# Quantitative, High-throughput Mechanistic Enzymology

> **NIH NIH R01** · STANFORD UNIVERSITY · 2024 · $501,803

## Abstract

Project Summary
This project will provide data critical to advance our understanding of enzyme sequence/structure/function
relationships. We will develop innovative microfluidic tools and techniques and apply them to systematically
study enzymes at a high-throughput and quantitative level. In the prior granting period, we developed HT-
MEK (High-Throughput Microfluidic Enzyme Kinetics), which enabled recombinant expression, purification,
and deep functional characterization of >1500 enzymes in parallel and returns kinetic and thermodynamic
constants (e.g. kcat/KM, kcat, KM, Ki) at unprecedented scale. Here, we extend this platform to provide a suite
of techniques (HT-MES, High-Throughput Microfluidic Enzyme Stability) capable of quantifying kinetic and
thermodynamic stabilities (e.g. ΔGfold, kunfold) with similar throughput. We will also enable site-specific
incorporation of noncanonical amino acids to provide the capability to isolate and systematically the impact
of particular physicochemical residue properties and to investigate the impacts of post-translational
modifications.
During the prior granting period, our application of HT-MEK to profile multiple substitutions at each position
and multiple functional parameters for the model alkaline phosphatase PafA revealed that residues with
similar functional effects on catalysis formed large and spatially contiguous ‘regions’ that extended from
the active site to distal surfaces, and that different regions affected different aspects of function. Here, we
will systematically apply HT-MEK/S to a variety of new systems to build upon these observations and
develop and test new models of how enzymes attain their functions and their observed kinetic and
thermodynamic constants. Specifically, we will map functional couplings between residues in PafA via
double and multi-mutant cycles and test the degree to which observations from PafA generalize by
applying HT-MEK/S to investigate other alkaline phosphatase superfamily members and to acyl
phosphatases, which provide ideal enzymes for developing and testing predictive computational models.
We will also profile impacts of all nonsynonymous single nucleotide substitutions on folding and catalysis
for protein tyrosine phosphatases (PTPs), providing clinically-relevant information about potential health
consequences of mutations that can direct development of mechanistically relevant therapies and
therapeutics. Finally, we will extend the reach of HT-MEK/S via collaborative projects with the Keedy and
Zalatan labs. In all cases, we seek to use high-throughput data to test computational predictions, provide
much-needed ground truth data for use by others, and reveal previously unattainable insights into the
functional and energetic inner workings of enzymes.

## Key facts

- **NIH application ID:** 10811509
- **Project number:** 2R01GM064798-13
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Polly Morrell Fordyce
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $501,803
- **Award type:** 2
- **Project period:** 2002-01-01 → 2027-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10811509

## Citation

> US National Institutes of Health, RePORTER application 10811509, Quantitative, High-throughput Mechanistic Enzymology (2R01GM064798-13). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/10811509. Licensed CC0.

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