Computational design of dynamic biosensors for emerging synthetic opioids

Domain: physics
Published: 2026-01-07
Editorial outcome: minor_revision

Abstract
Nitazenes are an emergent class of synthetic opioids that often rival or exceed fentanyl in their potency. These compounds have been detected internationally in illicit drugs and are the cause of increasing numbers of hospitalizations and overdoses. New analogs are consistently released, making detection challenging—new ways of testing a wider range of nitazenes and their metabolic products are urgently needed. Here, we develop a computational protocol to redesign the plant abscisic acid receptor PYR1 to bind diverse nitazenes and maintain its dynamic transduction mechanism. The best design has a low nanomolar limit of detection in vitro against nitazene and menitazene. Deep mutational scanning yielded sensors able to recognize a range of clinically relevant nitazenes and the common metabolic byproduct in a complex biological matrix with limited cross-specificity against unrelated opioids. Application of protein design tools on privileged receptors like PYR1 may yield general sensors for a wide range of applications in vitro and in vivo.

Introduction
The rise in overdose deaths from synthetic opioids has been well- documented in the US and worldwide, with prevalence rising in the 2010s to account for well over half of documented overdose deaths in the US in 20221–3. Synthetic opioids such as fentanyl are inexpensive to produce and highly potent, with fentanyl 50 to 100 times more potent than heroin and morphine2,4, and thus are often mixed with other drugs to cheaply increase strength and addictivity2,3. An emerging class of novel synthetic opioids are 2-benzyl benzimidazole compounds known as nitazenes. First identified in the US and Canadian drug supply in 2019, nitazenes are up to 40 times more potent than fentanyl5–8. Furthermore, the molecular structures of synthetic opioids, including nitazenes, are consistently altered to skirt laws banning known compounds3,7,9. While commercial tests exist for fentanyl, including lateral flow assay test strips10–12, there is an immediate need for diagnostic assays that surpass the sensitivity and breadth of current nitazene test strips to detect the range of nitazenes in the drug supply and enable the rapid detection of nitazene metabolites to assist medical providers in treating exposed patients13.

Diagnostic assays often contain a biomolecule to specifically recognize the drug(s). To develop nitazene biosensors, we chose to redesign the binding pocket of the plant abscisic acid (ABA) receptor PYR114–16. PYR1 binds a phosphatase HAB1 in the presence of ABA through an allosteric gate-latch lock mechanism17, representing a natural chemical induced dimerization (CID) module. This CID module presents a built-in transduction mechanism coupling protein interactions to a diverse range of signal outputs16,18,19. This is additionally advantageous because the molecular ratchet mechanism of the PYR1-HAB1 CID allows highly sensitive readouts from lower affinity ligand-receptor pairs14,20. Immunoassays with antibodies or other binding molecules can be developed by brute force screening, but the relatively small surface area of drugs limit the affinity and ability to bind multiple related drug variants. While drug host receptors can be modified into a sensor21, the same receptor can bind chemically dissimilar drugs, hampering unique identification of a drug class. Computational design has recently been used to create moderate affinity binders for largely apolar, rigid molecules22–26. However, binders are not sensors: the transduction of the binding event into a measurable signal must typically be engineered for each bespoke design, limiting generality and throughput. In contrast, repurposing the PYR1 binding pocket, while keeping its transduction mechanism intact, promises to be a more generalizable solution.

In this study, we use computational design of the PYR1 binding pocket to identify biosensors suitable for detection of nitazenes and their byproducts. We use deep mutational scanning, directed evolution, and computational modeling to create a pan-nitazene sensor that can detect multiple nitazene derivatives, including the common variant isotonitazene and its 4-hydroxy nitazene byproduct. We then develop a luciferase-based in vitro diagnostic platform that is label-free, fast, sensitive, and can be performed in a complex biological matrix like urine. The computational-experimental methodology represents a general way to rapidly develop biosensors to address the emergence of new synthetic opioids that can circumvent existing detection modalities.

Results
A computational design protocol for sensing the nitazene family of synthetic opioids
Design of an allosteric biosensor requires solving the challenge of protein-ligand binder design consistent with, and constrained by, a structural definition of the transduction mechanism27. A major advantage of redesigning PYR1 for new ligand sensing is that the receptor has an exceptionally well understood and characterized CID mechanism, where a bound water maintains hydrogen bonds between the ligand, the PYR1 receptor, and the HAB1 protein17 (Fig. 1A, B). We hypothesized that successful designs would recapitulate the spatial orientation of this ligand hydrogen bond acceptor.

To guide our design efforts, we first sought to understand the relationship between the molecular features of nitazenes and their potency. The nitazene family of 2-benzyl benzimidazole compounds contain substitutions observed at three positions (R1, R2, R3; Fig. 1C)7. We evaluated several regression models, including ordinary least squares, Ridge, Lasso, and elastic net, to assess the relative importance of nitazene substitutions on potency (Supplementary Fig. 1) using a previous study of nitazene substitutions on cell-based activation of the μ-opioid receptor7. This analysis showed that a nitro group at R2 and several larger aliphatic ethers at R1—instead of a hydroxyl group—increased potency significantly (Fig. 1D), consistent with known potent synthetic opioids like isotonitazene7,28, which contains an isopropoxy branched ether group at R1. Substitutions of different N-containing heterocycles or secondary and tertiary amine groups at R3 have less effects on molecular potency. Finally, isotonitazene and other nitazenes are metabolized at R1 to an hydroxyl7,29–31, forming 4-hydroxy nitazene and related metabolites. This leaves the R2 group as a defining feature of nitazene potency. Thus, a biosensor that can detect a range of nitazenes containing an R2 nitro group and with limited cross-reactivity to other opioids is imperative for sensing nitazenes in the drug supply. Note that nitazenes with hydrogen at R2 group may in some contexts be as potent as fentanyl. Thus, a secondary goal would be to sense such nitazenes.

Repurposing an existing pocket to sense nitazenes requires a way to down-select the astronomical number of possible ligand-protein conformations. We separated the design problem into two components: (1) defining the structural pose (the rigid body orientation of the ligand relative to the PYR1 backbone); and (2) identifying a likely PYR1 sequence for that pose. To define the structural pose, we developed a computational design protocol to identify likely ligand-protein conformations and ligand orientations in the receptor binding pocket that maintain the transduction mechanism (Fig. 1B, E). Sequences for these poses are then designed using the physically based algorithm Rosetta FastDesign32 followed by physics-based filtering metrics.

To identify likely conformers of isotonitazene, which contains 9 rotatable bonds, we performed temperature replica exchange MD simulations in solution. Three isotonitazene conformers were observed greater than 97% of the time (Supplementary Fig. 2). To orient these conformers in the binding pocket, we reasoned that likely configurations occur where the important nitro group hydrogen binds to the bound water critical to the dynamic transduction mechanism. Performing the alignment revealed that two isotonitazene conformers could dock without steric clashing when all 21 allowable positions in the binding pocket are mutated to glycine. These results match ligand docking experiments in a deeply mutagenized PYR1 library biased toward hydrophobic ligands (HMH33), which contains sequences that allow docking of nitazenes in the binding pocket (Supplementary Fig. 3).

