Proteasome Interactome and Its Role in the Mechanisms of Brain Plasticity.

Proteasomes are highly conserved multienzyme complexes responsible for proteolytic degradation of the short-lived, regulatory, misfolded, and damaged proteins. They play an important role in the processes of brain plasticity, and decrease in their function is accompanied by the development of neurodegenerative pathology. Studies performed in different laboratories both on cultured mammalian and human cells and on preparations of the rat and rabbit brain cortex revealed a large number of proteasome-associated proteins. Since the identified proteins belong to certain metabolic pathways, multiple enrichment of the proteasome fraction with these proteins indicates their important role in proteasome functioning. Extrapolation of the experimental data, obtained on various biological objects, to the human brain suggests that the proteasome-associated proteins account for at least 28% of the human brain proteome. The proteasome interactome of the brain contains a large number of proteins involved in the assembly of these supramolecular complexes, regulation of their functioning, and intracellular localization, which could be changed under different conditions (for example, during oxidative stress) or in different phases of the cell cycle. In the context of molecular functions of the Gene Ontology (GO) Pathways, the proteins of the proteasome interactome mediate cross-talk between components of more than 30 metabolic pathways annotated in terms of GO. The main result of these interactions is binding of adenine and guanine nucleotides, crucial for realization of the nucleotide-dependent functions of the 26S and 20S proteasomes. Since the development of neurodegenerative pathology is often associated with regioselective decrease in the functional activity of proteasomes, a positive therapeutic effect would be obviously provided by the factors increasing proteasomal activity. In any case, pharmacological regulation of the brain proteasomes seems to be realized through the changes in composition and/or activity of the proteins associated with proteasomes (deubiquitinase, PKA, CaMKIIα, etc.).

Modeling Side Chains in the Three-Dimensional Structure of Proteins for Post-Translational Modifications.

Amino acid substitutions and post-translational modifications (PTMs) play a crucial role in many cellular processes by directly affecting the structural and dynamic features of protein interaction. Despite their importance, the understanding of protein PTMs at the structural level is still largely incomplete. The Protein Data Bank contains a relatively small number of 3D structures having post-translational modifications. Although recent years have witnessed significant progress in three-dimensional modeling (3D) of proteins using neural networks, the problem related to predicting accurate PTMs in proteins has been largely ignored. Predicting accurate 3D PTM models in proteins is closely related to another fundamental problem: predicting the correct side-chain conformations of amino acid residues in proteins. An analysis of publications as well as the paid and free software packages for modeling three-dimensional structures showed that most of them focus on working with unmodified proteins and canonical amino acid residues; the number of articles and software packages placing emphasis on modeling three-dimensional PTM structures is an order of magnitude smaller. This paper focuses on modeling the side-chain conformations of proteins containing PTMs (nonstandard amino acid residues). We collected our own libraries comprising the most frequently observed PTMs from the PDB and implemented a number of algorithms for predicting the side-chain conformation at modification points and in the immediate environment of the protein. A comprehensive analysis of both the algorithms per se and compared to the common Rosetta and FoldX structure modeling packages was also carried out. The proposed algorithmic solutions are comparable in their characteristics to the well-known Rosetta and FoldX packages for the modeling of three-dimensional structures and have great potential for further development and optimization. The source code of algorithmic solutions has been deposited to and is available at the GitHub source.

SAFoldNet: A Novel Tool for Discovering and Aligning Three-Dimensional Protein Structures Based on a Neural Network.

The development and improvement of methods for comparing and searching for three-dimensional protein structures remain urgent tasks in modern structural biology. To solve this problem, we developed a new tool, SAFoldNet, which allows for searching, aligning, superimposing, and determining the exact coordinates of fragments of protein structures. The proposed search and alignment tool was built using neural networking. Specifically, we implemented the integrative synergy of neural network predictions and the well-known BLAST algorithm for searching and aligning sequences. The proposed method involves multistage processing, comprising a stage for converting the geometry of protein structures into sequences of a structural alphabet using a neural network, a search stage for forming a set of candidate structures, and a refinement stage for calculating the structural alignment and overlap and evaluating the similarity with the starting structure of the search. The effectiveness and practical applicability of the proposed tool were compared with those of several widely used services for searching and aligning protein structures. The results of the comparisons confirmed that the proposed method is effective and competitive relative to the available modern services. Furthermore, using the proposed approach, a service with a user-friendly web interface was developed, which allows for searching, aligning, and superimposing protein structures; determining the location of protein fragments; mapping onto a protein molecule chain; and providing structural similarity metrices (expected value and root mean square deviation).

