Персона:
Борисевич, София Станиславовна

Организационные подразделения

Организационная единица

Институт интеллектуальных кибернетических систем

Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.

Фамилия

Борисевич

Имя

София Станиславовна

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Результаты поиска

Теперь показываю 1 - 10 из 14

Только метаданные
KDM2 lysine demethylases: structure, functions, and anticancer targets
(2026) Osherov, P. M.; Kurkin, A. V.; Shtil, A. A.; Borisevich, S. S.; Ошеров, Петр Михайлович; Борисевич, София Станиславовна
Только метаданные
Prediction of the small molecule selectivity index against influenza virus strain A/H1N1 using machine learning methods
(2025) Egorov, A. D.; Gorohov, Ya. V.; Kuznetsov, M. M.; Borisevich, S. S.; Егоров, Алексей Дмитриевич; Борисевич, София Станиславовна
Только метаданные
Conjugates of amiridine and thiouracil derivatives as effective inhibitors of butyrylcholinesterase with the potential to block β-amyloid aggregation
(2024) Khudina, O. G.; Grishchenko, M. V.; Makhaeva, G. F.; Kovaleva, N. V; Borisevich, S. S.; Борисевич, София Станиславовна
Только метаданные
Structural Features of Statistically Significant Complexes Formed in the 0.5 mol/kg LiDFOB SL/DMC System
(2023) Evshchik, E. Y.; Ilyina, M. G.; Khamitov, E. M.; Borisevich, S. S.; Ильина, Маргарита Григорьевна; Хамитов, Эдуард Маратович; Борисевич, София Станиславовна
Только метаданные
Whether Molecular Dynamics Methods Can Explain Different Activities of Stereoisomers Against Respiratory Syncytial Virus or Not?
(2024) Borisevich, S. S.; Volcho, K. P.; Salakhutdinov, N. F.; Борисевич, София Станиславовна
Только метаданные
Machine learning prediction of acute toxicity with in vivo experiments on tetrazole derivatives
(2025) Zarezin,D.P.; Shtil, A. A.; Nenajdenko, V. G.; Borisevich, S. S.; Зарезин, Данил Петрович; Штиль, Александр Альбертович; Борисевич, София Станиславовна
Только метаданные
QC and MD Modelling for Predicting the Electrochemical Stability Window of Electrolytes: New Estimating Algorithm
(2022) Dobrovolsky, Y. A.; Ilyina, M. G.; Khamitov, E. M.; Borisevich, S. S.; Ильина, Маргарита Григорьевна; Хамитов, Эдуард Маратович; Борисевич, София Станиславовна
The electrolyte is an important component of lithium-ion batteries, especially when it comes to cycling high-voltage cathode materials. In this paper, we propose an algorithm for estimating both the oxidising and reducing potential of electrolytes using molecular dynamics and quantum chemistry techniques. This algorithm can help to determine the composition and structure of the solvate complexes formed when a salt is dissolved in a mixture of solvents. To develop and confirm the efficiency of the algorithm, LiBF4 solutions in binary mixtures of ethylene carbonate (EC)/dimethyl carbonate (DMC) and sulfolane (SL)/dimethyl carbonate (DMC) were studied. The structure and composition of the complexes formed in these systems were determined according to molecular dynamics. Quantum chemical estimation of the thermodynamic and oxidative stability of solvate complexes made it possible to establish which complexes make the most significant contribution to the electrochemical stability of the electrolyte system. This method can also be used to determine the additive value of the oxidation and reduction potentials of the electrolyte, along with the contribution of each complex to the overall stability of the electrolyte. Theoretical calculations were confirmed experimentally in the course of studying electrolytes by step-by-step polarisation using inert electrodes. Thus, the main aim of the study is to demonstrate the possibility of using the developed algorithm to select the optimal composition and solvent ratio to achieve predicted redox stability.
Только метаданные
In silico analysis of the γ-carbonic anhydrase EcoCAγ from Enterobacteria: assessing the role of the 72-residue N-terminal extension
(2025) Stoliarskaia, M. Y.; Nikonov, O. S.; Borisevich, S. S.; Столярская, Мария Ярославовна; Борисевич, София Станиславовна
Открытый доступ
New Inhibitors of Respiratory Syncytial Virus (RSV) Replication Based on Monoterpene-Substituted Arylcoumarins
(2023) Khomenko, T. M.; Shtro, A. A.; Galochkina, A. V.; Nikolaeva, Y. V.; Borisevich, S. S.; Борисевич, София Станиславовна
Respiratory syncytial virus (RSV) causes annual epidemics of respiratory infection. Usually harmless to adults, the RSV infection can be dangerous to children under 3 years of age and elderly people over 65 years of age, often causing serious problems, even death. At present, there are no vaccines and specific chemotherapeutic agents for the treatment of this disease, so the search for low-molecular weight compounds to combat RSV is a challenge. In this work, we have shown, for the first time, that monoterpene-substituted arylcoumarins are efficient RSV replication inhibitors at low micromolar concentrations. The most active compound has a selectivity index of about 200 and acts most effectively at the early stages of infection. The F protein of RSV is a potential target for these compounds, which is also confirmed by molecular docking and molecular dynamics simulation data.
Открытый доступ
The Nitro Group Reshapes the Effects of Pyrido[3,4-g]quinazoline Derivatives on DYRK/CLK Activity and RNA Splicing in Glioblastoma Cells
(2024) Borisevich, S. S.; Aksinina,T. E.; Ilyina, M. G.; Shender,V. O.; Борисевич, София Станиславовна; Ильина, Маргарита Григорьевна
Serine-threonine protein kinases of the DYRK and CLK families regulate a variety of vital cellular functions. In particular, these enzymes phosphorylate proteins involved in pre-mRNA splicing. Targeting splicing with pharmacological DYRK/CLK inhibitors emerged as a promising anticancer strategy. Investigation of the pyrido[3,4-g]quinazoline scaffold led to the discovery of DYRK/CLK binders with differential potency against individual enzyme isoforms. Exploring the structureў??activity relationship within this chemotype, we demonstrated that two structurally close compounds, pyrido[3,4-g]quinazoline-2,10-diamine 1 and 10-nitro pyrido[3,4-g]quinazoline-2-amine 2, differentially inhibited DYRK1-4 and CLK1-3 protein kinases in vitro. Unlike compound 1, compound 2 efficiently inhibited DYRK3 and CLK4 isoenzymes at nanomolar concentrations. Quantum chemical calculations, docking and molecular dynamic simulations of complexes of 1 and 2 with DYRK3 and CLK4 identified a dramatic difference in electron donor-acceptor properties critical for preferential interaction of 2 with these targets. Subsequent transcriptome and proteome analyses of patient-derived glioblastoma (GBM) neurospheres treated with 2 revealed that this compound impaired CLK4 interactions with spliceosomal proteins, thereby altering RNA splicing. Importantly, 2 affected the genes that perform critical functions for cancer cells including DNA damage response, p53 signaling and transcription. Altogether, these results provide a mechanistic basis for the therapeutic efficacy of 2 previously demonstrated in in vivo GBM models.