PowerNovo: de novo peptide sequencing via tandem mass spectrometry using an ensemble of transformer and BERT models.

The primary objective of analyzing the data obtained in a mass spectrometry-based proteomic experiment is peptide and protein identification, or correct assignment of the tandem mass spectrum to one amino acid sequence. Comparison of empirical fragment spectra with the theoretical predicted one or matching with the collected spectra library are commonly accepted strategies of proteins identification and defining of their amino acid sequences. Although these approaches are widely used and are appreciably efficient for the well-characterized model organisms or measured proteins, they cannot detect novel peptide sequences that have not been previously annotated or are rare. This study presents PowerNovo tool for de novo sequencing of proteins using tandem mass spectra acquired in a variety of types of mass analyzers and different fragmentation techniques. PowerNovo involves an ensemble of models for peptide sequencing: model for detecting regularities in tandem mass spectra, precursors, and fragment ions and a natural language processing model, which has a function of peptide sequence quality assessment and helps with reconstruction of noisy sequences. The results of testing showed that the performance of PowerNovo is comparable and even better than widely utilized PointNovo, DeepNovo, Casanovo, and Novor packages. Also, PowerNovo provides complete cycle of processing (pipeline) of mass spectrometry data and, along with predicting the peptide sequence, involves the peptide assembly and protein inference blocks.

Determination of Heterogeneous Proteomic and Metabolomic Response in anti-TNF and anti-IL-6 Treatment of Patients with Rheumatoid Arthritis.

Reduction in tumor necrosis factor (αTNF) and interleukin-6 (IL-6) activities is a widely utilized strategy for the treatment of rheumatoid arthritis (RA) with a high success rate. Despite both schemes targeting the deprivation of inflammatory reactions caused by the excessive activity of cytokines, their mechanisms of action and the final output are still unequal. This was a comparative longitudinal study that lasted for 24 weeks and aimed to find the answer to why the two schemes of therapy can pass out of proportion in attitude of their efficiency. What are the differences in metabolic and proteomic responses among patients who were being treated by either the anti-TNF or anti-IL-6 strategy? We found increased levels of immunoglobulins A and G (more than 2-fold in anti-IL-6 and more than 4-5-fold in anti-TNF groups) at the final stage (24 weeks) of monitoring but the most profound increase was determined for µ-chains of immunoglobulins in both groups of study. Metabolomic changes displayed main alterations with regard to arginine metabolism and collagen maintenance, where arginine increased 8.86-fold (p < 0.001) in anti-TNF and 5.71-fold (p < 0.05) in anti-IL-6 groups but patients treated by the anti-TNF scheme suffered a higher depletion of arginine before the start of therapy. Some indicators of matrix and bone tissue degradation also increased 4-hydroxyproline (4-HP) more than 6-fold (p < 0.001) in anti-TNF and more than 2-fold (p < 0.05) in the anti-IL-6 group, but the growth dynamics in the anti-IL6 group was delayed (gradually raised at week 24) compared to the anti-TNF group (raised at week 12) following a smooth reduction. The ELISA analysis of IL-6 and TNFα concentration in the study population supported proteomic and metabolomic data. A positive correlation between ΔCDAI and ΔDAS28 indicators and ESR and CRP was established for the majority of patients after 24 weeks of treatment where ESR and CRP reduced by 20% and 40% finally, respectively. A regression model using the Forest Plot was estimated to elucidate the impact of the most significant clinical, biochemical, and anthropometric indicators for the evaluation of differences between considered anti-TNF and anti-IL-6 schemes of therapy.

Proteomic and electron microscopy study of myogenic differentiation of alveolar mucosa multipotent mesenchymal stromal cells in three-dimensional culture.