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.

ß-synuclein potentiates synaptic vesicle dopamine uptake and rescues dopaminergic neurons from MPTP-induced death in the absence of other synucleins.

Synucleins, a family of three proteins highly expressed in neurons, are predominantly known for the direct involvement of α-synuclein in the etiology and pathogenesis of Parkinson's and certain other neurodegenerative diseases, but their precise physiological functions are still not fully understood. Previous studies have demonstrated the importance of α-synuclein as a modulator of various mechanisms implicated in chemical neurotransmission, but information concerning the involvement of other synuclein family members, ß-synuclein and γ-synuclein, in molecular processes within presynaptic terminals is limited. Here, we demonstrated that the vesicular monoamine transporter 2-dependent dopamine uptake by synaptic vesicles isolated from the striatum of mice lacking ß-synuclein is significantly reduced. Reciprocally, reintroduction, either in vivo or in vitro, of ß-synuclein but not α-synuclein or γ-synuclein improves uptake by triple α/ß/γ-synuclein-deficient striatal vesicles. We also showed that the resistance of dopaminergic neurons of the substantia nigra pars compacta to subchronic administration of the Parkinson's disease-inducing prodrug 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine depends on the presence of ß-synuclein but only when one or both other synucleins are absent. Furthermore, proteomic analysis of synuclein-deficient synaptic vesicles versus those containing only ß-synuclein revealed differences in their protein compositions. We suggest that the observed potentiation of dopamine uptake by ß-synuclein might be caused by different protein architecture of the synaptic vesicles. It is also feasible that such structural changes improve synaptic vesicle sequestration of 1-methyl-4-phenylpyridinium, a toxic metabolite of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, which would explain why dopaminergic neurons expressing ß-synuclein and lacking α-synuclein and/or γ-synuclein are resistant to this neurotoxin.

Tear Proteome Revealed Association of S100A Family Proteins and Mesothelin with Thrombosis in Elderly Patients with Retinal Vein Occlusion.

Tear samples collected from patients with central retinal vein occlusion (CRVO; n = 28) and healthy volunteers (n = 29) were analyzed using a proteomic label-free absolute quantitative approach. A large proportion (458 proteins with a frequency > 0.6) of tear proteomes was found to be shared between the study groups. Comparative proteomic analysis revealed 29 proteins (p < 0.05) significantly differed between CRVO patients and the control group. Among them, S100A6 (log (2) FC = 1.11, p < 0.001), S100A8 (log (2) FC = 2.45, p < 0.001), S100A9 (log2 (FC) = 2.08, p < 0.001), and mesothelin ((log2 (FC) = 0.82, p < 0.001) were the most abundantly represented upregulated proteins, and ß2-microglobulin was the most downregulated protein (log2 (FC) = −2.13, p < 0.001). The selected up- and downregulated proteins were gathered to customize a map of CRVO-related critical protein interactions with quantitative properties. The customized map (FDR < 0.01) revealed inflammation, impairment of retinal hemostasis, and immune response as the main set of processes associated with CRVO ischemic condition. The semantic analysis displayed the prevalence of core biological processes covering dysregulation of mitochondrial organization and utilization of improperly or topologically incorrect folded proteins as a consequence of oxidative stress, and escalating of the ischemic condition caused by the local retinal hemostasis dysregulation. The most significantly different proteins (S100A6, S100A8, S100A9, MSLN, and ß2-microglobulin) were applied for the ROC analysis, and their AUC varied from 0.772 to 0.952, suggesting probable association with the CRVO.

PSSNet-An Accurate Super-Secondary Structure for Protein Segmentation.

A super-secondary structure (SSS) is a spatially unique ensemble of secondary structural elements that determine the three-dimensional shape of a protein and its function, rendering SSSs attractive as folding cores. Understanding known types of SSSs is important for developing a deeper understanding of the mechanisms of protein folding. Here, we propose a universal PSSNet machine-learning method for SSS recognition and segmentation. For various types of SSS segmentation, this method uses key characteristics of SSS geometry, including the lengths of secondary structural elements and the distances between them, torsion angles, spatial positions of Cα atoms, and primary sequences. Using four types of SSSs (ßαß-unit, α-hairpin, ß-hairpin, αα-corner), we showed that extensive SSS sets could be reliably selected from the Protein Data Bank and AlphaFold 2.0 database of protein structures.

3ß-Corner Stability by Comparative Molecular Dynamics Simulations.

