# Quantitative, High-throughput Mechanistic Enzymology

> **NIH NIH R01** · STANFORD UNIVERSITY · 2022 · $560,779

## Abstract

Project Summary
 Enzymes are the primary catalysts of biological transformations and have enormous value in medicine and
industry. While decades of research have established that active site residues are essential for catalysis, we do
not yet understand in detail the contributions of residues beyond the active site to efficiency and specificity. Of
course a folded enzyme is required for catalysis, but beyond folding there is a complex functional interplay of
residues throughout an enzyme: allosteric ligand binding and remote mutations alter and enhance catalysis,
active site residues coevolve with remote residues and regions, and distal mutations arise in screens and
selections for enzymes with enhanced functional properties. Given this inherent complexity, our central premise
is that we need tools that extend the power of traditional mechanistic enzymology to systematically investigate
residues throughout the entire protein and their interconnectivity. Our central technological innovation delivers
the needed tools: High-throughput Microfluidics for Enzyme Kinetics (HT-MEK) and Stability (HT-MES)
expresses, purifies, and quantitatively assays 1200 enzyme variants in parallel, rapidly and inexpensively,
yielding accurate kinetic and thermodynamic constants for many substrates and ligands over many conditions.
With these measurements we will map functional regions and linkages throughout proteins, allowing enzymology
to address previously inaccessible challenges in mechanism, evolution, and biology.
 We first apply these tools to the Alkaline Phosphatase (AP) superfamily member E. meningoseptica PafA,
leveraging extensive prior structural, mechanistic, and phylogenetic insights to guide assay development and
test previously inaccessible models to deepen our understanding of enzyme catalysis. We will systematically
and quantitatively determine kinetic parameters for cognate and promiscuous PafA substrates and affinities for
ground and transition-state PafA inhibitors, and we will do so for multiple mutations of every PafA residue; these
measurements will provide a comprehensive map of enzyme regions that contribute to specific components of
catalytic function. Next, we will use multi-mutant cycles to determine the energetic and functional linkage of these
regions to active site residues and specific catalytic features, as well as the connections within and between
these regions. Extension to multiple AP superfamily members across evolutionary distances will identify the
range and limits of generality of functional maps and identify the alterations that rewire functional connectivity.
Expanding this approach to other targets, some of which are explored herein, will address fundamental and
practical problems of broad interest. This carefully-reasoned, stepwise approach will usher in a new era of
enzymology, in which the acquisition of multidimensional functional maps of enzymes addresses new questions
in mechanism, evolution, and biology, and in which...

## Key facts

- **NIH application ID:** 10477007
- **Project number:** 5R01GM064798-12
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Polly Morrell Fordyce
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $560,779
- **Award type:** 5
- **Project period:** 2002-01-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10477007

## Citation

> US National Institutes of Health, RePORTER application 10477007, Quantitative, High-throughput Mechanistic Enzymology (5R01GM064798-12). Retrieved via AI Analytics 2026-06-14 from https://api.ai-analytics.org/grant/nih/10477007. Licensed CC0.

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