Protein carbonylation, a marker of excessive oxidative stress, has been studied in the context of multiple human diseases related to oxidative stress. The variety of post-translational carbonyl modifications (carbonyl PTMs) and their low concentrations in plasma challenge their reproducible identification and quantitation. However, carbonyl-specific biotinylated derivatization tags (e.g., aldehyde reactive probe, ARP) allow for targeting carbonyl PTMs by enriching proteins and peptides carrying these modifications. In this study, an oxidized human serum albumin protein model (OxHSA) and plasma from a healthy donor were derivatized with ARP, digested with trypsin, and enriched using biotin-avidin affinity chromatography prior to nano reversed-phase chromatography coupled online to electrospray ionization tandem mass spectrometry with travelling wave ion mobility spectrometry (nRPC-ESI-MS/MS-TWIMS). The presented workflow addresses several analytical challenges by using ARP-specific fragment ions to reliably identify ARP peptides. Furthermore, the reproducible recovery and relative quantitation of ARP peptides were validated. Human serum albumin (HSA) in plasma was heavily modified by a variety of direct amino acid oxidation products and adducts from reactive carbonyl species (RCS), with most RCS modifications being detected in six hotspots, i.e., Lys10, Lys190, Lys199, Lys281, Lys432, and Lys525 of mature HSA.

Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can infect human cells by first attaching to the ACE-2 receptor via its receptor-binding domain (RBD) in the spike protein. Here, we report the influence of N-glycosylation sites of the RBD and the membrane (M) protein on IgG antibody binding in serum samples from patients infected with the original SARS-CoV-2 strain in Germany. The RBDs of the wildtype, alpha, beta, gamma, and kappa variants expressed in HEK293S GnTI− cells were all N-glycosylated at Asn331, Asn334, Asn343, and Asn360 or Asn370, whereas the M-protein was glycosylated at Asn5. An ELISA using a coated RBD and probed with anti-RBD IgG antibodies gave a sensitivity of 96.3% and a specificity of 100% for the wildtype RBD, while the sensitivity decreased by 5% to 10% for the variants of concern, essentially in the order of appearance. Deglycosylation of the wildtype RBD strongly reduced antibody recognition by ~20%, considering the mean of the absorbances recorded for the ELISA. This effect was even stronger for the unglycosylated RBD expressed in Escherichia coli, suggesting structural changes affecting epitope recognition. Interestingly, the N-glycosylated M-protein expressed in HEK293S GnTI− cells gave good sensitivity (95%), which also decreased to 65% after deglycosylation, and selectivity (100%). In conclusion, N-glycosylation of the M-protein, the RBD, and most likely the spike protein are important for proper antibody binding and immunological assays, whereas the type of N-glycosylation is less relevant.

Excessive oxidative stress, associated with various diseases, can induce protein carbonylation-nonenzymatic modifications involving aldehyde or keto group formation. These modifications are structurally diverse and low in abundance, which complicates their detection and quantitation. Here, we developed a strategy to identify and quantify protein carbonylation in human serum proteins from 39 rheumatoid arthritis patients and 29 healthy donors. Reactive carbonyl groups were derivatized with an aldehyde reactive probe (ARP), digested with trypsin, enriched via avidin affinity chromatography, and analyzed using RP-HPLC-ESI-IMS-MS/MS. Ion mobility spectrometry (IMS) was applied in both data-dependent (DDA) and data-independent acquisition (DIA) modes. DDA generated spectral libraries of ARP-derivatized peptides (ARP-peptides), which enabled peptide-centric detection in DIA data. We manually confirmed 86 ARP-peptides, with 93.8% of peak areas showing signal-to-background ratios >3. Among the 32 unique carbonylation sites, 28 were on human serum albumin, with hotspots at Cys58, Lys214, Lys219, Lys223, Lys456, Lys543, Lys549, and Lys565. Six previously unreported species were identified using IMS, DIA, ARP-reporter ions, and de novo sequencing. The ARP-peptides were quantified with ≥ 75% intrabatch reproducibility (coefficient of variation <20%). Similar modification levels were observed in both groups, suggesting basal, disease-independent carbonylation in abundant serum proteins.

Given the significant role of glycosylation in modulating protein structure and activity, glycoproteomics is gaining increased interest from the broad scientific community. Large-scale site-resolved N-glycoproteomics relies on automated MS2-based searches, but candidate glycopeptide assignments can remain ambiguous when isomeric or isobaric glycan structures, adducts, chemical modifications, in-source fragments, or incomplete MS2 evidence support more than one plausible interpretation. Here, we systematically categorize common challenges and misassignments in glycoproteomics and present a post-search validation workflow using Skyline software to identify and correct these. The workflow matches search-engine-derived candidate assignments to LC–MS/MS evidence for correct precursor monoisotope assignment, retention time behavior, and glycosite context. The workflow is demonstrated with Byonic-derived glycopeptide candidate lists and converts automated search results into curated, verifiable, site-resolved N-glycopeptide features for downstream quantification and reporting. We applied the workflow to data from 52 human serum samples, and reviewed 3,071 candidate N-glycopeptide IDs. From these, 1,722 MS2 candidate IDs were refuted as inconsistent with chromatographic and/or precursor-level evidence. Curation added 320 glycopeptide features, comprising 152 MS1-supported composition-level assignments and 168 additional LC-resolved isomer features, yielding a final curated feature set of 1,436 N-glycopeptides across the serum N-glycoproteome. Together, these results show that reviewing the raw LC-MS/MS data associated with search-engine results improves both the accuracy and comprehensiveness of detectable N-glycopeptides, supporting more transparent and reliable reporting. The curated dataset provides a resource for future method development, benchmarking and machine–learning efforts directed at automated glycopeptide validation.