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Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches

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Abstract

Diabetes mellitus (DM) is one of the major health problems worldwide. WHO have estimated that 439 million people may have DM by the year 2030. Several classes of drugs such as sulfonylureas, meglitinides, thiazolidinediones etc. are available to manage this disease, however, there is no cure for this disease. Salt inducible kinase 2 (SIK2) is expressed several folds in adipose tissue than in normal tissues and thus SIK2 is one of the attractive targets for DM treatment. SIK2 inhibition improves glucose homeostasis. Several analogues have been reported and experimentally proven against SIK for DM treatment. But, identifying potential SIK2 inhibitors with improved efficacy and good pharmacokinetic profiles will be helpful for the effective treatment of DM. The objective of the present study is to identify selective SIK2 inhibitors with good pharmacokinetic profiles. Due to the unavailability of SIK2 structure, the modeled structure of SIK2 will be an important to understand the atomic level of SIK2 inhibitors in the binding site pocket. In this study, different molecular modeling studies such as Homology Modeling, Molecular Docking, Pharmacophore-based virtual screening, MD simulations, Density Functional Theory calculations and WaterMap analysis were performed to identify potential SIK2 inhibitors. Five molecules from different databases such as Binding_4067, TosLab_837067, NCI_349155, Life chemicals_ F2565-0113, Enamine_7623111186 molecules were identified as possible SIK2 inhibitors.

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Abbreviations

ADME:

Absorption distribution metabolism excretion

CAMK:

Calcium/calmodulin kinase

CREB:

CAMP-response element binding protein

CRTCs:

CREB-regulated transcriptional coactivators

CADD:

Computer-aided drug design

DM:

Diabetes mellitus

DFT:

Density functional theory

DOPE:

Discrete optimized protein energy

GCMC:

Grand canonical Monte Carlo

GCAP:

Grand canonical alchemical perturbation

GPCR:

G protein-coupled receptor

GROMACS:

GROningen MAchine for chemical simulations

HDACs:

Histone deacetylases

HTVS:

High-throughput virtual screening

HOMO:

Highest occupied molecular orbital

IST:

Inhomogeneous solvation theory

LKB1:

Liver kinase B1

LUMO:

Lowest unoccupied molecular orbital

MDS:

Molecular dynamic simulation

MDCK:

Madin-Darby canine kidney

MM-PBSA:

Molecular mechanics Poisson-Boltzmann surface area

NVT:

Constant temperature, constant volume

NPT:

Constant temperature, constant pressure

OPLS3e:

Optimized potentials for liquid simulations

PDB:

Protein data bank

ProSA:

Protein structural analysis

PBF:

Poisson Boltzmann

SGLT2:

Sodium-glucose co-transporter-2 finite

SP:

Standard precision

SPC:

Simple point charge

STM:

Single trajectory method

T2DM:

Type 2 Diabetes Mellitus

WHO:

World Health Organization

SIK2:

Salt inducible kinase 2

WM:

WaterMap

XP:

Extra precision

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Acknowledgements

JJ thanks the TANSCHE [RGP/2019-20/ALU/HECP-0049], Indo-Taiwan [GITA/DST/TWIN/P-86/2019] dated: 04/03/2020, DBT(BIC)-No. BT/PR40154/BTIS/137/34/2021, DST-Fund for Improvement of S&T Infrastructure in Universities & Higher Educational Institutions (FIST) (SR/FST/LSI-667/2016) (C), DST Promotion of University Research and Scientific Excellence (PURSE) (No. SR/PURSE Phase 2/38 (G), 2017 and JP thanks ICMR-SRF (No: ISRM/11 (62)/2019 dated: 10/06/ 2019) to carry out this work.

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  1. Structural Biology and Bio-Computing Laboratory, Department of Bioinformatics, Alagappa University, Science Block, Karaikudi, Tamil Nadu, 630004, India

    Prajisha Jayaprakash, Jayashree Biswal, Raghu Rangaswamy & Jeyaraman Jeyakanthan

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  1. Prajisha Jayaprakash
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Jayaprakash, P., Biswal, J., Rangaswamy, R. et al. Designing of potent anti-diabetic molecules by targeting SIK2 using computational approaches. Mol Divers 27, 1101–1121 (2023). https://doi.org/10.1007/s11030-022-10470-0

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