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Towards machine learning prediction of the fluorescent protein absorption spectra

Roman Alekseevich Stepanyuk 1, 2
Roman Alekseevich Stepanyuk
, 
Igor Viktorovich Polyakov 1, 3
Igor Viktorovich Polyakov
, 
Anna Mikhailovna Kulakova 1
Anna Mikhailovna Kulakova
, 
Ekaterina Igorevna Marchenko 4
Ekaterina Igorevna Marchenko
, 
Mariya Grigor'evna Khrenova 1, 2
Mariya Grigor'evna Khrenova
1 Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
2 Federal Research Centre 'Fundamentals of Biotechnology' of the Russian Academy of Sciences, Moscow, Russian Federation
3 N.M. Emanuel Institute of Biochemical Physics, Russian Academy of Sciences, Moscow, Russian Federation
4 Department of Materials Science, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
10.1016/j.mencom.2024.10.007
Published 2024-10-22
Communication , Volume 34, Issue 6, 788-791
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Stepanyuk R. A. et al. Towards machine learning prediction of the fluorescent protein absorption spectra // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 788-791.
GOST all authors (up to 50) Copy
Stepanyuk R. A., Polyakov I. V., Kulakova A. M., Marchenko E. I., Khrenova M. G. Towards machine learning prediction of the fluorescent protein absorption spectra // Mendeleev Communications. 2024. Vol. 34. No. 6. pp. 788-791.
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TY - JOUR
DO - 10.1016/j.mencom.2024.10.007
UR - https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.007
TI - Towards machine learning prediction of the fluorescent protein absorption spectra
T2 - Mendeleev Communications
AU - Stepanyuk, Roman Alekseevich
AU - Polyakov, Igor Viktorovich
AU - Kulakova, Anna Mikhailovna
AU - Marchenko, Ekaterina Igorevna
AU - Khrenova, Mariya Grigor'evna
PY - 2024
DA - 2024/10/22
PB - Mendeleev Communications
SP - 788-791
IS - 6
VL - 34
ER -
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@article{2024_Stepanyuk,
author = {Roman Alekseevich Stepanyuk and Igor Viktorovich Polyakov and Anna Mikhailovna Kulakova and Ekaterina Igorevna Marchenko and Mariya Grigor'evna Khrenova},
title = {Towards machine learning prediction of the fluorescent protein absorption spectra},
journal = {Mendeleev Communications},
year = {2024},
volume = {34},
publisher = {Mendeleev Communications},
month = {Oct},
url = {https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.007},
number = {6},
pages = {788--791},
doi = {10.1016/j.mencom.2024.10.007}
}
MLA
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Stepanyuk, Roman Alekseevich, et al. “Towards machine learning prediction of the fluorescent protein absorption spectra.” Mendeleev Communications, vol. 34, no. 6, Oct. 2024, pp. 788-791. https://mendcomm.colab.ws/publications/10.1016/j.mencom.2024.10.007.

Keywords

dipole moment variation upon excitation.
fluorescent proteins
machine learning
QM/MM molecular dynamics

Abstract

We demonstrate that machine learning models trained on a set of features obtained from QM/MM molecular dynamic trajectories of fluorescent proteins can be used to predict the chromophore dipole moment variation upon excitation, the quantity related to the electronic excitation energy. Linear regression, gradient boosting, and artificial neural network- based models were considered using cross-validation on the training dataset. Gradient boosting approach proved to be the most accurate for both internal (R2 = 0.77) and external (R2 = 0.7) test sets.

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