This study explored the mechanisms by which the stability of super-secondary structures of the 3ß-corner type autonomously outside the protein globule are maintained in an aqueous environment. A molecular dynamic (MD) study determined the behavioral diversity of a large set of non-homologous 3ß-corner structures of various origins. We focused on geometric parameters such as change in gyration radius, solvent-accessible area, major conformer lifetime and torsion angles, and the number of hydrogen bonds. Ultimately, a set of 3ß-corners from 330 structures was characterized by a root mean square deviation (RMSD) of less than 5 Å, a change in the gyration radius of no more than 5%, and the preservation of amino acid residues positioned within the allowed regions on the Ramachandran map. The studied structures retained their topologies throughout the MD experiments. Thus, the 3ß-corner structure was found to be rather stable per se in a water environment, i.e., without the rest of a protein molecule, and can act as the nucleus or "ready-made" building block in protein folding. The 3ß-corner can also be considered as an independent object for study in field of structural biology.

In Silico Study of the Interactions of Anle138b Isomer, an Inhibitor of Amyloid Aggregation, with Partner Proteins.

Herein, we aimed to highlight current "gaps" in the understanding of the potential interactions between the Anle138b isomer ligand, a promising agent for clinical research, and the intrinsically disordered alpha-synuclein protein. The presence of extensive unstructured areas in alpha-synuclein determines its existence in the cell of partner proteins, including the cyclophilin A chaperone, which prevents the aggregation of alpha-synuclein molecules that are destructive to cell life. Using flexible and cascaded molecular docking techniques, we aimed to expand our understanding of the molecular architecture of the protein complex between alpha-synuclein, cyclophilin A and the Anle138b isomer ligand. We demonstrated the possibility of intricate complex formation under cellular conditions and revealed that the main interactions that stabilize the complex are hydrophobic and involve hydrogen.

Atomic Simulation of the Binding of JAK1 and JAK2 with the Selective Inhibitor Ruxolitinib.

Rheumatoid arthritis belongs to the group of chronic systemic autoimmune diseases characterized by the development of destructive synovitis and extra-articular manifestations. Cytokines regulate a wide range of inflammatory processes involved in the pathogenesis of rheumatoid arthritis and contribute to the induction of autoimmunity and chronic inflammation. Janus-associated kinase (JAK) and signal transducer and activator of transcription (STAT) proteins mediate cell signaling from cytokine receptors, and are involved in the pathogenesis of autoimmune and inflammatory diseases. Targeted small-molecule drugs that inhibit the functional activity of JAK proteins are used in clinical practice for the treatment of rheumatoid arthritis. In our study, we modeled the interactions of the small-molecule drug ruxolitinib with JAK1 and JAK2 isoforms and determined the binding selectivity using molecular docking. Molecular modeling data show that ruxolitinib selectively binds the JAK1 and JAK2 isoforms with a binding affinity of -8.3 and -8.0 kcal/mol, respectively. The stabilization of ligands in the cavity of kinases occurs primarily through hydrophobic interactions. The amino acid residues of the protein globules of kinases that are responsible for the correct positioning of the drug ruxolitinib and its retention have been determined.

TRIM28 Is a Novel Regulator of CD133 Expression Associated with Cancer Stem Cell Phenotype.

CD133 is an extensively studied marker of the most malignant tumor cell population, designated as cancer stem cells (CSCs). However, the function of this glycoprotein and its involvement in cell regulatory cascades are still poorly understood. Here we show a positive correlation between the level of CD133 plasma membrane expression and the proliferative activity of cells of the Caco-2, HT-29, and HUH7 cancer cell lines. Despite a substantial difference in the proliferative activities of cell populations with different levels of CD133 expression, transcriptomic and proteomic profiling revealed only minor distinctions between them. Nonetheless, a further in silico assessment of the differentially expressed transcripts and proteins revealed 16 proteins that could be involved in the regulation of CD133 expression; these were assigned ranks reflecting the apparent extent of their involvement. Among them, the TRIM28 transcription factor had the highest rank. The prominent role of TRIM28 in CD133 expression modulation was confirmed experimentally in the Caco2 cell line clones: the knockout, though not the knockdown, of the TRIM28 gene downregulated CD133. These results for the first time highlight an important role of the TRIM28 transcription factor in the regulation of CD133-associated cancer cell heterogeneity.

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.

Current Approaches in Supersecondary Structures Investigation.