To test these initial steps of our computational design process, we screened a nitazene panel (Supplementary Table 1) against the HMH library using a yeast two-hybrid (Y2H) assay34,35, identifying sensors for six nitazene family members with differences in R1, R2, and R3 groups (although not isotonitazene; see Supplementary Fig. 4 and Supplementary Table 1, 2). Initial nitazene hits were identified with a geometric mean minimum dose response (MDR) of 61µM in the Y2H growth selection, suggesting that their sequences were not optimized for binding. The hits had a mean of 6.4 mutations from the parental receptor; a previous computationally designed double site saturation mutagenesis library18 yielded no sensors when the same panel was screened (Supplementary Table 1), highlighting that many PYR1 mutations are necessary to repurpose this receptor for recognizing nitazenes.

Computational design protocol yields an M-responsive nitazene binder
The insights from screening and structural modeling were integrated into the design process for a nitazene-specific PYR1 library. We used the perturbed alignments of predicted conformations of isotonitazene for Rosetta sequence redesign (see Methods, Supplementary Table 3), fixing the identity of residues known to be essential for the allosteric transduction mechanism17,34. After manual curation, the library was ordered as an oligopool and constructed by a four part Golden Gate assembly36 in a thermally stabilized PYR1 background, PYR1HOT537 (Supplementary Table 4–5 and Supplementary Fig. 5). The library contained a mean of 9 mutations from the PYR1HOT5 background. We screened this library using Y2H assays against a panel of nitazenes (Fig. 2A), identifying 49 unique ligand-responsive sequences for six nitazene analogs, including isotonitazene (Supplementary Fig. 6 and Supplementary Table 6). Sensors with the lowest MDR for each compound are shown in Fig. 2B. Across all hits, the MDR was 19µM, representing a significant improvement over that of the previous HMH library (61µM; two-sided Wilcoxon rank-sum test, p value 8.5e-5). From this screen, we identified PYR1nita (PYR1HOT5/H60P/N90S with K59Q, S92M, F108V, Y120A, S122G, F159A, A160V) as the lead candidate. This mutant appeared in screens for nitazene (MDR: 5µM), menitazene (MDR: 1µM), and protonitazene (MDR: 50µM), and showed no detectable binding to the structurally unrelated synthetic opioid benzylfentanyl (Fig. 2C and Supplementary Fig. 6). To further characterize PYR1nita, we displayed it on the surface of yeast and performed cell surface titrations. No binding was observed in the presence of a designed thermostable HAB1 (ΔN-HAB1T+)18 alone or in the presence of the structurally unrelated ligand benzylfentanyl. Saturable responses were observed for nitazene EC50 250nM (95% c.i. 177–390nM) and menitazene EC50 383nM (95% c.i. 277–588nM), with a limit of detection (LOD) for nitazene of 43nM (Fig. 2D). To test binding in vitro, we optimized a previously described ex vivo split luciferase detection system18 using the NanoBiT complementation reporter system38. Here, LgBiT is fused to a PYR1 receptor construct, and SmBiT is fused to HAB139 (Supplementary Fig. 7). Ligand titrations showed no activation with benzylfentanyl and nanomolar sensitivity to both nitazene EC50 243nM (95% c.i. 216–276nM) and menitazene EC50 306nM (95% c.i. 240–405nM), with a LOD for nitazene of 3.2nM (Fig. 2E). Thus, the computational design pipeline enabled the isolation of a nitazene sensor with low nM sensitivity in vitro.

Optimization of nitazene biosensors by deep mutational scanning
Next, we used deep mutational scanning coupled with a directed evolution strategy to develop two distinct nitazene biosensors (Fig. 3A). The first of these is for the detection of 4-hydroxynitazene, the major metabolite byproduct of isotonitazene. While the current needed LOD is unknown40, an effective sensor should exhibit a low nM LOD and function in a complex biological matrix like urine. The second sensor is a pan-sensor capable of detecting nitazenes even in complex mixtures of powders. While the sensitivity requirement of this sensor is less strenuous, it must be specific for nitazenes compared with other synthetic opioids, fillers, or recreational drugs.

In a first step to designing these sensors, we performed a deep mutational scan (DMS) of 16 positions inside of the PYR1nita binding pocket using yeast display screening coupled to fluorescence activated cell sorting. Libraries were screened at 250nM of nitazene and menitazene (approx. 20% of the EC50). Additionally, a constitutive (no ligand but in the presence of 500nM HAB1T+ binding protein) and a reference control (sorting on all gates except the gate associated with binding) were included. Examples sorting gates are shown in Supplementary Fig. 8.

Following short-read deep sequencing of these populations, the frequencies of each mutant were assessed and normalized to the parental sequence by an enrichment ratio (Fig. 3B and Supplementary Fig. 9). Most mutations (298/320) were depleted, indicating a restricted sensor pocket. Nitazene and menitazene shared 10 non-constitutive beneficial mutations (V81CLI, V83ML, M92FL, A120G, A159H, V164I; Fig. 3C), largely in the aliphatic central cavity near the predicted binding site of the benzylbenzimidazole nitazene scaffold (Fig. 3D). The largest enriched mutation for both ligands was A159H. Our original design models did not include a residue which delocalizes the partial charge on the nitro group; we hypothesize that H159 satisfies this requirement (Fig. 3D). Indeed, redesigning the PYR1 sequence using the deep learning algorithm LigandMPNN41 identifies H159 in 8% of designs (Supplementary Fig. 10). This finding by an orthogonal design method is consistent with the proposed binding pose; additionally, the incorporation of a newer, deep learning packing algorithm for sequence design hints at future avenues to build on our ligand docking method to improve the computational design process. Overall, the mutational profile is largely consistent with the designed binding mode.

To develop a sensor capable of binding the major metabolite byproduct 4-hydroxynitazene, we used the DMS output to create a focused combinatorial library of 4608 members largely containing the beneficial mutations shared between nitazene and menitazene (Supplementary Table 7). Screening this library on nitazene and menitazene yielded two sensors, PYR1nitav2.1 (PYR1nita with V81I, M92F, A120G, G122E, E141D, A159H) and PYR1nitav2 (PYR1nita with V81I, V83L, E141D, A159H), containing 4-6 additional mutations from PYR1nita (Fig. 3E and Supplementary Fig. 10). Both sensors recognized 4-hydroxynitazene, menitazene, and nitazene at an average LOD of 100 pM and EC50 of approximately 2nM (Fig. 3F and Supplementary Fig. 11), representing a more than 100-fold improvement in affinity over the originally designed PYR1nita sensor. Neither sensor recognized the unrelated synthetic opioid benzylfentanyl at the highest concentration tested, indicating highly specific recognition of nitazene, its metabolic byproduct 4-hydroxynitazene, and a close analog menitazene over a competing synthetic opioid.

Our underlying hypothesis in the design process was the importance of the nitro group in molecular recognition by the PYR1-based sensors. One prediction resulting from this hypothesis is that two nitazenes that differ only by the nitro groups should have very different affinities. To examine this, we used yeast display to determine the EC50 for the PYR1nita, PYR1nitav2, and PYR1nitav2.1 sensors against metonitazene and metodesnitazene. Metonitazene and metodesnitazene are identical except the presence or absence of a nitro group at R2. All three sensors had markedly reduced or no responsiveness to metodesnitazene compared with metonitazene (Supplementary Fig. 12). For example, PYR1nitav2 showed at least a 650-fold difference in EC50 values for metonitazene compared with metodesnitazene. Overall, these results are consistent with the importance of the nitro group in the ligand-induced latch closure, consistent with the design poses. We note that there is small but measurable binding signal present at the higher desnitazene concentrations for the engineered sensors, suggesting that a pan sensor may be capable of recognizing even nitazenes lacking a nitro group at R2.

