Biology subjects

Fordyce, P. M.

Publications and source records attributed to Fordyce, P. M..

At least 19 recordsLinked to original sources

bioRxiv2026

Amplicon/Protein Bead Display enables quantitative in vitro biochemistry at scale

Scalable methods for producing and characterizing protein libraries are essential for generating standardized datasets needed to train models linking sequence to function. Here, we present Amplicon/Protein Bead Display (APB-Display), which expresses and purifies >100,000 protein variants in vitro in 1 day. APB-Display uses particle-templated emulsification to generate libraries of hydrogel beads that covalently display many copies of a given protein variant and its encoding DNA. By incubating beads with fluorescently-labeled ligands, sorting, and sequencing to generate titration curves (APB-TiteSeq), we simultaneously quantified expression levels and binding affinities (Kds) for >18,000 FLAG epitope variants interacting with M2 anti-FLAG antibody in 3 days, revealing chemistry-dependent epistasis between positions 4 and 5. Single-concentration binding measurements (APB-SortSeq) paired with neural network denoising further returned quantitative affinities for >88,000 variants. APB-Display requires only standard laboratory equipment and access to a FACS sorter, providing an accessible platform for quantitative in vitro biochemistry at scale.

biophysics↗

bioRxiv2026

Simple and Multiplexed Tracking of Bacterial Growth in Double Emulsion Droplets

Droplet-based microfluidics enable researchers to observe phenotypic heterogeneity within complex biological mixtures through parallel encapsulation of individual samples followed by imaging. Observing or quantifying dynamic heterogeneity remains challenging due to complexities associated with trapping and tracking many individual droplets. Current approaches for time-lapse imaging require specialized devices with droplet traps that limit accessibility and throughput. Here, using readily available materials and software, we demonstrate a simple method for stabilizing and monitoring many, individual droplets for up to 12 hours. We leveraged our method to track bacterial growth within droplets in a high-throughput manner. Our method allows tracking the changes and variation in growth rate within and across droplets, revealing heterogeneity in growth patterns hidden in batch assays. Improving the affordability and throughput of time-dependent phenotyping assays helps to advance biological discovery and biotechnology innovation.

bioengineering↗

bioRxiv2026

High-throughput biochemical phenotyping of SHP2 variants reveals molecular basis of diseases and allosteric drug inhibition

Interpreting clinical and functional consequences of genetic variants remains challenging due to limited quantitative biochemical data at scale. We applied high-throughput microfluidic enzyme kinetics to profile 190 clinical variants of SHP2, a protein tyrosine phosphatase linked to developmental disorders and cancers. Through >300,000 reaction progress curves, we derived kinetic and thermodynamic parameters quantifying variant effects on catalysis, autoinhibition, stability, phosphopeptide binding, and drug responses. This multidimensional dataset reveals that dysregulated autoinhibition, rather than altered stability or catalysis, predominantly determines SHP2-associated pathogenesis. Thermodynamic modeling reveals that clinical-stage allosteric inhibitors preferentially stabilize a previously underappreciated, partially active conformation over the fully inactive state, leading to variant-dependent drug responses. Our high-throughput biochemical framework establishes a general approach to decipher the biochemical logic connecting protein variants to clinical outcomes.

biochemistry↗

bioRxiv2026

FoTO1 orchestrates Taxol biosynthesis through catalytic and non-catalytic mechanisms

Taxol is a blockbuster chemotherapeutic derived from the Pacific Yew tree. Recent work in our group has identified a complete pathway to baccatin III, a key intermediate, that hinges on a novel accessory protein, Facilitator of Taxane Oxidation (FoTO1). This protein dramatically improves yield and alters enzyme product profiles when reconstituting the Taxol pathway in N. benthamiana. FoTO1 has been shown to act early in the biosynthetic pathway improving the yields of product generated by the combination of a plastidial diterpene synthase (taxadiene synthase) and an endoplasmic reticulum (ER) localized cytochrome P450 (T5-alpha-hydroxylase). Here, we show that FoTO1 is an enzyme capable of converting taxadiene-(4),5-epoxide, the likely product of T5H oxidation, into taxadien-5-ol. FoTO1 is also functional in yeast, resolving a key bottleneck for development of a bioproduction route to Taxol in this host. Targeted mutagenesis of key catalytic residues in FoTO1 abrogates function in vitro but not in planta, suggesting non-catalytic contributions of FoTO1 to the taxane pathway. A combination of proximity labelling, bimolecular fluorescence complementation assays, and co-immunoprecipitation studies revealed that FoTO1 interacts with and organizes various P450s in the Taxol pathway. These approaches highlight the importance of both FoTO1s catalytic and non-catalytic functions in improving yields in the early Taxol pathway. Beyond Taxol biosynthesis, FoTO1 boosts yields for diverse diterpene pathways from across phylogeny, suggesting a general role of this protein class in mediating metabolism across the plastid and ER in plants.

