1. Discovery of novel selective PI3Kγ inhibitors through combining machine learning-based virtual screening with multiple protein structures and bio-evaluation
- Author
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Yanfei Cai, Jingyu Zhu, Yun Chen, Jian Jin, Xinling Zhao, Kan Li, Lei Xu, Huazhong Li, and Gang Huang
- Subjects
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 - Abstract
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. more...
- Published
- 2022
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