To develop a sequence able to sense structurally diverse nitazenes, we used the structural model of PYR1nita to identify mutations which could allow binding breadth by increasing the pocket space for diverse functional R1 and R3 groups. We created a focused combinatorial library of 2048 variants encoding differences at positions 59, 141, 163, 164, & 167 to the PYR1nitav2.1 and PYR1nitav2 sensors (Supplementary Table 8). After screening against constitutive binders, the library was split and screened in parallel against butonitazene, isotonitazene, and N-pyrrolidino isotonitazene (Supplementary Fig. 13). These populations were deep sequenced; sequences that were enriched in all three ligand populations were individually tested (Supplementary Fig. 14). The best sensors, PANnita and PANnita.1, recognized the three diverse nitazenes and did not recognize benzylfentanyl (Fig. 3G and Supplementary Fig. 15). PANnita has in total 11 mutations in the receptor binding pocket out of 18 total mutable positions. Combined, computational design coupled to protein engineering can access a more diverse functional sequence space than accessed by previous libraries.

Engineered PYR1 sensors can discriminate nitazenes and metabolites in relevant diagnostic scenarios
To test whether PYRnitav2.1 and PANnita are capable of sensing nitazenes under relevant conditions, we repurposed our in vitro luminescence assay. Luminescence detection assays are susceptible to errors from varying experimental conditions including fluctuating activity in diverse biological matrices42. For these reasons, ease of single-sample detection is limited by the need for external calibration. A previously described calibrator luciferase43 enables single-sample ratiometric quantitative readouts (Fig. 4A). This assay compares the ratio of ligand dependent luminescence to background GFP fluorescence to mitigate experimental variance. We tested this new calibrator assay with the parental PYR1 sensor and a range of synthetic cannabinoid sensors18. All tested sensors had nearly identical dynamic range, LOD, and EC50 values between buffer and urine (Supplementary Fig. 16). Using this assay, PYR1nitav2.1 could recognize the metabolic byproduct 4-hydroxy nitazene with a LOD of 1nM (0.37 ng/mL) in urine, its most clinically relevant biological matrix, and with no cross-reactivity to the unrelated opioids benzylfentanyl, codeine, or heroin (Fig. 4B).

Another diagnostic use for sensors is discriminating nitazenes in unknown powders. To assess this use case, we tested whether PANnita.1 could recognize diverse nitazenes relative to these other opioids. PANnita.1 bound the nitazene variants butonitazene, isotonitazene, and N-pyrrolidino isotonitazene while exhibiting minimal perception of benzylfentanyl, codeine, and heroin. Ligands were dissolved in aqueous buffer with a cosolvent consisting of 0.42 (v/v)% methanol and 0.58 (v/v)% DMSO except for heroin, which was dissolved in 1 (v/v)% acetonitrile. At their highest tested concentration, this assay was able to detect a 6.4x, 7.2x, and 23x fold-change respectively for the target ligands butonitazene, isotonitazene, and N-pyrrolidino nitazene, with limited activation of off-target opioids (2-way ANOVA, p<0.0001) (Fig. 4C). Similar results were obtained for the PANnita sensor (Supplementary Fig. 17). To determine the wider range of efficacy of the PAN sensors, PANnita and PANnita.1 were tested against ten structurally diverse nitazenes (at 10μM) not used in sensor engineering. These included desnitazenes lacking the nitro group, and 5-aminoisotonitazene not yet detected by nitazene test strips41. Remarkably, these pan sensors combined to recognize five of these nitazenes, including two of the three desnitazenes and ethyleneoxynitazene (Supplementary Figs. 18–19). Pan sensors failed to recognize 5-aminoisotonitazene which contains an amine at R2 that is likely incompatible with the water-mediated latch closure. Additionally, pan sensors did not recognize some nitazenes (ethylenenitazene, ethylene isotonitazene) likely because of steric clashes. Still, the ability to sense diverse nitazenes, including desnitazenes, distinct from those used in the engineering process suggests that these or closely related engineered sensors may be able to identify the overwhelming majority of nitazenes in the drug supply.

In directed evolution, it is well established that optimizing for affinity and breadth can impact other properties like protein expression yields, stability against thermal challenge, and kinetic activation44. We assessed these properties in the pan-nitazene sensors and PYR1nitav2.1 compared with HOT5mandi37, the most stable engineered PYR1-based sensor developed thus far. All proteins had identical expression yields of 19 mg/L under standard E. coli expression conditions when expressed as LgBiT and MBP-tagged fusion proteins (Supplementary Fig. 20). To test stability against thermal challenge, we developed a luminescence-based thermal inactivation assay. All sensors showed a 44.8°C or higher temperature at which a 20 min incubation resulted in 50% loss in activity (T50 20). The T50 20 for PYR1nitav2.1 was 48.0oC (95% c.i. 47.3–48.6°C), which was not significantly different (p value=0.13) than that for HOT5mandi (T50 20=48.5, 95% c.i. 47.3–48.6°C)37 (Supplementary Fig. 21). We also tested the time the luminescence assay takes to equilibrate. At 10μM ligand and at room temperature, all sensors reached 90% of steady-state luminescence values within 17 min. Notably, PYR1nitav2.1 reached equilibrium faster than HOT5mandi (11 vs. 14 min, p value 0.0458) (Supplementary Fig. 22). Although the PAN sensors are intended for testing unidentified powders in vitro, we also tested whether they retain the ability to sense N-pyrrolidino isotonitazene in a biological matrix. In urine, both sensors maintained the ability to discriminate between benzylfentanyl and N-pyrrolidino isotonitazene (Supplementary Fig. 23). Thus, the tested sensors increased breadth and affinity for nitazenes without appreciably changing their underlying kinetic activation, expression yields, or stability against thermal challenge.

Methods
Chemicals
Nitazenes and other pharmaceutically active molecules for use in yeast surface display and split luciferase assays were purchased through Cayman Chemical, including 4’ hydroxy nitazene (#30218), benzyl fentanyl (hydrochloride, #19883), butonitazene (exempt preparation, #38653), codeine (CRM, #ISO60140), ethyleneoxynitazene (citrate, #34947), heroin-d9 (exempt preparation, #42070), isotonitazene (CRM, #30880), menitazene (citrate, #36634), metonitazene (citrate, CRM, #40808), N-desethyl etonitazene (#32512), nitazene (citrate, #36635), N-pyrrolidino etonitazene (CRM, #42194), N-pyrrolidino isotonitazene (citrate, #34909), protonitazene (hydrochloride, CRM, #38143), metodesnitazene (hydrochloride, #43513), etodesnitazene (CRM, #43092), isotodesnitazene (citrate, #30286), N-pyrrolidino isotonitazene (citrate, #34909), N,N-dimethylamino etonitazene (citrate, #34452), ethylene etonitazene (citrate, #35166), 5-aminoisotonitazene (#29318), ethylene nitazene (citrate, #34946), metonitazene (CRM, #43158), and (+)-WIN 55,212-2 (mesylate, #10009023). Chemicals acquired from Sigma include mandipropamid (#32805-100mg), JWH-015 (#J4252-5MG), JWH-016, and JWH-072. Abscisic acid was purchased from GoldBio (#A-050-2.5). Diazinon was acquired from ChemService Inc. (#N-116121-250MG). Hikarazine-108 was a gift from Dr. Yves Janin, Centre National de la Recherche Scientifique. Dry Hikarazine-108 was resuspended in 200μL DMSO and then diluted with 300μL of acidic ethanol. Acidic ethanol is prepared by adding 100μL of 37% hydrochloric acid to 12 mL of 100% ethanol. This resulted in a 4.22mM stock that can be stored at −20°C indefinitely.

Computational design protocol
The mutational library Nitazene Lib1 was generated using a structural replacement computational docking protocol to generate high-quality plausible protein-ligand alignments of simulated low-energy ligand conformers.

To generate ligand conformers by molecular dynamics, an initial 3D conformer was downloaded from Pubchem54. Conformers were generated by temperature replica exchange molecular dynamics simulations (TREMD)55 as described in Leonard et al56. Briefly, we used the default implemented settings which run 27 temperature replicas ranging from 300 to 450 K and all simulations were run for 100 ns with replica exchange occurring every 2 ps. The conformer with the highest observed occupancy in the lowest temperature replica was given a relative free energy of 0.0 kcal/mol, and the free energy of other conformers were calculated relative to the highest observed occupancy assuming a Boltzmann weighted ensemble. Generated conformers were plotted sequentially by free energy relative to the lowest energy conformer, categorizing conformers as either “high occupancy” (greater than 4% occupancy cutoff used for menitazene and isotonitazene) or “low occupancy” (less than 1% occupancy cutoff used for menitazene and isotonitazene) (Supplementary Fig. 2). Low-energy conformers only were exported as a single sdf file for ligand docking. For this paper, we developed an automated protocol with open-source code (https://github.com/ajfriedman22/SM_ConfGen).

Low-energy ligand conformers generated by TREMD were docked into the PYR1 protein ligand binding pocket using the “dock to sequence” approach of our method “Structural Replacement Biosensor Design”, available as custom python scripts at https://github.com/alisoncleonard/Structural-Replacement-Biosensor-Design. In the dock to sequence method, the wild-type protein sequence to design is mutated to match the sequence of a known low-affinity ligand binding sequence identified through library screening in yeast two-hybrid (Supplementary Fig. 4), enabling visualization of ligand alignments that could accommodate the known binding sequence. Each nitazene conformer was docked in space using a Singular Value Decomposition (SVD)-based superposition method to align the 3D dimensional coordinates of a given novel query ligand with a target molecule. To engineer PYR1, the PDB structure 3QN157 was used as the template, with atoms surrounding the conserved nitro group at nitazene position R2 superimposed on the target molecule abscisic acid ketone, with the nitro group acting as the required hydrogen bond acceptor needed for binding58.

The alignment process begins with two sets of atomic coordinates: one from the query molecule to be aligned and the other from the target molecule. These do not need to be the full set of atomic coordinates for the molecules but must include at least three atoms that are not collinear. Each coordinate set is arranged into a matrix, where each row in the matrix is the x, y, and z coordinates of an atom, and the corresponding row in the other matrix is the corresponding atom in the other molecule. To obtain a set of transformation matrices, each coordinate set is translated so that its center of mass (mean) is positioned at the origin, then a covariance matrix is calculated between the two centered coordinate sets, and finally the covariance matrix is decomposed into three components: two orthogonal matrices and a diagonal matrix of singular values which are used to calculate a rotation matrix. Once the rotation matrix is computed, the original query compound undergoes transformations applied using the apply_transform_Rx_plus_v function available in PyRosetta, where the query coordinates are first translated so that their center of mass is at the origin, to ensure that the rotation is applied correctly, next the computed rotation matrix is applied to the coordinates of the query compound, to put it in plane with the target molecule, and finally the query is translated to the center of mass of the target molecule coordinates, completing the alignment.

As it is unlikely that a novel ligand would dock in exactly the same orientation as abscisic acid, the aligned ligand was next perturbed in space using the PyRosetta Rigid Body Perturbation mover. The moved ligand was verified both to not clash with the protein backbone coordinates, determined using a fast-grid search of PYR1 subjected to a poly-glycine shave, and to contain a nitrogen or oxygen close enough to the latch water molecule of the PYR1 structure to form a hydrogen bond. If these conditions were satisfied, the PYR1-ligand complex was relaxed and repacked using the PyRosetta Packer function, and the repacked complex was checked for collisions with the full protein including both backbone and side chains by fast-grid search. Finally, complexes that pass clash check were scored using the standard PyRosetta score function, with complexes below −300 REU generally considered to be realistic protein-ligand complexes. This process can be repeated as many times as desired to appropriately map plausible ligand alignments in the protein pocket (Supplementary Table 2). The number of alignments tested per conformer and total per ligand are listed in Supplementary Table 3, along with the number of alignments passing clash check taken forward.

Modeled protein-ligand structures were used as inputs for Rosetta FastDesign32 (scripts available at https://github.com/alisoncleonard/Structural-Replacement-Biosensor-Design) for structure-informed rational design to select mutations for inclusion in the library Nitazene Lib1. The results presented in Supplementary Fig. 10 were a LigandMPNN41 sequence redesign of a similar set of modeled protein-ligand structures using (https://github.com/dauparas/LigandMPNN). The temperature was set to 0.2 and the noise level used was 0.1 Angstrom Gaussian noise using ligandmpnn_v_32_010_25.pt.

Plasmids and plasmid library generation
A full list of primer sets (Supplementary Table 9) and plasmids (Supplementary Table 10) used in the work are included. All plasmids were sequence verified using Oxford NanoPore (Plasmidsaurus).

To construct Nitazene Lib1, libraries containing desired combinations of mutations were encoded in oligopools (Agilent) divided into 3 cassettes (Supplementary Table 4), along with 2 constant region cassettes flanking the PYR1 gene, to be cloned into the PYR1 yeast two-hybrid destination vector pND00559. Three sub-libraries were prepared by Golden Gate assembly59, with each containing 2 of the 3 variable portions and one constant cassette (e.g. Library A contained variable cassettes 1 and 2, plus a constant cassette 3). Transformation efficiencies (Supplementary Table 5) were estimated using serial dilution, and library quality (Supplementary Fig. 5) determined by short-read deep sequencing (Azenta Amplicon-EZ). PYR1nita was assembled into pND003 to make pRMC075 by Golden Gate assembly; this plasmid was subsequently transformed into EBY100 S. cerevisiae cells (ATCC Catalog Number MYA-4941TM)60.

