Discovery of novel selective PI3Kγ inhibitors through combining machine learning-based virtual screening with multiple protein structures and bio-evaluation
| Autor: | Yanfei Cai, Jingyu Zhu, Yun Chen, Jian Jin, Xinling Zhao, Kan Li, Lei Xu, Huazhong Li, Gang Huang |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
SMILES
simplified molecular input line entry specification 0301 basic medicine Medicine (General) Science (General) CADD computer-aided drug design AUC area under receiver operations characteristic curve VS virtual screening Ionic ionic interactions computer.software_genre PI3Kγ Machine Learning Q1-390 Phosphatidylinositol 3-Kinases 0302 clinical medicine Protein structure Basic and Biological Science PI3K Phosphoinositide 3-kinase RTK receptor tyrosine kinases IMDM Iscove’s Modified Dulbecco’s Medium PDB protein data bank Cytotoxicity PARP poly ADP-ribose polymerase XP extra precision Phosphoinositide-3 Kinase Inhibitors Selective inhibitor chemistry.chemical_classification Multidisciplinary Chemistry JN-KI3 GPCR G protein-coupled receptors MD molecular dynamics Molecular Docking Simulation PAINS pan-assay interference compounds NBC naive Bayesian classifier RMSD root-mean-squared-deviation RMSF root-mean-squared-fluctuation MM/GBSA molecular mechanics/generalized born surface area 030220 oncology & carcinogenesis CDRA confirmatory dose–response assays SP standard precision DMEM Dulbecco’s Modified Eagle Medium Virtual screening Gene isoform H-bond hydrogen bond DS3.5 discovery studio 3.5 Molecular Dynamics Simulation Machine learning AKT protein kinase B 03 medical and health sciences R5-920 FBS fetal bovine serum ROC receiver operations characteristic REOS rapid elimination of swill Badapple bioactivity data associative promiscuity pattern learning engine PAGE polyacrylamide gel electrophoresis PI3K/AKT/mTOR pathway ComputingMethodologies_COMPUTERGRAPHICS Water Bridge hydrogen bonds through water molecular bridge business.industry Mechanism (biology) PSA primary screening assays 030104 developmental biology Enzyme Cell culture Hematologic malignancies Artificial intelligence SD standard deviation business ADMET absorption distribution metabolism excretion and toxicity computer |
| Zdroj: | Journal of Advanced Research Journal of Advanced Research, Vol 36, Iss, Pp 1-13 (2022) |
| ISSN: | 2090-1232 |
| DOI: | 10.1016/j.jare.2021.04.007 |
| Popis: | Graphical abstract Highlights • Virtual screening based on machine learning with multiple proteins was developed. • Discovery of a novel PI3Kγ inhibitor integrating virtual screening and bio-assays. • JN-KI3 selective inhibit PI3Kγ enzymatic activity and hematologic malignancies. • The selective γ-inhibition mechanism of JN-KI3 was highlighted using MD simulation. Introduction Phosphoinositide 3-kinase gamma (PI3Kγ) has been regarded as a promising drug target for the treatment of various diseases, and the diverse physiological roles of class I PI3K isoforms (α, β, δ, and γ) highlight the importance of isoform selectivity in the development of PI3Kγ inhibitors. However, the high structural conservation among the PI3K family makes it a big challenge to develop selective PI3Kγ inhibitors. Objectives A novel machine learning-based virtual screening with multiple PI3Kγ protein structures was developed to discover novel PI3Kγ inhibitors. Methods A large chemical database was screened using the virtual screening model, the top-ranked compounds were then subjected to a series of bio-evaluations, which led to the discovery of JN-KI3. The selective inhibition mechanism of JN-KI3 against PI3Kγ was uncovered by a theoretical study. Results 49 hits were identified through virtual screening, and the cell-free enzymatic studies found that JN-KI3 selectively inhibited PI3Kγ at a concentration as low as 3,873 nM but had no inhibitory effect on Class IA PI3Ks, leading to the selective cytotoxicity on hematologic cancer cells. Meanwhile, JN-KI3 potently blocked the PI3K signaling, finally led to distinct apoptosis of hematologic cell lines at a low concentration. Lastly, the key residues of PI3Kγ and the structural characteristics of JN-KI3, which both would influence γ isoform-selective inhibition, were highlighted by systematic theoretical studies. Conclusion The developed virtual screening model strongly manifests the robustness to find novel PI3Kγ inhibitors. JN-KI3 displays a specific cytotoxicity on hematologic tumor cells, and significantly promotes apoptosis associated with the inhibition of the PI3K signaling, which depicts PI3Kγ as a potential target for the hematologic tumor therapy. The theoretical results reveal that those key residues interacting with JN-KI3 are less common compared to most of the reported PI3Kγ inhibitors, indicating that JN-KI3 has novel structural characteristics as a selective PIK3γ inhibitor. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|