Structure Prediction and Computational Protein Design for Efficient Biocatalysts and Bioactive Proteins.

The ability to predict and design protein structures has led to numerous applications in medicine, diagnostics and sustainable chemical manufacture. In addition, the wealth of predicted protein structures has advanced our understanding of how life's molecules function and interact. Honouring the work that has fundamentally changed the way scientists research and engineer proteins, the Nobel Prize in Chemistry in 2024 was awarded to David Baker for computational protein design and jointly to Demis Hassabis and John Jumper, who developed AlphaFold for machine-learning-based protein structure prediction.

Enriching productive mutational paths accelerates enzyme evolution.

Darwinian evolution has given rise to all the enzymes that enable life on Earth. Mimicking natural selection, scientists have learned to tailor these biocatalysts through recursive cycles of mutation, selection and amplification, often relying on screening large protein libraries to productively modulate the complex interplay between protein structure, dynamics and function. Here we show that by removing destabilizing mutations at the library design stage and taking advantage of recent advances in gene synthesis, we can accelerate the evolution of a computationally designed enzyme.

A new physical and biological strategy to reduce the content of zearalenone in infected wheat kernels: the effect of cold needle perforation, microorganisms, and purified enzyme.

With the aim of reintroducing wheat grains naturally contaminated with mycotoxins into the food value chain, a decontamination strategy was developed in this study. For this purpose, in a first step, the whole wheat kernels were pre-treated using cold needle perforation. The pore size was evaluated by scanning electron microscopy and the accessibility of enzymes and microorganisms determined using fluorescent markers in the size range of enzymes (5 nm) and microorganisms (10 μm), and fluorescent microscopy.

Excelzyme: A Swiss University-Industry Collaboration for Accelerated Biocatalyst Development.

Excelzyme, an enzyme engineering platform located at the Zurich University of Applied Sciences, is dedicated to accelerating the development of tailored biocatalysts for large-scale industrial applications. Leveraging automation and advanced computational techniques, including machine learning, efficient biocatalysts can be generated in short timeframes. Toward this goal, Excelzyme systematically selects suitable protein scaffolds as the foundation for constructing complex enzyme libraries, thereby enhancing sequence and structural biocatalyst diversity.