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

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Hela Cell

SUBMITTER: Nigel Beaton

LAB HEAD: Lukas Reiter

PROVIDER: PXD019902 | Pride | 2020-09-09

REPOSITORIES: Pride

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Publications

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]

PMID: 32826910

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Project description:We used all five current human African trypanosomiasis drugs for genome-scale RNA interference (RNAi) target sequencing (RIT-seq) screens in Trypanosoma brucei, revealing the transporters, organelles, enzymes and metabolic pathways that function to facilitate anti-trypanosomal drug action. RIT-seq profiling links more than fifty additional genes to drug action. This data has been described in the following article [doi:10.1038/nature10771] and its further analysis can be freely submitted for publication. For information on the proper use of data shared by the Wellcome Trust Sanger Institute (including information on acknowledgement), please see http://www.sanger.ac.uk/datasharing/. Protocol - Assembly of the bloodstream-form T. brucei RNAi library and the RNAi target sequencing (RIT-seq) approach were reported previously (Alsford, S. et al. High-throughput phenotyping using parallel sequencing of RNA interference targets in the African trypanosome. Genome Res 21, 915-924, 264 doi:gr.115089.110 [pii] 265 10.1101/gr.115089.110 (2011). Briefly, a tetracycline-inducible RNAi plasmid library, containing randomly sheared genomic fragments (mean ~600 bp) under the control of head-to-head T7 promoters, was targeted to a single genomic locus that had been validated for robust expression. For this study, the library was grown under inducing conditions with drug selection and genomic DNA was isolated from surviving populations. For chemical RIT-seq profiling, adapter-ligated sequencing libraries were prepared from each genomic DNA sample and used to amplify DNA fragments containing RNAi cassette-insert junctions in semi-specific PCR reactions; one primer was specific for the RNAi vector and the other for the Illumina adapter. Size-selected DNA was sequenced with 76 cycle paired-end runs on an Illumina GAII. Illumina sequencing reads containing a nine-base RNAi vector junction sequence were then mapped to the T. brucei reference genome. Where loss of function increases drug tolerance, RNAi-target sequence representation is increased relative to the otherwise susceptible population, revealing hot spots.

Dataset Information

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

Publications

A machine learning-based chemoproteomic approach to identify drug targets and binding sites in complex proteomes.

Similar Datasets

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