Biology subjects

Bornscheuer, U.

Publications and source records attributed to Bornscheuer, U..

5 recordsLinked to original sources

bioRxiv2026

The Arabidopsis PAL3 is a carboxy-tyrosine ammonia lyase

Phenylalanine ammonia lyase (PAL) catalyses the deamination of L-phenylalanine, which is the first step of the plant-specific phenylpropanoid pathway. Its position at the intersection of primary and specialized metabolism, combined with its important role in plant growth and adaptive stress response, has made it a subject of extensive studies. We identified key amino acids in the catalytic pocket of several PAL enzymes, including PAL3 of Arabidopsis, that are different from the canonical PAL sites, suggesting these enzymes have a different substrate specificity and have been miscategorised for decades. By combining untargeted metabolomics with enzyme assays, we discovered that PAL3 converts 3-carboxy-tyrosine into carboxy-p-coumaric acid. Based on this specific function, we propose to denote it as a 3-carboxy-tyrosine ammonia lyase (CAL). In addition to its discovery as a novel enzyme class, paving the way for biotechnological applications, we introduce the carboxy-phenylpropanoids as a new class of specialised metabolites.

biochemistry↗

bioRxiv2026

Uncovering Functional Distant Mutations by Ultra-High-Throughput Screening of Dehalogenases

Conformational dynamics play a central role in enzyme function by controlling substrate access and productive binding. Yet mutations that beneficially modulate these properties are difficult to identify. Here, we used ultrahigh-throughput fluorescence-activated droplet sorting (FADS) with a bulky fluorogenic substrate derived from coumarin (COU-3) to impose steric selection pressure on the haloalkane dehalogenase LinB. Screening a focused library yielded five single substitutions located 11.5-15.5 [A] from the catalytic centre. Variant I138N showed a fourfold increase in catalytic efficiency toward COU-3 through reduced KM and increased kcat, associated with increased cap-domain flexibility and facilitated substrate entry. In contrast, variant P208S markedly reduced substrate inhibition and shifted specificity toward bulkier iodinated haloalkanes by reshaping its tunnel environment. Integrated kinetic and structural analyses revealed that screening with bulky substrates directs selection toward distal regions controlling substrate access and unproductive binding. These findings demonstrate that ultrahigh-throughput FADS can reveal dynamic mechanisms of enzyme adaptation that remain difficult to predict by rational design. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/713925v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@782038org.highwire.dtl.DTLVardef@8b43f3org.highwire.dtl.DTLVardef@11a403eorg.highwire.dtl.DTLVardef@6fcaea_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

SoluProtMut: Siamese Deep Learning for Predicting Solubility Effects of Protein Mutations with Experimental Validation

Protein solubility is an attractive engineering target because it is a critical property influencing the scalability of protein production and the success of therapeutic proteins in biomedical applications. However, predicting solubility changes upon mutation in silico is challenging due to data heterogeneity and protein bias. Here, we explore how different sources of solubility data can be used for machine learning and present SoluProtMut, a Siamese deep geometric neural network trained to predict the impact of mutations on protein solubility. Our final model was trained exclusively on deep mutational scanning data. We compare our model with five established solubility prediction methods. The model achieves state-of-the-art performance on an independent dataset of various proteins, especially in predicting the effects of multipoint mutations (informedness of 26.5 %). Our findings also reaffirm that the scarcity of solubility data continues to hamper progress in this field. To address this limitation, we experimentally quantified solubility changes for hundreds of single-point and multipoint mutants of haloalkane dehalogenase. Complemented with recent deep-mutational-scanning data on myoglobin, we employed both these data for external validation. Although the generalization to unseen proteins remains limited, our findings demonstrate the potential of integrating high-throughput assays with deep learning to improve the accuracy and scope of solubility prediction. Highlights- We present a novel anti-symmetric Siamese architecture for mutational prediction on structures built on graph-based convolutional neural networks ensuring SE(3) invariance. - We demonstrate that a model trained only on single-point mutants of a single protein derived from high-throughput experiments generalizes to multipoint mutants of unseen proteins, achieving the state-of-the-art binary prediction informedness of 26.5 %. - We address the key limitation in the domain by extending the available data with 277 single-point and multipoint mutants of haloalkane dehalogenase labelled in-house and a selection of recently published 1037 single-point mutants of myoglobin. - We systematically study how different data subsets affect the performance of the trained models, revealing that including yeast-derived high-throughput data in training hampers generalization to low-throughput assays but recovers the performance on the yeast-derived myoglobin dataset.

bioinformatics↗

bioRxiv2024

SusC/D-like proteins in Gammaproteobacteria that utilize fructans

Fructans are ubiquitous in terrestrial ecosystems, however, these glycans are unexplored in the marine environment. We have discovered that the Antarctic gammaproteobacterium Pseudoalteromonas distincta is highly adapted to the degradation of fructose-containing substrates. This is enabled by proteins encoded in several genomic regions, including a fructan polysaccharide utilization locus (PUL). In addition to a glycoside hydrolase from family 32 (GH32), the fructan PUL encodes two proteins that have been described as specific for Bacteroidota and were previously unknown for Gammaproteobacteria: a glycan-binding SusD-like protein and a SusC-like TonB-dependent transporter (TBDT), which work as a complex in glycan import. Proteome analyses and biochemistry results suggest that the SusC/D-like proteins of P. distincta shuttle small-sized inulin-type fructans directly into the cell, where they are degraded by a periplasmic exo-active GH32. A SusD-like protein could provide a competitive adavantage in the absence of extracelluar endo-active inulinases. Comparative genomics identified further SusC/D-like proteins in Gammaproteobacteria, most of which are co-encoded with GH32s, indicative of fructan PULs, and are frequently associated with the marine habitat. Our study thus shows the first known exception to the paradigm that only Bacteroidota use SusC/D-like proteins. It further suggests that fructans contribute to the marine glycan pool.

molecular biology↗

bioRxiv2024

From Bulk to Binding: Decoding the Entry of PET into Hydrolase Binding Pockets

Plastic-degrading enzymes hold promise for biocatalytic recycling of poly(ethylene terephthalate) (PET), a key synthetic polymer. Despite their potential, the current activity of PET hydrolases is not sufficient for industrial use. To unlock their full potential, a deep mechanistic understanding followed by protein engineering is required. Using cuttingedge molecular dynamics simulations and free energy analysis methods, we uncover the entire pathway from the initial binding of two PET hydrolases - the thermophilic leaf-branch compost cutinase (LCC) and polyester hydrolase 1 (PES-H1) - to an amorphous PET material to a PET chain entering the active site and adopting a hydrolyzable geometry. Our results reveal the initial PET binding and elucidate its non-specific nature driven by electrostatic and hydrophobic forces. Upon PET entry into the active site, we uncover that this process can occur via one of three key pathways and detect barriers to it arising from both PET-PET and PET-enzyme interactions, with specific residues identified by in silico and in vitro mutagenesis. These insights not only advance our understanding of PET degradation mechanisms and pave the way for targeted enzyme enhancement strategies, but also offer an innovative approach applicable to enzyme studies across disciplines.

bioengineering↗