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).

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.

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.

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.

Analysis of Structural Changes in the Protein near the Phosphorylation Site.

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, Cα 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.

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.

Tracking Health, Performance and Recovery in Athletes Using Machine Learning.

Training and competitive periods can temporarily impair the performance of an athlete. This disruption can be short- or long-term, lasting up to several days. We analyzed the health indicators of 3661 athletes during an in-depth medical examination. At the time of inclusion in the study, the athletes were healthy. Instrumental examinations (fluorography, ultrasound examination of the abdominal cavity and pelvic organs, echocardiography, electrocardiography, and stress testing "to failure"), laboratory examinations (general urinalysis and biochemical and general clinical blood analysis), and examinations by specialists (ophthalmologist, otolaryngologist, surgeon, cardiologist, neurologist, dentist, gynecologist (women), endocrinologist, and therapist) were performed. This study analyzed the significance of determining the indicators involved in the implementation of the "catabolism" and "anabolism" phenotypes using the random forest and multinomial logistic regression machine learning methods. The use of decision forest and multinomial regression models made it possible to identify the most significant indicators of blood and urine biochemistry for the analysis of phenotypes as a characterization of the effectiveness of recovery processes in the post-competitive period in athletes. We found that the parameters of muscle metabolism, such as aspartate aminotransferase, creatine kinase, lactate dehydrogenase, and alanine aminotransferase levels, and the parameters of the ornithine cycle, such as creatinine, urea acid, and urea levels, made the most significant contribution to the classification of two types of metabolism: catabolism and anabolism.

Molecular Modeling Insights into Upadacitinib Selectivity upon Binding to JAK Protein Family.

Rheumatoid arthritis (RA) is a chronic disease characterized by bone joint damage and incapacitation. The mechanism underlying RA pathogenesis is autoimmunity in the connective tissue. Cytokines play an important role in the human immune system for signal transduction and in the development of inflammatory responses. Janus kinases (JAK) participate in the JAK/STAT pathway, which mediates cytokine effects, in particular interleukin 6 and IFNγ. The discovery of small molecule inhibitors of the JAK protein family has led to a revolution in RA therapy. The novel JAK inhibitor upadacitinib (RinvoqTM) has a higher selectivity for JAK1 compared to JAK2 and JAK3 in vivo. Currently, details on the molecular recognition of JAK1 by upadacitinib are not available. We found that characteristics of hydrogen bond formation with the glycine loop and hinge in JAKs define the selectivity. Our molecular modeling study could provide insight into the drug action mechanism and pharmacophore model differences in JAK isoforms.

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.

Molecular Portrait of an Athlete.

Sequencing of the human genome and further developments in "omics" technologies have opened up new possibilities in the study of molecular mechanisms underlying athletic performance. It is expected that molecular markers associated with the development and manifestation of physical qualities (speed, strength, endurance, agility, and flexibility) can be successfully used in the selection systems in sports. This includes the choice of sports specialization, optimization of the training process, and assessment of the current functional state of an athlete (such as overtraining). This review summarizes and analyzes the genomic, proteomic, and metabolomic studies conducted in the field of sports medicine.