Biology subjects

Schwaneberg, U.

Publications and source records attributed to Schwaneberg, U..

7 recordsLinked to original sources

bioRxiv2026

Tuning inhibitor susceptibility of human 4-hydroxyphenylpyruvate dioxygenase to rescue tyrosine catabolism in the presence of β-triketones

Human 4-hydroxyphenylpyruvate dioxygenase (HPD) catalyses a key step in tyrosine degradation via homogentisate in liver and kidney cells. Pharmacological inhibition of HPD with nitisinone (NTBC) is standard treatment for tyrosinemia type 1 (HT1) and alkaptonuria (AKU). Although life-changing, continuous NTBC treatment limits metabolic flexibility and complicates emerging gene- and cell-based therapeutic approaches. Engineering HPD variants that retain catalytic activity during NTBC exposure could facilitate restoration of tyrosine metabolism, yet the molecular determinants of inhibitor tolerance remain poorly understood. Here, we applied Knowledge Gaining Directed Evolution (KnowVolution) to enhance NTBC tolerance while considering enzyme activity, predicted stability, and MHC-II peptide binding. Random mutagenesis, site-saturation mutagenesis, and computational analyses identified beneficial substitutions that improved enzyme activity in the presence of NTBC. These substitutions are predominantly located outside the catalytic cavity, suggesting that NTBC tolerance may involve distal structural effects rather than direct modification of inhibitor-binding residues. Recombination of selected substitutions, guided by activity and computational predictions of stability and MHC-II binding, yielded multi-substitution HPD variants with markedly increased NTBC tolerance. The leading variant, HPD-M8, exhibited up to 40-fold greater NTBC tolerance than wild-type HPD and increased tolerance to structurally related {beta}-triketone inhibitors, without an overall increase in predicted MHC-II binder burden. Computational analyses suggested altered catalytic access-pathway energetics without major perturbation of the conserved catalytic machinery, consistent with a possible contribution of distal substitutions to inhibitor tolerance. Together, these findings provide insight into HPD inhibitor tolerance and establish a foundation for future HT1 and AKU therapeutic strategies.

biochemistry↗

bioRxiv2026

Deciphering and Improving Human Homogentisate 1,2-Dioxygenase Function Through Knowledge Gaining Directed Evolution: Implications for Alkaptonuria

Human homogentisate 1,2-dioxygenase (HGD) catalyses the oxidative cleavage of homogentisic acid (HGA) to maleylacetoacetate (MAA), a key step in tyrosine degradation. Loss of HGD activity causes alkaptonuria (AKU), a rare inherited metabolic disorder characterized by toxic HGA accumulation. Current therapy with nitisinone lowers HGA levels but does not restore HGD function, motivating further investigation of HGD structure-function relationships. In this study, we applied the Knowledge Gaining Directed Evolution (KnowVolution) strategy to investigate how amino acid substitutions influence catalytic activity and structural integrity of human HGD. Catalytic activity was evaluated in Escherichia coli using an assay quantifying MAA formation over time. Across four KnowVolution phases, multiple substitutions were identified that modulated catalytic activity while preserving enzyme function. Notably, none of the influential substitutions were located within the catalytic pocket; instead, they occurred predominantly at surface-exposed or structural positions. Structural mapping, interface analysis, and computational stability predictions indicated that some substitutions contribute to hexamer stabilization, whereas others likely alter activity through indirect, non-catalytic mechanisms involving pocket remodelling. Combined substitutions showed non-additive effects that were either cooperative or antagonistic, demonstrating that their impact could not be predicted from individual contributions. Tunnel and pocket analyses showed that N31S, S54D and D86H produced a more compact hexamer, whereas a Q354P+P359E double mutant reduced catalytic pocket solvent accessibility and volume, supporting the observed activity differences. Overall, these findings demonstrate that HGD activity can be modulated by substitutions outside the catalytic pocket, providing new insight into HGD function and genotype-phenotype relationships underlying AKU.

biochemistry↗

bioRxiv2026

Genuine Directed Evolution In Test Tube (GENie)

Directed evolution has long been constrained by complex screening hardware and labor-intensive workflows. Here, we report the first genuine test-tube screening platform that uses His6-tagged peptide-functionalized magnetic beads and Fe3+-decorated E. coli cells to establish a phenotype-genotype linkage, thereby decoupling ultrahigh-throughput screening from specialized instrumentation and democratizing directed evolution. The platform demonstrated a screening throughput of > 108 events s-1 and an enrichment factor of up to 63-fold. Using galactose oxidase as a model, we identified variants with up to a 26-fold increase in catalytic efficiency. Extensions to D-amino acid oxidase and alcohol oxidase yielded variants with up to 5383-fold and 25-fold improvements over their respective wildtypes after a single round of screening. These results highlight the platforms capacity to rapidly engineer H2O2-generating oxidases and to advance AI-driven enzyme design through rapid data generation.

bioengineering↗

bioRxiv2024

KnowVolution of an efficient polyamidase through molecular dynamics simulations of incrementally docked oligomeric substrates

Management of synthetic polymer waste is one of the most pressing challenges for society today. Enzymatic recycling of polycondensates like polyamides (PA), however, remains limited due to a lack of efficient polyamidases. This study reports the directed evolution of the polyamidase NylCp2-TS. Key positions involved in enzyme-substrate interactions and PA 6 hydrolysis are identified through random mutagenesis and molecular dynamics (MD) simulations. The final variant, NylC-HP (NylCp2-TSF134W/D304M/R330A), exhibits a 6.9-fold increased specific activity (520 {+/-} 19 {micro}mol6-AHAeq. h-1 mgenzyme-1) and enhanced thermal stability (Tm = 90 {degrees}C, {Delta}Tm = 4.2 {degrees}C), making NylC-HP the fastest polyamidase for PA 6 and PA 6,6 hydrolysis. Despite the improved reaction rate, the degree of depolymerization remains below 1 %. To understand the molecular basis of achieved improvements and factors limiting the degree of depolymerization, intra- and intermolecular interactions of various enzyme-substrate complexes are analyzed by incremental docking of PA 6 tetramers and MD simulations. After optimizing the activity and stability of NylC-HP, the findings suggest that widening the substrate binding pocket is likely necessary to improve substrate accessibility to target more buried attack sites on the polymer surface, and thereby enhancing the degree of depolymerization.

bioengineering↗

bioRxiv2022

A hybrid model combining evolutionary probability and machine learning leverages data-driven protein engineering

Protein engineering through directed evolution and (semi-)rational approaches has been applied successfully to optimize protein properties for broad applications in molecular biology, biotechnology, and biomedicine. The potential of protein engineering is not yet fully realized due to the limited screening throughput hampering the efficient exploration of the vast protein sequence space. Data-driven strategies have emerged as a powerful tool to leverage protein engineering by providing a model of the sequence-fitness landscape that can exhaustively be explored in silico and capitalize on the high diversity potential offered by nature However, as both the quality and quantity of the inputted data determine the success of such approaches, the applicability of data-driven strategies is often limited due to sparse data. Here, we present a hybrid model that combines direct coupling analysis and machine learning techniques to enable data-driven protein engineering when only few labeled sequences are available. Our method achieves high performance in predicting a proteins fitness based on its sequence regardless of the number of sequences-fitness pairs in the training dataset. Besides reducing the computational effort compared to state-of-the-art methods, it outperforms them for sparse data situations, i.e., 50 - 250 labeled sequences available for training. In essence, the developed method is auspicious for data-driven protein engineering, especially for protein engineers who have only access to a limited amount of data for sequence-fitness landscape modeling.

synthetic biology↗

bioRxiv2022

Rational design yields molecular insights on leaf binding of the anchor peptide Macaque Histatin

1In times of a constantly growing world population and increasing demand for food, sustainable agriculture is crucial. To reduce the amount of applied nutrients, herbicides, and fungicides, the rainfastness of plant protection agents is of pivotal importance. As a result of protective agent wash-off, plant protection is lost, and soils and groundwater are severely polluted. To date, rainfastness of plant protection products is achieved by adding polymeric adjuvants to the agrochemicals. However, polymeric adjuvants will be regarded as microplastics in the future, and environmentally friendly alternatives are needed. Anchor peptides (APs) are promising biobased and biodegradable adhesion promoters. While the adhesion of anchor peptides to artificial surfaces, such as polymers, has already been investigated in theory and experimentally, exploiting the adhesion to biological surfaces remains challenging. The complex nature and composition of biological surfaces such as plant leaf and fruit surfaces complicate the generation of accurate models. Here, we present the first detailed three-layered atomistic model of the surface of apple leaves and use it to compute free energy profiles of the adhesion and desorption of APs to and from that surface. Our model is validated by a novel fluorescence-based MTP assay that mimicks these complex processes and allows quantifying them. For the AP Macaque Histatin, we demonstrate that aromatic and positively charged amino acids are essential for binding to the waxy apple leaf surface. The established protocols should generally be applicable for tailoring the binding properties of APs to biological interfaces.

bioengineering↗

bioRxiv2021

Structural and mechanistic insight into spectral tuning in flavin-binding fluorescent proteins

Determining the molecular origin of spectral tuning in photoactive biological systems is instrumental for understanding their function. Spectral-tuning efforts for flavin-binding fluorescent proteins (FbFPs), an emerging class of fluorescent reporters, are limited by their dependency on protein-bound flavins, whose structure and hence electronic properties, cannot be altered by mutation. To address those shortcomings, we here present the photophysical, computational and structural characterization of structurally uncharacterized blue-shifted FbFPs, carrying a previously described lysine substitution within their flavin-binding pocket. X-ray structures reveal displacement of the lysine away from the chromophore and opening up of the structure as cause for the blue shift. Site-saturation mutagenesis and high-throughput screening, yielded a red-shifted variant, in which the lysine side chain of the blue-shifted variant is stabilized in close distance to the flavin by a secondary mutation, mechanistically accounting for the red shift. Thus, a single secondary mutation in a blue-shifted variant is sufficient to generate a red-shifted FbFP. Using spectroscopy, X-ray crystallography and quantum mechanics molecular mechanics calculations, we provide a firm structural and functional understanding of spectral tuning in FbFPs. We also show that the identified blue- and red-shifted variants allow for two-color microscopy based on spectral separation. In summary, the generated blue- and red-shifted variants represent promising new tools that should find application in life sciences.

biophysics↗