Myocyte differentiation is featured by adaptation processes, including mitochondria repopulation and cytoskeleton re-organization. The difference between monolayer and spheroid cultured cells at the proteomic level is uncertain. We cultivated alveolar mucosa multipotent mesenchymal stromal cells in spheroids in a myogenic way for the proper conditioning of ECM architecture and cell morphology, which induced spontaneous myogenic differentiation of cells within spheroids. Electron microscopy analysis was used for the morphometry of mitochondria biogenesis, and proteomic was used complementary to unveil events underlying differences between two-dimensional/three-dimensional myoblasts differentiation. The prevalence of elongated mitochondria with an average area of 0.097 µm2 was attributed to monolayer cells 7 days after the passage. The population of small mitochondria with a round shape and area of 0.049 µm2 (p < 0.05) was observed in spheroid cells cultured under three-dimensional conditions. Cells in spheroids were quantitatively enriched in proteins of mitochondria biogenesis (DNM1L, IDH2, SSBP1), respiratory chain (ACO2, ATP5I, COX5A), extracellular proteins (COL12A1, COL6A1, COL6A2), and cytoskeleton (MYL6, MYL12B, MYH10). Most of the Rab-related transducers were inhibited in spheroid culture. The proteomic assay demonstrated delicate mechanisms of mitochondria autophagy and repopulation, cytoskeleton assembling, and biogenesis. Differences in the ultrastructure of mitochondria indicate active biogenesis under three-dimensional conditions.

Managing of Unassigned Mass Spectrometric Data by Neural Network for Cancer Phenotypes Classification.

Mass spectrometric profiling provides information on the protein and metabolic composition of biological samples. However, the weak efficiency of computational algorithms in correlating tandem spectra to molecular components (proteins and metabolites) dramatically limits the use of "omics" profiling for the classification of nosologies. The development of machine learning methods for the intelligent analysis of raw mass spectrometric (HPLC-MS/MS) measurements without involving the stages of preprocessing and data identification seems promising. In our study, we tested the application of neural networks of two types, a 1D residual convolutional neural network (CNN) and a 3D CNN, for the classification of three cancers by analyzing metabolomic-proteomic HPLC-MS/MS data. In this work, we showed that both neural networks could classify the phenotypes of gender-mixed oncology, kidney cancer, gender-specific oncology, ovarian cancer, and the phenotype of a healthy person by analyzing 'omics' data in 'mgf' data format. The created models effectively recognized oncopathologies with a model accuracy of 0.95. Information was obtained on the remoteness of the studied phenotypes. The closest in the experiment were ovarian cancer, kidney cancer, and prostate cancer/kidney cancer. In contrast, the healthy phenotype was the most distant from cancer phenotypes and ovarian and prostate cancers. The neural network makes it possible to not only classify the studied phenotypes, but also to determine their similarity (distance matrix), thus overcoming algorithmic barriers in identifying HPLC-MS/MS spectra. Neural networks are versatile and can be applied to standard experimental data formats obtained using different analytical platforms.

Detection of Circulating Serum microRNA/Protein Complexes in ASD Using Functionalized Chips for an Atomic Force Microscope.

MicroRNAs, which circulate in blood, are characterized by high diagnostic value; in biomedical research, they can be considered as candidate markers of various diseases. Mature microRNAs of glial cells and neurons can cross the blood-brain barrier and can be detected in the serum of patients with autism spectrum disorders (ASD) as components of macrovesicles, macromolecular protein and low-density lipoprotein particles. In our present study, we have proposed an approach, in which microRNAs in protein complexes can be concentrated on the surface of AFM chips with oligonucleotide molecular probes, specific against the target microRNAs. MicroRNAs, associated with the development of ASD in children, were selected as targets. The chips with immobilized molecular probes were incubated in serum samples of ASD patients and healthy volunteers. By atomic force microscopy (AFM), objects on the AFM chip surface have been revealed after incubation in the serum samples. The height of these objects amounted to 10 nm and 6 nm in the case of samples of ASD patients and healthy volunteers, respectively. MALDI-TOF-MS analysis of protein components on the chip surface allowed us to identify several cell proteins. These proteins are involved in the binding of nucleic acids (GBG10, RT24, RALYL), in the organization of proteasomes and nucleosomes (PSA4, NP1L4), and participate in the functioning of the channel of active potassium transport (KCNE5, KCNV2).

Proteomic and molecular dynamic investigations of PTM-induced structural fluctuations in breast and ovarian cancer.

