A streamlined platform for analyzing tera-scale DDA and DIA mass spectrometry data enables highly sensitive immunopeptidomics.

Integrating data-dependent acquisition (DDA) and data-independent acquisition (DIA) approaches can enable highly sensitive mass spectrometry, especially for imunnopeptidomics applications. Here we report a streamlined platform for both DDA and DIA data analysis. The platform integrates deep learning-based solutions of spectral library search, database search, and de novo sequencing under a unified framework, which not only boosts the sensitivity but also accurately controls the specificity of peptide identification. Our platform identifies 5-30% more peptide precursors than other state-of-the-art systems on multiple benchmark datasets. When evaluated on immunopeptidomics datasets, we identify 1.7-4.1 and 1.4-2.2 times more peptides from DDA and DIA data, respectively, than previously reported results. We also discover six T-cell epitopes from SARS-CoV-2 immunopeptidome that might represent potential targets for COVID-19 vaccine development. The platform supports data formats from all major instruments and is implemented with the distributed high-performance computing technology, allowing analysis of tera-scale datasets of thousands of samples for clinical applications.

[Human placenta hydrolysates: from V.P. Filatov to the present day: Review].

Works of V.P. Filatov and his school laid the foundation for the study and clinical use of human placenta hydrolysates (HPH). To date, the PubMed database contains more than 5,000 publications on basic and clinical research on HPH. Studies of the peptide composition of HPH, carried out using the methods of modern proteomics, have made it possible to propose a complex of molecular mechanisms of the action of HPH in various pathologies. The article discusses the effects of HPH on the treatment of liver diseases, atopic dermatitis, viral infections (herpes, COVID-19, viral hepatitis), iron overload and chronic fatigue syndrome. Stimulation of HPH regenerative capabilities of the body is important for accelerating and improving the quality of wound healing, treatment of diseases of the joints and the reproductive system.

A robust high-throughput fluorescent polarization assay for the evaluation and screening of SARS-CoV-2 fusion inhibitors.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a serious threat to global health. One attractive antiviral target is the membrane fusion mechanism employed by the virus to gain access to the host cell. Here we report a robust protein-based fluorescent polarization assay, that mimicking the formation of the six-helix bundle (6-HB) process during the membrane fusion, for the evaluation and screening of SARS-CoV-2 fusion Inhibitors. The IC50 of known inhibitors, HR2P, EK1, and Salvianolic acid C (Sal-C) were measured to be 6.1 nM, 2.5 nM, and 8.9 µM respectively. In addition, we found Sal-A has a slightly lower IC50 (3.9 µM) than Sal-C. Interestingly, simple caffeic acid can also disrupt the formation of 6-HB with a sub-mM concentration. Pilot high throughput screening (HTS) of a small marine natural product library validates the assay with a Z' factor close to 0.8. We envision the current assay provides a convenient way to screen SARS-CoV-2 fusion inhibitors and assess their binding affinity.

Application of spectral library prediction for parallel reaction monitoring of viral peptides.

A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here, we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Viral peptides were chosen as an example, because virology is an area where in silico library generation could significantly improve PRM assay design. With both libraries a total of 1174 precursors were identified. Notably, compared to the DDA-derived library, we could identify 101 more precursors by using the Prosit-derived library. Additionally, we show that Prosit can be applied to predict tandem mass spectra of synthetic viral peptides with different collision energies. Finally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab demonstrating that both libraries are suited for peptide identification by PRM. Summarized, Prosit-derived viral spectral libraries predicted in silico can be used for PRM data analysis, making DDA analysis for library generation partially redundant in the future.

Generation of SARS-CoV-2 S1 Spike Glycoprotein Putative Antigenic Epitopes in Vitro by Intracellular Aminopeptidases.

Presentation of antigenic peptides by MHCI is central to cellular immune responses against viral pathogens. While adaptive immune responses versus SARS-CoV-2 can be of critical importance to both recovery and vaccine efficacy, how protein antigens from this pathogen are processed to generate antigenic peptides is largely unknown. Here, we analyzed the proteolytic processing of overlapping precursor peptides spanning the entire sequence of the S1 spike glycoprotein of SARS-CoV-2, by three key enzymes that generate antigenic peptides, aminopeptidases ERAP1, ERAP2, and IRAP. All enzymes generated shorter peptides with sequences suitable for binding onto HLA alleles, but with distinct specificity fingerprints. ERAP1 was the most efficient in generating peptides 8-11 residues long, the optimal length for HLA binding, while IRAP was the least efficient. The combination of ERAP1 with ERAP2 greatly limited the variability of peptide sequences produced. Less than 7% of computationally predicted epitopes were found to be produced experimentally, suggesting that aminopeptidase processing may constitute a significant filter to epitope presentation. These experimentally generated putative epitopes could be prioritized for SARS-CoV-2 immunogenicity studies and vaccine design. We furthermore propose that this in vitro trimming approach could constitute a general filtering method to enhance the prediction robustness for viral antigenic epitopes.

