Computational Investigation of Thymus linearis Phytoconstituents for the Treatment of Rheumatoid Arthritis: A Promising Approach towards Herbal Therapy

Abstract

The current study investigated several popular artificial algorithms for efficient hit selection, which might be applied to the identification of TAK 1 and JAK inhibitors for the treatment of rheumatoid arthritis. To accomplish the intended objectives Molecular docking experiments, database retrieval, de novo drug design, knowledge-based drug design, and molecular dynamics simulations were carried out. Targeting particular inhibitors, such as TAK 1, IL-16, and JAK, is one of the main components of the current tactics for treating inflammation. Our work has significant therapeutic applications as it focuses on exploiting Thymus linearis to harness inflammatory activity. Among the six constituents found in the plant, thymol was identified as the primary constituent with the highest concentration and had appropriate ADMET data. Subsequently, two protein candidates were docked with it: 5V5N (TAK1) and 7TEU (JAK), which were recognized as viable ones based on their docking score. Following that, thymol demonstrated a robust binding interaction with TAK1 as well as with JAK which proved its efficacy against the TAK1 and JAK. The outcome of molecular docking and dynamics simulations has confirmed that the Thymol-JAK complex exhibits greater stability compared to the Thymol-TAK complex. However, to enhance the binding affinity towards both proteins further, a suitable hit compound was designed from Thymol using the de novo drug design technique. Both the docking and simulation studies demonstrated that the hit (DH) developed from the Thymol exhibited steady and efficient binding energies, indicating that it might serve as a useful treatment against rheumatoid arthritis.