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Biology subjects

Stoll, D.

Publications and source records attributed to Stoll, D..

3 recordsLinked to original sources

Computational design of a thermostable de novo biocatalyst for whole cell biotransformations

Over the last decades, transformative catalytic strategies have emerged, with biocatalysis cur-rently exerting a substantial influence on the pharmaceutical and fine chemical sectors. Fast pro-gress 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 de-signed 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 con-nected by five loops, creating an open central channel with two accessible cavities. To demon-strate the versatility of this scaffold, we designed variants with catalytic sites positioned at differ-ent 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 scat-tering, while a crystal structure of a surface-redesigned variant confirms the close match be-tween 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 sol-vent. These results establish a stable de novo protein scaffold with tunable functional sites, offer-ing a versatile platform for biocatalysis, biosensing, and biosynthetic systems.

biochemistry↗

Computational design of highly active de novo enzymes

Enzymes are broadly used as biocatalysts in industry and medicine due to their coverage of vast areas of chemical space, their exquisite selectivity and efficiency as well as the mild reaction conditions at which they operate. Custom designed enzymes can produce tailor-made biocatalysts with potential applications extending beyond natural reactions. However, current design methods require testing of high numbers of designs and mostly produce de novo enzymes with low catalytic activities. As a result, they require costly experimental optimization and high-throughput screening to be industrially viable. Here we present rotamer inverted fragment finder-diffusion (Riff-Diff), a hybrid machine learning and atomistic modelling strategy for scaffolding catalytic arrays in de novo proteins. We highlight the general applicability of Riff-Diff by designing enzymes for two mechanistically distinct chemical transformations, the retro-aldol reaction and the Morita-Baylis-Hillman reaction. We show that in both cases it is possible to generate catalysts exhibiting activities rivalling those optimized by in-vitro evolution, along with exquisite stereoselectivity. High resolution structures of six of the designs revealed an angstrom level of active site design precision. The design strategy can, in principle, be applied to any catalytically competent amino acid constellation. These findings enable the practical applicability of de novo protein catalysts in synthesis and shed light on fundamental principles of protein design and enzyme catalysis.

biochemistry↗

A novel PNGase Rc for improved protein N-deglycosylation in bioanalytics and HDX-MS epitope mapping under challenging conditions

N-linked glycosylation is a ubiquitous posttranslational modification of proteins. While it plays an important role in the biological function of proteins, it often poses a major challenge for their analytical characterization. Currently available peptide N-glycanases (PNGases) are often inefficient at deglycosylating proteins due to sterically inaccessible N-glycosylation sites. This usually leads to poor sequence coverage in bottom-up analysis using liquid chromatography with tandem mass spectrometry (LC-MS) and makes it impossible to obtain an intact mass signal in top-down MS analysis. In addition, most PNGases operate optimally only in the neutral to slightly acidic pH range and are severely compromised in the presence of reducing and denaturing substances, which limits their use for advanced bioanalysis based on hydrogen-deuterium exchange in combination with mass spectrometry (HDX-MS). Here, we present a novel peptide N-glycanase from Rudaea cellulosilytica (PNGase Rc) for which we demonstrate broad substrate specificity for N-glycan hydrolysis from multiply occupied and natively folded proteins. Our results show that PNGase Rc is functional even under challenging, HDX quench conditions (pH 2.5, 0 {degrees}C) and in the presence of 0.4 M Tris(2-carboxyethyl)phosphine (TCEP), 4 M urea and 1 M guanidinium chloride (GdmCl). Most importantly, we successfully applied the PNGase Rc in an HDX-MS workflow to determine the epitope of a nanobody targeting the extracellular domain of human signal-regulating protein alpha (SIRP).

biochemistry↗