Modelling PIP4K2A inhibitory activity of 1,7-naphthyridine analogues using machine learning and molecular docking studies

Abstract

PIP4K2A is a type II lipid kinase that catalyzed the rate-limiting step of the conversion of phosphatidylinositol-5-phosphate (PI5P) into phosphatidylinositol 4,5-bisphosphate (PI4,5P2). PIP4K2A has been intricately linked to the inhibition of various types of tumors via reactive oxygen species-mediated apoptosis, making it an important therapeutic target. In the quest of finding biologically active substances with efficient PIP4K2A inhibitory activity, machine learning algorithms were used to investigate the quantitative relationship between structures and inhibitory activities of 1,7-naphthyridine analogues. Three machine learning algorithms (MLR, ANN, and SVM) were used to develop QSAR models that can effectively predict the PIP4K2A inhibitory activity of a library of 1,7-naphthyridine analogues. The cascaded feature selection method was performed by sequential application of GFA and MP5 algorithms to identify a molecular descriptor subset that can best describe the PIP4K2A inhibitory activity of 1,7-naphthyridine analogues. PIP4K2A inhibitory activities predicted by the ML models were strongly correlated with the experimental values. The QSAR Modelling indicates that the best-performing ML model was SVM with the RBF kernel function. The SVM model performed very well in predicting PIP4K2A inhibitory activity of the 1,7-naphthyridine analogues with RTR and QEX values of 0.9845 and 0.8793 respectively. To further gain more structural insight into the origin of PIP4K2A inhibitory activity of 1,7-naphthyridine analogues, molecular docking studies were performed. The results indicate that five compounds; 15, 25, 13, 09, and 28 were found to have a high binding affinity with the receptor molecules. Hydrogen bonding, pi-pi interaction, and pi-cation interactions were found to modulate the binding interaction of the inhibitors. Although the SVM gives essentially a black-box model which cannot be readily interpreted, using SVM in tandem with MLR and ANN provides a unique perspective in building robust QSAR predictive models. The superior predictive performance of the ML models and the explanatory power of MLR models were combined to provide a unique insight into the structure-activity relationship of 1,7-naphthyridine inhibitors. This is relevant in that it provides information that can be invaluable as guidelines for the design of novel PIP4K2A inhibitors.

Fig. 1. Scatter plot of the experimental PIP4K2A inhibitory activity and values predicted using MLR.

Fig. 2. Williams plot of QSAR model using MLR.

Fig. 3. Scatter plot of the experimental PIP4K2A inhibitory activity and values predicted using ANN.

Fig. 4. Williams plot of QSAR model using ANN.

Fig. 5. Comparing validation metric of SVM models developed using different kernel functions.

Fig. 6. Scatter plot of the experimental PIP4K2A inhibitory activity and values predicted using SVM.

Fig. 7. Williams plot of QSAR model using AN.

Fig. 8. Superimposed co-crystallized ligand pose and computed ligand pose.

Fig. 9. 3D visualization of the interaction of compound 15 on the active site of PIP4K2A.

Fig. 10. 3D visualization of the interaction of compound 25 on the active site of PIP4K2A.

Fig. 11. 3D visualization of the interaction of compound 13 on the active site of PIP4K2A.

Fig. 12. 3D visualization of the interaction of compound 09 on the active site of PIP4K2A.

Fig. 13. 3D visualization of the interaction of compound 28 on the active site of PIP4K2A.

Fig. 14. Barcode representation of protein–ligand interaction fingerprint matrix of PIP4K2A inhibitors.

Fig. 15. Relationship between protein–ligand contact similarity and docking scores of PIP4K2A inhibitors.