Biology subjects

Posner, J. A.

Publications and source records attributed to Posner, J. A..

2 recordsLinked to original sources

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↗

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↗