Post-translational processing leads to conformational changes in protein structure that modulate molecular functions and change the signature of metabolic transformations and immune responses. Some post-translational modifications (PTMs), such as phosphorylation and acetylation, are strongly related to oncogenic processes and malignancy. This study investigated a PTM pattern in patients with gender-specific ovarian or breast cancer. Proteomic profiling and analysis of cancer-specific PTM patterns were performed using high-resolution UPLC-MS/MS. Structural analysis, topology, and stability of PTMs associated with sex-specific cancers were analyzed using molecular dynamics modeling. We identified highly specific PTMs, of which 12 modified peptides from eight distinct proteins derived from patients with ovarian cancer and 6 peptides of three proteins favored patients from the group with breast cancer. We found that all defined PTMs were localized in the compact and stable structural motifs exposed outside the solvent environment. PTMs increase the solvent-accessible surface area of the modified moiety and its active environment. The observed conformational fluctuations are still inadequate to activate the structural degradation and enhance protein elimination/clearance; however, it is sufficient for the significant modulation of protein activity.

Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia.

The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those undergoing cancer treatment and monitored for sufficient time to find shared features between the considered conditions. We performed mass spectrometry-based proteomic and metabolomic investigations of patients with different cancer phenotypes (breast, ovarian, renal, and prostate) and patients with schizophrenia. The resulting vast quantity of proteomic and metabolomic data were then processed using systems biology and one-dimensional (1D) convolutional neural network (1DCNN) machine learning approaches. Traditional systematic approaches permit the segregation of schizophrenia and cancer phenotypes on the level of biological processes, while 1DCNN recognized "signatures" that could segregate distinct cancer phenotypes and schizophrenia at the comorbidity level. The designed network efficiently discriminated unrelated pathologies with a model accuracy of 0.90 and different subtypes of oncophenotypes with an accuracy of 0.94. The proposed strategy integrates systematic analysis of identified compounds and application of 1DCNN model for unidentified ones to reveal the similarity between distinct phenotypes.

MAPK and Notch-Mediated Effects of Meso-Xanthin F199 Compounds on Proliferative Activity and Apoptosis of Human Melanocytes in Three-Dimensional Culture.

Meso-Xanthin (Meso-Xanthin F199™) is a highly active antiaging injection drug of the latest generation. The main acting compound is fucoxanthin, supplemented with several growth factors, vitamins, and hyaluronic acid. Previous examination of fucoxanthin on melanocytes showed its ability to inhibit skin pigmentation through different signaling pathways focused on suppression of melanogenic-stimulating receptors. In turn, the anticancer property of fucoxanthin is realized through MAPK and PI3K pathways. We aimed to evaluate the effect of fucoxanthin and supplemented growth factors on melanocyte growth and transformation at a proteomic level. The effect of fucoxanthin on melanocytes cultivated in three-dimensional (3D) condition was examined using high-throughput proteomic and system biology approaches to disclose key molecular events of the targeted action. Our results demonstrated significant inhibition of cell differentiation and ubiquitination processes. We found that the negative regulation of PSME1 and PTGIS largely determines the inhibition of NF-κB and MAPK2. Besides, fucoxanthin selectively inhibits cell differentiation via negative regulation of Raf signaling and the upstream activation of IL-1 signaling. It is assumed that inhibition of Raf influences the Notch-4 signaling and switches off the MAPK/MAPK2 cascade. Blockage of MAPK/MAPK2 is feasible due to suppression of Ras and NF-κB by the addressed action of IKKB, IKK2, and TRAF6. Suggestively, Meso-Xanthin F199™ can manage processes of proliferative activity and inhibition of apoptosis due to composition of fucoxanthin and growth-stimulating factors, which may increase the risk of skin cancer development under certain condition.

Revelation of Proteomic Indicators for Colorectal Cancer in Initial Stages of Development.

