Search for new inhibitors of protein kinase signaling pathways using molecular dynamics and molecular docking methods
Автор: Glushko A.A., Kodonidi I.P., Frantsiyants E.M., Kaplieva I.V., Chiryapkin A.S., Sergienko E.V.
Журнал: Cardiometry @cardiometry
Рубрика: Conference proceedings
Статья в выпуске: 29, 2023 года.
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The development of new therapeutic strategies for the treatment of various diseases associated with impaired protein kinase activity is one of the most urgent tasks in modern medicine. The search for new protein kinase inhibitors is an effective approach in the struggle against cancer. However, the existing protein kinase inhibitors may have limitations in their effectiveness or may produce undesired side effects. Therefore, the development of new methods for searching for new inhibitors, which should be more effective and safe, is a topical issue. One of the promising areas is the use of molecular modeling to search for new protein kinase inhibitors.
Protein kinase inhibitors, automated molecular docking, molecular dynamics, simulation, convolutional neural networks
Короткий адрес: https://sciup.org/148327379
IDR: 148327379 | DOI: 10.18137/cardiometry.2023.29.conf.7
Текст статьи Search for new inhibitors of protein kinase signaling pathways using molecular dynamics and molecular docking methods
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1Pyatigorsk Medical and Pharmaceutical Institute - branch of the Federal State Budgetary Educational Institution of Higher Education Volga State Medical University of the Russian Federation, Pyatigorsk
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2Federal State Budgetary Institution “National Medical Research Center for Oncology” of the Ministry of Health of Russia, Rostov-on-Don
Introduction . The development of new therapeutic strategies for the treatment of various diseases associated with impaired protein kinase activity is one of the most urgent tasks in modern medicine. The search for new protein kinase inhibitors is an effective approach in the struggle against cancer. However, the existing protein kinase inhibitors may have limitations in their effectiveness or may produce undesired side effects. Therefore, the development of new methods for searching for new inhibitors, which should be more effective and safe, is a topical issue. One of the promising areas is the use of molecular modeling to search for new protein kinase inhibitors.
The aim of our work was to develop a methodology for selecting new protein kinase inhibitors based on an analysis of their general properties and interactions with inhibitor molecules.
Materials and methods . In our work, automated molecular docking methods were applied in combination with the use of convolutional neural networks of deep learning and molecular dynamics to analyze the general properties of protein kinases and select the optimal method for searching for inhibitors. Molecular docking of a large array of molecules with known biological activity was performed, as well as a quantitative analysis of the relationship between their structure and the ability to inhibit protein kinases was completed [1]. The analysis was performed for the protein kinases MAPK (240 substances), MEPK (142
substances), B-Raf (325 substances), PI3K (325 substances), JAK (550 substances), as well as the receptor kinase EGFR (185 substances). Molecular docking was completed using the AutodockGPU software [2, 3], taking into account the mobility of the amino acids of the binding site.
Results . For each substance, 100 conformations of the ligand-enzyme complex were obtained. The docking results were analyzed based on the average and minimum binding energies. Molecular docking was also performed using the Gnina software [4]. In that case, the selection of the best conformations and prediction of the inhibition constant were provided using a convolutional neural network included in the Gnina software. For the obtained conformations, the ligand-enzyme complex was simulated by the molecular dynamics method in the Gromacs [5, 6] and Bioeureka [7] softwares. Molecular dynamics simulations made it possible to clarify the characteristics of the binding of ligands to the catalytic site (duration and energy of binding).
Conclusions . The results obtained make it possible to construct a high-performance method for a detailed study of the quantitative relationship between the structure and the biological activity for the targeted search for new protein kinase inhibitors.
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