Improving docking and virtual screening performance using AlphaFold2 multi-state modeling for kinases.
AlphaFold2
Ensemble screening
Kinase
Multi-state modeling
Protein-ligand docking
Structure-based virtual screening
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
24 Oct 2024
24 Oct 2024
Historique:
received:
13
08
2024
accepted:
04
10
2024
medline:
25
10
2024
pubmed:
25
10
2024
entrez:
25
10
2024
Statut:
epublish
Résumé
Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery.
Identifiants
pubmed: 39448664
doi: 10.1038/s41598-024-75400-6
pii: 10.1038/s41598-024-75400-6
doi:
Substances chimiques
Protein Kinase Inhibitors
0
Ligands
0
Protein Kinases
EC 2.7.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
25167Subventions
Organisme : National Research Foundation of Korea
ID : 2022M3E5F3081268
Organisme : Korea Environmental Industry and Technology Institute
ID : RS-2023-00219144
Organisme : Ministry of Science and ICT, South Korea
ID : IITP-2024-RS-2022-00156439
Organisme : College of Medicine, Korea University
ID : K2327351
Informations de copyright
© 2024. The Author(s).
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