RESUMO
Through a combination of comparative modeling, site-directed and classical random mutagenesis approaches, we previously identified critical residues for binding, recognition, and translocation of urea, and its inhibition by 2-thiourea and acetamide in the Aspergillus nidulans urea transporter, UreA. To deepen the structural characterization of UreA, we employed the artificial intelligence (AI) based AlphaFold2 (AF2) program. In this analysis, the resulting AF2 models lacked inward- and outward-facing cavities, suggesting a structural intermediate state of UreA. Moreover, the orientation of the W82, W84, N279, and T282 side chains showed a large variability, which in the case of W82 and W84, may operate as a gating mechanism in the ligand pathway. To test this hypothesis non-conservative and conservative substitutions of these amino acids were introduced, and binding and transport assessed for urea and its toxic analogue 2-thiourea, as well as binding of the structural analogue acetamide. As a result, residues W82, W84, N279, and T282 were implicated in substrate identification, selection, and translocation. Using molecular docking with Autodock Vina with flexible side chains, we corroborated the AF2 theoretical intermediate model, showing a remarkable correlation between docking scores and experimental affinities determined in wild-type and UreA mutants. The combination of AI-based modeling with classical docking, validated by comprehensive mutational analysis at the binding region, would suggest an unforeseen option to determine structural level details on a challenging family of proteins.
Assuntos
Inteligência Artificial , Furilfuramida , Simulação de Acoplamento Molecular , Ureia/metabolismo , Tioureia , Acetamidas , Transportadores de UreiaRESUMO
Prostate cancer is the most common cancer in men around the world. It is a complex and heterogeneous disease in which androgens and their receptors play a crucial role in the progression and development. The current treatment for prostate cancer is a combination of surgery, hormone therapy, radiation and chemotherapy. Therapeutic agents commonly used in the clinic include steroidal and non-steroidal anti-androgens, such as cyproterone acetate, bicalutamide and enzalutamide. These few agents have multiple adverse effects and are not 100% effective. Several plant compounds and mixtures, including grape seed polyphenol extracts, lycopene and tomato preparations, soy isoflavones, and green tea extracts, have been shown to be effective against prostate cancer cell growth. In vivo activity of some isolated compounds like capsaicin and curcumin was reported in prostate cancer murine models. We prepared a library of plant extracts from traditional Mayan medicine. These plants were selected for their use in the contemporaneous Mayan communities for the treatment of different diseases. The extracts were assessed in a phenotypic screening using LNCaP prostate cancer androgen sensitive cell line, with a fixed dose of 25 µg/mL. MTT assay identified seven out of ten plants with interesting anti-neoplastic activity. Extracts from these plants were subjected to a bioguided fractionation to study their major components. We identified three compounds with anti-neoplastic effects against LNCaP cells, one of which shows selectivity for neoplastic compared to benign cells.