Histones are the building units of nucleosomes, which constitute chromatin. Histone post-translational modifications (PTMs) play an essential role in epigenetic gene regulation. The Plasmodium falciparum genome encodes canonical and variant histones and a collection of conserved enzymes for histone PTMs and chromatin remodeling. Herein, we profiled the P. falciparum histone PTMs during the development of gametocytes, the obligatory stage for parasite transmission. Mass spectrometric analysis of histones extracted from the early, middle, and late stages of gametocytes identified 457 unique histone peptides with 90 PTMs, of which 50% were novel. The gametocyte histone PTMs display distinct patterns from asexual stages, with many new methylation sites in histones H3 and H3.3 (e.g., K14, K18, and K37). Quantitative analyses revealed a high abundance of acetylation in H3 and H4, mono-methylation of H3/H3.3 K37, and ubiquitination of H3BK112, suggesting that these PTMs play critical roles in gametocytes. Gametocyte histones also showed extensive and unique combinations of PTMs. These data indicate that the parasite harbors distinct transcription regulation mechanisms during gametocyte development and lay the foundation for further characterization of epigenetic regulation in the life cycle of the malaria parasite.

Large scale proteomic profiling of cell lines can reveal molecular signatures attributed to variable genotypes or induced perturbations, enabling proteogenomic associations and elucidation of pharmacological mechanisms of action. Although isobaric labeling has increased the throughput of proteomic analysis, the commonly used sample preparation workflows often require time-consuming steps and costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost-effective one-pot reaction workflow in a 96-well plate format (SimPLIT) that minimizes processing steps and demonstrates improved reproducibility compared to alternative approaches. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent cleanup step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. We showcase the applicability of the workflow in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in any proteomics laboratory for medium-to-large scale TMT-based studies for deep profiling of cell lines.

Kinases play important roles in cell signaling, and adenosine monophosphate (AMP) is known to modulate cellular energy homeostasis through AMP-activated protein kinase (AMPK). Here, we explored novel AMP-binding kinases by employing a desthiobiotin-conjugated AMP acyl-phosphate probe to enrich efficiently AMP-binding proteins. Together with a parallel-reaction monitoring-based targeted proteomic approach, we uncovered 195 candidate AMP-binding kinases. We also enriched desthiobiotin-labeled peptides from adenine nucleotide-binding sites of kinases and analyzed them using LC-MS/MS in the multiple-reaction monitoring mode, which resulted in the identification of 44 peptides derived from 43 kinases displaying comparable or better binding affinities toward AMP relative to adenosine triphosphate (ATP). Moreover, our proteomic data revealed a potential involvement of AMP in the MAPK pathway through binding directly to the relevant kinases, especially MEK2 and MEK3. Together, we revealed the AMP-binding capacities of a large number of kinases, and our work built a strong foundation for understanding how AMP functions as a second messenger to modulate cell signaling.

A key to improving vaccine design and vaccination strategy is to understand the mechanism behind the variation of vaccine response with host factors. Glycosylation, a critical modulator of immunity, has no clear role in determining vaccine responses. To gain insight into the association between glycosylation and vaccine-induced antibody levels, we profiled the pre- and postvaccination serum protein glycomes of 160 Caucasian adults receiving the FLUZONE influenza vaccine during the 2019-2020 influenza season using lectin microarray technology. We found that prevaccination levels of Lewis A antigen (Lea) are significantly higher in nonresponders than responders. Glycoproteomic analysis showed that Lea-bearing proteins are enriched in complement activation pathways, suggesting a potential role of glycosylation in tuning the activities of complement proteins, which may be implicated in mounting vaccine responses. In addition, we observed a postvaccination increase in sialyl Lewis X antigen (sLex) and a decrease in high mannose glycans among high responders, which were not observed in nonresponders. These data suggest that the immune system may actively modulate glycosylation as part of its effort to establish effective protection postvaccination.

Virtual screening of protein-protein and protein-peptide interactions is a challenging task that directly impacts the processes of hit identification and hit-to-lead optimization in drug design projects involving peptide-based pharmaceuticals. Although several screening tools designed to predict the binding affinity of protein-protein complexes have been proposed, methods specifically developed to predict protein-peptide binding affinity are comparatively scarce. Frequently, predictors trained to score the affinity of small molecules are used for peptides indistinctively, despite the larger complexity and heterogeneity of interactions rendered by peptide binders. To address this issue, we introduce PPI-Affinity, a tool that leverages support vector machine (SVM) predictors of binding affinity to screen datasets of protein-protein and protein-peptide complexes, as well as to generate and rank mutants of a given structure. The performance of the SVM models was assessed on four benchmark datasets, which include protein-protein and protein-peptide binding affinity data. In addition, we evaluated our model on a set of mutants of EPI-X4, an endogenous peptide inhibitor of the chemokine receptor CXCR4, and on complexes of the serine proteases HTRA1 and HTRA3 with peptides. PPI-Affinity is freely accessible at https://protdcal.zmb.uni-due.de/PPIAffinity.

