LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes
Piazza, I.; Beaton, N.; Bruderer, R.; Knobloch, T.; Barbisan, C.; Siepe, I.; Rinner, O.; de Souza, N.; Picotti, P.; Reiter, L.
Show 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 developing optimized small-molecule compounds. Current unbiased approaches cannot directly pinpoint the interaction surfaces between ligands and protein targets. To address his limitation we have developed a new drug target deconvolution approach based on limited proteolysis coupled with mass spectrometry that works across species including human cells (LiP-Quant). LiP-Quant features an automated data analysis pipeline and peptide-level resolution for the identification of any small-molecule binding sites, Here we demonstrate drug target identification by LiP-Quant across compound classes, including compounds targeting kinases and phosphatases. We demonstrate that LiP-Quant estimates the half maximal effective concentration (EC50) of compound binding sites in whole cell lysates. LiP-Quant identifies targets of both selective and promiscuous drugs and correctly discriminates drug binding to homologous proteins. We finally show that the LiP-Quant technology identifies targets of a novel research compound of biotechnological interest.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Novel substrate prediction for the TAM family of RTKs using phosphoproteomics and structure-based modeling 94%
- A large-scale bioinformatic study of graspimiditides and structural characterization of albusimiditide 94%
- Integrative x-ray structure and molecular modeling for the rationalization of procaspase-8 inhibitor potency and selectivity 93%
Similar papers in this journal
- Gemcitabine and ATR inhibitors synergize to kill PDAC cells by blocking DNA damage response 95%
- Behavioral fingerprints predict insecticide and anthelmintic mode of action 94%
- Proteome-scale amino-acid resolution footprinting of protein-binding sites in the intrinsically disordered regions of the human proteome 94%
Similar papers in this journal
- Armeniaspirols inhibit the AAA+ proteases ClpXP and ClpYQ leading to cell division arrest in Gram-positive bacteria 95%
- An automatic pipeline for the design of irreversible derivatives identifies a potent SARS-CoV-2 Mpro inhibitor. 94%
- Targeting the PI5P4K lipid kinase family in cancer using novel covalent inhibitors 94%
Similar papers in this journal
- Increasing protein stability by inferring substitution effects from high-throughput experiments 92%
- Leveraging a self-cleaving peptide for tailored control in proximity labeling proteomics 92%
- Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.