Role of Peptides in Diagnostics.

The specificity of a diagnostic assay depends upon the purity of the biomolecules used as a probe. To get specific and accurate information of a disease, the use of synthetic peptides in diagnostics have increased in the last few decades, because of their high purity profile and ability to get modified chemically. The discovered peptide probes are used either in imaging diagnostics or in non-imaging diagnostics. In non-imaging diagnostics, techniques such as Enzyme-Linked Immunosorbent Assay (ELISA), lateral flow devices (i.e., point-of-care testing), or microarray or LC-MS/MS are used for direct analysis of biofluids. Among all, peptide-based ELISA is considered to be the most preferred technology platform. Similarly, peptides can also be used as probes for imaging techniques, such as single-photon emission computed tomography (SPECT) and positron emission tomography (PET). The role of radiolabeled peptides, such as somatostatin receptors, interleukin 2 receptor, prostate specific membrane antigen, αß3 integrin receptor, gastrin-releasing peptide, chemokine receptor 4, and urokinase-type plasminogen receptor, are well established tools for targeted molecular imaging ortumor receptor imaging. Low molecular weight peptides allow a rapid clearance from the blood and result in favorable target-to-non-target ratios. It also displays a good tissue penetration and non-immunogenicity. The only drawback of using peptides is their potential low metabolic stability. In this review article, we have discussed and evaluated the role of peptides in imaging and non-imaging diagnostics. The most popular non-imaging and imaging diagnostic platforms are discussed, categorized, and ranked, as per their scientific contribution on PUBMED. Moreover, the applicability of peptide-based diagnostics in deadly diseases, mainly COVID-19 and cancer, is also discussed in detail.

Ultra-fast proteomics with Scanning SWATH.

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

Peptide Correlation Analysis (PeCorA) Reveals Differential Proteoform Regulation.

Shotgun proteomics techniques infer the presence and quantity of proteins using peptide proxies produced by cleavage of the proteome with a protease. Most protein quantitation strategies assume that multiple peptides derived from a protein will behave quantitatively similar across treatment groups, but this assumption may be false due to (1) heterogeneous proteoforms and (2) technical artifacts. Here we describe a strategy called peptide correlation analysis (PeCorA) that detects quantitative disagreements between peptides mapped to the same protein. PeCorA fits linear models to assess whether a peptide's change across treatment groups differs from all other peptides assigned to the same protein. PeCorA revealed that ∼15% of proteins in a mouse microglia stress data set contain at least one discordant peptide. Inspection of the discordant peptides shows the utility of PeCorA for the direct and indirect detection of regulated post-translational modifications (PTMs) and also for the discovery of poorly quantified peptides. The exclusion of poorly quantified peptides before protein quantity summarization decreased false-positives in a benchmark data set. Finally, PeCorA suggests that the inactive isoform of prothrombin, a coagulation cascade protease, is more abundant in plasma from COVID-19 patients relative to non-COVID-19 controls. PeCorA is freely available as an R package that works with arbitrary tables of quantified peptides.

Shortlisting SARS-CoV-2 Peptides for Targeted Studies from Experimental Data-Dependent Acquisition Tandem Mass Spectrometry Data.

Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a crucial tool for fighting the COVID-19 pandemic. This dataset brief presents the exploration of a shotgun proteomics dataset acquired on SARS-CoV-2 infected Vero cells. Proteins from inactivated virus samples were extracted, digested with trypsin, and the resulting peptides were identified by data-dependent acquisition tandem mass spectrometry. The 101 peptides reporting for six viral proteins were specifically analyzed in terms of their analytical characteristics, species specificity and conservation, and their proneness to structural modifications. Based on these results, a shortlist of 14 peptides from the N, S, and M main structural proteins that could be used for targeted mass-spectrometry method development and diagnostic of the new SARS-CoV-2 is proposed and the best candidates are commented.