Design strategy for creating catalytically active metal binding proteins

Metalloenzymes catalyze a wide variety of reactions in nature by taking advantage of the versatility and reactivity of transition metals. Despite the diversity of reactions catalyzed by natural proteins, there is still a demand for designer enzymes. In many cases, all that is needed is routine re-engineering of the native enzymes to perform efficiently under the demanded application conditions. In other cases, the reaction or reaction condition desired differs so much from natural conditions that mere redesign of natural proteins is not practical. De novo enzymes, which are generated entirely from first principles rather than modified from natural proteins, are ideal for these situations. These de novo enzymes would allow us to generate enzymes that can survive at much higher temperatures, work in many different solvents and solutions, or perform completely novel functions. Please click Additional Files below to see the full abstract

De Novo Scaffold Design: From Membrane β-Barrels to ɑ-helical Repeat Oligomers

Thesis (Ph.D.)--University of Washington, 2021De novo design of new protein folds, shapes, and features opens up new avenues for functional design. My thesis work covers the de novo design of membrane beta barrels, important considerations for making new metal binding proteins, and the design of large sets of homo-oligomers with a wide variety of pocket shapes and sizes. This work aims to shed structural and functional insights necessary to create well behaved proteins with specific desired features without being limited to the natural proteins that have been characterized. The quick and accurate generation of new protein scaffolds with a variety of folds or pocket sizes will contribute to enabling further functional design of de novo membrane pores, protein catalysts, and small-molecule binders

De novo design of high-affinity binders of bioactive helical peptides.

Many peptide hormones form an α-helix on binding their receptors1-4, and sensitive methods for their detection could contribute to better clinical management of disease5. De novo protein design can now generate binders with high affinity and specificity to structured proteins6,7. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion8 to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful