Dataset Information


LiP-Quant, an automated chemoproteomic approach to identify drug targets in complex proteomes, p3

ABSTRACT: LiP-Quant MS sample preparation for target deconvolution


ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hela Cell

SUBMITTER: Nigel Beaton  

LAB HEAD: Lukas Reiter

PROVIDER: PXD019902 | Pride | 2020-09-09


Dataset's files

Action DRS
E_D180308_S480-Exp18-RapaDoseResp-01_MHRM_R01_T0.raw Raw
Fig 1D.xls Xls
Fig 2C.xls Xls
Fig 2D.xls Xls
Fig 4C, S9A.xls Xls
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A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Piazza Ilaria I   Beaton Nigel N   Bruderer Roland R   Knobloch Thomas T   Barbisan Crystel C   Chandat Lucie L   Sudau Alexander A   Siepe Isabella I   Rinner Oliver O   de Souza Natalie N   Picotti Paola P   Reiter Lukas L  

Nature communications 20200821 1

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  ...[more]

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