biochemistry↗

bioRxiv2026

Engineering the mechanosensitivity of single DNA molecules via high-throughput microfluidic force spectroscopy

Single-molecule force spectroscopy (SMFS) is a powerful tool to measure how biomolecules respond to mechanical force, but limited sequence throughput constrains its potential. Here, we present a single-molecule, multiplexed, microfluidic force spectroscopy (SM3FS) assay that uses parallelized microfluidics to measure up to 80 sequence variants per experiment. Using SM3FS, we stretched, overstretched, or unzipped 241 different DNA structures and generated 131,847 single-molecule traces from a quarter-million observations. High sequence throughput allowed us to identify DNA structures that are kinetically stable yet mechanically fragile (Frupture < 3 pN at 0.5 pN s-1), revealing how mechanosensitivity can arise as an intrinsic property of multivalent systems. By enabling systematic sequence-function mapping under force, SM3FS opens a path to high-throughput nonequilibrium studies of biomolecules.

biophysics↗

bioRxiv2026

uSort-M: Scalable isolation of user-defined sequences from diverse pooled libraries

Advances in high-throughput sequencing and computational protein design are expanding the catalog of known protein sequences far more rapidly than they can be functionally characterized. Functional characterization through biochemical and biophysical assays often requires isolating variants to profile them individually, a process which is often laborious and expensive. To address this, we developed user-defined Sorted Mutants (uSort-M), which can rapidly isolate and identify individual variants from widely available pools of genes by leveraging automated cell sorting and long-read sequencing technologies. To develop the uSort-M pipeline, we first parsed a 328-member scanning mutagenesis library of a 300-bp gene. Direct comparison between short-read and long-read sequencing demonstrated that both methods resolve variants with high fidelity, enabling recovery of 96% of desired library members by sorting eight 384-well plates at fivefold lower cost than traditional synthesis. After optimizing for long-read sequencing, we demonstrated uSort-Ms generalizability to complex libraries by parsing a sequence- and length-diverse 500-member library, recovering 88% of variants from shallow oversampling (<3-fold). Library recoveries could be accurately predicted by simulating library uniformity, transformation number, sorting efficiency, and per-base error rates, and we extrapolated these simulations to predict the sampling depth needed to process large libraries containing thousands of members. To facilitate adoption, these simulations are packaged alongside data analysis and workflow management tools in an open-source Python toolkit with an interactive dashboard. By implementing standard instrumentation in a generalizable workflow, uSort-M provides an efficient and cost-effective solution for large library generation, thereby removing a key barrier to large-scale protein functional characterization.

synthetic biology↗

bioRxiv2025

ADAPT-M: A workflow for rapid, quantitative in vitro measurements of enriched protein libraries

Protein-protein interactions underpin most cellular interactions, and engineered binders present powerful tools for probing biology and developing novel therapeutics. One bottleneck in binder generation is the scalable, quantitative characterization of these interactions. We present ADAPT-M (Affinity Determination by Adaptation of ProTein binders for Microfluidics), a streamlined workflow that connects yeast surface display (YSD) with in vitro affinity and kinetic measurements using the high-throughput STAMMPPING microfluidic platform. ADAPT-M quantifies Kds and dissociation kinetic parameters for hundreds of enriched protein variants in under one week without requiring hands-on protein purification. We applied ADAPT-M to a computationally designed library targeting the SARS-CoV-2 Omicron BA.1 receptor binding domain, successfully recovering and measuring Kds for most highly enriched YSD variants. Measurements correlate strongly with biolayer interferometry and yeast titration assays. ADAPT-M further enabled selection of lead candidates for structural and mutational analysis, which revealed designed paratopes were preserved despite binding to off-target epitopes. By bridging YSD screening and in vitro validation, ADAPT-M accelerates protein binder discovery and supports data-driven protein engineering.

