Immunomodulatory potential of bioactive glycoside syringin: a network pharmacology and molecular modeling approach

Abstract

Many diseases, such as rheumatoid arthritis, neurodegenerative disease, lupus, autoimmune disease, and cancer, are described by chronic inflammation following tissue damage. Anti-inflammatory drugs like non-steroidal anti-inflammatory drugs and other steroids cause many side effects and generally need careful consideration and monitoring during usage. In recent years, a significant interest in plant-derived approaches has been warranted. The bioactive glycoside syringin might be one of the effective immunomodulatory agents. However, its immunomodulatory potential needs to be better known. In this study, we evaluated the immunomodulatory potential of syringin using network pharmacology, molecular docking, and molecular dynamics simulation-based approaches. First, we applied the GeneCards and OMIM databases to acquire the immunomodulatory agents. Then, the STRING database was utilized to get the hub genes. Interaction analysis and molecular docking described strong binding of the active site of immunomodulatory proteins with the bioactive syringin. Molecular dynamics simulations (200 ns) showed a very stable interaction of syringin with the immunomodulatory protein. Further, the optimized structure and molecular electrostatic potential of the syringin were calculated by a density-functional theory utilizing basis levels of B3LYP/6-31. Syringin investigated in this study holds the required drug-likeness characteristics and follows Lipinski’s rule of five. However, quantum-chemical estimations show the syringin has potent reactivity, demonstrating a lower energy gap. Furthermore, the gap between ^ELUMO and ^EHOMO was low, suggesting the excellent affinity of syringin towards the immunomodulatory proteins. The present study shows that syringin might be an effective immunomodulatory agent and can be further explored using different experimental methods.

Communicated by Ramaswamy H. Sarma

Keywords:

• DAVID database
• density functional theory
• immunomodulator
• MD simulations
• STRING database
• Syringin

Acknowledgments

Vipendra Kumar Singh acknowledges the Indian Council of Medical Research—Research Associateship (ICMR-RA) Fellowship, New Delhi, India, and D. C. Thakur acknowledges the ICMR for the Postdoctoral Fellowship. Naina Rajak acknowledges the Council of Scientific and Industrial Research-Junior Research Fellowship (CSIR-JRF), New Delhi, India. Siddharth Mall Bisen, Department of Physics, Institute of Science, Banaras Hindu University, Varanasi, for help in the DFT analysis. Dr. Neha Garg would like to acknowledge the seed grant under the Institute of Eminence Scheme, BHU, for financial support. Finally, the authors highly acknowledge the National Supercomputing Mission (NSM) for providing computing resources of 'PARAM Himalaya’ at IIT Mandi, which is implemented by C-DAC and supported by the Ministry of Electronics and Information Technology (MeitY) and the Department of Science and Technology (DST), Government of India.

Disclosure statement

The authors declare no conflict of interest in the present work.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.