Over the last decades, transformative catalytic strategies have emerged, with biocatalysis currently exerting a substantial influence on the pharmaceutical and fine chemical sectors. Fast progress in the design of efficient enzymatic processes, however, suffers from the lack of readily available, stable, and customizable protein scaffolds that can be adapted to different catalytic functions. Here, we detail the design and experimental characterization of computationally designed de novo proteins with a non-natural fold for biocatalytic applications. The initial design and several variants form a helical barrel structure comprised of six antiparallel straight helices connected by five loops, creating an open central channel with two accessible cavities. To demonstrate the versatility of this scaffold, we designed variants with catalytic sites positioned at different locations along the central channel. All designs show high thermal stability and excellent agreement between experimental and calculated scattering profiles from small-angle X-ray scattering, while a crystal structure of a surface-redesigned variant confirms the close match between the designed and experimental structures. Importantly, repositioning and engineering the catalytic sites enables substantial modulation of catalytic activity, with the best variant showing an approximately 11-fold increase in catalytic efficiency compared with the original design. Finally, the designs can be used for whole-cell biotransformations and tolerate up to 20% organic solvent. These results establish a stable de novo protein scaffold with tunable functional sites, offering a versatile platform for biocatalysis, biosensing, and biosynthetic systems.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
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It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or seque...
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.