Denis V. Petrovskiy

High physical activity often results in complex and ambiguous proteomic changes. This study aimed to analyze possible changes in the composition and content of proteins in plasma samples of elite athletes from sports with various activity levels and intensity. We performed proteomic profiling of blood plasma from 93 elite athletes with over eight years of continuous professional experience. High-resolution tandem mass spectra obtained in the data-independent acquisition mode were analyzed using the PLGS algorithm against a library of known protein sequences, and the PowerNovo algorithm for de novo protein sequencing. Combining the two protein identification strategies improved the completeness of the analysis and expanded the number of identified proteins. We identified changes in levels of circulating proteins that distinguish the high-intensity group from other groups by proteins involved in immune response, lipid transport and metabolism, and oxygen and iron transport. Changes in protein levels in biological samples of professional athletes may be associated with the training load intensity and type of sport.

Metabolomics investigates final and intermediate metabolic products in cells. Assessment of the human metabolome relies principally on the analysis of blood, urine, saliva, sweat, and feces. Tissue biopsy is employed less frequently. Understanding the metabolite composition of biosamples from athletes can significantly improve our knowledge of molecular processes associated with the efficiency of training and recovery. Such knowledge may also lead to new management opportunities. Successful execution of metabolomic studies requires simultaneous qualitative and quantitative analyses of numerous small biomolecules in samples under test. Unlike genomics and proteomics, which do not allow for direct assessment of enzymatic activity, metabolomics focuses on biochemical phenotypes, providing unique information about health and physiological features. Crucial factors in ensuring the efficacy of metabolomic analysis are the meticulous selection and pre-treatment of samples.

Biobanks are involved in a broad range of studies, including both basic and clinical research, so their functions and roles are evolving. Digital biobanks have emerged due to digitalization in this field; however, it also entails an increasing number of ethical and legal issues, in particular those related to the protection of donor data and potential commercial applications. The development of biobanks and the size of stored datasets lay the groundwork for proceeding to digital biobanks that intensely employ artificial intelligence tools. Digital biobanks can simplify the search for and access to biological specimens, thus contributing to the conduction of research and creating new collaborations. They are becoming an increasingly important tool for personalized medicine and an individualized approach to disease treatment, contributing to the elaboration of more accurate diagnostic methods and the development of innovative therapeutic strategies.

Traumatic brain injury (TBI) is one of the leading causes of mortality and disability among young and middle-aged individuals. Adequate and timely diagnosis of primary brain injuries, as well as the prompt prevention and treatment of secondary injury mechanisms, significantly determine the potential for reducing mortality and severe disabling consequences. Therefore, it is crucial to have objective markers that indicate the severity of the injury. A number of molecular factors—proteins and metabolites—detected in the blood immediately after trauma and associated with the development and severity of TBI can serve in this role. TBI is a heterogeneous condition with respect to its etiology, clinical form, and genesis, being accompanied by brain cell damage and disruption of blood–brain barrier permeability. Two oppositely directed flows of substances and signals are observed: one is the flow of metabolites, proteins, and nucleic acids from damaged brain cells into the bloodstream through the damaged blood–brain barrier; the other is the infiltration of immune cells (neutrophils and macrophages) and serological proteins. Both flows aggravate brain tissue damage after TBI. Therefore, it is extremely important to study the key signaling events that regulate these flows and repair the damaged tissues, as well as to enhance the effectiveness of treatments for patients after TBI.

Metabolic pathway prediction and reconstruction play crucial roles in solving fundamental and applied biomedical problems. In the case of fundamental research, annotation of metabolic pathways allows one to study human health in normal, stressed, and diseased conditions. In applied research, it allows one to identify novel drugs and drug targets and to design mimetics (biomolecules with tailored properties), as well as contributes to the development of such disciplines as toxicology and nutrigenomics. It is important to understand the role of a metabolite as a substrate (the product or intermediate participant of an enzymatic reaction) in cellular signaling and phenotype implementation according to the pivotal paradigm of biology: “one gene–one protein–one function (one trait)”. Due to the development of omics technologies, a vast body of data on the metabolome composition of living organisms has been accumulated over the past two decades. Systematization of the information on the roles played by metabolites in implementation of cellular signaling, as well as metabolic pathway reconstruction and refinement, have necessitated the development of bioinformatic tools for performing large-scale omics data mining. This paper reviews web-accessible databases relevant to metabolic pathways and considers the applications of the three types of bioinformatics methods for constructing metabolic networks (graphs for substrate–enzyme–product transformation; stoichiometric analysis of substrate–product transformation; and product retrosynthesis). It describes, step by step, a generalized algorithm for constructing biological pathway maps which explains to the researcher the workflow implemented in available bioinformatics tools and can be used to create new tools in projects requiring pathway reconstruction.

Modification of the protein after synthesis (PTM) often affects protein function as supported by numerous studies. However, there is no consensus about the degree of structural protein changes after modification. For phosphorylation of serine, threonine, and tyrosine, which is a common PTM in the biology of living organisms, we consider topical issues related to changes in the geometric parameters of a protein (Rg, RMSD, C_α displacement, SASA). The effect of phosphorylation on protein geometry was studied both for the whole protein and at the local level (i.e., in different neighborhoods of the modification site). Heterogeneity in the degree of protein structural changes after phosphorylation was revealed, which allowed for us to isolate a group of proteins having pronounced local structural changes in the neighborhoods of up to 15 amino acid residues from the modification site. This is a comparative study of protein structural changes in neighborhoods of 3–15 amino acid residues from the modified site. Amino acid phosphorylation in proteins with pronounced local changes caused switching from the inactive functional state to the active one.

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).

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.

The study is devoted to the creation of a dataset of protein structural motifs of the 3β-corner type. The relevance and importance of creating a dataset of 3β-corners is determined by the fact that this structure can be an embryo or a ready-made structural block in the process of protein folding, and can also act as an independent object of research in the field of structural biology. The dataset also contains 3β-corner-like structures that are geometrically similar to 3β-corners. The dataset consists of 45,896 structures. For each motif, its characteristics are presented: the name of the protein in which the 3β-corner is recognized, the method and resolution of the protein structure, the coordinates of localization in the protein, the secondary structure of the amino acid sequence, the gyration radius, the solvent-accessible area, and the composition of the elements of the secondary structure. The dataset will allow a comprehensive study of structures on a large scale and advance the understanding of the features and patterns of their structural organization.

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.