Comprehending interaction mechanism of natural actives of Colchicum autumnale L. for rheumatoid arthritis using integrative chemoinformatic approaches

Abstract

This research delves into the realm of therapeutic potential within natural compounds derived from Colchicum autumnale L., emphasizing a holistic perspective on medications used in human therapy. Rather than confining the study to their primary actions, the research endeavors to unveil molecular targets for these natural compounds, with a specific focus on their potential applicability in the treatment of rheumatoid arthritis (RA). The study focuses on understanding interactions between specific natural actives that target RA. Fifteen RA target proteins were identified from OMIM, GeneScan and PharmaGKB. Their structures were downloaded from RCSB PDB. Two active components of C. autumnale L. were chosen for mass spectrometry investigation. Ligand characteristics were determined using the ADMETlab and SwissADME software tools. Molecular docking was performed, and the top three complexes were simulated for 200 ns, along with identification of free binding energies. The compounds β-sitosterol–IL-10 (−6.50 kcal/mol), colchicine–IL-10 (−6.01 kcal/mol), linoleic acid–IL-10 (−7.22 kcal/mol) and linoleic acid–IL-10 (−7.22 kcal/mol) exhibited best binding energies. β-Sitosterol and colchicine showed the highest stability in simulations, confirmed by molecular mechanics free energy binding calculations. This work provides insights into the molecular interaction of natural compounds against RA targets, offering potential therapeutic anti-RA medications.

Communicated by Ramaswamy H. Sarma

Keywords:

• Rheumatoid arthritis
• Colchicum autumnale L.
• natural bioactive
• molecular docking and simulations
• free energy computations
• ADMET
• protein–protein interaction network

Acknowledgments

The authors are grateful to the Biotechnology Industry Research Assistance Council (BIRAC), New Delhi. The authors would like to acknowledge Dr. Shobha G., Professor, Department of Computer Science and Engineering, for providing us with the A100 GPU for high-performance computing and analysis. Thanks, in particular, to Eashita Suvarna, research intern, for being helpful with the final computations. Special thanks to Mr. Akshay Uttarkar for his valuable inputs.

Disclosure statement

The authors declare no conflict of interest.

Ethics statement

No ethical consent is required.

Author contributions

Sowmya H S (S.H.S)- Formal analysis, Investigation, Data analysis, Manuscript writing. Guruprasad V (G.V)- Conceptualization, providing the plant extract. Anagha S Setlur (A.S.S) and Chandrashekar K (C.K)- Reviewing the manuscript, providing valuable inputs, Methodology implementation. Jitendra Kumar (J.K), Vidya Niranjan (V.N) and Ningaraju T. M (N.T.M)- Conceptualization, Review and Supervisor.

Additional information

Funding

No funding was provided for carrying out this project.