Background: Colorectal cancer (CRC) at a current clinical level is still hardly diagnosed, especially with regard to nascent tumors, which are typically asymptotic. Searching for reliable biomarkers of early diagnosis is an extremely essential task. Identification of specific post-translational modifications (PTM) may also significantly improve net benefits and tailor the process of CRC recognition. We examined depleted plasma samples obtained from 41 healthy volunteers and 28 patients with CRC at different stages to conduct comparative proteome-scaled analysis. The main goal of the study was to establish a constellation of protein markers in combination with their PTMs and semi-quantitative ratios that may support and realize the distinction of CRC until the disease has a poor clinical manifestation. Results: Proteomic analysis revealed 119 and 166 proteins for patients in stages I-II and III-IV, correspondingly. Plenty of proteins (44 proteins) reflected conditions of the immune response, lipid metabolism, and response to stress, but only a small portion of them were significant (p < 0.01) for distinguishing stages I-II of CRC. Among them, some cytokines (Clusterin (CLU), C4b-binding protein (C4BP), and CD59 glycoprotein (CD59), etc.) were the most prominent and the lectin pathway was specifically enhanced in patients with CRC. Significant alterations in Inter-alpha-trypsin inhibitor heavy chains (ITIH1, ITIH2, ITIH3, and ITIH4) levels were also observed due to their implication in tumor growth and the malignancy process. Other markers (Alpha-1-acid glycoprotein 2 (ORM2), Alpha-1B-glycoprotein (A1BG), Haptoglobin (HP), and Leucine-rich alpha-2-glycoprotein (LRG1), etc.) were found to create an ambiguous core involved in cancer development but also to exactly promote tumor progression in the early stages. Additionally, we identified post-translational modifications, which according to the literature are associated with the development of colorectal cancer, including kininogen 1 protein (T327-p), alpha-2-HS-glycoprotein (S138-p) and newly identified PTMs, i.e., vitamin D-binding protein (K75-ac and K370-ac) and plasma protease C1 inhibitor (Y294-p), which may also contribute and negatively impact on CRC progression. Conclusions: The contribution of cytokines and proteins of the extracellular matrix is the most significant factor in CRC development in the early stages. This can be concluded since tumor growth is tightly associated with chronic aseptic inflammation and concatenated malignancy related to loss of extracellular matrix stability. Due attention should be paid to Apolipoprotein E (APOE), Apolipoprotein C1 (APOC1), and Apolipoprotein B-100 (APOB) because of their impact on the malfunction of DNA repair and their capability to regulate mTOR and PI3K pathways. The contribution of the observed PTMs is still equivocal, but a significant decrease in the likelihood between modified and native proteins was not detected confidently.

Serologic Markers of Autism Spectrum Disorder.

According to WHO data, about 67 million people worldwide are affected by autism, and this number grows by 14% annually. Among the possible causes of autism are genetic modifications, organic lesions of the central nervous system, metabolic disorders, influence of viral and bacterial infections, chemical influence to the mother's body during pregnancy, etc. The conducted research shows that research papers published until today do not name any potential protein markers that meet the requirements of the basic parameters for evaluating the efficiency of disease diagnostics, in particular high sensitivity, specificity, and accuracy. Conducting proteomic research on a big scale in order to detect serologic markers of protein nature associated with development of autism spectrum disorders seems to be highly relevant.

Detection of hepatitis C virus core protein in serum by atomic force microscopy combined with mass spectrometry.

A method for detection and identification of core antigen of hepatitis C virus (HCVcoreAg)-containing particles in the serum was proposed, with due account taken of the interactions of proteotypic peptides with Na(+), K(+), and Cl(-) ions. The method is based on a combination of reversible biospecific atomic force microscopy (AFM)-fishing and mass spectrometry (MS). AFM-fishing enables concentration, detection, and counting of protein complexes captured on the AFM chip surface, with their subsequent MS identification. Biospecific AFM-fishing of HCVcoreAg-containing particles from serum samples was carried out using AFM chips with immobilized antibodies against HCVcoreAg (HCVcoreAgim). Formation of complexes between anti-HCVcoreAgim and HCVcoreAg-containing particles on the AFM chip surface during the fishing process was demonstrated. These complexes were registered and counted by AFM. Further MS analysis allowed reliable identification of HCVcoreAg within the complexes formed on the AFM chip surface. It was shown that MS data processing, with account taken of the interactions between HCVcoreAg peptides and Na(+), K(+) cations, and Cl(-) anions, allows an increase in the number of peptides identified.