Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.08.17.745240

A Scalable and Robust Workflow for Cost-Effective Post-Translational Modifications Profiling by Chemical Proteomics

Zang, L.·Grandke, J.·Richter, J.·Kielkowski, P.
View original source ↗Export metadata ↓

Abstract

Mass spectrometry-based chemical proteomics is a powerful method to analyze proteins labelled by small molecules to identify protein targets of active compounds and to profile protein post-translational modifications. The throughput and high protein input for chemical proteomics workflows has been often a limiting factor for application of the technology for specialized and difficult to culture cell lines. The high protein input was necessary to gain significant difference of noise to signal ratio in proteomics readout. Here, we describe a general chemical proteomics workflow, which is performed in 96-well plate and necessitate only 25 g of protein input to profile post-translationally modified proteins including abundant O-GlcNAcylated proteins as well as low abundant AMPylated proteins. The workflow integrates advances in Cu(I)-catalyzed azide-alkyne cycloaddition to minimize chemical side-reactivity of the click reaction and data-independent acquisition mode during LC-MS/MS measurement. An iterative optimization of protein clean-up on carboxylate-coated paramagnetic beads led to significant saving of the beads usage and lowers the unspecific protein background that resulted in sensitivity gain.

Explore related subjects

biochemistryPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zang, L., Grandke, J., Richter, J., Kielkowski, P.. 2026-08-21. A Scalable and Robust Workflow for Cost-Effective Post-Translational Modifications Profiling by Chemical Proteomics. https://doi.org/10.64898/2026.08.17.745240

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Deep reinforcement learning-driven discovery of a MsbA-targeted small-molecule antibiotic for the treatment of Acinetobacter baumannii infection

Antibiotics with new mechanisms are highly pursued to address the threat of infections caused by drug-resistant Gram-negative bacteria. Targeting MsbA, a key protein of the lipopolysaccharide biosynthesis pathway, represents a promising strategy to discover new classes of antibiotics. However, currently available MsbA-targeted molecules either lack sufficient potency or have unfavorable properties, necessitating expansion of chemical space. In this study, we chose the most promising cerastecin Cpd 4 as the template, and used two Artificial Intelligence (AI)-based tools, i.e. Link-INVENT and AutoMolDesigner for molecular design, performed chemical derivatization and antibacterial activity evaluation, which led to the discovery of Y-11 (MIC for A. baumannii: 0.5 g/mL). Encouragingly, Y-11 showed equivalent potency to Cpd4 for carbapenem-resistant A. baumannii, and less cytotoxicity and hemolysis as well as lower spontaneous resistance frequency. In vivo efficacy study demonstrated that Y-11 could effectively reduce bacterial loads in the mice infected by A. baumannii. The following mechanism study including molecular dynamics simulation, biochemical assay, and transmission electron microscope (TEM) analysis suggested that Y-11 inhibited the transport of lipooligosaccharide and impaired the formation of outer membrane, probably by competitively binding to the substrate binding site of MsbA and modulating ATPase activity. Taken together, we have discovered a MsbA-targeted small molecule Y-11 via AI-driven drug design, which provides a foundation for future antibiotic development.

biochemistry↗
bioRxiv2026

Dynamic architecture of the Rixosome reveals mechanism of activation and ITS2 processing

Eukaryotic ribosome assembly requires the coordinated processing and extensive remodeling of pre-rRNAs. During late nuclear maturation of the 60S subunit, sequential removal of the internal transcribed spacer 2 (ITS2) is initiated by endonucleolytic cleavage at site C2 by the conserved Las1 nuclease. Las1 acts together with the kinase Grc3 and the Rix1 complex to form the Rixosome, which also functions in transcriptional regulation. However, the assembly of the Rixosome, its recruitment to pre-ribosomes, and its activation for ITS2 cleavage remain unclear. Here, we present cryo-EM structures of the human LAS1 complex, two structures of the isolated Rixosome and nine transition states of Rix1-bound pre-60S particles from Schizosaccharomyces pombe. These structures reveal a dynamic Rixosome architecture in which the heterotetrameric Las1 complex engages one or two copies of the Rix1 complex. Rix1 binding is highly flexible in the human Rixosome but rigid in the yeast complex. The isolated yeast Rixosome remains inactive, but binding to the pre-60S particle triggers a structural rearrangement that allows for substrate engagement and activation of the nuclease. Together, our results define the dynamic architecture of the Rixosome and provide a structural framework for ITS2 processing during nuclear maturation of the eukaryotic 60S ribosomal subunit.

biochemistry↗
bioRxiv2026

SGFP-Grid Split GFP Graphene Grids

Affinity graphene grids provide a promising approach for selective protein capture in cryo-EM. Here, we introduce a split-GFP graphene grid platform(SGFP-G), in which graphene-conjugated GFP 1-10 selectively captures GFP11 tagged proteins from low concentration samples or cell lysates. This platform enables rapid assessment of target protein enrichment and particle distribution before vitrification via fluorescence imaging, while the grid design positions captured proteins away from the graphene surface and air-water interface. We also introduce a unique strategy to minimize nonspecific protein adsorption, thereby improving the selective enrichment of target proteins on this grid. Using GFP11-tagged apoferritin, we demonstrate fluorescence guided protein capture and obtain a 2.58 [A] cryoEM reconstruction, establishing SGFP-G as an affinity grid platform for high resolution structural studies with reduced sample requirements.

biochemistry↗