<p><a>The over expression of Tumor
necrosis factor-α (TNFα) has been implicated in a variety of disease and is
classified as a therapeutic target for inflammatory diseases (Crohn disease,
psoriasis, psoriatic arthritis, rheumatoid arthritis).Commercially available
therapeutics are biologics which are associated with several risks and
limitations. Small molecule inhibitors and natural compounds (saponins) were
identified by researchers as lead molecules against TNFα, however, </a>they
were often associated with high IC50 values which can lead to their failure in clinical trials. This
warrants research related to identification of better small molecule inhibitors
by screening of large compound libraries. Recent developments have demonstrated
power of natural compounds as safe therapeutics, hence, in this work, we have
identified TNFα phytochemical inhibitors using high throughput <i>in silico </i>screening approaches of 6000
phytochemicals followed by 200 ns molecular dynamics simulations and relative binding
free energy calculations. The work yielded potent hits that bind to TNFα at its
dimer interface. The mechanism targeted was inhibition of oligomerization of TNFα
upon phytochemical binding to restrict its interaction with TNF-R1 receptor. MD
simulation analysis resulted in identification of two phytochemicals that showed
stable protein-ligand conformations over time. The two compounds were
triterpenoids: Momordicilin and Nimbolin A with relative binding energy-
calculated by MM/PBSA to be -190.5 kJ/Mol and -188.03 kJ/Mol respectively. Therefore,
through this work it is being suggested that these phytochemicals can be used
for further <i>in vitro</i> analysis to confirm their inhibitory action against
TNFα or can be used as scaffolds to arrive at better drug candidates.</p>
Algorithm for chemical space exploration with stochastic search (ACSESS) to develop structurally diverse chemical compound libraries for drug screening
