Chemists of RUDN showed how new antineoplastic medicine interacts with proteins of blood
Heterocyclic compounds, in which there are several organic “rings” with atoms of other elements, are of interest to chemists and pharmacists due to a wide range of biological activity. Previously, RUDN University created a heterocyclic compound with a pronounced antitumor effect. In structure, it resembles the drugs aphthazole and panoprofen, which are usually used to combat inflammation. At the same time, the mechanism of their action is not fully known to scientists. One of the indicators of the effectiveness of the drug is its binding to blood proteins, so the study of this process is important for understanding the work of the drug. RUDN chemists continued to study the new drug and studied the features of its biophysical interaction with the body. To do this, they investigated how the drug binds to the protein cyclooxygenase-1 (PTGS1) and bovine serum albumin (BSA).
“Sincethe molecules of the created drug compound are similar to the commercial drugs pranoprofenom and aphthazole,itis interesting to understand the mechanism of its binding interaction with PTGS1 using various biophysical methods. Understanding the interaction with BSA is important because it is a well-known model of proteinmolecules a, which can take various drugs and transfer them to a given molecular environment,” said Alexey Festa, Candidate of Chemical Sciences, Senior Lecturer at the Department of Organic Chemistry of RUDN University.
The chemists investigated the drug’s interaction with BSA and PTGS1 experimentally and theoretically, using fluorescence spectroscopy (fluorimetry)as well as computer molecular modeling. Fluorimetry allows you to determine the concentration of a substance by the intensity of fluorescence, which occurs when it is irradiated with a substance.
Fluorescence spectroscopy showed that when the drug interacts with albumin, the effect of the so-called static quenching occurs. This means that the substances have combined with each other, and a non-fluor natural product isobtained. This effect (together with subsequent theoretical simulation results) suggests that the compound follows the principle of hydrophobic interaction. It determines how the structure of the compound will look as a result. Molecular modeling using the molecular docking method showed that in conjunction with BSA and PTGS1, the drug is stable during the entire simulation time.
“Molecular docking studies show that the drug has a goodbinding ability both in combination with BSA and in combination with PTGS. At the same time, the theoretically calculated energyof the connection almost coincides with the one calculated experimentally.Analysis ofmolecular dynamics shows that the drug is stable both in the complex system with BSA and in the system with PTGS1,” — Cubramani Kartikean, postdoc Department of Organic Chemistry rudn University.
The results are published in the Journal of Molecular Structure.
Imagine a world where everyone has enough food, clean water, access to education, and decent work. A world where nature is protected and the future of our planet is cared for. These are the Sustainable Development Goals—to achieve a sustainable future for all! To this end, in 2015, the United Nations (UN) defined 17 Sustainable Development Goals (SDGs). The SDGs are a global plan that helps countries and people work together towards a better future. All 193 UN member states have joined the plan.
Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
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Imagine a world where everyone has enough food, clean water, access to education, and decent work. A world where nature is protected and the future of our planet is cared for. These are the Sustainable Development Goals—to achieve a sustainable future for all! To this end, in 2015, the United Nations (UN) defined 17 Sustainable Development Goals (SDGs). The SDGs are a global plan that helps countries and people work together towards a better future. All 193 UN member states have joined the plan.
Researchers from the Faculty of Artificial Intelligence at RUDN University conducted a large-scale study that revealed systemic errors in large language models (LLMs) when diagnosing depression based on text. This work, carried out in collaboration with colleagues from AIRI, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow Institute of Physics and Technology, and MBZUAI, not only identifies the problem but also lays the foundation for the creation of more reliable and secure tools for detecting depression and anxiety.
Alexandra Sentyabreva, a junior researcher at the Laboratory of Cell Technologies and Tissue Engineering at RUDN Research Institute of Molecular and Cellular Medicine at the Russian University of People's Friendship, won the competition for young scientists at the All-Russian Scientific Conference “Topical Issues of Morphogenesis in Norm and Pathology.” She was awarded the Academician A.P. Avtsyn Prize.