ArticlePLoS computational biology2023
Structure-based prediction of nucleic acid binding residues by merging deep learning- and template-based approaches.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- Protein-nucleic acid binding site prediction using interpretable Kolmogorov-Arnold networks with hypergraph representation learning.Bioinformatics (Oxford, England) · 2026Article
- MegSite: an accurate nucleic acid-binding residue prediction method based on multimodal protein language model.Briefings in bioinformatics · 2025Article
- INAB: identify nucleic acid binding domain via cross-modal protein language models and multiscale computation.Briefings in bioinformatics · 2025Article
- TransBind allows precise detection of DNA-binding proteins and residues using language models and deep learning.Communications biology · 2025Article
- Advances in Language-Model-Informed Protein-Nucleic Acid Binding Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- bindNode24: Competitive binding residue prediction with 60 % smaller model.Computational and structural biotechnology journal · 2025Article
- Improved prediction of post-translational modification crosstalk within proteins using DeepPCT.Bioinformatics (Oxford, England) · 2024Article
- A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.International journal of molecular sciences · 2024Article
- EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion.Briefings in bioinformatics · 2024Article
- ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction.Briefings in bioinformatics · 2024Article
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3 authors.
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Abstract
Accurate prediction of nucleic binding residues is essential for the understanding of transcription and translation processes. Integration of feature- and template-based strategies could improve the prediction of these key residues in proteins. Nevertheless, traditional hybrid algorithms have been surpassed by recently developed deep learning-based methods, and the possibility of integrating deep learning- and template-based approaches to improve performance remains to be explored. To address these issues, we developed a novel structure-based integrative algorithm called NABind that can accurately predict DNA- and RNA-binding residues. A deep learning module was built based on the diversified sequence and structural descriptors and edge aggregated graph attention networks, while a template module was constructed by transforming the alignments between the query and its multiple templates into features for supervised learning. Furthermore, the stacking strategy was adopted to integrate the above two modules for improving prediction performance. Finally, a post-processing module dependent on the random walk algorithm was proposed to further correct the integrative predictions. Extensive evaluations indicated that our approach could not only achieve excellent performance on both native and predicted structures but also outperformed existing hybrid algorithms and recent deep learning methods. The NABind server is available at http://liulab.hzau.edu.cn/NABind/.
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