A single site saturation mutagenesis (SSM) library at 16 positions (59, 81, 83, 92, 94, 108, 110, 117, 120, 122, 141, 159, 160, 163, 164, and 167) was prepared for PYR1nita (PYR1HOT5/H60P/N90S/K59Q/S92M/F108V/Y120A/S122G/F159A/A160V) by nicking mutagenesis61 using pRMC075 as a plasmid template. NNK mutagenic primers were designed using published SUNi mutagenesis scripts62. Cells were transformed into XL1-Blue electrocompetent E. coli. Plasmid DNA was extracted with a ZymoPURE II Plasmid midiprep kit. The library was transformed into EBY100 S. cerevisiae cells using the lithium acetate method60.

A pan-nitazene library (Nitazene Library 2, Supplementary Table 7) of 4608 members was made with mutations encoded into ultramers (IDT) with constrained degenerate mixed base codons and assembled according to59. A single forward ultramer (Nitz_Combo_Lib2_for) spanning residues 58 to 119 and encoding V81VIL (VTT), V83VILM (VTK), M92MILF (WTK), V108VI (RTH) was amplified with a mix of two reverse ultramers (Nitz_Combo_Lib2_rev_159AV, Nitz_Combo_Lib2_rev_159H) spanning residues 109-168 and encoding A120GA (GSS), G122DEAG (GVN), E141DE (GAW), and either A159GA (GYG) or A159H (CAT) generating a full length Golden Gate ready cassette. A single gBlock (IDT) (Nitz_ComboLib2_Block_1_3) encoding both N-terminal sequence (before residue 58) and C-terminal sequence (after residue 168) with appropriate Golden Gate cut sites was used to generate the full length CDS in the library with pND003 destination vector. The library was prepared for YSD as described above. PYR1nitav2 and PYR1nitav2.1 were isolated as colonies to obtain plasmids P8 and P5 after sorting, respectively.

Two libraries based off of PYR1nitav2 and PYR1nitav2.1 were constructed via combinatorial mutagenesis essentially according to (15) to make Nitazene Library 3 and Nitazene Library 4 (Supplementary Table 8), respectively. Each library had the following mutations added by long ultramers (IDT) (pZB717-pZB720) encoding Q59QVASLIMT (DYR), D141EDAG (GVN), A159VA (GYT) or A159H, V163VA (GYN), V164VILM (VTK), and N167SACGN (KSY), where A159VA and A159H were encoded on two separate ultramers, for a total of 2560 library members each. After Nitazene Libraries 3 and 4 were prepared for YSD as described above, they were pooled together to create Nitazene Library 5 containing 5120 library members total, which was screened according to conditions listed below. NGS was performed as described below to identify pan-nitazene contenders. Select individuals were cloned into pND003 with constant cassettes cass1_pND003_ACL and cass5_pND003_ACL and gBlocks (IDT) containing the corresponding mutations for each contender 1_Cas_2-4_Nit_pan_contender through 6_Cas_2-4_Nit_pan_contender were created via Golden Gate assembly according to Daffern et al., 202336.

pYTK047 was a gift from John Dueber (Addgene plasmid #65154; http://n2t.net/addgene:65154; RRID:Addgene_65154)63.

Yeast two-hybrid screening of mutant biosensor libraries
Selection experiments for mutant receptors that respond to new ligands were conducted as previously described by Beltrán58. Briefly, the indicated pBD-PYR1 mutant library was transformed into MAV99 (MATa trp1-901 leu2-3,112 hisΔ200 ade2-101 gal4Δ gal80Δ can1r-cyh2r LYS2::(GAL1::HIS3) GAL1::lacZ SPO13::10xGAL4site::URA3) harboring pAD-HAB1. Mav99 was a gift from Jeff Bachant and is authenticated by functional controls. A minimum transformation efficiency of 1 million CFU was achieved, typically by pooling two transformations. After transformations, negative selections were conducted twice to remove receptors that bind HAB1 in a ligand-independent fashion (i.e., constitutive receptors) by growing the library on solid media containing 1g/L 5-FOA; the purged library was collected and stored as 30 (v/v)% glycerol stocks until use58.

These glycerol stocks were used in subsequent selections for cells responsive to 100μM nitazene ligands described in Supplementary Table 1 on SD-Trp,-Leu,-Ura media. Colonies supporting uracil-independent growth at 30°C were isolated after 3 days, regrown on SD-Trp,-Leu,+1g/L 5-FOA to screen out constitutive biosensors, retested to confirm ligand-dependent growth on SD-Trp,-Leu,-Ura plates with and without test chemical. The minimum dose response was determined by growth validation after 3 days, in which yeast cells were grown as spot assays by suspending a colony in 100µL of 1X TE and dropping 2µL onto SD-Trp,-Leu,-Ura plates containing various concentrations of the target ligand.

Library sorting by yeast surface display
Sorting of the PYR1 yeast displayed libraries was performed as done in Steiner et al.64 except for the following differences. Cells were induced in SD/GCAA media containing a 9:1 ratio of galactose:dextrose. Induction was carried out at 22°C and 250 rpm for 20 h. Cells were washed in citrate-buffered saline (20mM trisodium citrate dihydrate, 147mM NaCl, 4.5mM KCl) supplemented with 0.1% w/v BSA adjusted to pH 8.0 with 1M NaOH (CBSF), while prepared ΔN-HAB1T+ from plasmid pJS72358 was resuspended in CBSF plus 1mM DTT and 5mM TCEP.

For the SSM library sort, each labeling condition had 250µL of OD600=2 cells, 3µL of ligand dissolved in DMSO for an overall solvent of 1 v/v%, ΔN-HAB1T+ to 500nM, and the volume of the reaction brought to 300µL with CBSF. Binding and labeling were performed as in Leonard et al.56. Cells were collected at approximately half of EC50 in a diagonal gate, excluding background, selecting 200,000 cells each of the top 10–15% of binders, a reference population, and non-binders. Cells were grown and stocks prepared according to56. Next generation sequencing (NGS) was performed on these screened cells as described below.

Nitazene Library 2 was screened with 250µl of OD600=2 cells and maintained solvent at 1 v/v%, 100nM of ΔN-HAB1T+, and a total volume of 300µL attained by adding CBSF in order to obtain PYR1nitav2 sensors. First, 200,000 cells of the top 10% of binders were sorted using 250nM menitazene. The second sort used identical reaction conditions, but only 200,000 cells of the top 5% of binders were collected. The final sort for the PYR1nitav2 sensors differed from the previous sort only by using 100nM menitazene, which yielded PYR1nitav2 and PYR1nitav2.1 sensors, isolated as individual colonies. DNA was extracted from PYR1nitav2 and PYR1nitav2.1 and plasmid sequenced (Plasmidsaurus) to identify sensor sequences.

A total of 250µl of OD600=2 cells of the combinatorial mutagenesis libraries of PYR1nitav2 and PYR1nitav2.1 was screened with 100nM ΔN-HAB1T+ and the first sort was performed with no ligand and 1% (v/v) DMSO to collect 200,000 cells that did not bind constitutively. The resulting cells were grown and screened against three ligands in parallel, butonitazene, isotonitazene, and N-pyrrolidino isotonitazene at 2.5µM, 2.5µM, and 23.8µM, respectively, where butonitazene and isotonitazene were dissolved in methanol and the final volume of the solvent was maintained at at 1 (v/v)% in all samples. The top 5% of binders were collected to get 200,000 cells and DNA was prepared for NGS as described below.

The top performing contenders for the PANnita sensor were identified as those that had the highest enrichment ratios for each ligand and gBlock cassettes containing the identified mutations were cloned into pND003 via Golden Gate assembly to make pRMC079-084. These sensors were tested with yeast surface display with 100nM ΔN-HAB1T+ and various concentrations of butonitazene, isotonitazene, N-pyrrolidino isotonitazene, and benzylfentanyl, maintaining a solvent concentration of 1% v/v.

Next generation sequencing of collected cell populations
DNA was extracted using freeze/thaw cycles and zymolase according to Medina-Cucurella & Whitehead60, except for the following differences. Instead of utilizing a Qiagen mini-prep column, an NEB miniprep column was utilized and DNA was washed with 550µL DNA wash buffer two times and eluted in 30µL nuclease free water. Product from the purification of plasmid with exonuclease I and lambda nuclease was cleaned using an NEB DNA & PCR cleanup kit according to the manufacturer’s instructions.

Plasmid DNA was prepared for NGS by amplifying with either primer sets RMC-P1035 and RMC-P1036 (length of 383bp) or RMC-P1037 and RMC-P1038 (length of 388bp). 10µL of plasmid DNA was amplified using 12.5µL Q5® High-Fidelity 2X Master Mix, 1.25µL 10µM forward primer, 1.25µL 10µM reverse primer, and 25µL nuclease free water (NFW) and placed in the thermocycler with the following cycles: 30s at 98°C, 18 cycles of 5s at 98°C, 20s at 57.5°C, and 30s at 72°C, followed by a final extension of 2 minutes at 72°C and a hold at 10°C. PCR product was purified using an NEB Monarch DNA Gel Extraction kit. DNA was pooled for nitazene and menitazene top binders, then reference populations, and non-binding populations at equal masses and sent to Quintara for AmpExpress next generation sequencing. Resulting sequences were merged with FLASh65, filtered for appropriate read length and amino acid start sequence, and heatmaps of calculated enrichment ratios were created as in Medina-Cucurella & Whitehead60.

Protein expression and purification
Proteins were expressed in E. coli BL21(DE3) under control of a T7 promoter, autoinduced, lysed using B-PER surfactant, and purified by Ni-NTA affinity chromatography58.

Split Luciferase in vitro Assay
Proteins, stored as ammonium sulfate precipitates, were pelleted at 17,000xg for 10 minutes and then resuspended in desalting buffer (50mM HEPES-KOH, 10% w/v glycerol, 200mM KCl, 1mM DTT, 5mM TCEP, pH 8.0). A 7K MWCO Zeba™ spin desalting column (ThermoFisher, Cat. No. 89882) was equilibrated with desalting buffer and then used to buffer equilibrate the resuspended protein, which was then stored on ice until use. Protein concentration was measured by diluting 20μL of desalted proteins into 180μL of 8M urea in a UV-Star plate (Item No. 655801). An absorbance value at 280nm was taken using a plate reader (BioTek Synergy™ H1 Microplate Reader), background subtracted from a blank, and scaled 10x to account for the dilution. The concentration was measured by the Edelhoch method66 using calculated extinction coefficients.

Ligand was diluted to 10x working concentration in desalting buffer and 20μL of this dilution was added to the bottom of Greiner™ 96-well, white, flat bottom plate (Cat. No. 655075). In a separate tube, desired proteins are combined with the Hikarazine-108, and diluted with desalting buffer to reach a final volume of 180μL per well. Add the prosubstrate, Hikarazine-108, last, just before adding the mixture to the wells to minimize its depletion before the reaction has begun in the plate. For bioassay measurements, 40μL urine was supplemented to this master mix per well such that the final concentration of urine was 20% v/v when combined with the ligand. Pooled human urine was sourced from Innovative Research Inc. (SKU# IRHUURE50ML) and was sterile filtered before use to remove any aggregates. Typical working concentrations for LgBiT-PYR1 and SmBiT-HAB1 are in the low nM range (2 and 20nM are an appropriate starting point for LgBiT-PYR1 and SmBiT-HAB1, respectively) while Hikarazine-108 is always used at 10μM. This 180μL master mix was scaled to encompass all wells and replicates. Using a multichannel pipette, 180μL of this mixture are added to each well. The wells are mixed up and down using the pipette, not aspirating to avoid bubbles.

The plate was placed in a plate reader (BioTek Synergy™ H1 Microplate Reader) and an auto gain feature was used to adjust the gain at a wavelength of 450nm for the brightest anticipated well, so that overflow does not occur. An integration time of 1s and a read height of 7mm was used. The plate was then run, and data was output in units of relative luminescence units (RLU) per second. While we used the readout at time=0, we observed luminescence at intervals for one hour. Doing so offered greater resolution into the kinetics of our system and was helpful for monitoring its behavior.

For ratiometric detection, inclusion of the calibrator luciferase includes mNeonGreen fused to the full length NanoBiT luciferase, enabling bioluminescence resonance energy transfer (BRET) between the pair43. This protein sequence was ordered as an eBlock from IDT (Supplementary Table 11) and cloned into a protein expression backbone from pJS723 using Gibson Assembly67. The NanoBiT luminesces blue light (λmax ~450nm) while mNeonGreen fluoresces with a λmax ~520nm, such that the optimized system yields two distinct spectrum peaks.

To resolve two distinct peaks, we ran a spectral scan of each well. With this method, samples volumes of 180μL (without the luminescence prosubstrate) were added to the plate and then covered and placed on a plate shaker at 25°C and 750 rpm. After 30 min, the foil was removed, and 20μL of Hikarazine-108 was added to bring the well up to a volume of 200μL and samples were mixed using a multichannel pipette. On the plate reader, we specified a range of wavelengths from 415 to 555 with a step size of 35nm. For each sample we obtained RLU/s at these 5 defined wavelengths. Complex formation is measured as the RLUs/sec at λ450 divided by the RLUs/sec at λ520. The specific concentration of calibrator used varied based upon the concentrations of LgBiT-PYR1 and SmBiT-HAB1 and typically requires adjustment for each PYR1-ligand pair with the working concentration typically being mid-high pM. An appropriate starting point for optimizing this concentration would be 100-fold less than the concentration of your limiting protein (LgBiT-PYR1 or SmBiT-HAB1).

For testing the PANnita sensors in the SpLuc assay (Fig. 4C and Supplementary Fig. 17), a 1 (v/v)% cosolvent of 42% methanol and 58% DMSO was used for all ligands except for heroin, which was dissolved solely in acetonitrile. Similarly, for testing these sensors versus the extended nitazene variants set (Supplementary Fig. 18–19), a cosolvent of 57% v/v DMSO, 39% v/v methanol, and 4% v/v acetonitrile was used for all ligands. Limit of detection (LOD) was calculated as the lowest ligand concentration at which the mean luminescence measurement exceeded three standard deviations above that of the corresponding blank luminescence measurement.

All measurements collected as distinct technical replicates by running multiple wells in parallel within the same plate. All statistical analyses were performed using Graphpad Prism.

Titrations via yeast surface display
Ligand-specific titrations of nitazene sensors were carried out through yeast surface display, run similarly to library sorting, but with the following differences. A total of 10µL of OD600=2 were labeled with various dilutions of ligand such that the ligand’s solvent was <1% of the reaction volume and ΔN-HAB1T+ was added to 500nM in the final reaction volume of 50µL. Data from 25,000 labeled cells was collected after resuspending cell pellets in 100µL CBSF, processed in FlowJo v10 software, and fitted to a specific binding curve with Hill slope in GraphPad Prism 10 to find EC50 values.

Thermal inactivation assay
This assay was adapted from Daffern et al37 for utilization within the split luciferase in vitro assay. 72µL of LgBiT-PYR1 variants at 20nM in desalting buffer were aliquoted to 12 PCR tubes with reducing agents. These tubes were placed in a thermocycler for 20 min with a gradient of temperatures spanning 38°C to 60°C after which each tube was brought back down to 4°C and placed on ice. 20µL of each was pipetted into a white, flat bottom plate using a multichannel pipette. 180µL of desalting buffer with SmBiT-HAB1, Hikarazine-108, and ligand were added to these wells to bring them up to a final volume of 200µL and reach working concentrations of 20nM, 2nM, 4.22µM and 10µM for SmBiT-HAB1, LgBiT-PYR1, Hikarazine-108, and each ligand respectively. Wells were pipetted up and down to mix, bubbles were removed, and luminescence was measured for each well using a plate reader in accordance with the split luciferase in vitro assay protocol as previously outlined.