Proteins expressed during the cell cycle determine cell function, topology, and responses to environmental influences. The development and improvement of experimental methods in the field of structural biology provide valuable information about the structure and functions of individual proteins. This work is devoted to the study of supersecondary structures of proteins and determination of their structural motifs, description of experimental methods for their detection, databases, and repositories for storage, as well as methods of molecular dynamics research. The interest in the study of supersecondary structures in proteins is due to their autonomous stability outside the protein globule, which makes it possible to study folding processes, conformational changes in protein isoforms, and aberrant proteins with high productivity.

Mass Spectrometric Identification of Proteins Enhanced by the Atomic Force Microscopy Immobilization Surface.

An approach to highly-sensitive mass spectrometry detection of proteins after surface-enhanced concentrating has been elaborated. The approach is based on a combination of mass spectrometry and atomic force microscopy to detect target proteins. (1) Background: For this purpose, a technique for preliminary preparation of molecular relief surfaces formed as a result of a chemical or biospecific concentration of proteins from solution was developed and tested on several types of chip surfaces. (2) Methods: mass spectrometric identification of proteins using trailing detectors: ion trap, time of flight, orbital trap, and triple quadrupole. We used the electrospray type of ionization and matrix-assisted laser desorption/ionization. (3) Results: It is shown that when using locally functionalized atomically smooth surfaces, the sensitivity of the mass spectrometric method increases by two orders of magnitude as compared with measurements in solution. Conclusions: It has been demonstrated that the effective concentration of target proteins on specially prepared surfaces increases the concentration sensitivity of mass spectrometric detectors-time-of-flight, ion trap, triple quadrupole, and orbital ion trap in the concentration range from up to 10-15 M.

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.

Challenges of the Human Proteome Project: 10-Year Experience of the Russian Consortium.

This manuscript collects all the efforts of the Russian Consortium, bottlenecks revealed in the course of the C-HPP realization, and ways of their overcoming. One of the main bottlenecks in the C-HPP is the insufficient sensitivity of proteomic technologies, hampering the detection of low- and ultralow-copy number proteins forming the "dark part" of the human proteome. In the frame of MP-Challenge, to increase proteome coverage we suggest an experimental workflow based on a combination of shotgun technology and selected reaction monitoring with two-dimensional alkaline fractionation. Further, to detect proteins that cannot be identified by such technologies, nanotechnologies such as combined atomic force microscopy with molecular fishing and/or nanowire detection may be useful. These technologies provide a powerful tool for single molecule analysis, by analogy with nanopore sequencing during genome analysis. To systematically analyze the functional features of some proteins (CP50 Challenge), we created a mathematical model that predicts the number of proteins differing in amino acid sequence: proteoforms. According to our data, we should expect about 100 000 different proteoforms in the liver tissue and a little more in the HepG2 cell line. The variety of proteins forming the whole human proteome significantly exceeds these results due to post-translational modifications (PTMs). As PTMs determine the functional specificity of the protein, we propose using a combination of gene-centric transcriptome-proteomic analysis with preliminary fractionation by two-dimensional electrophoresis to identify chemically modified proteoforms. Despite the complexity of the proposed solutions, such integrative approaches could be fruitful for MP50 and CP50 Challenges in the framework of the C-HPP.

200+ Protein Concentrations in Healthy Human Blood Plasma: Targeted Quantitative SRM SIS Screening of Chromosomes 18, 13, Y, and the Mitochondrial Chromosome Encoded Proteome.

This work continues the series of the quantitative measurements of the proteins encoded by different chromosomes in the blood plasma of a healthy person. Selected Reaction Monitoring with Stable Isotope-labeled peptide Standards (SRM SIS) and a gene-centric approach, which is the basis for the implementation of the international Chromosome-centric Human Proteome Project (C-HPP), were applied for the quantitative measurement of proteins in human blood plasma. Analyses were carried out in the frame of C-HPP for each protein-coding gene of the four human chromosomes: 18, 13, Y, and mitochondrial. Concentrations of proteins encoded by 667 genes were measured in 54 blood plasma samples of the volunteers, whose health conditions were consistent with requirements for astronauts. The gene list included 276, 329, 47, and 15 genes of chromosomes 18, 13, Y, and the mitochondrial chromosome, respectively. This paper does not make claims about the detection of missing proteins. Only 205 proteins (30.7%) were detected in the samples. Of them, 84, 106, 10, and 5 belonged to chromosomes 18, 13, and Y and the mitochondrial chromosome, respectively. Each detected protein was found in at least one of the samples analyzed. The SRM SIS raw data are available in the ProteomeXchange repository (PXD004374, PASS01192).

Next Steps on in Silico 2DE Analyses of Chromosome 18 Proteoforms.

