Decoding the substrate specificity landscape of a promiscuous enzyme through multi-substrate mutational scanning

Domain: chemistry
Published: 2026-02-26
Editorial outcome: minor_revision

Abstract
Substrate specificity is a defining feature of enzyme function, but its molecular underpinnings remain difficult to decode and engineer. Here, we leverage enzyme proximity sequencing (EP-Seq) to systematically map how single-point and combinatorial mutations reshape the substrate preferences of D-amino acid oxidase (DAOx) from Rhodotorula gracilis, a model promiscuous enzyme. We generate ~40,000 sequence–phenotype pairs, enabling us to profile the activities of ~6,500 unique DAOx variants against five D-amino acid substrates with distinct physicochemical properties. Our analysis reveals that substrate-specific mutations are distributed throughout the enzyme structure. Mutations near the active site drive strong specificity shifts but also incur catalytic penalties, while distal mutations subtly rewire intramolecular contacts in order to modulate specificity with minimal loss of activity. We identify and validate positional hotspots that act allosterically to influence specificity, and characterize key variants that acquire exclusive substrate specificity or exhibit up to 230-fold changes in substrate preference. Combining mutations with complementary effects further sharpens substrate discrimination, enabling rational design of highly selective biocatalysts. This work provides a powerful framework for decoding enzyme specificity and provides foundational datasets to advance AI-guided enzyme engineering.

Introduction
Tailoring substrate specificity in enzymes is crucial for biocatalyst engineering, therapeutic enzyme design, and synthetic biology. Despite major progress in structural and computational enzymology, our understanding of how enzymes discriminate between structurally similar substrates remains incomplete, particularly in enzymes that exhibit broad promiscuity. Substrate specificity was for many years considered a static property1, but is now appreciated as an emergent feature shaped by an interplay of active-site geometry, conformational dynamics, and long-range intramolecular interaction networks, all under complex evolutionary constraints2–4.

Substrate specificity manifests as a relative property, enabling enzymes to preferentially convert their primary substrate relative to structurally similar alternatives, while maintaining sufficient reaction flux in the crowded cellular environment3,5,6. However, the molecular principles governing substrate discrimination and its co-evolution with other enzyme properties (e.g., overall catalytic activity and folding stability) remain poorly understood. This knowledge gap limits our ability to predict how mutations will affect enzyme performance and impedes the rational engineering of enzymes for diverse applications.

Deep mutational scanning (DMS) has revolutionized the study of protein function by enabling high-throughput mapping of sequence-fitness relationships7–11. If successfully applied to the study of enzyme specificity, DMS could have significant potential to illuminate how sequence encodes discrimination between structurally similar substrates. However, applying DMS to enzymes presents challenges. DMS workflows applied to enzymes must directly link catalytic activity to a measurable cellular phenotype. This requirement has largely limited prior work to enzymes whose activity can be directly linked with cell growth or survival12–14. Prior DMS work on substrate specificity has therefore focused on antibiotic resistant β-lactamase15–17 and on an amide hydrolase18 whose function generates a nitrogen source required for cell proliferation.

These prior studies demonstrate that each substrate shapes the enzyme’s fitness landscape in a distinct way, and offer preliminary insights into molecular determinants of specificity. However, this line of research has been limited by reliance on intracellular reactions and growth-based selections, where reaction conditions may be poorly controlled, substrate concentrations cannot be precisely set, and the observed phenotypic fitness may conflate the influence of mutations on catalytic activity with their impact on enzyme abundance (i.e., expression level) or folding stability.

To overcome these challenges, we recently developed enzyme proximity sequencing (EP-Seq), a DMS workflow that leverages a high-throughput single-cell enzyme activity assay based on radical-mediated fluorescent proximity labeling19–24. EP-Seq enables quantification of enzyme activity for cells displaying an enzyme variant on their surface, where catalytic turnover can be linked, directly or indirectly via a cascade reaction, to the production of hydrogen peroxide (H2O2). We previously validated EP-Seq by mapping the mutational landscape of an oxidoreductase19, showing that the method can disentangle the effects of mutations on expression level, folding stability, and catalytic activity in a massively parallel and quantitative manner.

In the present work, we extend the use of EP-Seq to examine how mutations affect substrate selectivity in a D-amino acid oxidase from Rhodotorula gracilis, a naturally promiscuous enzyme with diverse industrial and biomedical applications25–28. By displaying a comprehensive library of single-substitution enzyme variants on the surface of yeast cells, we measured the activity of thousands of variants against five substrates with distinct biophysical properties. This setup allowed precise control over reaction conditions and facilitated direct comparison of fitness scores across datasets.

By analyzing the fitness landscape of DAOx on five substrates, we identified hundreds of single amino acid substitutions that differentially modulate enzyme performance, revealing how mutations that control substrate preference are distributed across the entire protein structure. Mutations near the active site produced large specificity shifts but often incurred catalytic penalties, while distal substitutions more subtly fine-tuned substrate preferences with minimal loss of activity. This highlights distinct mechanisms for achieving specificity depending on the extent of functional cost that can be tolerated during evolution. We further developed a substrate specificity score, and validated this statistical metric using 140 single-clone measurements of enzymatic discrimination factors. This high-throughput DMS-based phenotypic metric enables rapid and quantitative assessment of enzyme selectivity between substrate pairs. Finally, we showed that combining individually beneficial mutations can synergistically sharpen substrate specificity, offering a scalable route to engineering highly selective biocatalysts. Together, these findings establish EP-Seq as a robust platform for decoding and engineering enzyme specificity at scale.

Results
EP-Seq workflow applied to study substrate specific effects of mutations

To systematically map how mutations affect substrate specificity in Rhodotorula gracilis D-amino acid oxidase (DAOx) we applied EP-Seq19, a high-throughput platform that links enzyme activity to a fluorescence readout at the single-cell level. In this workflow, a library of enzyme variants fused to the Aga2 anchor protein is displayed on the surface of Saccharomyces cerevisiae. Enzymatic turnover is coupled to production of H2O2, which in the presence of horseradish peroxidase (HRP) initiates a tyramide-based radical labeling reaction that links fluorophores to the cell surface. The resulting cellular fluorescence intensity correlates with the catalytic turnover of the variant borne by that particular cell. Following labeling, cells are sorted by fluorescence activated cell sorting (FACS) into bins, and next-generation sequencing (NGS) analysis of the DNA barcodes reveals the identities of the enzyme variants in each bin. Fitness scores for each variant are then computed by comparing their bin distributions to that of the wild-type enzyme, allowing quantitative measurements of activity. In a parallel experiment, the expression levels of all variants in the library are monitored by fluorescently labeling a C-terminal epitope tag (e.g., 6×His tag) on the displayed enzyme construct, and again quantifying the expression fitness by FACS and NGS. By decoupling activity and expression measurements, EP-Seq enables the construction of orthogonal fitness landscapes for catalytic activity and display levels.

We applied EP-Seq to evaluate the effects of single-point mutations on the catalytic activity of DAOx across five distinct D-amino acid substrates (Fig. 1A). The substrate panel included three hydrophobic amino acids (D-alanine (D-Ala), D-methionine (D-Met), and D-phenylalanine (D-Phe)) with progressively larger side chains (methyl, S-methyl thioether, and phenyl, respectively). Additionally, we included two hydrophilic substrates (D-asparagine (D-Asn), and D-glutamine (D-Gln)) bearing polar amide groups on two- and three-carbon side chains, respectively.

We first cloned the wild type DAOx (WT-DAOx) gene in-frame with Aga2, and confirmed that the surface-displayed fusion protein on S. cerevisiae strain EBY100 retained activity and substrate preferences comparable to the soluble enzyme. We found that the hydrophobic substrates were processed with higher affinity than the polar ones (Supplementary Note 1, Supplementary Fig. 1, Supplementary Table 1). For the EP-Seq assays, each substrate was assayed at its experimentally determined KM concentration. Time-course optimization of the tyramide reaction showed that a 60-minute reaction time yielded optimal labeling for the hydrophobic substrates, while 120 minutes were required for the less reactive polar substrates. These conditions ensured comparable signal intensities and dynamic ranges across all five substrates (Supplementary Note 2, Supplementary Fig. 2).

We next transformed EBY100 yeast cells with a site-saturation mutagenesis library of DAOx variants, covering 94.4% of all possible single amino acid substitutions (6,530 of 6,916 possible missense variants; Fig. 1B). After the tyramide labeling assay, cells were sorted by fluorescence intensity. A low fluorescence gate captured cells either not displaying the enzyme or bearing inactive variants, while the remaining cells were equally divided into three bins of increasing signal (Fig. 1C, Supplementary Fig. 3). Barcode sequencing of each bin was then used to reveal the identity and frequency of the variants, from which we computed an activity fitness score for each variant relative to WT (Fig. 1D-E, Supplementary Table 2, Eqs. 1-3).

Validation of the EP-Seq workflow and analysis of fitness scores

To assess the reproducibility of the workflow, we calculated Pearson correlation coefficients (r values) between fitness scores obtained from two biological replicates. This analysis included DNA single-mutant variants represented by at least 10 cells per replicate at the screening stage (n = 6573). Across all five substrates, Pearson’s r values ranged from 0.94 to 0.97, indicating high reproducibility of the assay (Fig. 2A).

Within each screen, we computed a consensus score for each DAOx variant (Methods, Eq. 4), and examined the distributions of scores for missense, nonsense and synonymous mutations (Fig. 2B-C). Missense mutations were generally associated with reduced catalytic activity (average score = -0.31 ± 0.05; n = 5,821 variants in all five datasets) (Fig. 2B-C, Supplementary Table 3). Nonsense mutations had strongly negative effect on activity (average score -0.58 ± 0.05; n = 316 in all five datasets), indicating that loss of the C-terminal region beginning anywhere from position 1 to position 354 had a deleterious effect on the catalytic activity of the enzyme, regardless of the substrate used (Fig. 2C, Supplementary Table 3). In contrast, variants carrying premature stop codons beyond position 356, which resulted in loss of only the last ~10 amino acids, exhibited fitness scores comparable to WT (average score -0.01 ± 0.03; n = 9 variants in all five datasets) (Fig. 2C), suggesting that this C-terminal region is largely unnecessary for catalytic function, regardless of the substrate. Synonymous mutations did not substantially alter fitness, with scores similar to WT (avg score = 0.00 ± 0.0035; n = 281 in all five datasets) (Fig. 2B-C, Supplementary Table 3).

To validate the fitness scores obtained from EP-Seq, we used an Amplex Red assay to measure the catalytic activity of 18 yeast-displayed single-mutant DAOx variants on each of the five substrates. Initial reaction rates were normalized to that of the WT enzyme assayed in parallel and compared to the corresponding DMS-derived fitness scores. The two measurements were highly consistent, with Pearson’s r values ranging from 0.93 to 0.98 across all tested conditions (Fig. 2D).

Comparative analysis of EP-Seq scores across five DAOx substrates

We then combined the five high-quality EP-Seq datasets. The resulting data comprised 6,418 unique variants, including 5,821 missense, 316 nonsense, and 281 synonymous mutations. To account for differences in enzyme display (i.e. expression) levels, activity scores were normalized to expression scores obtained from our previously published dataset19. We further only considered variants with expression levels higher than the mean expression of nonsense variants plus one standard deviation. Synonymous variants were used to define a reference range for WT-like behavior (average score across all datasets: 1.00 ± 0.037). Nonsense and synonymous mutations were subsequently excluded, leaving 5,800 missense variants (83.9% of the total) shared across all five substrate screens that were used for comparative downstream analysis.

To qualitatively compare the effects of mutations, we visualized the fitness scores using heatmaps, which revealed broadly similar activity landscapes across all five screens (Supplementary Fig. 4). Quantitative analysis further confirmed strong concordance in these datasets, with Pearson’s r values ranging from 0.93 to 0.96 in pairwise comparisons between different substrates (Supplementary Fig. 5). The weakest correlation (r = 0.93) was observed between D-Ala and D-Gln (i.e. the smallest hydrophobic and the largest polar substrates, respectively). In contrast, the strongest correlations (r = 0.96) were found between D-Met and each of D-Phe, D-Ala, D-Asn, highlighting the intermediate size and chemical features of D-Met in comparison to the other substrates tested.

We attribute the high level of agreement to the inherently promiscuous and generalist nature of R. gracilis DAOx, a trait that is also observed in more recently diverged eukaryotic homologs such as human DAOx. Additionally, our use of standardized assay conditions (i.e. KM-matched substrate concentrations and optimized timepoints) facilitated a consistent dynamic range across all substrate screens, despite significant substrate-dependent differences in catalytic efficiency. This minimized potential detection biases and enhanced scoring accuracy, particularly for low-activity variants where substrate-specific effects are most likely to occur due to functional trade-offs in enzyme evolution 3,15,19.

Identification of specificity-determining positions in DAOx

To identify mutations that alter substrate preference, we computed z-scores for each missense variant across all pairwise combinations of the five substrate datasets. These z-scores quantify how much a variant’s fitness difference between two substrates deviates from the average difference observed across all mutations between the same two substrates. A positive z-score indicates enhanced activity on one substrate relative to its paired counterpart, while a negative z-score indicates the opposite (Supplementary Fig. 6). We refer to this metric as the substrate specificity score (Sp-score), which captures both the magnitude and direction of a mutation’s impact on substrate specificity (Methods, Eq. 5).

To define a significance threshold, we used synonymous mutations as an internal control population to estimate background noise. Mutations with |Sp-score| ≥ 3, corresponding to three standard deviations from the mean, were classified as substrate-specific mutations. This threshold excluded >99% of synonymous variants and retained 601 missense mutations (332 unique variants) that were then classified as substrate-specific (Fig. 3A-B, Supplementary Fig. 6). These mutations were broadly distributed throughout the DAOx sequence, affecting 166 of 364 positions (~45%) (Fig. 3A).

When mapped onto the 3D structure, substrate-specific mutations appeared throughout the enzyme, with an average distance of 18–24 Å from the catalytic center depending on the substrate. However, mutations ≤ 8 Å from the active site tended to show the strongest effects (i.e., highest |Sp-score|), highlighting the important contribution of active-site and first shell residues to specificity (Fig. 3C).

While most specificity-determining positions were unique to one or two substrates, a small set showed broader effects. We found three positions (residues 213, 215, and 221) that exhibited differential substrate-specific effects across all five tested substrates, depending on the identity of the introduced amino (Fig. 3D, Supplementary Fig. 7). The greatest overlap in substrate-specific positions was observed between D-Ala and D-Met (n = 15), suggesting a convergent mutational pattern shaped by their similar physicochemical profiles. In contrast, D-Phe and D-Gln shared only one specificity-associated site despite being similarly bulky. This limited overlap highlights the influence of chemical composition over steric similarity in shaping the specificity landscape (Fig. 3D).

Validation and global analysis of substrate specificity scores

To validate the EP-Seq-derived substrate specificity scores (Sp-scores), we performed kinetic measurements on 17 single-mutant DAOx variants exhibiting high Sp-scores towards at least one of the five substrates. Each variant was displayed on yeast, and Michaelis-Menten kinetics were measured on all five substrates. For each variant and substrate pair, we then calculated discrimination factors (D-factors). The D-factor, defined as (kcat/KM)sub1 / (kcat/KM)sub2 (Methods, Eq.8) provides a standardized metric of specificity, representing catalytic efficiency on a cognate substrate divided by that on a non-cognate substrate3,6. We then compared Sp-scores with D-factors across 140 variant-substrate combinations. The two measures were strongly correlated (Pearson’s r = 0.76, p < 0.0001, n = 140, Fig. 4A), confirming that the EP-Seq-derived Sp-scores accurately reflect substrate-selective catalytic behavior.

We next analyzed the distribution of substrate-specific mutations across all five assays. Among 445 unique single-mutant variants with |Sp-score| ≥ 3 towards at least one of the tested substrates, the majority favored D-Met (n = 121), D-Asn (n = 120), or D-Ala (n = 110) (Fig. 4B, pie chart). The ten most selective variants for the smallest two substrates D-Ala or D-Asn (irrespective of the non-cognate substrate) exhibited significantly higher average Sp-scores (D-Asn: 7.91 ± 1.10, n = 10; D-Ala: 6.28 ± 0.54, n = 10) as compared to the top ten most specific variants for the three larger substrates, including hydrophobic D-Phe (4.87 ± 0.53, n =10) and D-Met (5.59 ± 1.00, n = 10), or the polar D-Gln (5.45 ± 0.88, n = 10) (Fig. 4C). These trends suggest that the strongest substrate-selective effects arise when mutations act to exclude sterically larger non-cognate substrates.

To quantify patterns of substrate discrimination, we next calculated the mean Sp-score values for each substrate pair by multiplying the number of selective variants (|Sp-score| ≥ 3) for a given substrate pair by their average Sp-score, and normalizing by the total number of variants selective for the same primary substrate. This produced a single value reflecting both the frequency and intensity of discrimination across all substrate pairs (Fig. 4B, bars). Discrimination was most pronounced when substrate pairs differed substantially in terms of size or polarity. For example, D-Ala- and D-Asn-specific variants most effectively excluded the bulkier substrates D-Phe and D-Gln. Conversely, D-Phe-specific variants discriminated strongly against D-Gln but less so against the smaller substrates D-Ala, D-Asn, and D-Met. A similar pattern held for D-Gln-specific variants, which discriminated against D-Phe but not against smaller substrates. These trends suggest that accommodating the larger substrates requires an open active site that also admits smaller substrates, limiting selectivity based on steric exclusion. The strong mutual discrimination between D-Phe and D-Gln likely reflects changes in electrostatic interactions. Finally, D-Met-specific variants exhibit moderate and balanced selectivity across all four other substrates, consistent with its intermediate size and chemical features (Fig. 4B, bars).

A detailed analysis of the amino acid substitutions in the most selective variants revealed distinct trends, especially for D-Ala and D-Asn. Among the top 10 variants selective for D-Ala, 7 introduced residues bulkier than the WT residue, typically tyrosine (Y) or tryptophan (W) at positions near the catalytic site (average distance = 4.62 Å ± 2.54; Supplementary Table 4). This suggests that steric exclusion of larger non-cognate substrates is a dominant mechanism for enhancing selectivity on D-Ala. In contrast, only 2 of the top 10 D-Asn-selective variants showed increased bulkiness while 8 introduced charged or polar side chains (Supplementary Table 4), suggesting that substrate exclusion alone was insufficient to generate selectivity on D-Asn, which the wild-type enzyme poorly accommodates (KM > 10 mM).

Analysis of the 10 most selective variants on D-Phe, D-Met, and D-Gln, which exhibited lower average Sp-scores than the 10 most selective variants on D-Ala or D-Asn, did not reveal any consistent substitution trends (Supplementary Table 4), suggesting that specificity for these substrates arises through more complex or context-dependent mechanisms.

Together, these results indicate that steric clashes and electrostatic interactions play dominant roles in driving substrate specificity. This observation supports the broader idea that positive selection (i.e., enhancing activity on a cognate substrate) is typically a slow and incremental evolutionary process. By contrast, specificity can emerge more rapidly through negative selection, where mutations exclude non-cognate substrates via structural or electrostatic incompatibility3,4,29,30. This strategy also applies in many laboratory evolution campaigns, where undesired activity is penalized while variants enhancing specificity are often reached by excluding secondary substrates from the catalytic site31–33.

Evolutionary interplay between substrate specificity, catalytic activity and structural stability

Engineering enzymes for improved activity often compromises structural stability, a well-established trade-off reflecting the delicate balance between function and fold19,34–37. To investigate how this balance shifts during evolution of substrate specificity, we analyzed substrate-specific DAOx variants by combining the catalytic activity data with yeast display expression scores, known to be a valid proxy for folding and stability19,38–40.

Among the 445 specificity-conferring single mutants, 24.9% (n = 111) retained catalytic activity toward the primary substrate comparable to WT. This suggests that specificity can emerge through functionally neutral drift. These mutations were mildly destabilizing (average expression score: 0.95 ± 0.08; WT score: 1) and generally located away from the active site (average distance: 13.60 ± 10.29 Å) (Fig. 4 D-E, beige). An additional 20.7% (n = 92) of substrate-specific variants exhibited reduced catalytic activity toward the primary substrate, but were similarly stable or slightly more stable than WT (average expression score: 1.00 ± 0.06; WT score: 1). These mutations tended to occur closer to the active site (average distance: 7.96 ± 6.61 Å) and reflect a trade-off in which enhanced specificity is achieved at the cost of catalytic efficiency (Fig. 4 D-E, gray).

The remaining 54.4% of variants (n = 242) increased both specificity and catalytic activity but imposed the most substantial reduction in stability, with the lowest expression scores (0.87 ± 0.08) and the greatest average distance from the active site (average distance: 17.1 ± 8.81 Å) (Fig. 4 D-E, colored). These results highlight how functional gains can often originate from distal mutations, even when accompanied by destabilizing effects. Notably, the most impactful specificity-conferring mutations clustered near the active site (Fig. 3C), suggesting that large shifts in substrate preference often involve functional compromises, passing through regions of reduced fitness along the evolutionary trajectory.

Single mutations reshape substrate preference via distinct structural mechanisms

To understand in more detail the structural mechanisms underlying substrate specificity, we characterized the catalytic properties of 30 single-mutant DAOx variants chosen for their high Sp-scores. Each variant, along with WT-DAOx was expressed on yeast and assayed for Michaelis-Menten kinetic parameters against all five substrates. We determined Vmax and KM values and compared them to WT values to assess changes in substrate affinity and catalytic rate (Supplementary Table 5).

Among D-Ala-selective variants, F58W and F58Y exhibited the highest specificity. Located at the entrance of the substrate tunnel, these mutations increase steric hindrance, restricting access to larger substrates. While WT-DAOx already contains a bulky phenylalanine at this position, introducing even larger residues (Trp or Tyr) significantly shifted the enzyme’s preference toward D-Ala. In the F58W variant, the D-factor for D-Ala over D-Phe increased by >200-fold (D : F58W = 42.9, WT = 0.2), while an average ~20-fold increase in D-factor was observed in F58W for D-Ala over D-Met and D-Asn (Fig. 5 i, Supplementary Table 5).

For the D-Phe substrate, N54L emerged as the most selective variant. In the WT-DAOx catalytic site, Asn54 coordinates a water molecule28 and orients substrates through its polar side chain that projects toward the center of the catalytic pocket. Replacing Asn54 with hydrophobic Leu without increasing steric bulk likely alters local solvent interactions, enhancing binding of large hydrophobic substrates. This enhances compatibility with D-Phe and D-Met over the smaller D-Ala (D : N54L = 22.9, WT = 5.1). The structurally similar N54I showed a comparable selectivity profile as N54L (D : N54I = 23.3, WT = 5.1) (Fig. 5 ii, Supplementary Table 5).

On D-Met, the T93M mutation located just outside the active site increased selectivity by modestly improving D-Met affinity while broadly reducing catalytic efficiency on the other substrates. A similar effect was observed in T56M, likely due to stabilization of nearby contacts involving Met213. For T93M, discrimination between D-Met and D-Phe increased ~40-fold (D : T93M = 39.8, WT-DAOx = 0.9,) (Fig. 5 iii, Supplementary Table 5).

For the polar substrate D-Asn, the charged substitution M213E conferred strong selectivity, consistent with prior reports implicating this position in electrostatic tuning of the active site41,42. M213E reduced overall enzyme activity but preserved or enhanced affinity for D-Asn and D-Gln, with absolute selectivity against D-Ala and an average ~30-fold increase in D-factor for D-Asn over the other two hydrophobic substrates D-Phe and D-Met (D : M213E = 0.7, WT-DAOx = 0.02; D : M213E = 0.73, WT-DAOx = 0.02). The similar variant M213D further reinforced these observations (Supplementary Table 5).

For the polar substrate D-Gln, we found that the variant S341I exhibited striking selectivity despite the mutation being >8 Å away from the catalytic site. The mutation at position S341 can alter the orientation of nearby residues Y338 and Q339, which are directly involved in substrate coordination. While KM for D-Gln slightly increased, Vmax dropped to ~10% of WT. No measurable activity was observed for any of the other four substrates, even under extended assay conditions, making this variant fully selective for D-Gln, which is notably one the least favored substrates of WT-DAOx. This highlights how even distal mutations can fine-tune specificity through subtle geometric or allosteric effects (Fig. 5 v, Supplementary Table 5).

To reinforce the validity of our observations, we selected the five DAOx variants that exhibited the most pronounced shifts in substrate specificity toward each of the five tested substrates (F58W, N54L, T93M, M213E, S341I) for soluble expression in E. coli and subsequent purification. These variants, together with soluble WT-DAOx, were comprehensively characterized for their Michaelis-Menten kinetics against each of the five substrates used in this study (Table 1, Supplementary Fig. 8). We then calculated discrimination factors and compared them to the corresponding profiles obtained from the yeast-displayed formats (Supplementary Fig. 9). For all unique substrate pairs across these five variants, the determined D-factors showed strong agreement between the soluble and yeast-displayed forms (n = 36, r = 0.87, p < 0.0001; Supplementary Fig. 9A). All variants retained the specificity profiles observed in the yeast-displayed format. Notably, S341I maintained absolute selectivity for D-Gln, even when tested at concentrations 20-fold higher than WT and under extended incubation times (Supplementary Fig. 9B).

Distal mutations as modulators of substrate specificity

Distal mutations can significantly influence enzyme activity and substrate specificity, although their identification and mechanistic roles are challenging to detect. These effects are often mediated through conformational or allosteric changes and are highly context-dependent43–45. Previous deep mutational scanning studies have reported contrasting patterns. In some cases, specificity-determining mutations are mainly distal (>9 Å from the catalytic site)18, while in others, they cluster within the first shell residues around the active site15,17,46.

In DAOx, we found that the strongest specificity-shifting mutations were predominantly located within 8 Å of the catalytic site (Fig. 3C), yet these accounted for only 30% (n = 101) of all such mutations. The remaining 70% (n = 231) were scattered across more distal regions of the enzyme. Notably, we identified a hotspot along α-helix 10 (residues 248–263), where positions 254, 258, and 262 were among the most frequently mutated positions affecting specificity (Fig. 6A). These residues occupy alternating helical turns and project their side chains in the same direction, forming interactions with β-strands 9–11 (residues 212–235). This region shapes the catalytic cleft and contains critical substrate-coordinating residues, including M213, S215, and P221 (Fig. 6B) which are among the most important positions for substrate specificity across all tested substrates (Fig. 3D, Supplementary Fig. 7).

For these distal positions, we characterized two variants per site at the single clone level. At positions 254 and 258, substitutions such as R254C/D and H258W/R resulted in a twofold increase in substrate selectivity toward the bulky hydrophobic substrate D-Phe over all other tested substrates. In contrast, substitutions at position L262 (L262V/A), which introduce smaller hydrophobic side chains, led to a twofold increase in specificity for the smaller substrate D-Ala over all other substrates, with the exception of D-Gln (Fig. 6C). These results show how distal mutations outside the catalytic site can reshape the protein’s structural network, subtly modifying the catalytic pocket’s geometry and influencing substrate preference. The close proximity of α-helix 10 to the dimer interface hints that oligomerization-induced structural reorganization can further modulate the catalytic site and contribute to substrate specificity.

Combinatorial mutations enable control over substrate specificity

To further refine substrate specificity for engineering applications, we tested whether combining substrate-specific mutations would produce additive or synergistic effects on substrate preference (Fig. 7). Since the most selective single-point mutations were clustered within the catalytic site and also enhanced structural stability, we hypothesized that combining these mutations could further sharpen substrate specificity without significantly compromising stability or expression levels.

We constructed six double mutants and measured their kinetic parameters across all five substrates, comparing the specificity profiles to those of the corresponding single mutants (Fig. 7A-B). For example, the F58W mutation that introduced strong steric hindrance at the substrate tunnel and consistently reduced activity toward D-Phe. When combined with mutations that favor D-Ala such as S215Y or A222M, we observed a sharp drop-in activity for all other substrates and a pronounced enhancement in D-Ala specificity. By contrast, combining F58W with M213E, which introduces a negative charge at the active site, abolished D-Ala activity and shifted specificity toward the polar substrates D-Asn and D-Gln (Fig. 7C, top). The M213E/S215Y double mutant further intensified this specificity shift, redirecting enzyme activity almost entirely toward D-Asn and D-Gln, two substrates that are only minimally processed by WT-DAOx.

We also combined N54L and H258W, both of which favor D-Phe individually, and observed a strong additive effect: the double mutant retained over 75% catalytic efficiency for D-Phe while showing greatly reduced activity on all other substrates. However, not all combinations were additive. The T56H/T93M mutant, designed to enhance specificity on D-Met, maintained activity on multiple hydrophobic substrates without a clear preference, demonstrating that certain mutations can interfere with each other’s effects (Fig. 7C, bottom).

Methods
Expression and Surface Display of DAOx Monoclonal Populations and Variant Library

The pYDKan WT-DAOx plasmid and DAOx variant library used in this study were previously constructed and described19. The plasmids were transformed into Saccharomyces cerevisiae strain EBY100 using the lithium acetate method54, and positive colonies were selected on synthetic defined (SD) plates or liquid medium containing 2% (w/v) glucose and lacking tryptophan (-Trp).

As previously described19, to induce expression and surface display of the Aga2-DAOx fusion proteins, positive transformants were first cultivated in SD -Trp liquid medium with 2% (w/v) glucose at 30 °C for 24 h with shaking at 200 rpm, until reaching an OD600 of approximately 8. Cells were then transferred to fresh SD -Trp induction medium containing 0.2% (w/v) glucose, 1.8% (w/v) galactose, and 100 mM citrate/phosphate buffer (pH 7.0) and incubated at 20 °C for 48 h. Following induction, yeast cells were pelleted, washed with PBS containing 0.1% (w/v) bovine serum albumin (BSA), and labeled with antibodies targeting the C-terminal 6×His tag to assess surface display of the fusion proteins.

Yeast antibody staining to detect surface displayed DAOx

As outlined in our previous work19, following induction of protein expression and surface display, yeast cells were washed twice with PBS supplemented with 0.1% (w/v) BSA. Cells were then resuspended at a density of 2 × 106 cells per 100 μl in the same buffer containing a 1:500 dilution of mouse monoclonal anti-6xHis antibodies (Thermo Fisher Scientific, Cat# MA1-21315, RRID: AB_557403). Samples were incubated with the primary antibody for 30 minutes at room temperature, followed by two additional washes with PBS/BSA. Next, cells were resuspended in PBS/BSA(0.1%) at the same concentration (2 × 106 cells/100 μl) and incubated with a 1:500 dilution of Alexa Fluor™ 594-conjugated goat anti-mouse secondary antibody (Thermo Fisher Scientific, Cat# A-11005, RRID: AB_2534073) for 30 minutes at 4 °C.

After incubation, samples were washed twice more and prepared for analysis. Flow cytometry was performed using an Attune NxT cytometer (Thermo Fisher Scientific) equipped with 488 nm and 561 nm lasers. Surface expression of DAOx was quantified by recording and analyzing 10,000 events per sample.

Soluble Expression of WT-DAOx

The wild-type D-amino acid oxidase was expressed in Escherichia coli. The gene encoding the wild-type enzyme was cloned into the pET28 bacterial expression vector. Plasmids were transformed into BL21 (DE3) cells and plated on kanamycin-selective LB plates. From a single colony, a liquid LB Kanamycin culture (3 mL) was started and grown overnight at 37 °C with shaking (200 rpm). One milliliter of this overnight culture was used to inoculate 100 mL LB Kan and allowed to grow for 2.5 hours at 37 °C to an OD of 0.5. To induce expression, IPTG was added to a final concentration of 0.5 mM, and the culture was grown overnight for 18 hours at 25 °C with shaking (200 rpm). The following day, cells were centrifuged at 4000 x g for 15 minutes, and the supernatant was discarded. The pellet was placed on ice for 15 minutes and then resuspended in 10 mL of lysis buffer (50 mM TRIS, pH 8.0, 50 mM NaCl, 0.1% (v/v) Triton X-100, 5 mM MgCl2). Cells were lysed by sonication for 10 minutes (Sonifier cell disruptor Branson Digital Sonifier, USA) and then centrifuged at 18,000 x g for 20 minutes. The supernatant was loaded onto a gravitational flow column packed with 2 mL of HisPur Ni-NTA resin. Columns were washed with 6 column volumes of PBS buffer first and then 6 column volumes of PBS buffer containing 20 mM imidazole. Finally, the bound protein was eluted in 5 mL PBS buffer containing 500 mM Imidazole. Elution fractions were concentrated using Vivaspin concentration columns (10 kDa cutoff). Subsequently, purity was increased, and residual salts from the affinity purification protocols were removed by size exclusion chromatography at a speed of 0.5 mL/min. The most abundant fraction was collected and the purity and molecular weight of the eluted protein were tested and confirmed by running 2 µg of eluted protein through a 12% polyacrylamide SDS gel.

Kinetic Characterization and Comparison of Soluble and Yeast-Displayed WT-DAOx

Kinetics measurements for soluble RgDAOx wild type were performed through Amplex Red activity assay by incubating the purified wild type enzyme with increasing concentrations of each tested substrates together with 5.6 μM HRP and 100 μM Amplex Red in PBS (pH 7.5). For measurement we used the following enzyme and substrate concentration. D-alanine, D-phenylalanine, D-methionine, D-leucine were tested at 0.27 mM, 0.55 mM, 1.1 mM, 2.2 mM, 4.4 mM, 8.8mM, 17.5 mM and 35 mM with a WT-DAOx concentration of 0.058 nM. D-histidine was tested at 0.55 mM, 1.1 mM, 2.2 mM, 4.4 mM, 8.8mM, 17.5 mM and 35 mM with a WT-DAOx concentration of 0.058 nM. D-serine was tested at 1.1 mM, 2.2 mM, 4.4 mM, 8.8mM, 17.5 mM 35 mM, 70 mM and 140 mM with a WT-DAOx concentration of 0.058 nM. D-Threonine was assayed at 1.1 mM, 2.2 mM, 4.4 mM, 8.8mM, 17.5 mM 35 mM, 70 mM and 140 mM with a WT-DAOx concentration of 0.29 nM. D-glutamine and D-asparagine were assayed at 0.4 mM, 0.8 mM, 1.6 mM, 3.2 mM, 6.3 mM 12.5 mM, 25 mM and 50 mM with a WT-DAOx concentration of 0.29 nM.

The fluorescence signal was read over time at 590 nm and converted to concentration of H2O2 using a calibration curve. The linear range of each reaction was used to extrapolate the reaction velocity and finally plotted versus substrate concentration to fit Michaelis Menten model and extract KM. kcat was then calculated by dividing the measured Vmax for each respective substrate by the concentration of enzyme used in the reaction.

Yeast cell populations positive for the display of DAOx (1’000’000 for D-Threonine D-glutamine and D-asparagine, 200’000 for D-alanine, D-phenylalanine, D-methionine, D-leucine, D-histidine and D-serine) were assayed for D-amino acid oxidase activity by mixing the cells with, 5.6 μM HRP, and 100 μM Amplex Red and increasing concentrations of each substrate in PBS (pH 7.5). D-alanine, D-phenylalanine, D-methionine, D-leucine were tested at 0.27 mM, 0.55 mM, 1.1 mM, 2.2 mM, 4.4 mM, 8.8mM, 17.5 mM and 35 mM. D-histidine was tested at 0.55 mM, 1.1 mM, 2.2 mM, 4.4 mM, 8.8mM, 17.5 mM. D-serine and D-threonine were tested at 1.1 mM, 2.2 mM, 4.4 mM, 8.8mM, 17.5 mM 35 mM, 70 mM and 140 mM. D-glutamine was assayed at 0.4 mM, 0.8 mM, 1.6 mM, 3.2 mM, 6.3 mM 12.5 mM, 25 mM and 50 mM. D-asparagine was assayed at 0.5 mM, 1 mM, 2.2 mM, 4.4 mM, 8.8 mM, 17.5 mM, 35 mM and 70 mM. The fluorescence signal was read over time at 590 nm and converted to concentration of H2O2 using a calibration curve. The linear range of each reaction was used to extrapolate the reaction velocity and finally plotted versus substrate concentration to fit Michaelis Menten function and extract KM and Vmax.

DAOx single cell tyramide/peroxidase proximity labeling assay

General procedure: the activity of yeast-displayed DAOx wild type was evaluated using a tyramide-based single-cell assay. Cells were diluted to a final concentration of 500 cells/μl and incubated with a 1:200 dilution of Alexa Fluor™ 488 Tyramide Reagent (Thermo Fisher Scientific, B40953), 55 μM horseradish peroxidase (HRP; Sigma-Aldrich, 77332), and the appropriate concentration of D-amino acid substrate, adjusted according to the specific substrate and experimental application. Reactions were carried out at 25 °C for a defined period depending on the assay conditions. Following incubation, cells were pelleted by centrifugation at 13,000 × g for 3 min using a benchtop centrifuge. The pellet was washed twice with PBS containing 0.1% (w/v) BSA and 0.05% TWEEN-20, then resuspended in the same buffer for flow cytometric analysis.

Time course tyramide assay: time course analyses of the tyramide assay were conducted under the same conditions as described above, with each substrate concentration set to its respective calculated KM. Specifically, the concentrations were as follows: 7.3 mM D-alanine, 2 mM D-methionine, 1.5 mM D-phenylalanine, 14.4 mM D-glutamine, and 45.8 mM D-asparagine. For reactions utilizing D-alanine as a substrate, incubation times were set at 5, 10, 20, 30, 40, 50, 60, 70, 80, and 90 minutes, respectively. Reactions involving D-phenylalanine and D-methionine were incubated for 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, and 90 minutes, respectively. Lastly, reactions using D-asparagine and D-glutamine as substrates were incubated for 10, 20, 30, 40, 50, 60, 80, 100, 120, and 140 minutes, respectively.

DAOx variants library tyramide assay: catalytic activity of displayed DAOx variant enzymes was assayed through a single-cell tyramide/peroxidase proximity labeling method. After induction of protein expression and cell surface staining of the yeast population as described above, 25M yeast cells were mixed at a concentration of 500 cells/μl with 1/200 dilution of Alexa fluor™ 488 Tyramide Reagent (Thermo Fisher, B40953), 56.8 μM HRP (Sigma-Aldrich, 77332) and one of the five substrates at concentration equal to KM in 1xPBS and 0.75% (w/v) sodium alginate. The reactions performed with D-alanine, D-phenylalanine and D-methionine were incubated for 60 minutes at 25°C. The reactions performed with D-glutamine and D-asparagine were incubated for 120 minutes at 25°C. Afterwards, the cells were spun down for 3 min at 13000 x g in a table top centrifuge. The cell pellet was washed twice with PBS 0.1% (wt/vol) BSA + 0.05% (vol/vol) Tween 20 and then used for flow cytometry and single cell sorting experiments.

Activity level sorting

EBY100 yeast cells expressing DAOx enzyme variants were stained to assess surface expression and assayed for activity using the single-cell tyramide assay. Cells were subsequently sorted on a FACSMelody cell sorter (BD Biosciences) equipped with 488 nm and 561 nm lasers and a 100 μm nozzle, following the protocol described in our previous study19 with minor modifications. Cells were sorted into pre-wetted 5 ml FACS tubes containing 0.5 ml of 2× concentrated SD -Trp medium supplemented with 4% (w/v) glucose and 1% (w/v) BSA. Yeast cells were first gated for singleton events and then the population divided into four bins based on the level of green fluorescent signal. Bin 1 was designed in order to include 99% of the population of cells negative to display or displaying inactive DAOx variants, using as reference the fluorescence level of the non-displaying yeast populations. The remaining part of the population of cells was equally divided into three yeast sub-populations with increasing fluorescent signal. We performed the sorting of two independently assayed yeast populations sorting each time between 5 M and 12 M total yeast cells therefore covering the entire variability of the library of 200’000 maximum variants at least 25 fold. After each cycle of sorting, yeast cells part of the same gated population were merged into 50 ml falcon tubes and pelleted 10 min at 4000 x g in a table top centrifuge. Afterwards, the supernatant was discarded and the cell pellet resuspended in 10 ml -Trp medium with 2% glucose supplemented with 100 μg/ml Pen Strep. After sorting, all the cell populations were grown for 48h at 30°C shaking at 200 rpm before being sampled into aliquots of 50M cells each and stored at -80°C in 25% (v/v) glycerol.

Preparation of DNA Libraries for Illumina Sequencing and Data Analysis

As previously outlined19, approximately 50 million yeast cells per sorted population were used as starting material for the preparation of Illumina sequencing libraries. Frozen cell pellets stored at −80°C were thawed and incubated at room temperature for 5 minutes, then centrifuged at 13,000 × g for 1 minute in a benchtop centrifuge. The supernatant was discarded, and the cell pellet was resuspended in 250 μl of miniprep resuspension solution (GeneJET Plasmid Miniprep Kit, Thermo Fisher) supplemented with 4 μl of Zymolyase (5 U/μl). Samples were incubated at 37°C for 2 hours with shaking at 900 rpm to digest the yeast cell walls. Following enzymatic treatment, plasmid DNA was extracted according to the manufacturer’s protocol for the GeneJET Plasmid Miniprep Kit. The purified plasmid DNA was eluted in 15 μl of nuclease-free water. The region containing the 15N unique molecular identifier (UMI) was amplified by PCR using NEBNext Ultra II Q5 Master Mix (New England Biolabs). Each 50 μl PCR reaction contained 25 μl of master mix, 5 μl of forward primer (1 μM), 5 μl of reverse primer (1 μM), and 15 μl of template DNA. Primers were designed to target the region of interest and to be compatible with Nextera indexing library preparation. To enhance sequence diversity at one end of the amplicon, four staggered forward primers were used in separate PCR reactions, and the resulting products were pooled for sequencing. PCR cycling conditions were as follows: initial denaturation at 98°C for 30 seconds; 18 cycles of 98°C for 10 seconds and 72°C for 30 seconds; followed by a final extension at 72°C for 2 minutes. Reactions were held at 4°C until further processing. PCR products were verified by electrophoresis on a 1% (w/v) agarose gel and purified using a standard DNA gel extraction kit. DNA concentration was measured, and samples were further purified using the DNA Clean and Concentrator-5 kit (Zymo Research). Unique Nextera indexing sequences were then added, samples were pooled, and sequencing was performed on an Illumina NovaSeq 6000 platform. Illumina read counts per sample are detailed in Supplementary Table 2. Demultiplexed sequencing reads were processed using a computational pipeline to extract UMI sequences and align them against a look-up table. UMI sequences were mapped using the BBMap alignment algorithm 55 with the reference sequence file (ill_ref.fa)

bbmap.sh in = *.fastq ref = ill_ref.fa out =*.sam

BBMap-generated sam files were further processed using the C script pib.c (process Illumina barcodes). This step extracted the sequences and associated quality scores for each UMI mapped by BBMap, saving the output in FASTQ format.

./pib *.sam> *.fq

Each mapped UMI was then aligned against the sequences stored in the look-up table (lut_m.tsv) using the C script rib.c (read Illumina barcodes). Each barcode was assigned one of the following tags based on the alignment and quality criteria: 0 – not found in the look-up table; 1 – found in the look-up table; 2 – read quality below Q20; 3 – UMI length different from 15 nucleotides. We applied a quality filter at Q20, tagging all UMIs associated with reads of lower quality with tag 2.

./rib -t lut_m.tsv -q 20 *.fq > *.tsv

Finally, UMI sequences with tag 1 were extracted, sorted alphabetically and grouped by identity.

grep^1 *.tsv |sort|uniq -c|sed -E ‘s/^*//; s/ /	/’>t1sct_*.tsv

All Illumina read files were processed using the computational workflow described above. The UMI sequence data and counts from each bin (t1sct_Bin#.tsv) were subsequently merged into a single file using the Jupyter notebook ill_tag1_bins. The C programs employed in this analysis were compiled with GCC version 7.4.0 on a 32-bit processor. Raw sequencing reads are accessible via the NCBI Sequence Read Archive under the Bioproject accession number: PRJNA1289092 [https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1289092]. The scripts, look up table and output data are available at https://doi.org/10.5281/zenodo.15846928

Fitness scores calculation

As in our previous work 19, the number of sequencing reads corresponding to each enzyme variant (rv) was converted into the number of sorted cells for that variant (cv) per sorted bin using the following equation (1):

rv / rtot = cv / ctot (1)

Here, rtot represents the total number of Illumina reads in the bin, and ctot is the total number of cells sorted into the same bin. The final activity scores for each variant and substrate were then calculated as the expected fluorescent intensity across all four bins in the experiment. We computed a weighted mean (β) of the cell counts (cv), using the median fluorescence intensity of the yeast population in each bin (ω) as the weighting factor:

β = ∑bin1 ωi∙cvi / ∑bin1 cvi (2)

The fitness score (F) per variant were finally calculated as follow:

F = log2 ( βv / βwt ) (3)

where βv represents the weighted mean expression score of the variant enzyme, and βwt corresponds to the score of WT-DAOx. To calculate the final consensus fitness score (Ffin) for each variant, we computed a weighted mean of the individual fitness scores (Fv) obtained from each replicate assay. The weighting factor used was the number of cells (cv) associated with the measured fitness in each experiment (Eq. 4).

Ffin = ∑rep1 Fvj∙cvj / ∑rep1 cvj (4)

Normalization of activity fitness scores by expression levels

To account for differences in enzyme display (i.e., expression) levels, and before comparative analyses, activity scores were exponentiated (i.e., inverted from log2 scale) and normalized to expression scores obtained from our previously published dataset 19. For this procedure, we included only variants with expression levels exceeding the mean expression of nonsense variants plus one standard deviation, to prevent inflated normalized activity scores due to extremely low expression values.

Comparative analysis and Sp-score

To identify mutations altering substrate preference, we calculated z-scores for each missense variant across all pairwise substrate combinations using the formula (Eq. 5):

z-score = Sp-score = (X-μ) / σ (5)

where X is the normalized activity fitness difference of the variant measured between two substrates, and μ and σ are the mean and standard deviation of fitness differences across all variants for the same substrate pair. This metric, termed substrate specificity score (Sp-score), captures both the magnitude and direction of substrate preference changes. Using synonymous mutations as controls, we set a threshold of |Sp|≥3 (three standard deviations) to identify substrate-specific mutations, excluding >99% of synonymous variants.

Quantification of substrate discrimination across substrate pairs

The intensity of discrimination for variants specific to a primary substrate against all non-cognate substrates presented in Fig. 4B was calculated in two steps. First, for each substrate pair, we computed the proportion of number of cognate substrate – specific variants (e.g. D-Ala vs D-Phe specific variants, |Sp-score| ≥ 3) relative to the total number of specific variants for the primary substrate (total D-Ala specific variants, |Sp-score| ≥ 3). This proportion was then multiplied by the mean Sp-score of these variants toward the primary substrate within the same substrate pair. The resulting metric integrates both the prevalence and magnitude of substrate discrimination.

DAOx structural properties calculations from 3D structure

Coordinates of all atoms in DAOx residues were extracted from the PDB structure 1C0P (PDB ID: 1C0P [http://doi.org/10.2210/pdb1c0p/pdb]). The minimum distance from each residue to the crystallized substrate D-Ala was calculated and used as a metric for proximity to the catalytic site in the analysis.

Validation of EP-Seq fitness scores

Eighteen single mutant DAOx variants were selected based on DMS scores spanning negative to positive values across D-Ala, D-Phe, D-Met, D-Asn, and D-Gln. Genes encoding S48C, K109L, K109W, K109Y, D110I, D110M, D110W, L153R, A187E, A187K, G199E, Q200W, S215A, S215R, T237M, Y238W, S268Y and L310P were synthesized and cloned into the pYDKan plasmid in-frame with the Aga2p coding sequence using a BioXP 3250 synthetic biology workstation (Codex DNA) or generated via PCR and KLD cloning to introduce mutations into the wild-type DAOx sequence. All sequences were codon-optimized for expression in Saccharomyces cerevisiae and verified by Sanger sequencing. Final plasmids were transformed into S. cerevisiae strain EBY100 using the lithium acetate method54. Positive colonies were selected on synthetic defined (SD) agar plates (-Trp) with 2% (wt/vol) glucose and cultivated in -Trp liquid medium with 2% (wt/vol) glucose for 24 h at 30 °C to an OD ~8 with continuous shaking (200 rpm). Expression and display of Aga2-DAOx wild-type and mutant constructs were induced by transferring cells at OD = 0.4 to fresh -Trp liquid medium containing 0.2% (wt/vol) glucose, 1.8% (wt/vol) galactose, and 100 mM citrate/phosphate buffer (pH 7). Cells were grown for 48 h at 20 °C, then pelleted, washed with PBS containing 0.1% (wt/vol) BSA, and used for antibody labeling to detect the C-terminal 6×His tag of the displayed fusion protein and confirm expression. The cell populations expressing DAOx wild-type and selected variants were then assayed for activity using the Amplex Red method and the five substrates from the EP-Seq screen (D-Ala, D-Phe, D-Met, D-Asn, D-Gln). A total of 250,000 yeast cells were mixed with 5.6 μM HRP and 100 μM Amplex Red in PBS (pH 7.5), and each substrate was added at the same final concentration used in the EP-Seq workflow (7.3 mM D-Ala, 1.5 mM D-Phe, 2 mM D-Met, 46 mM D-Asn, 14 mM D-Gln). Fluorescence was measured at 590 nm every 50–60 s for at least 60 min and at a temperature of 27-30°C. The linear range of the reaction for all enzyme variants and substrates was determined between 0 and 15 min, and the reaction slope was calculated. The single-clone activity score for each variant was obtained by dividing the reaction slope of the variant by that of the wild-type enzyme. Final scores were averaged from three independent measurements per cell population and plotted against the corresponding EP-Seq activity scores.

Kinetic study on yeast displayed DAOx substrate specific variants

Based on our EP-Seq comparative analysis, we selected DAOx variants with the highest z-scores and specificity towards each of the tested substrates. Gene-encoding DAOx variants (Supplementary Table 5) were generated via PCR and KLD cloning to introduce mutations into the wild-type DAOx sequence. All sequences were codon-optimized for expression in Saccharomyces cerevisiae and verified by Sanger sequencing. Final plasmids were transformed into S. cerevisiae strain EBY100 using the lithium acetate method54. Positive colonies were selected on synthetic defined (SD) agar plates (-Trp) with 2% (wt/vol) glucose and cultivated in -Trp liquid medium with 2% (wt/vol) glucose for 24 h at 30 °C to an OD ~8 with continuous shaking (200 rpm). Expression and display of Aga2-DAOx wild-type and mutant constructs were induced by transferring cells at OD = 0.4 to fresh -Trp liquid medium containing 0.2% (wt/vol) glucose, 1.8% (wt/vol) galactose, and 100 mM citrate/phosphate buffer (pH 7). Cells were grown for 48 h at 20 °C, then pelleted, washed with PBS containing 0.1% (wt/vol) BSA, and used for antibody labeling to detect the C-terminal 6×His tag of the displayed fusion protein. Yeast cells expressing DAOx wild-type and mutant variants were analyzed by flow cytometry.

Yeast populations were first gated for single events, then divided into four gates based on Alexa Fluor™ 594 fluorescence to cover the entire fluorescence range. A total of 10,000 single cells from each population were analyzed. The median fluorescence value and percentage of cells in each gate were recorded. Each population was assayed in duplicate. A weighted mean (β) of the cell distribution (%c) was calculated, with the median fluorescence of each gate (ω) as the weighting factor (Eq. 6).

β = ∑gate1 ωi ∙ %ci / ∑gate1 %ci (6)

Finally, the single-clone expression score (Fsing.clone) was calculated by dividing the weighted mean expression of each DAOx variant (βv) by the weighted mean expression of the wild-type DAOx (βwt), both assayed using the same procedure (Eq. 7).

Fsing.clone = βv / βwt (7)

Yeast populations expressing variants were used to determine the kinetic parameters KM and Vmax of each DAOx mutant towards the five substrates D-Ala, D-Phe, D-Met, D-Asn, D-Gln. The number of cells used for each kinetic measurement was fixed to 400 '000 and calculated by measuring the OD of each population using as reference value 1 OD corresponding to 10M cells in 1 ml. By maintaining fixed the number of cells and taking into account the the expression level of each variant enzyme we wanted to allow not only relative comparative analysis towards substrates for the same variant but also compare the performance of the DAOx single mutants to the wild type enzyme. Each yeast population was assayed for D-amino acid oxidase activity by mixing the cells with 5.6 μM HRP, 100 μM Amplex Red and increasing concentrations of each substrate in PBS (pH 7.5) at room temperature (25-30°C). Specifically, D-alanine, D-phenylalanine, D-methionine, were tested at 0.3125 mM, 0.625 mM, 1.25 mM, 2.5 mM, 5 mM, 10mM, 20 mM and 40 mM, while D-asparagine and D-glutamine were assayed at 1.5625 mM, 3.125 mM, 6.25 mM, 12.5 mM, 25 mM, 50 mM, 100 mM, 200 mM. The fluorescence signal was read for at least 20 min at 590 nm.The linear range of each reaction was determined and used to extrapolate the reaction velocity and finally plotted versus substrate concentration to fit Michaelis Menten function and extract KM and Vmax. Reaction velocities were determined for at least two independent replicate reactions at six or more substrate concentrations. These data were used to fit the Michaelis–Menten kinetic model. Due to a technical issue affecting one of the replicate reactions, the R254C variant tested with D-Ala was assessed using a single reaction only. Fitting results were considered reliable and accepted if the coefficient of determination (R²) was approximately 0.90 or higher. In cases where enzyme activity was too low to be accurately detected and the data did not meet the criteria for Michaelis–Menten fitting, reactions were repeated using longer incubation times and/or increased enzyme concentrations. If no improvement was observed, kinetic parameters (KM and Vmax) were classified as not detected (n.d.).

Discrimination factor calculation

The selectivity (D) of an enzyme for a primary substrate is defined as the ratio of the kcat/KM values for the primary substrate over the same ratio for the secondary substrate. This can be expressed as (Eq. 8):

D = (kcat/KM)sub1 / (kcat/KM)sub2 (8)

In this manuscript, when yeast surface-displayed enzymes were used to measure catalytic performance, and the absolute enzyme concentration in the reaction could not be determined, we define the discrimination factor (D) between two substrates as the ratio of Vmax and KM (Eq. 9)

D = (Vmax/KM)sub1 / (Vmax/KM)sub2 (9)

This relationship allows to calculate reliable discrimination factor values towards different substrates that reflect the catalytic properties of the soluble counterparts. For each variant we first normalize the Vmax value to the single clone expression score (Fsing. clone) in order to remove the effect of different levels of expression from the detected reaction velocity. Then we calculated the ratio between Vmax/KM for all of the 5 substrates. And the discrimination factor (D) for all possible substrate combinations.

Soluble expression of WT and substrate specific DAOx variants

DAOx variants (Table 1) were generated by site-directed mutagenesis using PCR and KLD cloning to introduce specific mutations into the wild-type DAOx gene, which was previously cloned into the pET28 expression vector. All constructs were verified by Sanger sequencing and transformed into E. coli NiCo21(DE3) for subsequent expression and purification. Cultures were grown in super optimal broth (SOB, 20 g/L tryptone, 5 g/L yeast extract, 2.5 mM KCl, 10 mM MgCl2, MgSO4, 10 mM NaCl, pH 7.0) at 37 °C with shaking until reaching an O.D.600 of 0.6-0.8. The cultures were then induced using 1 mM isopropyl β-d-1-thiogalactopyranoside (IPTG) at 16 °C, with shaking, for 20 h. After harvesting via centrifugation, the cell pellets were stored at -80 °C. When needed, the cell pellets were thawed and resuspended at 4 °C in BugBuster® protein extraction reagent (MilliporeSigma, Burlington, MA, USA) according to the manufacturers protocol. The resulting cell lysate then underwent centrifugation at 30,000 x g for 30 min and was passed through a 0.45 µM filter. The clarified lysate was then supplemented with 0.1% (w/v) Tween 20 and 10 mM Imidazole, and incubated with Ni-NTA Resin (HisPur™, Thermo Fisher Scientific, Massachusetts, United States) at 4 °C, with shaking, for 1 h. Ni-NTA Resin was reconstituted by centrifugation 800 x g for 5 mins and washed three times with 0.1% (w/v) Tween 20 and 10 mM Imidazole supplemented phosphate-buffered saline (PBS, 137 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, and 1.8 mM KH2PO4, pH 7.4). The Ni-NTA Resin was then transferred to gravity flow columns (Pierce™, Thermo Fischer Scientific, Massachusetts, United States) and washed using an imidazole gradient, 20-250 mM imidazole-supplemented PBS. Protein-containing elution fractions were then concentrated in centrifugal filters (Amicon®, Merck, Darmstadt, Germany) and further processed using size-exclusion chromatography (SEC, Superrose 12 10/300 GL column, Cytiva, Marlborough, United States) into PBS. SEC elution fractions containing the protein of interest were again concentrated using centrifugal filters and stored at -80 °C, until used in experiments. Protein concentrations were measured by spectrophotometry at 280 nm (NanoDrop 1000, 6 Thermo Scientific, DE, USA).

Kinetic Study of Soluble WT and Substrate-Specific DAOx Variants

Soluble WT-DAOx and its variants (Table 1) were used to determine the kinetic parameters KM and kcat toward five substrates: D-Ala, D-Phe, D-Met, D-Asn, and D-Gln. Each enzyme variant was assayed for D-amino acid oxidase activity by mixing the cells with 5.6 μM HRP, 100 μM Amplex Red, and increasing concentrations of each substrate in PBS (pH 7.5) at room temperature (25–30 °C). WT-DAOx was tested at a concentration of 0.25 nM, while the variants were tested at the following concentrations: N54L at 2 nM, F58W at 2 nM, T93M at 5 nM, M213E at 1 nM, and S341I at 5 nM. For each variant,D-Ala, D-Phe and D-Met were tested at concentrations of 0.3125, 0.625, 1.25, 2.5, 5, 10, 20, and 40 mM, while D-Asn and D-Gln e were assayed at 1.5625, 3.125, 6.25, 12.5, 25, 50, 100, and 200 mM. The fluorescence signal was read for at least 20 min at 590 nm. The linear range of each reaction was determined and used to extrapolate the reaction velocity and finally plotted versus substrate concentration to fit Michaelis Menten function and extract KM and Vmax. Reaction velocities were determined for at least three independent replicate reactions at six or more substrate concentrations. These data were used to fit the Michaelis–Menten kinetic model. Fitting results were considered reliable and accepted if the coefficient of determination (R²) was approximately 0.90 or higher. In cases where enzyme activity was too low to be accurately detected and the data did not meet the criteria for Michaelis–Menten fitting, reactions were repeated using longer incubation times and/or increased enzyme concentrations. If no improvement was observed, kinetic parameters (KM and kcat) were classified as not detected (n.d.).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study used the enzyme-proximity sequencing method (EP-Seq) developed by the authors to comprehensively map, at single-mutation resolution, the substrate specificity of Rhodotorula gracilis D-amino acid oxidase (RgDAOx), and to identify mutations that confer preferences for particular D-amino acid substrates together with their structural implications. The results indicate that, at least in this enzyme system, EP-Seq can serve as a powerful platform for high-throughput enzyme design.\n\nThe strengths of the study are threefold: (i) a comprehensive, large-scale screening dataset is carefully supported by rigorous statistical processing and assessments of reproducibility; (ii) the D-factor obtained from single-clone kinetic measurements agrees well with the Sp-score derived from deep mutational scanning (DMS), thereby reinforcing the validity of the measurement approach; and (iii) the work illuminates not only hotspots near the active site but also distal mutations, demonstrating that substrate specificity can be governed through diverse proximal and distal routes.\n\nOn the other hand, for some of the key mutations that drive substrate preferences, the structural interpretation remains ambiguous or is not fully presented. In representative cases, further concretizing the mechanistic hypotheses, based on three-dimensional models, interaction networks, and conformational changes, and, where appropriate, supplementing them with in silico modeling or validation using substrate analogs would deepen readers’ understanding.\n\nOverall, the manuscript offers strong methodological contributions and high-quality data, providing important advances for the practical utility of EP-Seq and for understanding the evolution of substrate specificity. A deeper examination of the structural mechanisms underlying the major mutations would further strengthen the paper.\n\nComments:\n1. The authors used five types of D-amino acids as substrates. Three of these are hydrophobic (D-Ala, D-Met, D-Phe), and two are polar/hydrophilic (D-Asn, D-Gln). Supplementary Material S2 outlines the rationale for these choices, but can the authors explain why the substrates were limited to five types and why other valid candidates (e.g., moderately sized hydrophobic substrates like D-Val or D-Ile, polar substrates like D-Thr, or acidic D-amino acids) were not included? Additionally, why was the contribution of salt bridge effects to substrate specificity not investigated using an acidic D-amino acids?\n2. It appears that coenzyme FAD was not added to the enzyme assay system. Could the effect of the amino acid mutation on FAD binding (i.e., the difference in catalytic activity) be influencing the results?\n3. Regarding the discussion of the structural mechanisms by which amino acid mutations affect substrate specificity, several points remain ambiguous. For example, concerning S341I, I believe a detailed explanation should be added, such as: “S341 forms a hydrogen bond with the main-chain carbonyl group of Y338, which is adjacent to Q339 forming the substrate-binding site; therefore, it may influence the orientation of Q339.” Is it possible to provide a more detailed analysis of the structural mechanism by which the S341I mutation confers absolute activity toward D-Gln, or the structural mechanism by which the T93M mutation enhances binding affinity for D-Met? Readers are likely to seek an understanding of the structural mechanisms by which amino acid mutations located away from the active site can influence substrate specificity.\n4．Fig. 7. Why weren't combinations of amino acid mutations at positions distant from the active site verified?\n\nMinor comments:\n1. As shown below, “Fig. 2C” should be added to the following sentence.\nNonsense mutations had strongly negative effect on activity (average score -0.58 ± 0.05; n = 316 in all five datasets), indicating that loss of the C-terminal region beginning anywhere from position 1 to position 354 had a deleterious effect on the catalytic activity of the enzyme, regardless of the substrate used (Table S3, Fig. 2C)\n2. The data for M213D do not appear to be present in Fig. 5 iv and Table S5.", "reviewer_2": "The authors present compelling work exploring enzyme substrate specificity in the model oxidoreductase D-amino acid oxidase (DAOx). They apply their previously developed enzyme proximity sequencing (EP-Seq) method to perform deep mutational scanning of DAOx across five diverse D-amino acid substrates. The high-throughput fluorescence-based measurements are highly reproducible and show excellent agreement with clonal measurements of activity and specificity. The resulting data provide a detailed, quantitative map of activity and specificity across the DAOx sequence, revealing a complex substrate-specificity landscape. The primary contribution of this work is the rigorous and insightful analysis of this large dataset. The authors identify hundreds of mutations throughout the enzyme that significantly impact substrate preference. They find that active-site mutations can cause large specificity shifts but often reduce catalytic activity, whereas distal mutations exert subtler effects that fine-tune specificity. They introduce a quantitative substrate-specificity score (Sp-score) that correlates strongly with experimentally determined discrimination factors, enabling large-scale analysis of specificity directly from high-throughput data. They identify structural factors driving specificity and a key mutational hotspot along helix 10 that modulates substrate preference via long-range interactions. Additionally, the authors show individual mutations can be stacked into multi-mutants, leading to highly specific enzymes. Overall, this study provides a comprehensive single-mutation resolution map of enzyme specificity and demonstrates the power of EP-Seq for decoding and engineering enzyme function.\n\nSpecific comments:\n- The results are interesting and make sense. It would be helpful if the authors more explicitly discussed their findings in the context of related DMS studies from Mikkelsen, Whitehead, Tokuriki, and others. Are there general principles about enzyme specificity that emerge across these systems?\n- The authors should also discuss the potential limitations of their approach. For example, in yeast surface display, it is common to find enzyme variants that perform well on the yeast surface but lose activity in soluble form. Could this influence the observed activity or specificity trends? Are there other technical or biological limitations that should be acknowledged?\n- Figure S6 is one of the most informative figures in the paper and essential for understanding the definition and interpretation of the Sp-score. It would strengthen the manuscript to move all or at least part of this figure into the main text (perhaps alongside Fig. 3).\n- Several figures could also benefit from clearer explanation. For instance, in Fig. 3, are the plotted Sp-scores for all 10 substrate pairs combined and summed? This should be explained in the caption. In Fig. 3C, is it meaningful to sum specificity scores across substrates given that each substrate has unique mutational preferences? In Fig. 5B, the substrate discrimination plots were not clearly explained--this information was only briefly mentioned in the SI and should be included directly in the legend. the authors should check to make sure all panels of all figures are clearly explained in the text or legends.\n- A question on terminology because this is not my field. I normally think of allostery referring to a ligand or effector molecule binding at a site distinct from the active site and altering enzyme activity. This seems a little different than the allostery referred to in Fig 6 where mutations far from the active site in helix 10 alter specificity. Are these related? The former is an evolved conformational mechanism linking distant sites, wheras the later is simply structurally coupled regions of the enzyme.\n- A question on terminology: I typically think of allostery as referring to a ligand or effector binding at a site distinct from the active site and altering enzyme activity. In Fig. 6, however, “allosteric hotspots” refer to mutations in helix 10 that influence specificity through structural coupling rather than ligand binding. Are these two uses of the term related? The classical definition implies an evolved conformational mechanism linking distant sites, whereas the paper’s use of the term seems to reflect structurally coupled regions of the enzyme.\n\nThe experimental reproducibility metrics are impressive. For Fig. 2A, the scatter plots would be more informative if they showed point density, either using transparent markers or density-based coloring (as commonly done in FACS plots).\n- The authors also stated “We attribute the high level of agreement to the inherently promiscuous and generalist nature of R. gracilis DAOx, a trait that is conserved in more evolutionarily recent homologs such as human DAOx.” Is the human enzyme more recent than R. gracilis?", "reviewer_3": "The authors previously developed a yeast display assay to decouple enzymatic activity measurement from covariant factors (expression and folding stability) on a single pair of enzyme-substrate interaction. Here, they extended their work to the interaction between D-amino acid oxidase (DAOx) and five different substrates. Through a series of comparative analysis, they showed that while substrate-specific mutations can be distal from the active site, those within have stronger impacts at the cost of reduced activity.\n\nOverall, the technical analysis of this submission is solid and innovative, but its experimental technique and findings are not novel. On yeast display assay, Amorosi et al. have already applied a similar technique to measure mutational impact on P450 activity [1]. On direct kinetic measurements, an excellent article from Markin et al. showcased a microfluidic platform to measure Michaelis-Menten constants for phosphatase [2]. These highly-relevant studies restrict the novelty of this submission to substrate specificity. Despite the thorough analysis presented, the finding is either already established in other DMS studies (on allosteric modulation) or too restrictive (structural insights on specific substrate-sidechain interactions). If possible, establishing key identifier(s) for allosteric modulation would be highly impactful in the field.\n\nIn conclusion, this submission is technically well-established, and I recommend referring it to other journals with stronger focus on technically sound research such as Scientific Reports.\n\nReferences\n1. Amorosi, C. J. et al. Massively parallel characterization of CYP2C9 variant enzyme activity and abundance. Am. J. Hum. Genet. 108, 1735–1751 (2021). doi:10.1016/j.ajhg.2021.07.001.\n2. Markin, C.J. et al. Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics. Science 373, eabf8761 (2021). doi:10.1126/science.abf8761.\n\nMajor edits on possible ways to introduce novelty\n1. identify quantitative and statistically significant structural/biophysical features that\n1.1 segregate impactful allosteric mutations from neglectible ones, or\n1.2 segregate allosteric mutations by substrate specificity\n2. establish generalizability of insights found in this work\n2.1 engineer and test new beneficial/substrate-specific mutations, and\n2.2 compare the success rate with other baselines\n\nMinor edits\n1. Figure 3A: highlight first-shell positions\n2. Figure 3B: label the distribution explicitly as CDF/PDF\n3. Heatmap S4: unify color scale and normalize to 1.0\n4. Inconsistent wordings\n4.1 “labeling” and “labelling”\n4.2 “Sp-scores” and “SP-scores”\n4.3 “gray” and “grey”"}, "author_response": "General comments for all the reviewers:\nWe thank the reviewers for their valuable comments and insight. Below we address each comment\npoint-by-point. The reviewer’s queries are written in black text, with our corresponding responses\nwritten in blue.\nReviewer #1:\nThis study used the enzyme-proximity sequencing method (EP-Seq) developed by the authors to\ncomprehensively map, at single-mutation resolution, the substrate specificity of Rhodotorula gracilis\nD-amino acid oxidase (RgDAOx), and to identify mutations that confer preferences for particular D-\namino acid substrates together with their structural implications. The results indicate that, at least in\nthis enzyme system, EP-Seq can serve as a powerful platform for high-throughput enzyme design.\nThe strengths of the study are threefold: (i) a comprehensive, large-scale screening dataset is carefully\nsupported by rigorous statistical processing and assessments of reproducibility; (ii) the D-factor\nobtained from single-clone kinetic measurements agrees well with the Sp-score derived from deep\nmutational scanning (DMS), thereby reinforcing the validity of the measurement approach; and (iii)\nthe work illuminates not only hotspots near the active site but also distal mutations, demonstrating\nthat substrate specificity can be governed through diverse proximal and distal routes.\nOn the other hand, for some of the key mutations that drive substrate preferences, the structural\ninterpretation remains ambiguous or is not fully presented. In representative cases, further\nconcretizing the mechanistic hypotheses, based on three-dimensional models, interaction networks,\nand conformational changes, and, where appropriate, supplementing them with in silico modeling or\nvalidation using substrate analogs would deepen readers’ understanding.\nOverall, the manuscript offers strong methodological contributions and high-quality data, providing\nimportant advances for the practical utility of EP-Seq and for understanding the evolution of substrate\nspecificity. A deeper examination of the structural mechanisms underlying the major mutations would\nfurther strengthen the paper.\nWe thank the reviewer for the overall positive evaluation of the work.\nComments:\n1. The authors used five types of D-amino acids as substrates. Three of these are hydrophobic (D-Ala,\nD-Met, D-Phe), and two are polar/hydrophilic (D-Asn, D-Gln). Supplementary Material S2 outlines the\nrationale for these choices, but can the authors explain why the substrates were limited to five types\nand why other valid candidates (e.g., moderately sized hydrophobic substrates like D-Val or D-Ile, polar\nsubstrates like D-Thr, or acidic D-amino acids) were not included? Additionally, why was the\ncontribution of salt bridge effects to substrate specificity not investigated using an acidic D-amino\nacids?\nAcidic D-amino acids are poorly processed by RgDAOx, and other substrates, such as positively\ncharged D-amino acids, are turned over at activity levels that fall below the detection limit of our\ntyramide-based high-throughput assay (https://doi.org/10.1021/acsbiomaterials.4c00895). Our\nchoice of these particular five substrates represents a practical compromise, where we examined\nsmall, medium, and large hydrophobic side chains (D-Ala, D-Met, D-Phe) and two polar substrates of\ndifferent sizes (D-Asn, D-Gln) for which activity could be reliably measured by EP-Seq. This substrate\nset allowed us to cover a range of chemical properties and steric sizes while ensuring a good dynamic\nrange of measurable activity for each substrate, which in turn makes the comparisons across the\nvarious screening feasible.\n2. It appears that coenzyme FAD was not added to the enzyme assay system. Could the effect of the\namino acid mutation on FAD binding (i.e., the difference in catalytic activity) be influencing the results?\nThe wild-type enzyme binds FAD tightly (Kd ≈ 20 nM) and contemporaneously folds upon FAD binding.\nIn our experience, we have never observed any difference in activity with or without added FAD. It is\ntrue that overall catalytic activity can depend on FAD binding, and in our previous publication we\nshowed that mutations that interfere with FAD binding led to a dramatic loss of activity\n(https://doi.org/10.1038/s41467-024-45630-3). In the context of this work, however, we do not\nexpect global FAD affinity to influence changes in substrate preference. Our measurements are made\nfor the same variant library on all 5 substrates, therefore the differences in substrate specificity that\nwe observe are unlikely to arise from weakened FAD binding. It is possible that variants exhibiting\nunchanged substrate preferences but altered (overall lower) catalytic activity could be affected by\ndifferences in FAD binding affinity, although this was not examined in detail here.\n3. Regarding the discussion of the structural mechanisms by which amino acid mutations affect\nsubstrate specificity, several points remain ambiguous. For example, concerning S341I, I believe a\ndetailed explanation should be added, such as: “S341 forms a hydrogen bond with the main-chain\ncarbonyl group of Y338, which is adjacent to Q339 forming the substrate-binding site; therefore, it\nmay influence the orientation of Q339.” Is it possible to provide a more detailed analysis of the\nstructural mechanism by which the S341I mutation confers absolute activity toward D-Gln, or the\nstructural mechanism by which the T93M mutation enhances binding affinity for D-Met? Readers are\nlikely to seek an understanding of the structural mechanisms by which amino acid mutations located\naway from the active site can influence substrate specificity.\nWe thank the reviewer for this thoughtful suggestion. We agree that a deeper understanding of the\nstructural mechanisms underlying the specificity changes observed for variants such as S341I and\nT93M would be valuable. However, providing such detailed mechanistic insight would require\nextensive molecular dynamics simulations together with high-resolution structural data (e.g., from\ncrystallography), which are beyond the scope of the present study. Where the available structural\ninformation allowed a clear interpretation, we have provided it in the manuscript (p. 13-14). For S341I,\nwe appreciate the reviewer’s suggestion and are happy to include the proposed explanation regarding\nits possible effect on the orientation of Q339. We have included the following in the main text:\n“The mutation at position S341 can alter the orientation of nearby residues Y338 and Q339, which are\ndirectly involved in substrate coordination”\n4．Fig. 7. Why weren't combinations of amino acid mutations at positions distant from the active site\nverified?\nThe relative impact of distal mutations on substrate specificity is generally lower than that of\nmutations in the active site first shell (Fig. 3C, bottom). Our goal in combining single mutations was to\ntest for additive effects on specificity, and we focused on mutations that individually caused strong\nchanges. This approach allowed us to maximize the chance of observing meaningful combinatorial\neffects.\n1. As shown below, “Fig. 2C” should be added to the following sentence.\nNonsense mutations had strongly negative effect on activity (average score -0.58 ± 0.05; n = 316 in all\nfive datasets), indicating that loss of the C-terminal region beginning anywhere from position 1 to\nposition 354 had a deleterious effect on the catalytic activity of the enzyme, regardless of the substrate\nused (Table S3, Fig. 2C)\nWe thank the reviewer for this suggestion. We have added the reference to Fig. 2C in the sentence as\nrecommended.\n2. The data for M213D do not appear to be present in Fig. 5 iv and Table S5.\nThank you for pointing this out. The data for M213D are presented only in Table S5 (raw 10). The\nreference to Fig. 5 iv in the main text was intended to compare with the M213E variant. We have\nremoved the reference to Fig. 5 iv in this context to avoid any confusion.\nReviewer #2:\nThe authors present compelling work exploring enzyme substrate specificity in the model\noxidoreductase D-amino acid oxidase (DAOx). They apply their previously developed enzyme\nproximity sequencing (EP-Seq) method to perform deep mutational scanning of DAOx across five\ndiverse D-amino acid substrates. The high-throughput fluorescence-based measurements are highly\nreproducible and show excellent agreement with clonal measurements of activity and specificity. The\nresulting data provide a detailed, quantitative map of activity and specificity across the DAOx\nsequence, revealing a complex substrate-specificity landscape. The primary contribution of this work\nis the rigorous and insightful analysis of this large dataset. The authors identify hundreds of mutations\nthroughout the enzyme that significantly impact substrate preference. They find that active-site\nmutations can cause large specificity shifts but often reduce catalytic activity, whereas distal\nmutations exert subtler effects that fine-tune specificity. They introduce a quantitative substrate-\nspecificity score (Sp-score) that correlates strongly with experimentally determined discrimination\nfactors, enabling large-scale analysis of specificity directly from high-throughput data. They identify\nstructural factors driving specificity and a key mutational hotspot along helix 10 that modulates\nsubstrate preference via long-range interactions. Additionally, the authors show individual mutations\ncan be stacked into multi-mutants, leading to highly specific enzymes. Overall, this study provides a\ncomprehensive single-mutation resolution map of enzyme specificity and demonstrates the power of\nEP-Seq for decoding and engineering enzyme function.\nWe thank the reviewer for the overall positive evaluation of the work.\nSpecific comments:\n- The results are interesting and make sense. It would be helpful if the authors more explicitly\ndiscussed their findings in the context of related DMS studies from Mikkelsen, Whitehead, Tokuriki,\nand others. Are there general principles about enzyme specificity that emerge across these systems?\nWe thank the reviewer for this comment. We have touched on this point on page 16 of main text (first\nand second paragraphs), where we compare our findings with previous DMS studies from Mikkelsen,\nWhitehead, Tokuriki, and others. As reported there:\n“Previous deep mutational scanning studies have reported contrasting patterns. In some cases,\nspecificity-determining mutations are mainly distal (>9 Å from the catalytic site)¹⁶, while in others, they\ncluster within the first-shell residues around the active site¹³,¹⁵,⁴⁴. In DAOx, we found that the strongest\nspecificity-shifting mutations were predominantly located within 8 Å of the catalytic site (Fig. 3C), yet\nthese accounted for only 30% (n = 101) of all such mutations. The remaining 70% (n = 231) were\nscattered across more distal regions of the enzyme.”\nThis comparison highlights that the classical view, where first-shell residues drive the largest changes\nin substrate specificity, is consistent with our findings and with those of Mikkelsen and Tokuriki. At the\nsame time, our data also show that distal mutations can modulate specificity in a more gradual manner\nand generally with a lower impact on overall enzyme fitness. This is in agreement with observations\nfrom Whitehead and colleagues. In our view, the evidence of this dual behaviour is one of the most\ninteresting findings of the study, as it suggests that multiple evolutionary trajectories are available to\nachieve substrate specificity, depending on the selective pressure acting on substrate preference and\non the fitness costs that the enzyme can tolerate.\n- The authors should also discuss the potential limitations of their approach. For example, in yeast\nsurface display, it is common to find enzyme variants that perform well on the yeast surface but lose\nactivity in soluble form. Could this influence the observed activity or specificity trends? Are there other\ntechnical or biological limitations that should be acknowledged?\nWe thank the reviewer for this comment. Our approach is compatible only with enzymes that can be\nfunctionally expressed on the yeast surface, and it is essential to verify that surface-displayed activity\nreflects the behavior of the soluble enzyme. In the present work, we have confirmed this for RgDAOx:\nthe catalytic K values on several substrates agree closely between the soluble and yeast-displayed\nM\nforms (Supplementary Fig. S1). Furthermore, for the five representative single mutants with altered\nspecificity, we compared activity on the yeast surface with that of the corresponding purified soluble\nproteins and observed full agreement (Supplementary Fig. S9). We nevertheless acknowledge that this\ncorrespondence is not guaranteed and must be evaluated case by case for other enzymes.\nA detailed discussion of the EP-Seq method and its technical limitations was provided in our previous\npublication (https://doi.org/10.1038/s41467-024-45630-3), where we stated: “Our workflow is\ncompatible with enzymes that can be functionally displayed on the cell surface and whose activity can\nbe directly or indirectly (via enzymatic cascade) linked to the production of peroxide. This includes\nenzymes with immediate therapeutic relevance such as Arginase and Asparaginase, as well as\nbiocatalysts with diagnostic or industrial applications, like Glucose Oxidase.”\nFollowing the reviewer’s suggestion, we have added the following sentence also to the conclusion\nparagraph of the current manuscript:\n“Beyond these evolutionary insights, our work establishes EP-Seq as a robust platform for high-\nthroughput engineering of enzymes that can be functionally displayed on the cell surface and whose\nactivity can be coupled, directly or via a cascade, to peroxide production.”\n- Figure S6 is one of the most informative figures in the paper and essential for understanding the\ndefinition and interpretation of the Sp-score. It would strengthen the manuscript to move all or at\nleast part of this figure into the main text (perhaps alongside Fig. 3).\nWe thank the reviewer for this suggestion. We did consider putting Figure S6 in the main text, but in\nour view, it would be somewhat repetitive since it shows all 10 possible combinations of substrates\nand cannot be partially shown without losing important information. Panel A of Figure 3 combines\ninformation on the regions that give specificity across all pairs, providing an overview of the main\npoints. Keeping Figure S6 in the Supplementary Information allows readers to examine all pairwise\ncomparisons of the substrate screening outcomes in detail, but we feel it is too much information for\nthe main text.\n- Several figures could also benefit from clearer explanation. For instance, in Fig. 3, are the plotted Sp-\nscores for all 10 substrate pairs combined and summed? This should be explained in the caption.\nIn Fig. 3A, the plot provides an overview of all individual z-scores for missense and synonymous\nvariants across the library for each of the 10 substrate pairs. The plot shows how the z-score varies\nalong the enzyme sequence and highlights that the z-scores of synonymous variants remain\nconsistently below 3, which was the threshold we used to define mutations that cause a change in\nsubstrate specificity. To improve the clarity of the caption we have modified to the following:\n“Scatter plot of absolute specificity scores across DAOx positions for all missense and synonymous\nvariants and for each of the 10 substrate pairs. Missense variants with |Sp-score| ≥ 3 are shown as\nyellow dots.”\nIn Fig. 3C, is it meaningful to sum specificity scores across substrates given that each substrate has\nunique mutational preferences?\nWe agree that each substrate screen reveals its own mutational patterns, driven by the biochemical\nfeatures of the substrate used. However, our goal in Figure 3C is to highlight broader trends in\nsubstrate specificity that depend on the position of the mutation rather than on the substrate itself.\nFor this reason, we summed all specificity scores >3 at each DAOx position to estimate the overall\ncontribution of individual positions to specificity. This analysis shows that positions directly interacting\nwith the substrates (first-shell residues) contribute most strongly to specificity, independent of the\nsubstrate considered. To improve clarity, we have revised the figure caption accordingly and added\nthe missing summation symbol (Σ) to the color bar next to the 3D structure.\n“ (Top) DAOx crystal structure (PDB ID: 1C0P) with substrate-specific positions highlighted as sticks.\nColor intensity (grey to yellow) represents the cumulative absolute Sp-score per position, considering\nonly Sp-scores >3. This reflects the contribution of each site to the global substrate-specificity\nlandscape. The FAD cofactor and D-Ala substrate found in the catalytic site of the enzyme are shown\nas orange sticks. Bottom: bar plot showing the relationship between the distance of substrate-specific\nmutations from the catalytic site and their cumulative absolute specificity scores.”\nIn Fig. 5B, the substrate discrimination plots were not clearly explained--this information was only\nbriefly mentioned in the SI and should be included directly in the legend. the authors should check to\nmake sure all panels of all figures are clearly explained in the text or legends.\nA detailed explanation of how the discrimination score was calculated is provided on page 12 of the\nSupplementary Information (“Discrimination factor calculation”). To make the figure captions more\nself-explanatory, we have now added the following lines to the captions of Fig. 5B, Fig. 6C, and\nSupplementary Fig. S9, where the discrimination-factor heatmaps are shown.\n\"Heatmaps of substrate discrimination factor (D) for all pairwise combinations of primary (y-axis) and\nsecondary (x-axis) substrates, calculated as the catalytic efficiency (V / K ) for the primary substrate\nmax M\ndivided by that for the secondary substrate.”\nWe also note that we have checked all figure captions and modified them according to the reviewer’s\nsuggestions to improve readability and clarity. All changes are highlighted in the submitted manuscript\nwith tracked changes.\n- A question on terminology because this is not my field. I normally think of allostery referring to a\nligand or effector molecule binding at a site distinct from the active site and altering enzyme activity.\nThis seems a little different than the allostery referred to in Fig 6 where mutations far from the active\nsite in helix 10 alter specificity. Are these related? The former is an evolved conformational mechanism\nlinking distant sites, wheras the later is simply structurally coupled regions of the enzyme.\nThis point appears to be a duplicated comment from the reviewer, and we address it in detail in our\nresponse below.\n- A question on terminology: I typically think of allostery as referring to a ligand or effector binding at\na site distinct from the active site and altering enzyme activity. In Fig. 6, however, “allosteric hotspots”\nrefer to mutations in helix 10 that influence specificity through structural coupling rather than ligand\nbinding. Are these two uses of the term related? The classical definition implies an evolved\nconformational mechanism linking distant sites, whereas the paper’s use of the term seems to reflect\nstructurally coupled regions of the enzyme.\nClassical allostery refers to dynamic, ligand-driven regulation, but in our work, “allosteric hotspots”\ndescribe regions where mutations can influence enzyme activity or specificity through long-range\nstructural coupling, even without ligand binding. Mutations can cause allostery by changing a protein's\nstructure and function at a site distant from the original mutation, a phenomenon known as allosteric\nmutation. In other words, these mutations act at a distance through similar structural contact\nnetworks that could enable classical allosteric regulation through ligand binding.\nThe experimental reproducibility metrics are impressive. For Fig. 2A, the scatter plots would be more\ninformative if they showed point density, either using transparent markers or density-based coloring\n(as commonly done in FACS plots).\nWe thank the reviewer for this helpful suggestion. We have updated Fig. 2A to use transparent\nmarkers, which makes the point density easier to see, and believe that the plot is now more\ninformative.\nThe authors also stated “We attribute the high level of agreement to the inherently promiscuous and\ngeneralist nature of R. gracilis DAOx, a trait that is conserved in more evolutionarily recent homologs\nsuch as human DAOx.” Is the human enzyme more recent than R. gracilis?\nWe thank the reviewer for this comment, which helped us clarify the sentence. The human DAOx\nsequence represents a lineage that diverged more recently from the common ancestor shared with\nother eukaryotic DAOx, whereas R. gracilis branched off earlier in evolution. We have revised the\nsentence in the manuscript accordingly to make this point clear.\n“We attribute the high level of agreement to the inherently promiscuous and generalist nature of R.\ngracilis DAOx, a trait that is also observed in more recently diverged eukaryotic homologs such as\nhuman DAOx.”\nReviewer #3:\nThe authors previously developed a yeast display assay to decouple enzymatic activity measurement\nfrom covariant factors (expression and folding stability) on a single pair of enzyme-substrate\ninteraction. Here, they extended their work to the interaction between D-amino acid oxidase (DAOx)\nand five different substrates. Through a series of comparative analysis, they showed that while\nsubstrate-specific mutations can be distal from the active site, those within have stronger impacts at\nthe cost of reduced activity.\nOverall, the technical analysis of this submission is solid and innovative, but its experimental technique\nand findings are not novel. On yeast display assay, Amorosi et al. have already applied a similar\ntechnique to measure mutational impact on P450 activity [1]. On direct kinetic measurements, an\nexcellent article from Markin et al. showcased a microfluidic platform to measure Michaelis-Menten\nconstants for phosphatase [2]. These highly-relevant studies restrict the novelty of this submission to\nsubstrate specificity. Despite the thorough analysis presented, the finding is either already established\nin other DMS studies (on allosteric modulation) or too restrictive (structural insights on specific\nsubstrate-sidechain interactions). If possible, establishing key identifier(s) for allosteric modulation\nwould be highly impactful in the field.\nIn conclusion, this submission is technically well-established, and I recommend referring it to other\njournals with stronger focus on technically sound research such as Scientific Reports.\nReferences\n1. Amorosi, C. J. et al. Massively parallel characterization of CYP2C9 variant enzyme activity and\nabundance. Am. J. Hum. Genet. 108, 1735–1751 (2021). doi:10.1016/j.ajhg.2021.07.001.\n2. Markin, C.J. et al. Revealing enzyme functional architecture via high-throughput microfluidic\nenzyme kinetics. Science 373, eabf8761 (2021). doi:10.1126/science.abf8761.\nWe thank the reviewer for the comment and assessment of related work. We have added the\nreferences to the main text of the manuscript (Ref. 10 and 11). Nevertheless, we want to specify that\nthe two cited studies employ methodological approaches that are not really directly comparable to\nthe platform used in our current study.\n1. Regarding Amorosi et al.\nThis work does not use yeast display, but rather a fluorescence-based yeast assay employing CYP-\nspecific activity probes that irreversibly stain enzymatically competent P450 variants. This platform\ncannot be used for steady-state kinetic measurements and operates inside cells under fixed reaction\nconditions. In contrast, our platform allows systematic control of key reaction parameters, including\nsubstrate concentration, buffer, temperature, and incubation time, which is essential for the\nquantitative kinetic characterization of substrate specificity. Although the overall workflows share\nconceptual elements, the experimental capabilities and aims of the two methods differ.\nRegarding Markin et al.\nThis study was extensively acknowledged in our earlier Nature Communications publication\n(https://doi.org/10.1038/s41467-024-45630-3) describing the development of the EP-Seq method\nand its comparison to the microfluidic platform developed by Markin et al. Nevertheless, we can again\nacknowledge its relevance in the current work and have inserted the reference.\nMajor edits on possible ways to introduce novelty\n1. identify quantitative and statistically significant structural/biophysical features that\n1.1 segregate impactful allosteric mutations from neglectible ones, or\n1.2 segregate allosteric mutations by substrate specificity\nIn the main text, we dedicate a full paragraph to the characterization of structural allosteric sites with\nan impact on substrate specificity. Specifically, α-helix 10 exhibits these features. We additionally\ntested the performance of six single-mutant variants (R254C, R254D, H258W, H258R, L262A, L262V)\nin this region toward each of the five substrates used in the study, demonstrating that each mutation\nresults in a distinct specificity profile (Fig. 6C). Given this analysis and the existing experimental\nevidence, we believe the current data already address the reviewer’s comment, and we respectfully\nnote that further experimentation is not warranted within the scope of this work.\n2. establish generalizability of insights found in this work\n2.1 engineer and test new beneficial/substrate-specific mutations, and\n2.2 compare the success rate with other baselines\nAgain, we feel what the reviewer is asking for was already done. In the current work, in addition to\nour high-throughput data, we characterized 36 single-mutant variants that were detected to have\nsignificantly altered substrate specificity. This included detailed measurements of Michaelis–Menten\nkinetic parameters on five substrates. For each variant, we report K and V values in supplementary\nM max\nTable S5. Moreover, the five most relevant substrate-specific mutants (F58W, N54L, T93M, M213E\nand S341I) were also expressed as soluble proteins, and their kinetic properties were re-evaluated,\nshowing strong concordance with the yeast-display data (Table1, Supplementary Fig. S8, S9). Given\nthe breadth and depth of these measurements, we believe this dataset sufficiently addresses the\nreviewer’s request.\nMinor edits\n1. Figure 3A: highlight first-shell positions\nWe have added a heatmap along the x-axis of Figure 3A showing the distance of each residue from\nthe catalytic site and have updated the figure caption accordingly.\nFigure 3B: label the distribution explicitly as CDF/PDF\nIn Figure 3B, the y-axis represents the number of variants observed at each specificity score, i.e., a raw\nhistogram, rather than a probability density or cumulative distribution.\n3. Heatmap S4: unify color scale and normalize to 1.0\nWe thank the reviewer for this suggestion. In Figure S4, the color scale reflects the actual measured\nactivity of each variant relative to the wild-type, with 1 corresponding to WT activity. The observed\nrange of activities varies between different conditions and substrates, and normalizing each heatmap\nto 1.0 would obscure these biologically meaningful differences. Therefore, we have retained the\ncurrent color scaling, which preserves the true variation and functional interpretation of the data.\n4. Inconsistent wordings\n4.1 “labeling” and “labelling”\n4.2 “Sp-scores” and “SP-scores”\n4.3 “gray” and “grey”\nWe have standardized spelling and terminology: “labeling,” “Sp-scores,” and “gray” are now used\nconsistently. We have also standardized the method name to “EP-Seq” (instead of Ep-Seq) throughout\nthe text and updated it in Figures 4 and S9."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have carefully addressed all of my previous comments, and the revised manuscript has been improved accordingly.\n\nThe responses are appropriate, and the manuscript is now clear and scientifically sound.\n\nI recommend acceptance of the manuscript without further revision.", "reviewer_2": "The authors have satisfactorily addressed all my comments.", "reviewer_3": "The primary concern on the novelty of this submission stems from the fact that the authors have previously published in the same journal on the same enzyme (DAOx) and one of the five substrates reported here (D-alanine) utilizing the same EP-Seq assay they developed [1]. Therefore, the scientific merit of this work should be based on the contrastive and comparative insights gained from these five substrates collectively. While the data collection is technically well-established, as noted in the previous reviewer comment, the current analysis relies heavily on qualitative case studies rather than establishing general principle(s) of enzyme specificity through quantifiable metrics. This needs not to be addressed through additional experiments.\n\nWhile the authors highlighted notable mutations like those on α-helix 10, observations are generally descriptive and lack statistical analysis on quantifiable biophysical metrics. In fact, precedent quantitative analysis has been done in their previous publication [1]. Figure 3C of their prior work identified biophysical properties that concurrently or differentially impacted enzyme activity and expression. Similarly, Krustal-Wallis test can be performed here to identify outstanding structural features that explain the differential response amongst the substrates with statistical confidence. For instance, features can be changes in interactive energy with the substrates, conservation score, or distance from the active site. The community would benefit tremendously from either positive or negative results consolidated in a quantifiable manner.\n\nReferences\nVanella, R., Küng, C., Schoepfer, A. A. et al. Understanding activity-stability tradeoffs in biocatalysts by enzyme proximity sequencing. Nat. Commun. 15, 1807 (2024). doi:10.1038/s41467-024-45630-3."}, "author_response": "General comments for all the reviewers:\nWe thank the reviewers for their comments. Below we address each comment point-by-point. The\nreviewer’s queries are written in black text, with our corresponding responses written in blue.\nReviewer #1 (Remarks to the Author):\nThe authors have carefully addressed all of my previous comments, and the revised manuscript has\nbeen improved accordingly.\nThe responses are appropriate, and the manuscript is now clear and scientifically sound.\nI recommend acceptance of the manuscript without further revision.\nWe thank reviewer for the positive evaluation of the work.\nReviewer #2 (Remarks to the Author):\nThe authors have satisfactorily addressed all my comments.\nWe thank reviewer for the positive evaluation of the work.\nReviewer #3 (Remarks to the Author):\nThe primary concern on the novelty of this submission stems from the fact that the authors have\npreviously published in the same journal on the same enzyme (DAOx) and one of the five substrates\nreported here (D-alanine) utilizing the same EP-Seq assay they developed [1]. Therefore, the scientific\nmerit of this work should be based on the contrastive and comparative insights gained from these five\nsubstrates collectively. comment, the current analysis relies heavily on qualitative case studies rather\nthan establishing general principle(s) of enzyme specificity through quantifiable metrics. This needs\nnot to be addressed through additional experiments. While the data collection is technically well-\nestablished, as noted in the previous reviewer\nWhile the authors highlighted notable mutations like those on α-helix 10, observations are generally\ndescriptive and lack statistical analysis on quantifiable biophysical metrics. In fact, precedent\nquantitative analysis has been done in their previous publication [1]. Figure 3C of their prior work\nidentified biophysical properties that concurrently or differentially impacted enzyme activity and\nexpression. Similarly, Krustal-Wallis test can be performed here to identify outstanding structural\nfeatures that explain the differential response amongst the substrates with statistical confidence. For\ninstance, features can be changes in interactive energy with the substrates, conservation score, or\ndistance from the active site. The community would benefit tremendously from either positive or\nnegative results consolidated in a quantifiable manner.\nWe thank the reviewer for raising this point regarding the identification of general principles of\nsubstrate specificity. While we agree that the scientific merit of this work lies in comparative insights\nacross substrates, we respectfully disagree with the assessment that our study lacks quantitative\nmetrics to address substrate specificity.\nOur comparative analysis of high-throughput EP-Seq data was performed using stringent quantitative\nand statistical criteria. Internal reference populations (e.g., synonymous variants) controlled for\nexperimental noise, excluding apparent substrate-specific effects arising from measurement\nvariability (Fig. 3A, B). In addition, dozens of single-variant kinetic measurements were quantitatively\ncharacterized to validate the high-throughput data. The Sp-score metric demonstrates strong\nagreement between high-throughput measurements and single-clone enzymatic assays, providing a\nrobust quantitative basis for assessing substrate specificity across all five substrates (Fig. 4A).\nBuilding on this foundation, we established quantitative relationships between substrate specificity\nand multiple biophysical and functional features, including protein expression level, structural\nstability, mutation distance from the catalytic site, and catalytic performance. These analyses reveal a\ncomplex, interconnected landscape in which functional and structural parameters jointly influence\nspecificity (Fig. 4E). Quantitative comparisons across substrates show that specificity effects depend\nstrongly on substrate identity: smaller substrates exhibit the strongest and more straightforward\neffects, whereas larger substrates display complex specificity landscapes that likely arise from the\ncoordinated action of multiple elements, such as steric factors and electrostatic interactions (Fig. 4B–\nC).\nRegarding structural determinants, our data highlight the dominant role of catalytic-site mutations in\ndriving specificity, often at the expense of overall activity, while distal mutations modulate specificity\nmore subtly. This illustrates the primary importance of the catalytic site in determining enzyme\nspecificity, as well as a secondary importance of distributed specificity-determining mutations across\nthe protein structure (Fig. 3C). In line with this, we identified and quantitatively validated the\ncontributions from α-helix 10, demonstrating how substitution in secondary structural elements\noutside the catalytic center can influence active-site geometry and substrate recognition (Fig. 6).\nWhile this feature is specific to DAOx, it exemplifies a broader principle of distributed structural\ninfluence on specificity.\nAs part of our original analysis, we also explored correlations between substrate-specific effects and\nindividual structural and biophysical properties, including amino acid bulkiness, polarity,\nhydrophobicity, solvent exposure, and residue conservation. These analyses did not yield strong or\nconsistent relationships for any of the features, and therefore we did not include them in the paper,\nas they offered no simple or intuitive trends for understanding specificity.\nIn conclusion, we believe that the inherent complexity of substrate recognition in enzyme catalysis\nhighlights the high value of our approach. By combining high-throughput mutational mapping with\ncross-substrate comparative analysis, we provide a detailed, quantitative view of the DAOx specificity\nlandscape and reveal evolutionary pathways toward highly specific yet functional enzyme variants,\ninsights that are largely inaccessible through classical low-throughput methods."}]

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