Metaproteomics has been increasingly utilized for high-throughput characterization of proteins in complex environments and has been demonstrated to provide insights into microbial composition and functional roles. However, significant challenges remain in metaproteomic data analysis, including creation of a sample-specific protein sequence database. A well-matched database is a requirement for successful metaproteomics analysis, and the accuracy and sensitivity of PSM identification algorithms suffer when the database is incomplete or contains extraneous sequences. When matched DNA sequencing data of the sample is unavailable or incomplete, creating the proteome database that accurately represents the organisms in the sample is a challenge. Here, we leverage a de novo peptide sequencing approach to identify the sample composition directly from metaproteomic data. First, we created a deep learning model, Kaiko, to predict the peptide sequences from mass spectrometry data and trained it on 5 million peptide-spectrum matches from 55 phylogenetically diverse bacteria. After training, Kaiko successfully identified organisms from soil isolates and synthetic communities directly from proteomics data. Finally, we created a pipeline for metaproteome database generation using Kaiko. We tested the pipeline on native soils collected in Kansas, showing that the de novo sequencing model can be employed as an alternative and complementary method to construct the sample-specific protein database instead of relying on (un)matched metagenomes. Our pipeline identified all highly abundant taxa from 16S rRNA sequencing of the soil samples and uncovered several additional species which were strongly represented only in proteomic data.

Rapid identification of existing respiratory viruses in biological samples is of utmost importance in strategies to combat pandemics. Inputting MALDI FT-ICR MS (matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry) data output into machine learning algorithms could hold promise in classifying positive samples for SARS-CoV-2. This study aimed to develop a fast and effective methodology to perform saliva-based screening of patients with suspected COVID-19, using the MALDI FT-ICR MS technique with a support vector machine (SVM). In the method optimization, the best sample preparation was obtained with the digestion of saliva in 10 μL of trypsin for 2 h and the MALDI analysis, which presented a satisfactory resolution for the analysis with 1 M. SVM models were created with data from the analysis of 97 samples that were designated as SARS-CoV-2 positives versus 52 negatives, confirmed by RT-PCR tests. SVM1 and SVM2 models showed the best results. The calibration group obtained 100% accuracy, and the test group 95.6% (SVM1) and 86.7% (SVM2). SVM1 selected 780 variables and has a false negative rate (FNR) of 0%, while SVM2 selected only two variables with a FNR of 3%. The proposed methodology suggests a promising tool to aid screening for COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Here we report a novel strategy for the rapid detection of SARS-CoV-2 based on an enrichment approach exploiting the affinity between the virus and cellulose sulfate ester functional groups, hot acid hydrolysis, and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Virus samples were enriched using cellulose sulfate ester microcolumns. Virus peptides were prepared using the hot acid aspartate-selective hydrolysis and characterized by MALDI-TOF MS. Collected spectra were processed with a peptide fingerprint algorithm, and searching parameters were optimized for the detection of SARS-CoV-2. These peptides provide high sequence coverage for nucleocapsid (N protein) and allow confident identification of SARS-CoV-2. Peptide markers contributing to the detection were rigorously identified using bottom-up proteomics. The approach demonstrated in this study holds the potential for developing a rapid assay for COVID-19 diagnosis and detecting virus variants from a variety of sources, such as sewage and nasal swabs.

Fasciola hepatica, the common liver fluke and causative agent of zoonotic fasciolosis, impacts on food security with global economic losses of over $3.2 BN per annum through deterioration of animal health, productivity losses, and livestock death and is also re-emerging as a foodborne human disease. Cathepsin proteases present a major vaccine and diagnostic target of the F. hepatica excretory/secretory (ES) proteome, but utilization in diagnostics of the highly antigenic zymogen stage of these proteins is surprisingly yet to be fully exploited. Following an immuno-proteomic investigation of recombinant and native procathepsins ((r)FhpCL1), including mass spectrometric analyses (DOI: 10.6019/PXD030293), and using counterpart polyclonal antibodies to a recombinant mutant procathepsin L (anti-rFhΔpCL1), we have confirmed recombinant and native cathepsin L zymogens contain conserved, highly antigenic epitopes that are conformationally dependent. Furthermore, using diagnostic platforms, including pilot serum and fecal antigen capture enzyme-linked immunosorbent assay (ELISA) tests, the diagnostic capacities of cathepsin L zymogens were assessed and validated, offering promising efficacy as markers of infection and for monitoring treatment efficacy.