Biology subjects

Pinney, M. M.

Publications and source records attributed to Pinney, M. M..

5 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↗

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↗

bioRxiv2024

Conformational Ensembles Reveal the Origins of Serine Protease Catalysis

Enzymes exist in ensembles of states that encode the energetics underlying their catalysis. Conformational ensembles built from 1231 structures of 17 serine proteases reveal atomic-level changes across their reaction states, identify molecular features that provide catalysis, and quantify their energetic contributions to catalysis. These enzymes precisely position their reactants in destabilized conformers, creating a downhill energetic gradient that selectively favors the motions required for reaction while limiting off-pathway conformational states. A local catalytic motif, the "nucleophilic elbow", has repeatedly evolved, generating ground state destabilization in 50 proteases and 52 additional enzymes spanning 32 distinct structural folds. Ensemble-function analyses reveal previously unknown catalytic features, provide quantitative models based on simple physical and chemical principles, and identify motifs recurrent in Nature that may inspire enzyme design. One sentence summary: Ensemble-function analyses provide a quantitative model for serine protease catalysis, reveal previously unknown conformational features that contribute to their catalysis, and identify a structural motif that underlie these features and has evolved in >100 different enzymes from 32 protein folds.

biophysics↗

bioRxiv2019

Assessing positioning in enzymatic catalysis via ketosteroid isomerase conformational ensembles

How enzymes achieve their enormous rate enhancements remains a central question in biology, and our understanding to date has impacted drug development, influenced enzyme design, and deepened our appreciation of evolutionary processes. While enzymes position catalytic and reactant groups in active sites, physics requires that atoms undergo constant motion. Numerous proposals have invoked positioning or motions as central for enzyme function, but a scarcity of experimental data has limited our understanding of positioning and motion, their relative importance, and their changes through the enzymes reaction cycle. To examine positioning and motions and test catalytic proposals, we collected "room temperature" X-ray crystallography data for P. putida ketosteroid isomerase (KSI), and we obtained conformational ensembles for this and a homologous KSI from multiple PDB crystal structures. Ensemble analyses indicated limited change through KSIs reaction cycle. Active site positioning was on the 1-1.5 [A] scale, and was not exceptional compared to non-catalytic groups. The KSI ensembles provided evidence against catalytic proposals invoking oxyanion hole geometric discrimination between the ground state and transition state or highly precise general base positioning. Instead, increasing or decreasing positioning of KSIs general base reduced catalysis, suggesting optimized [A]ngstrom-scale conformational heterogeneity that allows KSI to efficiently catalyze multiple reaction steps. Ensemble analyses of surrounding groups for WT and mutant KSIs provided insights into the forces and interactions that allow and limit active site motions. Most generally, this ensemble perspective extends traditional structure-function relationships, providing the basis for a new era of "ensemble-function" interrogation of enzymes.

biophysics↗