Photocatalytic technologies represent intriguing approaches for long-term environmental remediation strategies; however, approaches to sustainably generate the catalytic materials remain limited. Many methods require the use of toxic surfactants and potentially harsh conditions. As an alternative, bioinspired approaches present pathways toward the production of functional structures under ambient conditions. In this contribution, the effects of amino acids in the low-temperature production of Cu2O-based materials is examined, providing first principle information for the eventual de novo design of peptides that can control the structure/function relationship of these inorganic materials. These studies demonstrate that only a fraction of the 20 canonical amino acids (Arg, Cys, Glu, His, Lys, and Trp) possess specific control over the morphology and size of Cu2O materials during the synthetic process. This level of control is shown to directly affect the photocatalytic activity of the materials for the degradation of model organic pollutants. Taken together, these results provide intriguing new directions for the rational design of sustainable synthetic approaches for the production of catalytically important semiconductor metal oxide materials applied to long-term environmental remediation capabilities.
Simple acid-containing amino acids are capable of producing CuS nanodisks with plasmon bands shifted into the near IR. The materials also demonstrated photocatalytic reactivity for the degradation of model organic compounds.
The need for novel antimalarials is apparent given the continuing disease burden worldwide, despite significant drug discovery advances from the bench to the bedside. In particular, small-molecule agents with potent efficacy against both the liver and blood stages of Plasmodium parasite infection are critical for clinical settings as they would simultaneously prevent and treat malaria with a reduced selection pressure for resistance. While experimental screens for such dual-stage inhibitors have been conducted, the time and cost of these efforts limit their scope. Here, we have focused on leveraging machine learning approaches to discover novel antimalarials with such properties. A random forest modeling approach was taken to predict small molecules with in vitro efficacy versus liver-stage Plasmodium berghei parasites and a lack of human liver cell cytotoxicity. Empirical validation of the model was achieved with the realization of hits with liver-stage efficacy after prospective scoring of a commercial diversity library and consideration of structural diversity. A subset of these hits also demonstrated promising blood-stage Plasmodium falciparum efficacy. These 18 validated dual-stage antimalarials represent novel starting points for drug discovery and mechanism of action studies with significant potential for seeding a new generation of therapies.
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