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The TMEM176B protein is an ion channel expressed in cells of the immune system and is related to the biological processes of oncogenes, tumor suppression, and biomarkers. This protein is found in homo and heterodimeric forms with a mechanism that has not yet been fully elucidated. To identify molecules from natural products with an effective and safe inhibitory profile for the TMEM176B protein in its heterodimeric and homodimeric forms. We used the virtual screening technique to identify molecules with biological activity for TMEM176B, using the NuBBE natural products database and the AutoDockVina program.We applied several filter: binding energy; calculation of ligand efficiency; molecular interactions detected by the BINANA algorithm; simulation of ADME-Tox properties (absorption, distribution, metabolism, excretion, and toxicity), using SwissADME, pkCSM, ADMETLab, EmolTox, and PHARMIT tools. The application of the virtual screening filters made it possible to identify natural products with binding affinity for the two forms of TMEM176B. For the heterodimer, the chemical classes of the molecules that stood out were flavonoids (flavone), alkaloids (indole alkaloid), aromatic derivatives (styryl pyron and xanthone), and mostly terpenes (sesquiterpenoid). For the homodimer, the best candidates belong to the following classes: polyketides (polyketide linea), flavonoids (flavone), terpenes (diterpenoid and meroterpenoid), alkaloids (quinazoline alkaloid and indole alkaloid) and aromatic derivatives (coumarin). The molecular scaffolds (chemotypes) of the 1959 molecules evaluated for the two forms of TMEM176B resulted in 20 interesting candidates for the heterodimer and 23 candidates for the homodimer, which will be studied further. The NuBBE_2292 and NuBBE_950 complexes had their stability evaluated by molecular dynamics, for the hete/homodimer forms of TMEM176B respectively. Based on the partial results, it was possible to identify candidate molecules for the TMEM176B inhibition with satisfactory physicochemical profiles, which will allow further structural optimization studies.
This work was supported by FAPESP (Grant number: 2023/01920-8), CNPq (CABBIO - Grant number: 423717/2021-9) and CAPES.
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