ArticleResearch (Washington, D.C.)2022
Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network.
Article in Research (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Accurate Identification of Protein Binding Sites for All Drug Modalities Using ALLSites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computational approaches to druggable site identification: Current status and future perspective.Acta pharmaceutica Sinica. B · 2026Review
- Targeting the Reactive Proteome: Recent Advances in Activity-Based Protein Profiling and Probe Design.Biomolecules · 2025Review
- Article
- Illuminating the Druggable Human Proteome with an AI Protein Profiling Platform.Research square · 2025Article
- Illuminating the Druggable Human Proteome with an AI Protein Profiling Platform.bioRxiv : the preprint server for biology · 2025Article
- Structure-based rational design of covalent probes.Communications chemistry · 2025Review
- Application of Spatial Omics in the Cardiovascular System.Research (Washington, D.C.) · 2025Review
- Machine Learning Models to Interrogate Proteome-Wide Covalent Ligandabilities Directed at Cysteines.JACS Au · 2024Article
- Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines.bioRxiv : the preprint server for biology · 2024Article
- Exploring Protein Bioconjugation: A Redox-Based Strategy for Tryptophan Targeting.Research (Washington, D.C.) · 2024Article
- A flexible data-free framework for structure-basedChemical science · 2023Article
- A Transformer-Based Ensemble Framework for the Prediction of Protein-Protein Interaction Sites.Research (Washington, D.C.) · 2023Article
- AP-2δ Is the Most Relevant Target of AP-2 Family-Focused Cancer Therapy and Affects Genome Organization.Cells · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Covalent ligands have attracted increasing attention due to their unique advantages, such as long residence time, high selectivity, and strong binding affinity. They also show promise for targets where previous efforts to identify noncovalent small molecule inhibitors have failed. However, our limited knowledge of covalent binding sites has hindered the discovery of novel ligands. Therefore, developing in silico methods to identify covalent binding sites is highly desirable. Here, we propose DeepCoSI, the first structure-based deep graph learning model to identify ligandable covalent sites in the protein. By integrating the characterization of the binding pocket and the interactions between each cysteine and the surrounding environment, DeepCoSI achieves state-of-the-art predictive performances. The validation on two external test sets which mimic the real application scenarios shows that DeepCoSI has strong ability to distinguish ligandable sites from the others. Finally, we profiled the entire set of protein structures in the RCSB Protein Data Bank (PDB) with DeepCoSI to evaluate the ligandability of each cysteine for covalent ligand design, and made the predicted data publicly available on website.
Identifiers
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.