar et al.· Advances in Protein Chemistr...· 0 citations
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.
Jia Mi, Ya-Wen Liu, Chong Chu et al.· IEEE Transactions on Neural...· 0 citations
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