Biology subjects

Welner, D. H.

Publications and source records attributed to Welner, D. H..

7 recordsLinked to original sources

bioRxiv2026

Engineering Biosensors to Enhance Monoterpene Indole Alkaloid Production in Yeast

Monoterpene Indole Alkaloids (MIAs) are a diverse family of plant natural products with various medicinal applications. Although MIAs, such as vinblastine and reserpine, are clinically validated, sourcing of MIAs for clinical use or drug discovery from natural resources or via chemical synthesis is hampered due to their scarcity and chemical complexity. Refactoring MIA biosynthesis pathways in microbial cell factories could offer an alternative, more stable and potentially sustainable manufacturing route for alkaloid medicines and novel therapies. However, reaching commercially attractive titers, rates and yields remains challenging owing to the length and complexity of these metabolic pathways. One critical bottleneck is the low screening throughput and very high cost of the analytical methods used to quantify MIA for optimizing production. In this study, we evolved RamR, a promiscuous bacterial transcription factor to respond to five different MIAs, resulting in highly sensitive and selective sensor variants (EC50<10 M). X-ray crystallography and computational modeling provided insight into the MIA binding of the evolved biosensor variants. The RamR biosensing platform was functionalized in yeast and subsequently applied in a cost-effective semi-throughput screening campaign of a 188-gene overexpression library to identify high-performing cell factory designs for strictosidine, the common precursor for all MIAs. The fluorescent biosensor signal correlated with HPLC quantification (r2 = 0.932) allowing identification of single metabolic engineering hits which when combined yielded a maximum titer of >220 mg/L strictosidine, 3-fold higher than the parental reference strain. This study demonstrates the development of selective biosensors for MIAs and the cost-effective identification of novel metabolic engineering hits for optimizing MIA production in microbial cell factories.

synthetic biology↗

bioRxiv2026

Data-efficient distal engineering of fluorinase using zero-shot models

Fluorinases have high potential for industrial biofluorination but any applications have been precluded by low catalytic efficiency and resistance to active site engineering. In this work, we employed PRIZM, a computational workflow utilizing an existing low-N dataset and zero-shot models for in silico prediction of activity-enhancing mutations at distal sites. The combination of these predictions with expert opinion led to the identification of 21 fluorinase mutants with enhanced relative activities, while 3 variants showed increased melting temperatures. A mutation in the hexameric interface, K237R, resulted in the largest stability gain, a more than 3.2-fold improvement in catalytic efficiency at 57{degrees}C, and an 8-fold increase in relative activity at 62{degrees}C. These results highlight the potential of distal fluorinase engineering for improving properties required to realize its industrial applications.

bioengineering↗

bioRxiv2026

PRIZM: Combining Low-N Data and Zero-shot Models to Design Enhanced Protein Variants

Machine learning has repeatedly shown the ability to accelerate protein engineering, but many approaches demand large amounts of robust, high-quality training data as well as substantial computational expertise. While large pre-trained models can function as zero-shot proxies for predicting variant effects, selecting the best model for a given protein property is often non-trivial. Here, we introduce Protein Ranking using Informed Zero-shot Modelling (PRIZM), a two-phase workflow that first uses a high-quality low-N dataset to identify the most suitable pre-trained zero-shot model for a target protein property and then applies that model to rank and prioritize an in silico variant library for experimental testing. Across diverse benchmark datasets spanning multiple protein properties, PRIZM reliably separated low- from high-performing models using datasets of [~]20 labelled variants. We further demonstrate PRIZM in enzyme engineering case studies targeting sucrose synthase thermostability and glycosyltransferase activity, where PRIZM-guided selection identified improved variants, including gains of [~]3{degrees}C in apparent melting temperature and [~]20% higher relative activity. PRIZM provides an accessible, data-efficient route to leverage foundation models for protein design while requiring minimal experimental data.

bioinformatics↗

bioRxiv2026

Stability engineering of sucrose synthase for robust UDP-glucose regeneration

Glycosyltransferase-driven glycosylation enables environmentally mild synthesis of high-value chemicals, but industrial implementation is constrained by the cost of UDP-glucose. Sucrose synthase (SuSy) offers an attractive route for UDP-glucose recycling, yet inadequate operational stability has limited its use. Here, we report an integrated engineering workflow that overcomes the longstanding trade-off between efficiency and robustness in glycosyl donor recycling enzymes. We engineered GmSuSy wild-type and obtained variants combining supra-wildtype activity (178%) with enhanced thermostability ({Delta}Tmapp = 13.3 {degrees}C), solvent tolerance (70% retained activity in 25% DMSO) and a 123-fold longer half-life. The variants achieved total turnover numbers of [~]1 million (60 {degrees}C), supporting their industrial relevance. Mechanistic analyses revealed that long-range residue communication networks couple oligomeric interfaces with active sites, shifting conformational populations toward stable, catalytically competent states while increasing hydrophobic packing and reducing solvent accessibility. This enhanced robustness enabled significant process-level gains, including >90% conversion yields in indoxyl and MANT glycosylation. Techno-economic and life-cycle assessments indicate the potential to halve reaction costs and reduce environmental impacts threefold, establishing SuSy robustness as a key lever for sustainable industrial glycosylation.

