Covalent Inhibition of SARS-CoV-2 RBD-ACE2 Interaction by Aptamers with Multiple Sulfur(VI) Fluoride Exchange Modifications

The SARS-CoV-2 spike protein uses its receptor-binding domain (RBD) to interact with the angiotensin-converting enzyme 2 (ACE2) receptor on host cells, establishing the first step of SARS-CoV-2 infection. Inhibitors of RBD-ACE2 interaction, therefore, have shown great promise in preventing SARS-CoV-2 infection. Currently known RBD-ACE2 inhibitors are all based on reversible binding and must compete with ACE2 or RBD at the equilibrium. On the other hand, covalent inhibitors, such as those based on sulfur(VI) fluoride exchange (SuFEx) chemistry, can form irreversible chemical bonds with target proteins and offer advantages including higher potency and longer duration of inhibition. Here, we report covalent aptamer inhibitors that can block RBD-ACE2 by forming covalent bonds with RBD. These covalent aptamer inhibitors were developed by equipping known RBD aptamers with multiple SuFEx (mSuFEx) modifications. The mSuFEx-aptamer 6C3-7SF underwent strong covalent bonding with RBD and some of its variants at fast rates (t1/2 = 20 ~ 29 min−1) and induced more efficient RBD-ACE2 inhibition (IC50 = 26 ~ 37 nM) than the original aptamer (IC50 > 200 nM) according to an in vitro enzyme-linked immunosorbent assay (ELISA). The covalent bond formation was highly selective to RBD over human serum albumin (HSA) and ACE2, and could occur efficiently in diluted human serum. Peptide fragmentation analyses of the RBD-6C3-7SF adducts revealed multiple sites of covalent bonding on RBD, including K378, R408, Y422, Y424, Y453, and K458. The surprising R408 suggests that context-specific non-N-terminal arginine could be a new type of targetable residue by SuFEx-based covalent inhibitors, which were never reported as reactive with any non-N-terminal arginine in target proteins. In addition, RBD R408 is responsible for binding with ACE2 N90 glycan, and this arginine is conserved in SARS-CoV-2 variants of concern or interest, suggesting that R408 could be the potential site of interest for developing SuFEx-based covalent inhibitors against threatening SARS-CoV-2 variants. Although the compatibility of mSuFEx-based covalent aptamers in cellular and in vivo systems should be further investigated, our study demonstrated the promise of mSuFEx chemistry in constructing potent covalent aptamers to inhibit important protein-protein interactions (PPIs).

Characterization of a novel + 70 Da modification in rhGM-CSF expressed in E. coli using chemical assays in combination with mass spectrometry

Granulocyte-macrophage colony-stimulating factor (GM-CSF) is a cytokine and a white blood cell growth factor that has found usage as a therapeutic protein. During analysis of different fermentation batches of GM-CSF recombinantly expressed in E. coli, a covalent modification was identified on the protein by intact mass spectrometry. The modification gave a mass shift of + 70 Da and peptide mapping analysis demonstrated that it located to the protein N-terminus and lysine side chains. The chemical composition of C4H6O was found to be the best candidate by peptide fragmentation using tandem mass spectrometry. The modification likely contains a carbonyl group, since the mass of the modification increased by 2 Da by reduction with borane pyridine complex and it reacted with 2,4-dinitrophenylhydrazine. On the basis of chemical and tandem mass spectrometry fragmentation behavior, the modification could be attributed to crotonaldehyde, a reactive compound formed during lipid peroxidation. A low recorded oxygen pressure in the reactor during protein expression could be linked to the formation of this compound. This study shows the importance of maintaining full control over all reaction parameters during recombinant protein production.

Features of peptide fragmentation spectra in single cell proteomics

The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single cell proteomics specializes in identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single cell spectra, including loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity and distinct fragmentation pattern compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.

Proteome and phospho-proteome profiling for deeper phenotype characterization of colorectal cancer heterogeneity.

e15536 Background: The rise of precision oncology therapeutics requires deep understanding of the molecular mechanisms implicated in cancer biology. Colorectal cancer (CRC) is one of the first solid tumors to be molecularly characterized by defined genes and pathways. Advances in tumor profiling have revealed a profound molecular heterogeneity in CRC leading to the definition of several consensus molecular subtypes (CMS). However, this molecular heterogeneity is still largely defined on the genomic and transcriptomics level. To complement the understanding of genetically defined molecular subgroups, we performed large-scale deep proteomic and phospho-proteomic profiling of CRC patient biopsies and adjacent healthy control tissue, which has enabled to explore the phenotype and obtain more functional insights in cancer biology. Methods: Sample processing from 5-10 mg of tissue per sample was performed using a liquid handling robot. Phospho-peptide enrichment was carried out with a Kingfisher Flex device and MagReSyn Ti-IMAC magnetic beads. Data-Independent Acquisition (DIA) LC-MS/MS was performed on multiple platforms consisting of a Thermo Scientific Q Exactive HF-X mass spectrometer coupled to a Waters M-Class LC. Chromatography was operating at 5 µL/min, and separation was achieved using 45 min (whole proteome) and 60 min (phospho-proteome) gradients. Results: Indivumed has built IndivuType, the world’s first multi-omics database for individualized cancer therapy, analyzing the highest quality cancer biospecimens to generate the most comprehensive dataset, including genomics, transcriptomics, proteomics, and clinical outcome information. Enabled by the DIA technology, a mass spectrometric method developed by Biognosys that obtains peptide fragmentation data in a highly parallelized way with high sensitivity, more than 7,000 proteins in the whole proteome and 20,000 phospho-peptides in the phospho-proteome workflow were profiled across more than 900 resected tissue samples of various CMS of CRC. The resulting proteome and phospho-proteome data were integrated into the IndivuType database and cross-analyzed with genomic and transcriptomic markers. Through this combined analysis, novel insights in clinically relevant signaling pathways in CRC subtypes were revealed. Conclusions: The deep phenotypic profiling of cancer samples, using next generation proteomics and phospho-proteomics, has enabled us to go beyond the genomic level in the characterization of tumor molecular heterogeneity. This multi-omics approach provides a solid foundation to advance the understanding of cancer biology, unravel key molecular events, and support the identification of novel therapeutic targets for precision medicine in CRC.

SMITER—A Python Library for the Simulation of LC-MS/MS Experiments

SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER features a modular design, allowing for an easy implementation of different noise and fragmentation models. By default, SMITER uses an established noise model and offers several methods for peptide fragmentation, and two models for nucleoside fragmentation and one for lipid fragmentation. Due to the rich Python ecosystem, other modules, e.g., for retention time (RT) prediction, can easily be implemented for the tailored simulation of any molecule of choice. This facilitates the generation of defined gold-standard LC-MS/MS datasets for any type of experiment. Such gold standards, where the ground truth is known, are required in computational mass spectrometry to test new algorithms and to improve parameters of existing ones. Similarly, gold-standard datasets can be used to evaluate analytical challenges, e.g., by predicting co-elution and co-fragmentation of molecules. As these challenges hinder the detection or quantification of co-eluents, a comprehensive simulation can identify and thus, prevent such difficulties before performing actual MS experiments. SMITER allows the creation of such datasets easily, fast, and efficiently.