ArticleNature communications2020
A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 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.
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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
80 citing papers in PubMed, 1 synthesis or guideline pooled it, 174 citations in OpenAlex.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
- Peptide-centric local stability assay (PELSA) for sensitive identification of ligand-targeting proteins and binding sites at proteome scale.Nature protocols · 2026Review
- Paradoxical non-catalytic kinase functions are driven by inhibitor-induced displacement of autoinhibitory domains.Molecular systems biology · 2026Article
- Metabolic reprogramming drives pancreatic β cell neogenesis from α cells.Nature chemical biology · 2026Article
- DT-TRAP enables accurate target recognition via simplified dosing and low-temperature incubation.Journal of pharmaceutical analysis · 2026Article
- Recent advances in proteomic strategies for target identification of traditional Chinese medicine.Journal of pharmaceutical analysis · 2026Review
- Global protein-ligand binding affinity profiling via photocatalytic labeling.Nature communications · 2026Article
- Review
- Fructose-1,6-bisphosphate couples glycolytic activity to cell adhesion.Nature cell biology · 2026Article
- 3D Proteomics: Structural, Functional, Chemical and Biomarker Discovery Proteomics With LiP-MS.Molecular & cellular proteomics : MCP · 2026Review
- Label-Free Target Discovery Strategy for Natural Active Products.Biomolecules · 2026Review
- Above-Filter Digestion Proteomics Reveals Drug Targets and Localizes Ligand Binding Site.Journal of proteome research · 2026Article
- Limited proteolysis-coupled mass spectrometry captures proteome-wide protein structural alterations and biomolecular condensation in living cells.Molecular systems biology · 2026Article
- Mechanistic Insights into Active Components of Rosa Roxburghii Juice Against Fluoride-Induced Osteoarthritis.Antioxidants (Basel, Switzerland) · 2026Article
- Advancing DIA-Based Limited Proteolysis Workflows: Introducing DIA-LiPA.Analytical chemistry · 2026Article
- MGF-DTA: A Multi-Granularity Fusion Model for Drug-Target Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- Article
- Mass Spectrometry Proteomics: A Key to Faster Drug Discovery.Journal of medicinal chemistry · 2026Review
- Decoding protein dynamics with limited proteolysis coupled to mass spectrometry: A comprehensive review.Journal of pharmaceutical analysis · 2025Review
- A human metabolic map of pharmacological perturbations reveals drug modes of action.Nature biotechnology · 2025Article
20 more citing papers are in PubMed but not listed here.
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 at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chemoproteomics is a key technology to characterize the mode of action of drugs, as it directly identifies the protein targets of bioactive compounds and aids in the development of optimized small-molecule compounds. Current approaches cannot identify the protein targets of a compound and also detect the interaction surfaces between ligands and protein targets without prior labeling or modification. To address this limitation, we here develop LiP-Quant, a drug target deconvolution pipeline based on limited proteolysis coupled with mass spectrometry that works across species, including in human cells. We use machine learning to discern features indicative of drug binding and integrate them into a single score to identify protein targets of small molecules and approximate their binding sites. We demonstrate drug target identification across compound classes, including drugs targeting kinases, phosphatases and membrane proteins. LiP-Quant estimates the half maximal effective concentration of compound binding sites in whole cell lysates, correctly discriminating drug binding to homologous proteins and identifying the so far unknown targets of a fungicide research compound.
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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.