biophysics↗

bioRxiv2025

Large field of view fluorescence imaging of microfluidic devices with a tandem-lens macroscope

Microfluidic devices enable high-throughput sample processing with remarkable parallelization and miniaturization. While fluorescence microscopy provides a convenient method for reading out signal from microfluidic assays, commercially-available microscopes impose a fundamental tradeoff between temporal resolution, spatial resolution, and numerical aperture (NA). Spatially tiled imaging enables high-resolution and high-NA imaging over a large area but reduces temporal resolution. Conversely, low magnification, low NA imaging captures large areas in one shot, but typically sacrifices spatial resolution and fluorescence sensitivity. To address this, we introduce an automated transfluorescence tandem-macro-lens optomechanical system (macroscope) capable of sensitive, multi-channel fluorescence imaging over a very large field of view (34 mm diameter, 740 mm2), with resolution determined by the sensor pixel size. We demonstrate bright-field resolution of low-micron features and detection of low-to mid-nanomolar concentrations of common fluorophores within microfluidic device channels. To demonstrate the utility of this macroscope, we image enzyme turnover within valved microfluidic devices (the HT-MEK system, for High-Throughput Microfluidic Enzyme Kinetics) and achieve >50-fold increased temporal resolution over common commercial instruments while maintaining high sensitivity. This macroscope imaging solution costs substantially less than commercially available alternatives, providing a powerful new imaging approach for microfluidic applications requiring sensitive and rapid wide-field fluorescence imaging.

bioengineering↗

bioRxiv2025

Continuous FACS sorting of double emulsion picoreactors with a 3D printed vertical mixer.

High-throughput screening and directed evolution using microfluidic picoreactors have produced high-activity enzymes. In this approach, a substrate is co-encapsulated with a candidate enzyme and individual picoreactors are sorted based on an activity reporter. While many approaches use water-in-oil droplets (single emulsions) for fluorescence-activated droplet sorting (FADS) on custom-fabricated microfluidic devices that require integrated optics and electronics, recent approaches have lowered the engineering barriers to adoption by using simple microfluidic droplet generators to produce water-in-oil-in-water droplets (double emulsion picoreactors, DEs) that can be sorted with commercial FACS (fluorescence-activated cell sorting). Despite the simplified engineering requirements, high variability in loading rates and low yield during loading are barriers to efficient DE FACS sorting. Here, we optimized surfactants to enhance DE stability and demonstrated that a 3D-printed corkscrew on the sample line acts as a vertical mixer to enable more continuous loading. With these optimized loading conditions, we analyzed 1.17 million DEs in four 10-minute sorting rounds with a mean frequency of 480 Hz (390 Hz including sample exchanges); in a mock sort of 10% fluorescent DEs, we achieved 89.2{+/-}1.1% accuracy and 78{+/-}0.9% recovery with our optimized loading protocol. Overall, improved ease-of-use and throughput for FACS sortable DEs should expand the accessibility of directed evolution in controlled in vitro environments.

bioengineering↗

bioRxiv2025

Quantifying protein unfolding kinetics with a high-throughput microfluidic platform

Even after folding, proteins transiently sample unfolded or partially unfolded intermediates, and these species are often at risk of irreversible alteration (e.g. via proteolysis, aggregation, or post-translational modification). Kinetic stability, in addition to thermodynamic stability, can directly impact protein lifetime, abundance, and the formation of alternative, sometimes disruptive states. However, we have very few measurements of protein unfolding rates or how mutations alter these rates, largely due to technical challenges associated with their measurement. To address this need, we developed SPARKfold (Simultaneous Proteolysis Assay Revealing Kinetics of Folding), a microfluidic platform to express, purify, and measure unfolding rate constants for >1000 protein variants in parallel via on-chip native proteolysis. To demonstrate the power and potential of SPARKfold, we determined unfolding rate constants for 1,104 protein samples in parallel. We built a library of 31 dihydrofolate reductase (DHFR) orthologs with up to 78 chamber replicates per variant to provide the statistical power required to evaluate the systems ability to resolve subtle effects. SPARKfold rate constants for 5 constructs agreed with those obtained using traditional techniques across a 150-fold range, validating the accuracy of the technique. Comparisons of mutant kinetic effects via SPARKfold with previously published measurements impacts on folding thermodynamics provided information about the folding transition state and pathways via {varphi} analysis. Overall, SPARKfold enables rapid characterization of protein variants to dissect the nature of the unfolding transition state. In future work, SPARKfold can reveal mutations that drive misfolding and aggregation and enable rational design of kinetically hyperstable variants for industrial use in harsh environments.

