ArticleeLife2024
Genome-scale annotation of protein binding sites via language model and geometric deep learning.
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Advances in the Application of Protein Language Modeling for Nucleic Acid Protein Binding Site Prediction.Genes · 2024Pooled it
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- TripleBind: a generalizable deep learning framework for protein-nucleic acid and protein-ligand binding sites prediction based on pre-trained protein language models.Molecular diversity · 2026Article
- An interpretable framework applying protein words to predict protein-small molecule complementary pairing rules.Chemical science · 2026Article
- Surf2Spot: a surface-informed geometry-aware model for predicting partner-independent binder and nanobody design hotspots.Briefings in bioinformatics · 2026Article
- Article
- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- AI-enabled protein design facilitates future plant research and crop breeding.Plant physiology · 2026Review
- Review
- Mutational analyses reveal PLP-independent functions at PipY, the cyanobacterial paradigm for pyridoxal-phosphate binding proteins.Scientific reports · 2026Article
- DGSite: A Well-Calibrated Deep Learning Framework for Protein-RNA Binding Site Prediction.ACS omega · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- MPBind: a multitask protein binding site predictor using protein language models and equivariant GNNs.Bioinformatics (Oxford, England) · 2025Article
- Plant mobile domain protein-DNA motif modules counteract Polycomb silencing to stabilize gene expression.Nature plants · 2025Article
- Predicting nucleic acid binding sites by attention map-guided graph convolutional network with protein language embeddings and physicochemical information.Briefings in bioinformatics · 2025Article
- LABind: identifying protein binding ligand-aware sites via learning interactions between ligand and protein.Nature communications · 2025Article
- Gated-GPS: enhancing protein-protein interaction site prediction with scalable learning and imbalance-aware optimization.Briefings in bioinformatics · 2025Article
- An Online Server for Geometry-Aware Protein Function Annotations Through Predicted Structure.Methods in molecular biology (Clifton, N.J.) · 2025Article
- A Survey of Deep Learning Methods and Tools for Protein Binding Site Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.