Computer-assisted discovery and evaluation of potential ribosomal protein S6 kinase beta 2 inhibitors.
Molecular dynamics simulation
Protein model prediction
Ribosomal protein S6 kinase beta 2
Virtual screening
mTOR signaling pathway
Journal
Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250
Informations de publication
Date de publication:
06 Mar 2024
06 Mar 2024
Historique:
received:
23
11
2023
revised:
11
02
2024
accepted:
19
02
2024
medline:
15
3
2024
pubmed:
15
3
2024
entrez:
14
3
2024
Statut:
aheadofprint
Résumé
S6K2 is an important protein in mTOR signaling pathway and cancer. To identify potential S6K2 inhibitors for mTOR pathway treatment, a virtual screening of 1,575,957 active molecules was performed using PLANET, AutoDock GPU, and AutoDock Vina, with their classification abilities compared. The MM/PB(GB)SA method was used to identify four compounds with the strongest binding energies. These compounds were further investigated using molecular dynamics (MD) simulations to understand the properties of the S6K2/ligand complex. Due to a lack of available 3D structures of S6K2, OmegaFold served as a reliable 3D predictive model with higher evaluation scores in SAVES v6.0 than AlphaFold, AlphaFold2, and RoseTTAFold2. The 150 ns MD simulation revealed that the S6K2 structure in aqueous solvation experienced compression during conformational relaxation and encountered potential energy traps of about 19.6 kJ mol
Identifiants
pubmed: 38484695
pii: S0010-4825(24)00288-9
doi: 10.1016/j.compbiomed.2024.108204
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
108204Informations de copyright
Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.