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

[Display omitted] 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 enzym...

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Bibliographic Details
Published inMendeleev communications Vol. 34; no. 6; pp. 776 - 779
Main Authors Polyakov, Igor V., Miroshnichenko, Kirill D., Mulashkina, Tatiana I., Moskovsky, Alexander A., Marchenko, Ekaterina I., Khrenova, Maria G.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2024
Subjects
ATP hydrolysis
Laplacian of electron density
machine learning
myosin
QM/MM molecular dynamics
myosin
Laplacian of electron density
QM/MM molecular dynamics
ATP hydrolysis
machine learning
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Summary:[Display omitted] 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.
ISSN:0959-9436
DOI:10.1016/j.mencom.2024.10.003