Integration of regression models and MD simulations for virtual screening of natural compounds: Identification of novel hit scaffolds against SphK1 proteins

Abstract

Sphingosine kinase 1 (Sphk1) has emerged as a crucial target in the signalling pathways triggered by cancer, making it a current focus in the development of anticancer drugs. This paper used a combined computational method involving machine learning-driven regression models and molecular modelling methods to discover new Sphk1 inhibitors. Several multiple regression models, such as random forest (RF), support vector machine with radial basis function (SVM-RBF), and gradient boosting machine (GBM), k-nearest neighbors (KNN) and elastic-net regularized generalized linear model (GLMNET) were trained using combined descriptors and molecular fingerprint to make a prediction about the new scaffolds and their potency against the Sphk1 protein. The best-performing models were utilised for virtually screening by using the natural product library (TargetMol ∼ 4533 compounds), and the identified candidate molecules were allowed to perform molecular docking to determine their binding interaction with the Sphk1 protein. Upon close inspection, we have found a total six best hits, which have an estimated binding affinity much higher than that of the co-crystallised ligand. The stability and binding behaviour of the six best compounds were further investigated by performing 200 ns molecular dynamics (MD) simulations. Based on docking scores and dynamic stability, two hit candidates (T5711 and T6670) were selected for further analysis. These included MM/PBSA (molecular mechanics/Poisson-Boltzmann surface area) calculations to estimate binding free energies, as well as principal component analysis (PCA) and free energy landscape (FEL) evaluations to assess the conformational flexibility and thermodynamic stability of the protein-ligand complexes. This paper presents a strong virtual screening pipeline involving ML with support of molecular docking, MD simulations, and free energies analysis to discover potential virtual Sphk1 inhibitors. The results provide a platform, on and further additional experimentation and validation/optimisations can be done by medicinal chemists that will lead to the production of effective anticancer agents against the Sphk1 Protein. Graphical abstract showing workflow adopted for identification of new potential natural hits against Sphk1 protein by integrating ML-driven regression and virtual screening-based strategies.