student
Blagoveschensk, Blagoveshchensk, Russian Federation
student
Harbin, China
Blagoveshchensk, Blagoveshchensk, Russian Federation
VAC 03.01.2009 Математическая биология, биоинформатика
UDK 57 Биологические науки
GRNTI 34.15 Молекулярная биология
GRNTI 34.55 Биокибернетика
OKSO 06.05.01 Биоинженерия и биоинформатика
BBK 28 Биологические науки
TBK 6005 Прочие издания
BISAC MED008000 Biochemistry
BISAC MED090000 Biostatistics
In this work, using the in-silico experiment modeling method, the receptor and its ligands were docked in order to obtain the data necessary to study the possibility of using machine learning and hard intermolecular docking methods to predict potential ligands for various receptors. The protein TRPM8 was chosen, which is a member of the TRP superfamily of proteins and its classic agonist menthol as a ligand. It is known that menthol is able to bind to tyrosine 745 of the B chain. To carry out all the manipulations, we used the Autodock software and a special set of graphic tools designed to work with in silico models of chemicals. As a result of all the manipulations, the menthol conformations were obtained that can bind to the active center of the TRPM8 receptor.
protein TRPM8, bioinformatics, molecular docking
1. http://mgltools.scripps.edu/
2. http://autodock.scripps.edu/
3. https://www.rcsb.org/
4. https://www.rcsb.org/structure/6O6A
5. https://pubchem.ncbi.nlm.nih.gov/compound/1254
6. https://pubmed.ncbi.nlm.nih.gov/19886999/
7. https://pytorch.org
8. Estrada T. et.al., “Graphic Encoding of Macromolecules for Efficient High-Throughput Analysis”. https://www.researchgate.net/publication/327215386_Graphic_Encoding_of_Macromolecules_for_Efficient_High-Throughput_Analysis?enrichId=rgreq-5906aa511772b9138e942ed98f2a4faf-XXX&enrichSource=Y292ZXJQYWdlOzMyNzIxNTM4NjtBUzo2NjU0OTkwNzU2MTY3NzBAMTUzNTY3ODc1MjUwNA==&el=1_x_2&_esc=publicationCoverPdf
