PXD015446-1

PXD015446 is an original dataset announced via ProteomeXchange.

Title	LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes
Description	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 leads. We have developed a drug target deconvolution approach with an automated data analysis pipeline, based on limited proteolysis coupled with mass spectrometry that works across species including in human cells (LiP-Quant). Here we demonstrate drug target identification by LiP-Quant across compound classes, including with drugs targeting kinases and phosphatases . We demonstrate that LiP-Quant identifies drug binding sites, a unique feature of this approach, and that it can be used to estimate drug EC50s 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.
HostingRepository	PRIDE
AnnounceDate	2020-09-09
AnnouncementXML	Submission_2020-09-09_04:07:17.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Ilaria Piazza
SpeciesList	scientific name: Botryotinia fuckeliana (Noble rot fungus) (Botrytis cinerea); NCBI TaxID: 40559; scientific name: Homo sapiens (Human); NCBI TaxID: 9606; scientific name: Saccharomyces cerevisiae (Baker's yeast); NCBI TaxID: 4932;
ModificationList	monohydroxylated residue; acetylated residue; iodoacetamide derivatized residue
Instrument	Q Exactive

Revision	Datetime	Status	ChangeLog Entry
0	2019-09-13 04:56:16	ID requested
⏵ 1	2020-09-09 04:07:17	announced
2	2022-02-13 16:34:05	announced	2022-02-14: Updated project metadata.
3	2024-10-22 05:12:11	announced	2024-10-22: Updated project metadata.

Piazza I, Beaton N, Bruderer R, Knobloch T, Barbisan C, Chandat L, Sudau A, Siepe I, Rinner O, de Souza N, Picotti P, Reiter L, A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes. Nat Commun, 11(1):4200(2020) [pubmed]

curator keyword: Technical, Biomedical

submitter keyword: Drug identification, limited proteolysis, DIA-MS, LiP-MS

Dr. Lukas Reiter and Prof. Dr. Paola Picotti
contact affiliation	Biognosys AG, Schlieren, Switzerland (Dr. Lukas Reiter, lukas.reiter@biognosys.com) Institute of Molecular System Biology, ETH Zurich, Zurich, Switzerland (Prof. Dr. Paola Picotti, picotti@imsb.biol.ethz.ch)
contact email	lukas.reiter@biognosys.com
lab head
Ilaria Piazza
contact affiliation	ETH Zurich
contact email	ilaria.piazza@bc.biol.ethz.ch
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2020/09/PXD015446

PRIDE project URI

• PRIDE
  1. PXD015446
     1. Label: PRIDE project
     2. Name: LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes