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Towards accurate machine learning predictions of properties of the P-O bond cleaving in ATP upon enzymatic hydrolysis

Igor Viktorovich Polyakov 1
Igor Viktorovich Polyakov
, 
Kirill Dmitrievich Miroshnichenko 1
Kirill Dmitrievich Miroshnichenko
,  , 
Alexander Aleksandrovich Moskovsky 1
Alexander Aleksandrovich Moskovsky
, 
Ekaterina Igorevna Marchenko 2
Ekaterina Igorevna Marchenko
, 
Mariya Grigor'evna Khrenova 1, 3
Mariya Grigor'evna Khrenova
1 Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
2 Department of Materials Science, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
3 A.N. Bach Institute of Biochemistry, Russian Academy of Sciences, Moscow, Russian Federation
10.1016/j.mencom.2024.10.003
Published 2024-10-22
Communication , Volume 34, Issue 6, 776-779
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Polyakov I. V. et al. Towards accurate machine learning predictions of properties of the P-O bond cleaving in ATP upon enzymatic hydrolysis // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 776-779.
GOST all authors (up to 50) Copy
Polyakov I. V., Miroshnichenko K. D., Mulashkina T. I., Moskovsky A. A., Marchenko E. I., Khrenova M. G. Towards accurate machine learning predictions of properties of the P-O bond cleaving in ATP upon enzymatic hydrolysis // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 776-779.
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TY - JOUR
DO - 10.1016/j.mencom.2024.10.003
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.003
TI - Towards accurate machine learning predictions of properties of the P-O bond cleaving in ATP upon enzymatic hydrolysis
T2 - Mendeleev Communications
AU - Polyakov, Igor Viktorovich
AU - Miroshnichenko, Kirill Dmitrievich
AU - Mulashkina, Tatiana Igorevna
AU - Moskovsky, Alexander Aleksandrovich
AU - Marchenko, Ekaterina Igorevna
AU - Khrenova, Mariya Grigor'evna
PY - 2024
DA - 2024/10/22
PB - Mendeleev Communications
SP - 776-779
IS - 6
VL - 34
ER -
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@article{2024_Polyakov,
author = {Igor Viktorovich Polyakov and Kirill Dmitrievich Miroshnichenko and Tatiana Igorevna Mulashkina and Alexander Aleksandrovich Moskovsky and Ekaterina Igorevna Marchenko and Mariya Grigor'evna Khrenova},
title = {Towards accurate machine learning predictions of properties of the P-O bond cleaving in ATP upon enzymatic hydrolysis},
journal = {Mendeleev Communications},
year = {2024},
volume = {34},
publisher = {Mendeleev Communications},
month = {Oct},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.003},
number = {6},
pages = {776--779},
doi = {10.1016/j.mencom.2024.10.003}
}
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Polyakov, Igor Viktorovich, et al. “Towards accurate machine learning predictions of properties of the P-O bond cleaving in ATP upon enzymatic hydrolysis.” Mendeleev Communications, vol. 34, no. 6, Oct. 2024, pp. 776-779. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.003.

Keywords

ATP hydrolysis
Laplacian of electron density.
machine learning
myosin
QM/MM molecular dynamics

Abstract

Molecular dynamic simulations using QM/MM potentials are performed for the enzyme-substrate complex of adenosine triphosphate (ATP) with the motor protein myosin. Machine learning methods are applied to a dataset consisting of the geometry parameters of the active site in the enzyme-substrate complex to predict the Laplacian of electron density at the bond critical point of the PG-O3B bond being broken in ATP. Using a gradient boosting machine learning model, a mean absolute error of 0.01 a.u. and an R2 score of 0.99 are achieved, and it is found that the PG-O3B bond length is the most important feature, contributing 2/3, while other geometry features contribute 1/3.

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