Major histocompatibility complex class I (MHC I) plays a crucial role in the development of adaptive immune response in vertebrates. MHC molecules are cell surface protein complexes loaded with short peptides and recognized by the T-cell receptors (TCR). Peptides associated with MHC are named immunopeptidome. The MHC I immunopeptidome is produced by the proteasome degradation of intracellular proteins. The knowledge of the immunopeptidome repertoire facilitates the creation of personalized antitumor or antiviral vaccines. A huge number of publications on the immunopeptidome diversity of different human and mouse biological samples—plasma, peripheral blood mononuclear cells (PBMCs), and solid tissues, including tumors—appeared in the scientific journals in the last decade. Significant immunopeptidome identification efficiency was achieved by advances in technology: the immunoprecipitation of MHC and mass spectrometry-based approaches. Researchers optimized common strategies to isolate MHC-associated peptides for individual tasks. They published many protocols with differences in the amount and type of biological sample, amount of antibodies, type and amount of insoluble support, methods of post-fractionation and purification, and approaches to LC-MS/MS identification of immunopeptidome. These parameters have a large impact on the final repertoire of isolated immunopeptidome. In this review, we summarize and compare immunopeptidome isolation techniques with an emphasis on the results obtained.
The experimental data obtained by Simats A. et al. (Molecular and Cellular Proteomics, 2020, 19(12), 1921-1936) was analysed using a bioinformatic approach. Original experimental results available in the ProteomeXchange database were obtained using a comprehensive multidomain approach to identify potential blood biomarkers in ischemic stroke in mice. The identification of peptides with post-translational modification (PTM) was performed by us using the raw data (accession code PXD016538). Only phosphorylation and deamination were considered as PTMs. Different combinations of data sets (ischemic tissue with intact tissue, ischemic tissue with control taken from mice after sham surgery, etc.) were compared both in terms of the ratio of abundance for the modified peptide to the unmodified variant and in terms of absolute values of abundance. The most likely change in precisely PTM levels was shown for 27 proteins, which include dynamin, glycogen phosphorylase and 70 kDa heat shock protein.
The scale of virtual pKa values for calculating the isoelectric point of peptides and proteins with chemical and post-translational modifications (PTM) is presented. The learning set of pKa values is based on data from 25 experiments of isoelectric focusing of peptides with subsequent mass spectrometric identification (ProteomeXchange accession codes: PXD000065, PXD005410, PXD006291, PXD010006 and PXD017201). In order to enrich the resulting sets with peptides containing modifications the identification of peptides was repeated using raw mass spectrometry data of all datasets. In the final learning set have included peptides satisfying the following conditions: the peptide was found in the fraction with scoring function maximum and maximum peptide abundance; the peptide was found in more than one experiment, and differences of the pI value between experiments was less than 0.15 pH unit. Two variants of the scales were created. In the first variant, pKa values depended only on the residue position relative to the ends of the sequence (N- or C-terminal residue or inside the chain). In the second variant, the effect of neighboring residues was also taken into account. The prediction accuracy of the second variant was higher. The comparison with other methods of pI prediction was carried out. Although the scale was calculated from set containing only peptides, it would be applicable for pI prediction of proteins with and without PTM. The software for prediction of pI values using the resulting pKa scales is available at http://pIPredict3.ibmc.msk.ru.
The experimental results available in the ProteomeXchange database (accession code PXD016538) (Simats et al. (2020) Molecular and Cellular Proteomics, 19(12), 1921-1936) obtained using a comprehensive multi-omics approach were analyzed in mouse blood to identify potential biomarkers of ischemic stroke. Acetylation, methylation, and ubiquitination were considered as post-translational modifications. The analysis of the significance of changes in the level of protein modification was evaluated for ischemic tissue in comparison with tissue undamaged by stroke and control taken from mice after sham operation. At the level of statistically significant differences according to the Mann-Whitney test (p < 0.05), 2 proteins were found (Q02248 and Q8BL66); for additional 7 proteins, the differences were at the level of a statistical trend (p < 0.1). For 7 of 9 selected proteins there are reports in the literature, for their association with cerebral ischemia.
The paper analyzes a set of equations that adequately predict the IC50 value for SARS-CoV-2 main protease inhibitors. The training set was obtained using filtering by criteria independent of prediction of target value. It included 76 compounds, and the test set included nine compounds. We used the values of energy contributions obtained in the calculation of the change of the free energy of complex by MMGBSA method and a number of characteristics of the physical and chemical properties of the inhibitors as independent variables. It is sufficient to use only seven independent variables without loss of prediction quality (Q² = 0.79; R²prediction = 0.89). The maximum error in this case does not exceed 0.92 lg(IC50) units with a full range of observed values from 1.26 to 4.95.
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