Generative artificial intelligence (AI) models trained on natural protein sequences have been used to design functional enzymes. However, their ability to predict individual reaction steps in enzyme catalysis remains unclear, limiting the potential use of sequence information for enzyme engineering. In this study, we demonstrated that sequence information can predict the rate of the S2 step of a haloalkane dehalogenase using a generative maximum-entropy (MaxEnt) model.
View Article and Find Full Text PDFCarbon monoxide (CO) is notorious for its toxic effects but is also recognized as a gasotransmitter with considerable therapeutic potential. Due to the inherent challenges in its delivery, the utilization of organic CO photoreleasing molecules (photoCORMs) represents an interesting alternative to CO administration characterized by high spatial and temporal precision of release. This paper focused on the design, synthesis, and photophysical and photochemical studies of 20 3-hydroxyflavone (flavonol) and 3-hydroxyflavothione derivatives as photoCORMs.
View Article and Find Full Text PDFCarbon monoxide (CO) is an endogenous signaling molecule that regulates diverse physiological processes. The therapeutic potential of CO is hampered by its intrinsic toxicity, and its administration poses a significant challenge. Photoactivatable CO-releasing molecules (photoCORMs) are an excellent tool to overcome the side effects of untargeted CO administration and provide precise spatial and temporal control over its release.
View Article and Find Full Text PDFCarbon monoxide (CO) is an endogenous signaling molecule that controls a number of physiological processes. To circumvent the inherent toxicity of CO, light-activated CO-releasing molecules (photoCORMs) have emerged as an alternative for its administration. However, their wider application requires photoactivation using biologically benign visible and near-infrared (NIR) light.
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