biochemistry↗

bioRxiv2025

Enzymatic Glycosylation of Anthranilates for Enhanced Functionality

Anthranilate (ANT) is a precursor for the synthesis of valuable compounds, including its alkyl esters (AEANTs), such as methyl anthranilate (MANT). These derivatives are industrial petrochemical products used as flavouring agents and bird repellents. Due to the mandatory green transition, their biological industrial production must be considered. However, their antimicrobial activity and physicochemical properties inhibit efficient microbial production and challenge their practical use. To overcome this, we explored enzymatic glycosylation using UDP-dependent glycosyltransferases (UGTs). Screening identified three UGTs with activity on a selected AEANT panel, with UGT72B68 from Solanum lycopersicum showing the highest efficiency (840 s-1 M-1) for MANT. Rational engineering produced a mutant (F145M) with improved activity for bulkier AEANTs. We scaled up enzymatic synthesis, producing 9.3 g of MANT-N-glucose (>99% purity, 74% yield). With this in hand, we observed that MANT-N-glucose has a significantly lower impact on the growth of E. coli and P. putida, supporting microbial production. Furthermore, we found that MANT-N-glucose completely inhibited sunflower seed consumption, compared to a 70% reduction observed in a previous study using MANT, when tested on captured red-winged blackbirds. Finally, a preliminary life-cycle assessment demonstrated that microbially produced MANT-N-glucose is a viable alternative to chemically synthesised MANT as a bird repellent.

biochemistry↗

bioRxiv2020

A benchmark of protein solubility prediction methods on UDP-dependent glycosyltransferases

UDP-dependent glycosyltransferases (UGTs) are enzymes that glycosylate a wide variety of natural products, thereby modifying their physico-chemical properties, i.e. solubility, stability, reactivity, and function. To successfully leverage the UGTs in biocatalytic processes, we need to be able to screen and characterise them in vitro, which requires efficient heterologous expression in amenable hosts, preferably Escherichia coli. However, many UGTs are insoluble when expressed in standard and attempted optimised E. coli conditions, resulting in many unproductive and costly experiments. To overcome this limitation, we have investigated the performance of 11 existing solubility predictors on a dataset of 57 UGTs expressed in E. coli. We show that SoluProt outperforms other methods in terms of both threshold-independent and threshold-dependent measures. Among the benchmarked methods, only SoluProt is significantly better than random predictors using both measures. Moreover, we show that SoluProt uses a threshold for separating soluble and insoluble proteins that is optimal for our dataset. Hence, we conclude that using SoluProt to select UGT sequences for in vitro investigation will significantly increase the success rate of soluble expression, thereby minimising cost and enabling efficient characterisation efforts for biocatalysis research.

biochemistry↗

bioRxiv2019

Evolution-guided engineering of small-molecule biosensors

Allosteric transcription factors (aTFs) have proven widely applicable for biotechnology and synthetic biology as ligand-specific biosensors enabling real-time monitoring, selection and regulation of cellular metabolism. However, both the biosensor specificity and the correlation between ligand concentration and biosensor output signal, also known as the transfer function, often needs to be optimized before meeting application needs. Here, we present a versatile and high-throughput method to evolve and functionalize prokaryotic aTF specificity and transfer functions in a eukaryote chassis, namely bakers yeast Saccharomyces cerevisiae. From a single round of directed evolution of the effector-binding domain (EBD) coupled with various toggled selection regimes, we robustly select aTF variants of the cis, cis-muconic acid-inducible transcription factor BenM evolved for change in ligand specificity, increased dynamic output range, shifts in operational range, and a complete inversion of function from activation to repression. Importantly, by targeting only the EBD, the evolved biosensors display DNA-binding affinities similar to BenM, and are functional when ported back into a non-native prokaryote chassis. The developed platform technology thus leverages aTF evolvability for the development of new host-agnostic biosensors with user-defined small-molecule specificities and transfer functions.

synthetic biology↗