ArticleBriefings in bioinformatics2025
INAB: identify nucleic acid binding domain via cross-modal protein language models and multiscale computation.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Heuristic multi-site optimization for protein sequence design using Masked Protein Language Models.PLoS computational biology · 2026Article
- PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.PLoS computational biology · 2026Article
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Authors and funding
4 authors.
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Abstract
Protein-nucleic acid interactions play a crucial role in biological processes, including gene regulation and editing. Accurately identifying nucleic acid-binding domains in proteins is essential to unravel these interactions, yet traditional experimental methods like X-ray crystallography remain costly and time-intensive. Computational approaches have thus emerged as indispensable tools to complement wet-lab techniques. Here, we introduce a framework for nucleic acid-binding domain prediction by integrating cross-modal protein language models with a multiscale computational architecture. The proposed method leverages a structurally annotated benchmark dataset, which quantifies binding likelihood through hierarchical, proximity-based labels derived from experimental complexes. Evaluations demonstrate that the approach achieves state-of-the-art performance, providing a new insight into the design of multimodal learning systems in protein-nucleic acid interaction analysis and an open resource to accelerate discoveries in functional genomics and drug design.
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Registered trials
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