In the boundaries of the chromosome-centric Human Proteome Project (c-HPP) to obtain information about proteoforms coded by chromosome 18, several cell lines (HepG2, glioblastoma, LEH), normal liver, and plasma were analyzed. In our study, we have been using proteoform separation by two-dimensional electrophoresis (2DE) (a sectional analysis) and a semivirtual 2DE with following shotgun mass spectrometry using LC-ESI-MS/MS. Previously, we published a first draft of this research, where only HepG2 cells were tested. Here, we present the next step using more detailed analysis and more samples. Altogether, confident (2 significant sequences minimum) information about proteoforms of 117 isoforms coded by 104 genes of chromosome 18 was obtained. The 3D-graphs showing distribution of different proteoforms from the same gene in the 2D map were generated. Additionally, a semivirtual 2DE approach has allowed for detecting more proteoforms and estimating their pI more precisely. Data are available via ProteomeXchange with identifier PXD010142.

Increased Sensitivity of Mass Spectrometry by Alkaline Two-Dimensional Liquid Chromatography: Deep Cover of the Human Proteome in Gene-Centric Mode.

Currently, great interest is paid to the identification of "missing" proteins that have not been detected in any biological material at the protein level (PE1). In this paper, using the Universal Proteomic Standard sets 1 and 2 (UPS1 and UPS2, respectively) as an example, we characterized mass spectrometric approaches from the point of view of sensitivity (Sn), specificity (Sp), and accuracy (Ac). The aim of the paper was to show the utility of a mass spectra approach for protein detection. This sets consists of 48 high-purity human proteins without single aminoacid polymorphism (SAP) or post translational modification (PTM). The UPS1 set consists of the same 48 proteins at 5 pmols each, and in UPS2, proteins were grouped into 5 groups in accordance with their molar concentration, ranging from 10-11 to 10-6 M. Single peptides from the 92% and 96% of all sets of proteins could be detected in a pure solution of UPS2 and UPS1, respectively, by selected reaction monitoring with stable isotope-labeled standards (SRM-SIS). We also found that, in the presence of a biological matrix such as Escherichia coli extract or human blood plasma (HBP), SRM-SIS makes it possible to detect from 63% to 79% of proteins in the UPS2 set (sensitivity) with the highest specificity (∼100%) and an accuracy of 80% by increasing the sensitivity of shotgun and selected reaction monitoring combined with a stable-isotope-labeled peptide standard (SRM-SIS technology) by fractionating samples using reverse-phase liquid chromatography under alkaline conditions (2D-LC_alk). It is shown that this technique of sample fractionation allows the SRM-SIS to detect 98% of the single peptides from the proteins present in the pure solution of UPS2 (47 out of 48 proteins). When the extracts of E. coli or Pichia pastoris are added as biological matrixes to the UPS2, 46, and 45 out of 48 proteins (∼95%) can be detected, respectively, using the SRM-SIS combined with 2D-LC_alk. The combination of the 2D-LC_alk SRM-SIS and shotgun technologies allows us to increase the sensitivity up to 100% in the case of the proteins of the UPS2 set. The usage of that technology can be a solution for identifying the so-called "missing" proteins and, eventually, creating the deep proteome of a particular chromosome of tissue or organs. Experimental data have been deposited in the PeptideAtlas SRM Experiment Library with the dataset identifier PASS01192 and the PRIDE repository with the dataset identifier PXD007643.

FractionOptimizer: a method for optimal peptide fractionation in bottom-up proteomics.

Recent advances in mass spectrometry and separation technologies created the opportunities for deep proteome characterization using shotgun proteomics approaches. The "real world" sample complexity and high concentration range limit the sensitivity of this characterization. The common strategy for increasing the sensitivity is sample fractionation prior to analysis either at the protein or the peptide level. Typically, fractionation at the peptide level is performed using linear gradient high-performance liquid chromatography followed by uniform fraction collection. However, this way of peptide fractionation results in significantly suboptimal operation of the mass spectrometer due to the non-uniform distribution of peptides between the fractions. In this work, we propose an approach based on peptide retention time prediction allowing optimization of chromatographic conditions and fraction collection procedures. An open-source software implementing the approach called FractionOptimizer was developed and is available at http://hg.theorchromo.ru/FractionOptimizer . The performance of the developed tool was demonstrated for human embryonic kidney (HEK293) cell line lysate. In these experiments, we improved the uniformity of the peptides distribution between fractions. Moreover, in addition to 13,492 peptides, we found 6787 new peptides not identified in the experiments without fractionation and up to 800 new proteins (or 25%). Graphical abstract The analysis workflow employing FractionOptimizer software.