Biology subjects

Muir, D. F.

Publications and source records attributed to Muir, D. F..

4 recordsLinked to original sources

bioRxiv2026

Processing and Analyzing High-Throughput Microfluidic Enzymology Data: A Practical Guide To Rate Fitting and Quality Control

High-throughput enzymology enables quantitative characterization of enzyme function across hundreds to thousands of sequence variants and experimental conditions. Nevertheless, the scale and complexity of these datasets create substantial challenges for analysis and quality control. High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), for example, generates large microscopy datasets that must pass through multiple analytical stages, including image processing, initial-rate fitting, and kinetic modeling. Choices or errors made at any stage can propagate into the final kinetic parameters without being evident from fit statistics alone. Here, we provide a broadly applicable guide for analyzing high-throughput enzymology data from the HT-MEK platform, using Michaelis-Menten kinetics as a representative example. We first outline the conceptual workflow from raw fluorescence measurements to estimates of kcat, KM, and kcat/KM. We then discuss common experimental and analytical failure modes and present a framework for deciding when data should be refitted, filtered, qualified, or repeated. Finally, we provide a step-by-step workflow using Mercury, an open-source Python framework that integrates scalable HT-MEK data processing with traceable quality control and diagnostic visualization. This workflow preserves the connection between reported parameters and their underlying measurements and can be adapted to other kinetic models and biochemical assays.

biochemistry↗

bioRxiv2026

Coupling high-throughput protease enzymology with viral replication reveals biochemical constraints of viral fitness

Proteases govern essential biological processes and are key drug targets, yet how protease sequence variation quantitatively reshapes biochemical parameters and constrains biological fitness remains poorly understood. Here, we integrate high-throughput in vitro enzymology with cellular assays to link protease sequence, biochemistry, and fitness. We extend a microfluidic platform for high-throughput protease enzymology (HT-MEKpro), which is broadly applicable across protease families and catalytic classes, enabling measurement of catalytic turnover (kcat), Michaelis constant (KM), inhibitor potency (IC50), and relative substrate specificity for 102-103 variants. Applied to the SARS-CoV-2 main protease (Mpro), HT-MEKpro generated parallel catalytic and inhibitory landscapes for >400 variants. Integration with viral replication and in-cell cleavage assays reveals that variants with altered substrate specificity fail to support replication, suggesting imbalanced polyprotein processing as a constraint on viral fitness. More broadly, these data can enable mechanistically grounded modeling of protease sequence-property relationships and inform strategies for pharmacological modulation beyond active-site inhibition.

biochemistry↗

bioRxiv2025

Immortalization and Characterization of Schwann Cell Lines Derived from NF1 Associated Cutaneous Neurofibromas

Neurofibromatosis type 1 (NF1) is an autosomal dominant condition in which patients are heterozygous for a disruptive pathogenic variant in the NF1 gene. The most characteristic feature of the condition NF1 is the neurofibroma, a benign, multi-cellular tumor which initiates when a cell of the Schwann cell lineage gains a somatic pathogenic variant of the other NF1 allele. Neurofibromas developing at nerve termini in the skin are termed "cutaneous" neurofibromas (cNFs), while those developing within larger nerves are termed "plexiform." Most patients develop cNFs beginning in late childhood or early adulthood, continuing throughout life at variable rates. Some patients may develop only a few cNFs, while others suffer from thousands. There are no reliably effective physical or pharmaceutical therapies besides surgical removal. Although these are not life-threatening, they are disfiguring and can interfere with normal life functions. To provide a resource for research, we developed short-term cNF Schwann cell cultures from NF1 patients, from which we subsequently established the first semi-immortalized cNF cell lines through transduction with wild-type human telomerase reverse transcriptase (hTERT) and murine cyclin-dependent kinase 4 (mCdk4) genes. Here we present molecular, cellular, and functional characterization of these cell lines, which will be of utility for investigating and developing NF1 cNF therapies.

genomics↗

bioRxiv2024

Evolutionary-Scale Enzymology Enables Biochemical Constant Prediction Across a Multi-Peaked Catalytic Landscape

Quantitatively mapping enzyme sequence-catalysis landscapes remains a critical challenge in understanding enzyme function, evolution, and design. Here, we expand an emerging microfluidic platform to measure catalytic constants--kcat and KM--for hundreds of diverse naturally occurring sequences and mutants of the model enzyme Adenylate Kinase (ADK). This enables us to dissect the sequence-catalysis landscapes topology, navigability, and mechanistic underpinnings, revealing distinct catalytic peaks organized by structural motifs. These results challenge long-standing hypotheses in enzyme adaptation, demonstrating that thermophilic enzymes are not slower than their mesophilic counterparts. Combining the rich representations of protein sequences provided by deep-learning models with our custom high-throughput kinetic data yields semi-supervised models that significantly outperform existing models at predicting catalytic parameters of naturally occurring ADK sequences. Our work demonstrates a promising strategy for dissecting sequence-catalysis landscapes across enzymatic evolution and building family-specific models capable of accurately predicting catalytic constants, opening new avenues for enzyme engineering and functional prediction.

biochemistry↗