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

Shovonlal Bhowmick,Jozaa Zaidan Altamimi,Tahany Saleh Aldayel,Achintya Saha,Nora Abdullah Alfaris,Saikh Mohammad Wabaidur,Md Ataul Islam,Zeid A Alothman

doi:10.1007/s11030-021-10297-1

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

Highlights

Over the last few years, several studies indicate the emergence of the family of galectins as interesting drug targets for various pathophysiological conditions [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]
The exhaustive computational approaches combining the machine-learningbased recognition of the ligand binding site on the galectin-1 protein, absolute binding affinity predictions between the dietary polyphenols and galectin-1, extra-precision molecular docking, and all atomistic long-range 100 ns molecular dynamics (MD) simulation studies, and molecular mechanics–generalized born surface area (MM–GBSA)-based binding free energy estimation were implemented for identifying the potential polyphenols to be used as galectin-1 inhibitors/ modulators
The probable active binding sites on the galectin-1 protein were predicted by the DeepSite tool using a novel knowledge-based convolutional neural networks approach

Summary

Introduction

Over the last few years, several studies indicate the emergence of the family of galectins as interesting drug targets for various pathophysiological conditions [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]. The family of galectins is involved in a wide range of biological processes, such as cellular growth regulation, cell transformation, adhesion/migration, immunoregulation, chemotaxis and angiogenesis, invasion and metastasis, immune escape, and various key aspects of carcinogenesis-associated implications and other medical applications [23, 27]. These proteins are deeply involved in a variety of biological activities, such as the recognition and destruction of pathogens to alleviate their entry into the host cells [19, 28,29,30]. The exhaustive computational approaches combining the machine-learningbased recognition of the ligand binding site on the galectin-1 protein, absolute binding affinity predictions between the dietary polyphenols and galectin-1, extra-precision molecular docking, and all atomistic long-range 100 ns molecular dynamics (MD) simulation studies, and molecular mechanics–generalized born surface area (MM–GBSA)-based binding free energy estimation were implemented for identifying the potential polyphenols to be used as galectin-1 inhibitors/ modulators

Materials and methods

Results and discussion

Conclusion