Structure-based identification of galectin-1 selective modulators in dietary food polyphenols: a pharmacoinformatics approach

Abstract

In this study, a set of dietary polyphenols was comprehensively studied for the selective identification of the potential inhibitors/modulators for galectin-1. Galectin-1 is a potent prognostic indicator of tumor progression and a highly regarded therapeutic target for various pathological conditions. This indicator is composed of a highly conserved carbohydrate recognition domain (CRD) that accounts for the binding affinity of β-galactosides. Although some small molecules have been identified as galectin-1 inhibitors/modulators, there are limited studies on the identification of novel compounds against this attractive therapeutic target. The extensive computational techniques include potential drug binding site recognition on galectin-1, binding affinity predictions of ~ 500 polyphenols, molecular docking, and dynamic simulations of galectin-1 with selective dietary polyphenol modulators, followed by the estimation of binding free energy for the identification of dietary polyphenol-based galectin-1 modulators. Initially, a deep neural network-based algorithm was utilized for the prediction of the druggable binding site and binding affinity. Thereafter, the intermolecular interactions of the polyphenol compounds with galectin-1 were critically explored through the extra-precision docking technique. Further, the stability of the interaction was evaluated through the conventional atomistic 100 ns dynamic simulation study. The docking analyses indicated the high interaction affinity of different amino acids at the CRD region of galectin-1 with the proposed five polyphenols. Strong and consistent interaction stability was suggested from the simulation trajectories of the selected dietary polyphenol under the dynamic conditions. Also, the conserved residue (His44, Asn46, Arg48, Val59, Asn61, Trp68, Glu71, and Arg73) associations suggest high affinity and selectivity of polyphenols toward galectin-1 protein. Graphic Abstract

Application of Molecular Simulation Methods in Food Science: Status and Prospects

“…Because of their universality, AutoDock Vina and NNScore 1.0 are better solutions in the absence of known ligands . To improve the description of the complexes, the energy-describing scoring function was adopted by the updated AutoDock Vina. , Driven by the developments of deep learning, end-to-end frameworks for predicting protein–ligand binding affinities, like K DEEP , have become a trend in the upgrading of SFs and successfully applied in the identification of potent food constituents against SARS-CoV-2 papain-like protease, verifying the better binding affinity of proposed dietary food compounds with SARS-CoV-2 PLpro protein compared to the standard compound VBY; similarly, K DEEP has been used for the binding affinity prediction of dietary polyphenols for the selective identification of the potential selective inhibitors/modulators for galectin-1 . Like other ML models, accurate ML scoring functions require high-quality data sets.…”

Identification of potential 3CLpro inhibitors-modulators for human norovirus infections: An advanced virtual screening approach

“…Molecular docking has already been recognized as a prototype of structure-based virtual screening [33][34][35][36][37][38][39][40][41][42][43][44][45][46]. In this approach, potential molecules are identified from large chemical databases and binding interaction affinity is predicted toward the protein targets of interest.…”