Dataset Information

Enantioselective OTUD7B fragment discovery through chemoproteomics screening and high-throughput optimisation

ABSTRACT: Here we present a chemoproteomics fragment screening platform for identifying novel DUB-specific hit matter, that combines activity-based protein profiling with high-throughput chemistry direct-to-biology optimisation to enable rapid elaboration of initial fragment hits against OTU DUBs. Applying these approaches, we identify an enantioselective tool compound for OTUD7B, and validate it using chemoproteomics and biochemical DUB activity assays.

INSTRUMENT(S): timsTOF Pro 2

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Aini Vuorinen

LAB HEAD: Katrin Rittinger

PROVIDER: PXD054883 | Pride | 2025-01-13

REPOSITORIES: pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20K_Human_Proteome_1_seq_per_gene.fasta	Fasta
	DC2513_ELN56378_H00AL9K_P4_A02_S1-A2_1_3880.d.zip	Other
	DC2513_ELN56378_H00AL9K_P4_A03_S1-A3_1_3896.d.zip	Other
	DC2513_ELN56378_H00AL9K_P4_A04_S1-A4_1_3814.d.zip	Other
	DC2513_ELN56378_H00AL9K_P4_A05_S1-A5_1_3842.d.zip	Other

Items per page:

1 - 5 of 1033

Similar Datasets

Project description:Although current mass spectrometry (MS)-based proteomics identifies and quantifies thousands of proteins and (modified) peptides, only a minority of them are subjected to in-depth downstream analysis. With the advent of automated processing workflows, biologically or clinically important results within a study are rarely validated by visualization of the underlying raw information. Although several tools for this are in principle available, they are often not integrated into the overall analysis nor readily extendable with new approaches. To remedy this, we developed AlphaViz, an open-source Python package to superimpose output from common analysis workflows on the raw data for quick and easy visualization and validation of protein and peptide identifications. AlphaViz takes advantage of recent breakthroughs in the deep learning-assisted prediction of experimental peptide properties to allow manual assessment of the expected and measured peptide result deviation. We focused on the visualization of the 4-dimensional data cuboid provided by Bruker TimsTOF instruments, where the ion mobility dimension, besides intensity and retention time, can be predicted and used for verification. We illustrate how AlphaViz can quickly validate or invalidate peptide identifications regardless of the score given to them by automated workflows. Furthermore, we provide a â€˜predict modeâ€™ that can locate peptides present in the raw data but not reported by the search engine. This is illustrated with dilution series and the recovery of missing values from experimental replicates. Applied to phosphoproteomics of the EGF-signaling pathway, we show how key signaling nodes can be validated to enhance confidence for downstream interpretation or follow-up experiments. AlphaViz follows accepted standards for open-source software development, including extensive documentation, testing and continuous integration. It features an easy-to-install graphical user interface for end-users and a modular Python package for bioinformaticians. We hope that AlphaViz can help to make validation of critical proteomics results a standard feature in MS-based proteomics.

Dataset Information

Enantioselective OTUD7B fragment discovery through chemoproteomics screening and high-throughput optimisation

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets