Bambu and its applications in the discovery of active molecules against melanoma

Interaction of DisBa01 peptide from Bothrops alternatus venom with BRAF melanoma receptors: Modeling and molecular docking

Metastatic melanoma is highly aggressive and challenging, often leading to a grim prognosis. Its progression is swift, especially when mutations like BRAFV600E continuously activate pathways vital for cell growth and survival. Although several treatments target this mutation, resistance typically emerges over time. In recent decades, research has underscored the potential of snake venoms and peptides as bioactive substances for innovative drugs, including anti-coagulants, anti-microbial, and anti-cancer agents. Leveraging this knowledge, we propose employing a bioinformatics simulation approach to: a) Predict how well a peptide (DisBa01) from Bothrops alternatus snake venom binds to the melanoma receptor BRAFV600E via Molecular Docking. b) Identify the specific peptide binding sites on receptors and analyze their proximity to active receptor sites. c) Evaluate the behavior of resulting complexes through molecular dynamics simulations. d) Assess whether this peptide qualifies as a candidate for anti-melanoma therapy. Our findings reveal that DisBa01 enhances stability in the BRAFV600E melanoma receptor structure by binding to its RGD motif, an interaction absent in the BRAF WT model. Consequently, both docking and molecular dynamics simulations suggest that DisBa01 shows promise as a BRAFV600E inhibitor.

Comprehensive modular analyses of scar subtypes illuminate underlying molecular mechanisms and potential therapeutic targets

Pathological scarring resulting from traumas and wounds, such as hypertrophic scars and keloids, pose significant aesthetic, functional and psychological challenges. This study provides a comprehensive transcriptomic analysis of these conditions, aiming to illuminate underlying molecular mechanisms and potential therapeutic targets. We employed a co-expression and module analysis tool to identify significant gene clusters associated with distinct pathophysiological processes and mechanisms, notably lipid metabolism, sebum production, cellular energy metabolism and skin barrier function. This examination yielded critical insights into several skin conditions including folliculitis, skin fibrosis, fibrosarcoma and congenital ichthyosis. Particular attention was paid to Module Cluster (MCluster) 3, encompassing genes like BLK, TRPV1 and GABRD, all displaying high expression and potential implications in immune modulation. Preliminary immunohistochemistry validation supported these findings, showing elevated expression of these genes in non-fibrotic samples rich in immune activity. The complex interplay of different cell types in scar formation, such as fibroblasts, myofibroblasts, keratinocytes and mast cells, was also explored, revealing promising therapeutic strategies. This study underscores the promise of targeted gene therapy for pathological scars, paving the way for more personalised therapeutic approaches. The results necessitate further research to fully ascertain the roles of these identified genes and pathways in skin disease pathogenesis and potential therapeutics. Nonetheless, our work forms a strong foundation for a new era of personalised medicine for patients suffering from pathological scarring.

Machine-Learning-Enabled Ligand Screening for Cs/Sr Crystallizing Separation

The reprocessing of spent nuclear fuel is critical for the sustainability of the nuclear energy industry. However, several key separation processes present challenges in this regard, calling for continuous research into next-generation separation materials. Herein, we propose a high-throughput screening framework to improve efficiency in identifying potential ligands that selectively coordinate metal cations of interest in liquid wastes that considers multiple key chemical characteristics, including aqueous solubility, pKa, and coordination bond length. Machine-learning models were designed for the fast and accurate prediction of these characteristics by using graph convolution and transfer-learning techniques. Suitable ligands for Cs/Sr crystallizing separation were identified through the "computational funnel", and several top-ranking, nontoxic, low-cost ligands were selected for experimental verification.