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Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer’s disease

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Abstract

Alzheimer’s disease (AD) is a complex neurological disorder and no effective drug is available for its treatment. Numerous pathological conditions are believed to be responsible for the initiation and development of AD including c-Jun N-terminal kinases (JNKs). The JNKs are one of the enzymes from the mitogen-activated protein kinase (MAPK) family that controls the phosphorylation of various transcription factors on serine and threonine residues, and hold significant responsibilities in tasks like gene expression, cell proliferation, differentiation, and apoptosis. Since, JNK3 is primarily expressed in the brain hence its increased levels in the brain are associated with the AD pathology promoting neurofibrillary tangles, senile plaques, neuroinflammation, and nerve cell apoptosis. The current research work is focused on the development of novel JNK inhibitors as therapeutics for AD employing a structure-based virtual screening (SBVS) approach. The ZINC database (14634052 compounds) was investigated after employing pan assay interference (PAINs), drug-likeness, and diversity picking filter to distinguish molecules interacting with JNK3 by following three docking precision criteria: High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) & MMGBSA. Five lead molecules showed a better docking score in the range of -13.091 to -14.051 kcal/mol better than the reference compound (− 11.828 kcal/mol). The lead compounds displayed acceptable pharmacokinetic properties and were subjected to molecular dynamic simulations of 100 ns and binding free energy calculations. All the lead molecules showed stable RMSD and hydrogen bond interactions throughout the trajectory. The ∆GMM/PBSA_total score for the lead compounds ZINC220382956, ZINC147071339, ZINC207081127, ZINC205151456, ZINC1228819126, and CC-930 was calculated and found to be − 31.39, − 42.8, − 37.04, − 39.01, − 36.5, − 34.16 kcal/mol, respectively. Thus, it was concluded that the lead molecules identified in these studies have the potential to be explored as potent JNK3 inhibitors.

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Data availability

The datasets employed for conducting the computational study are readily accessible and available for utilization at https://doi.org/https://doi.org/10.5281/zenodo.8278365.

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Acknowledgements

KJ would like to acknowledge the Indian Council of Medical Research (ICMR), New Delhi, India for awarding a Senior Research Fellowship (File No. 45/29/2022-BIO/BMS). VK is thankful to CSIR New Delhi for providing grant reference number 02/(0354)/19/EMRII and also to DST-STARS (SR/FST/CS-I/2020/154). Authors are gratefully acknowledged for the access to the ‘PARAM Smriti Facility’ under the National Supercomputing Mission, Government of India at NABI, Mohali. NK and Vinay Kumar are grateful to UGC and CSIR, respectively for providing a Senior Research Fellowship.

Funding

Indian Council of Medical Research, 45/29/2022-BIO/BMS,Council of Scientific and Industrial Research,India,02/(0354)/19/EMRII,MoE STARS,IISc Bangolre,STARS2/2023-0040

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  1. Bharti Devi and Kailash Jangid have contributed equally to this work.

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  1. Laboratory of Organic and Medicinal Chemistry, Department of Chemistry, School of Basic Sciences, Central University of Punjab, Bathinda, Ghudda, 151401, India

    Bharti Devi, Naveen Kumar, Vinay Kumar & Vinod Kumar

  2. Laboratory of Organic and Medicinal Chemistry, Department of Chemistry and Department of Pharmaceutical Sciences and Natural Products, Central University of Punjab, Bathinda, Ghudda, 151401, India

    Kailash Jangid

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VK: has designed and instructed the study and is involved in manuscript preparation. BD and KJ: have performed computational work, analyzed the results, and prepared a draft of the manuscript with equal contribution. NK and VK: have performed ADME, MM/PBSA, and helped in editing and finalizing the manuscript.

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Correspondence to Vinod Kumar.

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Devi, B., Jangid, K., Kumar, N. et al. Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer’s disease. Mol Divers (2024). https://doi.org/10.1007/s11030-024-10820-0

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