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Combined Deep Learning and Molecular Modeling Techniques on the Virtual Screening of New mTOR Inhibitors from the Thai Mushroom Database
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Document Title
Combined Deep Learning and Molecular Modeling Techniques on the Virtual Screening of New mTOR Inhibitors from the Thai Mushroom Database
Author
Posansee K. Liangruksa M. Termsaithong T. Saparpakorn P. Hannongbua S. Laomettachit T. Sutthibutpong T.
Affiliations
Theoretical and Computational Physics Group Department of Physics King Mongkut抯 University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand; National Nanotechnology Center (NANOTEC) National Science and Technology Development Agency (NSTDA) Pathum Thani 12120 Thailand; Learning Institute King Mongkut抯 University of Technology Thonburi (KMUTT) Bangkok 10140 Thailand; Department of Chemistry Faculty of Science Kasetsart University Bangkok 10900 Thailand; Bioinformatics and Systems Biology Program School of Bioresources and Technology King Mongkut抯 University of Technology Thonburi (KMUTT) Bangkok 10150 Thailand; Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut抯 University of Technology Thonburi (KMUTT) 126 Pracha Uthit Road Bangkok 10140 Thailand
Type
Article
Source Title
ACS Omega
ISSN
24701343
Year
2023
Volume
8
Issue
41
Page
38373-38385
Open Access
All Open Access Gold Green
Publisher
American Chemical Society
DOI
10.1021/acsomega.3c04827
Abstract
The mammalian target of rapamycin (mTOR) is a protein kinase of the PI3K/Akt signaling pathway that regulates cell growth and division and is an attractive target for cancer therapy. Many reports on finding alternative mTOR inhibitors available in a database contain a mixture of active compound data with different mechanisms which results in an increased complexity for training the machine learning models based on the chemical features of active compounds. In this study a deep learning model supported by principal component analysis (PCA) and structural methods was used to search for an alternative mTOR inhibitor from mushrooms. The mTORC1 active compound data set from the PubChem database was first filtered for only the compounds resided near the first-generation inhibitors (rapalogs) within the first two PCA coordinates of chemical features. A deep learning model trained by the filtered data set captured the main characteristics of rapalogs and displayed the importance of steroid cores. After that another layer of virtual screening by molecular docking calculations was performed on ternary complexes of FKBP12-FRB domains and six compound candidates with high 揳ctive� probability scores predicted by the deep learning models. Finally all-atom molecular dynamics simulations and MMPBSA binding energy analysis were performed on two selected candidates in comparison to rapamycin which confirmed the importance of ring groups and steroid cores for interaction networks. Trihydroxysterol from Lentinus polychrous Lev. was predicted as an interesting candidate due to the small but effective interaction network that facilitated FKBP12-FRB interactions and further stabilized the ternary complex. ? 2023 The Authors. Published by American Chemical Society.
Industrial Classification
Knowledge Taxonomy Level 1
Knowledge Taxonomy Level 2
Knowledge Taxonomy Level 3
License
CC BY-NC-ND
Rights
Authors
Publication Source
Scopus