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.
De novo-designed enzymes have recently achieved high catalytic activity and stereoselectivity while demonstrating exceptional thermostability in entirely novel protein scaffolds. Among these, α-helical barrel protein scaffolds are attractive structures for biocatalysis due to their structural simplicity, high thermosta...
Wael Elaily, Markus Braun, David Stoll et al.· bioRxiv· 0 citations
Peptide asparaginyl ligases (PALs) hold great promise for peptide macrocyclization and protein bioconjugation, yet their broader application is limited by insufficient thermal stability and low recombinant expression. Here, we report FortiPAL-1, a diffusion-enabled PAL developed through an integrated computational-expe...
Shuo Pang, Yu-Xing Hao, Jia-Rong Mo et al.· Chemical Science· 0 citations
Abiotic foldamers that switch between distinct, well-defined geometries are rare. Here, we report a structurally simple series of oligomers with alternating ortho-phenylene and 2,3-pyrazinylene repeat units. Deconvolution of variable-temperature 1H NMR spectra, with the help of ab initio chemical shift predictions, sho...
Ying Liao, Vipul Batra, G. P. Devkota et al.· Chemistry· 0 citations
Peptide-based materials have enormous potential for applications including therapeutics, sensing, catalysis, and flexible electronics. Recent material discovery screenings through peptide sequence space have identified a class of amphiphilic heme-containing peptides that self-assemble at the nanoscale while efficient...
Jesse L. Prelesnik, A. P. Lau, Nathan C. Laud et al.· Journal of the American Chem...· 0 citations
Enhancing the robustness of functional proteins remains a central challenge in biotechnology, with implications for catalysis, pharmaceuticals, and industrial synthesis. Enzyme immobilization in porous materials such as metal–organic frameworks (MOFs) is widely used to enhance enzyme stability; however, the structural...
Siene Swinnen, Maxim Lox, Marika Di Berto Mancini et al.· Journal of the American Chem...· 0 citations
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.