Ursolic acid interaction with transcription factors BRAF, V600E, and V600K: a computational approach towards new potential melanoma treatments

Context

Melanoma is one of the cancers with the highest mortality rate for its ability to metastasize. Several targets have undergone investigation for the development of drugs against this pathology. One of the main targets is the kinase BRAF (RAF, rapidly accelerated fibrosarcoma). The most common mutation in melanoma is BRAFV600E and has been reported in 50–90% of patients with melanoma. Due to the relevance of the BRAFV600E mutation, inhibitors to this kinase have been developed, vemurafenib-OMe and dabrafenib. Ursolic acid (UA) is a pentacyclic triterpene with a privileged structure, the pentacycle scaffold, which allows to have a broad variety of biological activity; the most studied is its anticancer capacity. In this work, we reported the interaction profile of vemurafenib-OMe, dabrafenib, and UA, to define whether UA has binding capacity to BRAFWT, BRAFV600E, and BRAFV600K. Homology modeling of BRAFWT, V600E, and V600K; molecular docking; and molecular dynamics simulations were carried out and interactions and residues relevant to the binding of the inhibitors were obtained. We found that UA, like the inhibitors, presents hydrogen bond interactions, and hydrophobic interactions of van der Waals, and π-stacking with I463, Q530, C532, and F583. The ΔG of ursolic acid in complex with BRAFV600K (− 63.31 kcal/mol) is comparable to the ΔG of the selective inhibitor dabrafenib (− 63.32 kcal/mol) in complex to BRAFV600K and presents a ΔG like vemurafenib-OMe with BRAFWT and V600E. With this information, ursolic acid could be considered as a lead compound for design cycles and to optimize the binding profile and the selectivity towards mutations for the development of new selective inhibitors for BRAFV600E and V600K to new potential melanoma treatments.

Methods

The homology modeling calculations were executed on the public servers I-TASSER and ROBETTA, followed by molecular docking calculations using AutoGrid 4.2.6, AutoDockGPU 1.5.3, and AutoDockTools 1.5.6. Molecular dynamics and metadynamics simulations were performed in the Desmond module of the academic version of the Schrödinger-Maestro 2020–4 program, utilizing the OPLS-2005 force field. Ligand–protein interactions were evaluated using Schrödinger-Maestro program, LigPlot + , and PLIP (protein–ligand interaction profiler). Finally, all of the protein figures presented in this article were made in the PyMOL program.

Graphical Abstract