Melanoma is one of the most aggressive types of cancer, which has shown a tremendous surge in the last 50 years. Therapy for advanced-type melanoma is still a challenge because of the low response rate and 10-year survival. Therefore, drug discovery efforts need to be made to fight this cancer. To date, the development of big data and 3D has made it easier for researchers to understand the structure of proteins or enzymes that play an important role as receptors in melanoma cancer to be used as specific targets for diagnosis and therapy, for instance, the B-Raf V600E. This study examined the potential of active compounds from microalgae for developing melanoma anticancer drugs. The database was constructed using data mining from MarinLit and the related publications from 1970 to 2020. In silico methods such as molecular docking, virtual screening, and molecular dynamic simulations were used to find the most potential candidates. A total of 25 compounds passed the virtual screening stage. The top three compounds based on the binding free energy compared to a natural ligand and commercial drug are cholesta-5,7-dien-3beta-ol, 24-oxocholesterol acetate, lathosterol, and two additional compounds, phycocyanin and phycocyanobilin, were also selected due to their massive production from the most commonly cultured microalgae worldwide, Arthrospira sp. (previously known as Spirulina sp.). Furthermore, ADME analysis and toxicity tests were also carried out. Molecular dynamics simulation showed that phycocyanin was the best potential candidate for melanoma anticancer drugs, with free binding energies ranging from −65 to −80 kcal/mol. This result was also supported by root mean square deviation, root mean square fluctuation, and distance parameter data. This study may accelerate molecular research in producing therapeutic compounds for melanoma cancer, thus allowing it to continue developing pharmaceutical products that benefit human health.
Key words: B-Raf V600E, Melanoma, Molecular Dynamics, Phycocyanin, Virtual Screening
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