biochemistry↗

bioRxiv2024

Multiplexed perturbation of yew reveals cryptic proteins that enable a total biosynthesis of baccatin III and Taxol precursors

Plants make complex and potent therapeutic molecules, but difficulties in sourcing from natural producers or chemical synthesis can challenge their use in the clinic. A prominent example is the anti-cancer therapeutic paclitaxel (Taxol(R)). Identification of the full paclitaxel biosynthetic pathway would enable heterologous drug production, but it has eluded discovery despite a half century of intensive research. Within the search space of Taxus large, enzyme-rich genome, we suspected the complex paclitaxel pathway would be difficult to resolve using conventional gene co-expression analysis and small sample sets. To improve the resolution of gene set identification, we developed a multiplexed perturbation strategy to transcriptionally profile cell states spanning tissues, cell types, developmental stages, and elicitation conditions. This approach revealed a set of paclitaxel biosynthetic genes that segregate into expression modules that suggest consecutive biosynthetic sub-pathways. These modules resolved seven new genes that, when combined with previously known enzymes, are sufficient for the de novo biosynthesis and isolation of baccatin III, an industrial precursor for Taxol, in Nicotiana benthamiana leaves at levels comparable to the natural abundance in Taxus needles. Included are taxane 1{beta}-hydroxylase (T1{beta}H), taxane 9-hydroxylase (T9H), taxane 7{beta}-O-acyltransferase (T7AT), taxane 7{beta}-O-deacetylase (T7dA), taxane 9-O-deacetylase (T9dA), and taxane 9-oxidase (T9ox). Importantly, the T9H we discovered is distinct and independently evolved from those recently reported, which failed to yield baccatin III with downstream enzymes. Unexpectedly, we also found a nuclear transport factor 2 (NTF2)-like protein (FoTO1) crucial for high yields of taxanes; this gene promotes the formation of the desired product during the first taxane oxidation step, resolving a longstanding bottleneck in paclitaxel pathway reconstitution. Together with a new {beta}-phenylalanine-CoA-ligase, the eight genes discovered in this study enables the complete reconstitution of 3-N-debenzoyl-2-deoxy-paclitaxel with a 20-enzyme pathway in Nicotiana plants. More broadly, we establish a generalizable approach for pathway discovery that scales the power of co-expression studies to match the complexity of specialized metabolism, enabling discovery of gene sets responsible for high-value biological functions.

biochemistry↗

bioRxiv2024

Hydrophilic/ Omniphobic droplet arrays for high-throughput and quantitative enzymology

Engineered enzymes with enhanced or novel functions are specific catalysts with wide-ranging applications in industry and medicine. Here, we introduce Droplet Array Microfluidic Enzyme Kinetics (DA-MEK), a high-throughput enzyme screening platform that combines water-in-air droplet microarrays formed on patterned superhydrophilic/omniphobic surfaces with cell-free protein synthesis to enable cost-effective expression and quantitative kinetic characterization of enzyme variants. By printing DNA templates encoding enzyme variants onto hydrophilic spots, stamping slides to add cell-free expression mix, and imaging the resulting arrays, we demonstrate reproducible expression of hundreds of enzyme variants per slide. Subsequent stamping of fluorogenic substrates and time-lapse imaging allows determination of Michaelis-Menten parameters for each variant, with measured catalytic efficiencies spanning 5 orders of magnitude and agreeing well with values obtained via traditional microtiter plate assays. DA-MEK consumes orders of magnitude less reagents than plate-based assays while providing accurate and detailed kinetic information for both beneficial and deleterious mutations. In future work, we anticipate DA-MEK will provide a powerful and versatile platform to accelerate enzyme engineering and enable screening of large variant libraries under diverse conditions.

bioengineering↗

bioRxiv2024

FACS-Sortable Picoreactors for Ultra High-throughput Screening of Catalysts in Biphasic Environments

