Construction of a high-throughput screening model for mitochondrial function of Aconiti Lateralis Radix Praeparata by machine learning algorithm and mechanism analysis.
10.19540/j.cnki.cjcmm.20220216.402
- Author:
Ying-Li ZHU
1
;
Hong-Bin YANG
2
;
Jia-Rui WU
1
;
Xin SUN
3
;
Bing ZHANG
1
Author Information
1. School of Chinese Materia Medica, Beijing University of Chinese Medicine Beijing 100029, China Center for Pharmacovigilance and Rational Use of Chinese Medicine, Beijing University of Chinese Medicine Beijing 100029, China.
2. Center for Molecular Science Informatics, University of Cambridge Cambridge CB2 1EW, United Kingdom.
3. School of Traditional Chinese Medicine, Beijing University of Chinese Medicine Beijing 100029, China.
- Publication Type:Journal Article
- Keywords:
Aconiti Lateralis Radix Praeparata;
benzoylmesaconine;
deoxyaconitine;
machine learning;
mitochondria
- MeSH:
Aconitum/chemistry*;
Algorithms;
Drugs, Chinese Herbal/chemistry*;
High-Throughput Screening Assays;
Machine Learning;
Mitochondria;
Reactive Oxygen Species;
bcl-2-Associated X Protein
- From:
China Journal of Chinese Materia Medica
2022;47(9):2509-2515
- CountryChina
- Language:Chinese
-
Abstract:
A high-throughput screening machine learning model for mitochondrial function was constructed, and compounds of Aco-niti Lateralis Radix Praeparata were predicted. Deoxyaconitine with the highest score and benzoylmesaconine with the lowest score among the compounds screened by the model were selected for mitochondrial mechanism analysis. Mitochondrial function data were collected from PubChem and Tox21 databases. Random forest and gradient boosted decision tree algorithms were separately used for mo-deling, and ECFP4(extended connectivity fingerprint, up to four bonds) and Mordred descriptors were employed for training, respectively. Cross-validation test was carried out, and balanced accuracy(BA) and overall accuracy were determined to evaluate the performance of different combinations of models and obtain the optimal algorithm and hyperparameters for modeling. The data of Aconiti Lateralis Radix Praeparata compounds in TCMSP database were collected, and after prediction and screening by the constructed high-throughput screening machine learning model, deoxyaconitine and benzoylmesaconine were selected to measure mitochondrial membrane potential, reactive oxygen species(ROS) level and protein expression of B-cell lymphoma 2(Bcl-2), Bcl-2-associated X protein(Bax) and peroxisome proliferator-activated receptor-γ-coactivator 1α(PGC-1α). The results showed that the model constructed using gradient boosted decision tree+Mordred algorithm performed better, with a cross-validation BA of 0.825 and a test set accuracy of 0.811. Deoxyaconitine and benzoylmesaconine changed the ROS level(P<0.001), mitochondrial membrane potential(P<0.001), and protein expression of Bcl-2(P<0.001, P<0.01) and Bax(P<0.001), and deoxyaconitine increased the expression of PGC-1α protein(P<0.01). The high-throughput screening model for mitochondrial function constructed by gradient boosted decision tree+Mordred algorithm was more accurate than that by random forest+ECFP4 algorithm, which could be used to build an algorithm model for subsequent research. Deoxyaconitine and benzoylmesaconine affected mitochondrial function. However, deoxyaconitine with higher score also affected mitochondrial biosynthesis by regulating PGC-1α protein.