Biphasic environments can enable successful chemical reactions where any single solvent results in poor substrate solubility or poor catalyst reactivity. For screening biphasic reactions at high-throughput, a platform based on microfluidic double emulsions could use widely available FACS (Fluorescence Activated Cell Sorting) machines to screen millions of picoliter reactors in a few hours. However, encapsulating biphasic reactions within double emulsions to form FACS-sortable droplet picoreactors requires optimized solvent phases and surfactants to produce triple emulsion droplets that are stable over multi-hour assays and compatible with desired reaction conditions. This work demonstrates such FACS-sortable triple emulsion picoreactors with a fluorocarbon shell and biphasic octanol-in-water core. First, surfactants were screened to stabilize octanol-in-water emulsions for the picoreactor core. With these optimized conditions, stable triple emulsion picoreactors were produced (>70% of droplets survived to 24 hours), and the ability to produce protein in the biphasic core was demonstrated via cell-free protein synthesis. Finally, triple emulsion picoreactors were sorted based on fluorescence using commercial FACS sorters at >100 Hz with 75-80% of droplets recovered. These triple emulsion picoreactors have potential for future screening bead-encoded catalyst libraries, including enzymes such as lipases for biofuel production.

bioengineering↗

bioRxiv2023

High-throughput thermodynamic and kinetic measurements of transcription factor/DNA mutations reveal how conformational heterogeneity can shape motif selectivity

Transcription factors (TFs) bind DNA sequences with a range of affinities, yet the mechanisms determining energetic differences between high- and low-affinity sequences ( selectivity) remain poorly understood. Here, we investigated two basic helix-loop-helix TFs, MAX (H. sapiens) and Pho4 (S. cerevisiae), that bind the same high-affinity sequence with highly similar nucleotide-contacting residues and bound structures but are differentially selective for non-cognate sequences. By measuring >1700 Kds and >500 rate constants for Pho4 and MAX mutant libraries binding multiple DNA sequences and comparing these measurements with thermodynamic and kinetic models, we identify the biophysical mechanisms by which changes to TF sequence alter both bound and unbound conformational ensembles to shape specificity landscapes. These results highlight the importance of conformational heterogeneity in determining sequence specificity and selectivity and can guide future efforts to engineer nucleic acid-binding proteins with enhanced selectivity.

biophysics↗

bioRxiv2023

Design Automation of Microfluidic Single and Double Emulsion Droplets with Machine Learning

Droplet microfluidics enables kHz screening of picoliter samples at a fraction of the cost of other high-throughput approaches. However, generating stable droplets with desired characteristics typically requires labor-intensive empirical optimization of device designs and flow conditions that limit adoption to specialist labs. Here, we compile the most comprehensive droplet dataset to date and use it to train machine learning models capable of accurately predicting device geometries and flow conditions required to generate stable aqueous-in-oil and oil-in-aqueous single and double emulsions from 15 to 250 m at rates up to 12000 Hz for different fluids commonly used in life sciences. Novel device geometries predicted by our models for as-yet-unseen fluids yield accurate predictions, establishing their generalizability. Finally, we generate an easy-to-use design automation tool that yield droplets within 3 m (< 8%) of the desired diameter, facilitating tailored droplet-based platforms for new applications and accelerating their utility in life sciences.

bioengineering↗

bioRxiv2023

Global substrate identification and high throughput in vitro dephosphorylation reactions uncover PP1 and PP2A-B55 specificity principles

Phosphoprotein phosphatases (PPPs) dephosphorylate Serine (Ser)/Threonine (Thr) residues to regulate major signaling pathways and cellular transitions. Despite the central role of PPPs the substrates in most cellular processes and the determinants of phosphatase specificity are poorly understood. This is because methods to investigate this at scale are lacking. Here we develop a novel in vitro assay, MRBLE:Dephos, that allows multiplexing of dephosphorylation reactions to determine phosphatase preferences. Using MRBLE:Dephos, we establish amino acid preferences of the residues surrounding the dephosphorylation site for PP1 and PP2A- B55, which reveals common and unique preferences for the two phosphatases. To compare the MRBLE:Dephos results to cellular substrates, we focused on mitotic exit that requires extensive dephosphorylation by PP1 and PP2A-B55. We use specific inhibition of PP1 and PP2A-B55 in mitotic exit lysates coupled with quantitative phosphoproteomics to identify more than 2000 regulated phosphorylation sites. Importantly, the sites dephosphorylated during mitotic exit reveal key signatures that are consistent with the MRBLE:Dephos results. We use these insights to specifically alter INCENP dephosphorylation kinetics at mitotic exit, resulting in defective cytokinesis thus underscoring the biological relevance of our determined specificity principles. Finally, we provide a comprehensive characterization of PP1 binding motifs and demostrate how binding of phosphatases to substrates shapes dephosphorylation specificity. Collectively, we develop novel approaches to advance our ability to investigate protein phosphatases and use these to provide a framework for understanding mitotic exit regulation by dephosphorylation.

cell biology↗

bioRxiv2023

De novo distillation of thermodynamic affinity from deep learning regulatory sequence models of in vivo protein-DNA binding

Transcription factors (TF) are proteins that bind DNA in a sequence-specific manner to regulate gene transcription. Despite their unique intrinsic sequence preferences, in vivo genomic occupancy profiles of TFs differ across cellular contexts. Hence, deciphering the sequence determinants of TF binding, both intrinsic and context-specific, is essential to understand gene regulation and the impact of regulatory, non-coding genetic variation. Biophysical models trained on in vitro TF binding assays can estimate intrinsic affinity landscapes and predict occupancy based on TF concentration and affinity. However, these models cannot adequately explain context-specific, in vivo binding profiles. Conversely, deep learning models, trained on in vivo TF binding assays, effectively predict and explain genomic occupancy profiles as a function of complex regulatory sequence syntax, albeit without a clear biophysical interpretation. To reconcile these complementary models of in vitro and in vivo TF binding, we developed Affinity Distillation (AD), a method that extracts thermodynamic affinities de-novo from deep learning models of TF chromatin immunoprecipitation (ChIP) experiments by marginalizing away the influence of genomic sequence context. Applied to neural networks modeling diverse classes of yeast and mammalian TFs, AD predicts energetic impacts of sequence variation within and surrounding motifs on TF binding as measured by diverse in vitro assays with superior dynamic range and accuracy compared to motif-based methods. Furthermore, AD can accurately discern affinities of TF paralogs. Our results highlight thermodynamic affinity as a key determinant of in vivo binding, suggest that deep learning models of in vivo binding implicitly learn high-resolution affinity landscapes, and show that these affinities can be successfully distilled using AD. This new biophysical interpretation of deep learning models enables high-throughput in silico experiments to explore the influence of sequence context and variation on both intrinsic affinity and in vivo occupancy.

genomics↗

bioRxiv2022

High-throughput enzymology reveals mutations throughout a phosphatase that decouple catalysis and transition state analog affinity

Using High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), we measured over 9,000 inhibition curves detailing impacts of 1,004 single-site mutations throughout the Alkaline Phosphatase PafA on binding affinity for two transition state analogs (TSAs), vanadate and tungstate. As predicted by catalytic models invoking transition state complementary, mutations to active site and active site-contacting residues had highly similar impacts on catalysis and TSA binding. Unexpectedly, most mutations to more distal residues which reduced catalysis had little or no impact on TSA binding and many even increased affinity for tungstate. These disparate effects are accounted for by a model in which distal mutations alter the enzymes conformational landscape and increase occupancy of microstates that are catalytically less effective but better able to accommodate larger transition state analogs. In support of this model, glycine substitutions (rather than valine) were more likely to increase tungstate affinity, presumably due to increased conformational flexibility and increased occupancy of previously disfavored microstates. These results indicate that residues throughout an enzyme provide specificity for the transition state and discriminate against analogs that are larger only by tenths of an [A]ngstrom. Thus, engineering enzymes that rival the most powerful natural enzymes will likely require consideration not just of residues in and around the active site, but also of more distal residues that shape the enzymes conformational landscape and finetune the active site. In addition, the extensive functional communication between the active site and remote residues may provide interconnections needed for allostery and make allostery a highly evolvable trait. Significance StatementTransition state analogs (TSAs) resemble fleeting high-energy transition states and have been used to inhibit enzymes in nature and medicine, to learn about enzyme active site features, and to design and select new enzymes. While TSAs mimic transition states, they differ from actual TSs, and we exploit these differences here. Systematic TSA affinity measurements for 1,004 mutants of PafA (a model phosphatase enzyme) revealed effects in and around the active site that mirror their effects on catalysis, but TSA-binding and catalytic effects diverge more distally. These observations suggest that residues throughout an enzyme adjust its conformational landscape on the tenth-[A]ngstrom scale to optimize the active site for catalysis, rendering allostery more evolvable in nature but likely complicating enzyme design.

biochemistry↗