Statistics and reproducibility
For replication, we used 3 or 4 replicates (two technical per day, two replicates on separate days) for all experimental data reported in the paper, except where noted. Deep mutational scanning was performed once. No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The Investigators were not blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This reviewer enjoyed reading this very interesting manuscript, encompassing quite a feat of molecular engineering to answer a timely and interesting research question. The manuscript builds further on existing expertise and published work (e.g. Beltrán et al., Nat Biotechnol, 2022, Li et al., ACS Sensors, 2023, Leonard et al., ACS Chem Biol, 2024) to intelligently modify the plant hormone sensor PYR1 to recognize other ligands than the natural ligand abscisic acid, and to set up a bioassay based on the interaction of this modified receptor with HAB1. The most closely related work is likely the work published by Leonard et al. in ACS Chem Biol, where a biosensor for the synthetic cannabinoid WIN-55,212 was set up. The work presented in the manuscript under consideration seems of sufficient novelty to warrant potential publication in Nature Communications, pending some extra (straightforward but essential) experiments.\nWhile this reviewer is overall positive about this manuscript, and certainly about the novelty and the massive amount of work that went into it, the text was not always easy to follow, and for a reader not acquainted with the technology it will undoubtedly be complicated to fully comprehend how the authors come to some conclusions (it is really necessary to read the supplementary for a near-full comprehension). While this may be imposed by limitations in word count, the authors are encouraged to read the manuscript themselves again, as if they would not be acquainted with the technologies that are applied. Moreover, there seem to be some critical issues (primarily related to the ‘subject’ of the sensor, i.e. nitazenes) that appear to have been neglected. Hence, rather than focusing on the sensor methodology as such, this reviewer primarily focused on the nitazene class of opioids, and to what extent the claims made in the manuscript are sufficiently supported by the data.\nSome experiments are explicitly requested. Overall, these experiments should be easy to perform, as they solely relate to applying the final assay format on some extra compounds and/or in a biological matrix. These experiments are not only essential, they should also lead to a better balanced and even higher quality manuscript, as they will allow a better (perhaps more balanced) view on some claims that are made, and on the overall (potential) utility of the technology for detecting nitazenes in particular.\nMajor Comments\n1. An essential comment relates to one of the starting points of the technology, mentioned on page 4: (referring to the structure of nitazenes) “This leaves the R2 group as a defining feature of nitazene potency.” The importance of the R2 group is also highlighted on page 2 of the Suppl Data. This R2 group corresponds to the nitro group found in all but the ‘des’nitazenes. In essence, this implies that, while the technology aims / claims to be ‘pan’-nitazene, ‘des’nitazenes will (most likely) not be recognized by the sensor. It is essential that this is experimentally addressed, for two reasons: on the one hand, this would demonstrate that indeed the nitro group is essential for recognition by the sensor (i.e. for the bound water that is essential for the PYR1-HAB1 interaction); on the other hand, this would be a key limitation of the technology (as this implies that *not* all nitazenes would be recognized). Of note (and relevant to discuss):\na. while loss of the nitro group has a profound impact on the potency of nitazenes, several ‘des’nitazenes still have a fentanyl-like potency. This aspect has been totally neglected in this manuscript.\nb. also nitazene test strips (which are antibody-based) have as a limitation that ‘des’nitazenes are essentially not recognized (reference 41 of the manuscript; but not discussed)\n2. Somewhat related to the above: the authors also ordered ethyleneoxynitazene and N-desethyl etonitazene from Cayman (and these compounds were used at some point – page 11 of suppl data), but no data are shown on the performance of the final assay for these compounds, which differ from the other nitazenes in that ethyleneoxynitazene has a deviating R1 functional group (i.e. no ‘conventional’ alkoxy tail) and N-desethyl etonitazene has a deviating R3 group.\nSimilarly, only ‘intermediate’ results are shown for N-pyrrolidino etonitazene (page 12 of suppl data) – of note, this compound is not mentioned in the Materials & Methods section on page 1 of Suppl data (can the authors confirm that there is not a mix-up with N-pyrrolidino isotonitazene, for which ‘final’ data are available in Fig 4?).\nIn essence, the performance of the final ‘pan’ nitazene sensor was only shown for a total of 3 nitazenes (isotonitazene, butonitazene and N-pyrrolidino isotonitazene) (Figure 4). Recent literature shows that over 40 nitazenes have been functionally characterized to date. Here, only the performance on 3 nitazenes is shown and, while promising, it seems premature to refer to a ‘pan’ nitazene sensor based on the available data. The manuscript would greatly benefit (and it is a specific request of this reviewer) if more (structurally) divergent nitazenes would be tested – including the above-mentioned nitazenes (for which the authors already have the standards), as well as a nitazene with a dimethylamino (rather than a diethylamino) functional group at R3 (e.g. N,N-dimethylamino etonitazene), and an ethylene (rather than a methylene) bridge containing nitazene (e.g. ethylene etonitazene) – the latter is particularly relevant, as the nitazene test strips (which failed to detect ‘des’nitazenes, as mentioned above), also failed to detect that nitazene (perhaps the technology reported in this paper can do better than the test strips in detecting ‘ethylene’ nitazenes? – this is relevant, as several of these newer nitazenes are emerging / have emerged).\n3. The authors tested their sensor on urine and state that it “could recognize 4-hydroxy nitazene with a LOD of 1 nM in urine”. There are several issues here:\na. First, the data shown in Figure 4B show that the 1 nM LOD holds true for buffer, and not for urine – the signal obtained for urine at 1 nM is essentially the same as the one obtained for urine spiked at a 100-fold lower concentration. Given the absence of matrix controls for non-nitazene opioids, and the fact that there is a matrix effect (urine yielding a different start signal than buffer), this 1 nM LOD for urine is not correct.\nb. Second, both in the abstract and in the manuscript itself, the authors refer to “biological matrices” (plural), whereas only urine was tested. Moreover, only data for a single urine sample is shown, which served as its own control, which is somewhat problematic, as outlined further.\nc. Third, in reality, there is no ‘blank’ correlate of the urine to be tested. In other words: every matrix is different, and it is readily clear that testing of urine yields a different background than buffer.\nd. Fourth, only one urine sample was tested. It has been demonstrated (even in nanoluciferase-based assays) that the composition of urine may have a massive impact on the (background) signal.\ne. Finally, the other opioids have not been tested in urine as a matrix – most likely also an elevated signal (as compared to buffer) would be obtained for those. How does this impact the chance of scoring false positives, knowing that the concentration of 4-hydroxy nitazene in urine may be 100 to 1000 times lower than the concentration of other opioids?\nBased on all the above, it is imperative that the authors experimentally test:\n(i) the impact of different urine compositions on the sensor – there are examples of how to do this in literature (e.g. in a doping context, looking at different urine compositions is standard). Concrete: different urine samples, spiked at different concentrations, need to be tested, and based on that a realistic LOD (rather than a theoretical one, derived in optimal conditions) should be derived. It should be noted that a given urine cannot be used as its own control, as in real life, a positive urine needs to be detected ‘as such’ – i.e. there is no ‘negative’ correlate available. This implies that the actual LOD will most likely be (a lot) higher than the one specified in the manuscript.\n(ii) to what extent other opioids spiked in urine may give rise to signals that would overlap with low-4-OH-nitazene-spiked urine samples (i.e. to what extent false positives may appear owing to a different urinary composition).\n4. The title on page 8 and of Figure 4 is misleading, in a way that the ‘pan’ nitazene sensor was not applied on biological matrices. Also the abstract can be considered misleading in this context and requires rephrasing. It should be made more clear (also in the abstract) that only the nitav2 sensor was tested in (and may be suitable for) a biological matrix (urine), and that application of the PAN sensors are not to be situated in biological matrix testing (because of insufficient sensitivity; on page 6, the authors mention detection of nitazenes in “mixtures” using the PAN sensor – it should be specified what is meant with this). Based on Fig 4 and Fig S13, it is clear that in ideal conditions (buffer), concentrations ranging upwards from 500 nM can be detected. In contrast, low-nM or even sub-nM levels are relevant in biological matrices. The PAN sensors may still be of value for e.g. pill or powder testing, similar to the nitazene test strips. This is an essential remark.\n5. The discussion only handles the sensor’s design aspect, with zero discussion about nitazenes, and the (relevance, possibilities, but also limitations (cfr. critical remarks raised above), as well as real-life application potential) of the sensors generated in this manuscript. E.g. a discussion related to the performance of antibodies or activity-based testing (pan-opioid, rather than pan-nitazene) would be relevant.\nOther comments:\n1. Title: this manuscript specifically focuses on nitazenes and not on synthetic opioids in general. Whilst there are technologies (cell-based assays) that look at opioids (and more specifically opioid activity) in general, the topic and specific aim of this manuscript is to solely focus on one specific class of synthetic opioids, i.e. the 2-benzylbenzimidazoles, colloquially referred to as ‘nitazenes’ – hence, a suggestion to rephrase the 2nd part of the title to “… biosensors for ‘nitazene’ synthetic opioids”\n2. Page 3 & Figure S1: it is not clear what (biological?) data were used to get to the “contribution strength” of nitazene structural features. This requires clarification.\n3. Page 6 – it is essential to mention in the manuscript itself how LOD is defined. Furthermore, when talking about detection in biological matrices, it is relevant to additionally mention concentrations in ng/mL.\n4. Page 6: When talking about the “split Nanoluc” system described in reference 39 (Dixon et al.), it would be good (and actually more accurate) to additionally refer to the term “NanoBiT” (as done elsewhere in the manuscript).\n5. Page 4, line 6 of the 1st paragraph: “A nitro group at R2 and larger aliphatic ethers at R1 increased potency significantly”: whilst this holds true for the nitro group, this is not true for the aliphatic ether. As a matter of fact, the ‘sweet spot’ is to be found in etonitazene (ethoxy side chain) – both shorter and longer alkoxy tails result in a reduced potency (e.g. metonitazene and butonitazene). A more compact tail (e.g. isobutonitazene vs butonitazene and isotonitazene vs protonitazene) is also beneficial. Hence, this statement about “larger aliphatic ethers at R1 increased potency” requires rephrasing.\n6. There seem to be some Tables missing: Tables S9 and S10 (listing primers and plasmids) and S11 (protein sequence mNeonGreen-NanoBiT) are not present in the supplementary data.\n7. In the protocol for the split luciferase assay it is not entirely clear what exactly happens with the urine: the text states “urine was added to this mixture such that it reaches a final concentration of 20% v/v”. To this reviewer it is not clear what “this mixture” refers to. It seems as if urine is added to the 180 µL, but this is unlikely, as further on it is clear that the entire (180 µL) volume is transferred to the wells containing the recombinant proteins. The authors are requested to make this a bit more clear.\nAs a matter of fact, a ‘final protocol’, combining the information of page 6 with information from the actual manuscript would be really helpful and is kindly requested, as this reviewer can deduce that optimal performance of the assay requires a very dedicated protocol, with optimized concentrations of the different parts of the biosensor and of the mNeonGreen-nanoluc fusion ‘calibrator’. Currently, based on the available information, it would be hard to replicate the authors’ findings, even if one would have the constructs or recombinant proteins. Ideally, the information should be presented in a way that allows another researcher to independently replicate the findings.\n8. Suppl Fig S14: rather than specifying in the legend “low – mid – max”, specify the concentrations.\n9. Comments related to the text:\na. Page 2: “as well as nitazene metabolic byproducts to assist medical providers with patient care.” : this is a bit a strange phrasing. Likely the authors wish to say that a rapid detection of nitazene exposure (e.g. by detecting nitazene metabolites in urine) would aid in patient treatment. Please rephrase.\nb. Page 2: The existence of nitazene test strips is particularly relevant in the context of this manuscript (see also previous comments), and can readily be mentioned when talking about the fentanyl strips.\nc. Page 8, first line: should “benzylimidazole” be “benzylbenzimidazole”?\nd. Page 9: “these other synthetic opioids” – delete “other”, as this part of the sentence also refers to codeine and heroin, which are natural and semi-synthetic opioids.\ne. Page 9, next line and second-to-last line: “N-pyrrolidino nitazene” should be “N-pyrrolidino isotonitazene”\nf. Page1 (suppl): “dymanics” should be “dynamics”\ng. Page 5 (suppl): rewrite sentence containing « … and samples run maintained …”\nh. Page 8 (suppl): “indicates the relative contribution”\ni. Page 24 (suppl, Fig. S15, legend): “error bars show 1 s.e.m.” (instead of “are shown”)\nj. Please note that some known nitazene analogues (e.g., 4’-OH nitazene) are less potent than fentanyl – the authors are asked to add this nuance to their manuscript, for example in the following instances:\ni. Abstract, first sentence: “… class of synthetic opioids that often rival or exceed fentanyl…” (add e.g. “often”)\nii. Introduction, first paragraph: “First identified in the US and Canadian drug supply in 2019, different nitazenes are considered more potent than fentanyl” (add e.g. “different”; suggestion to remove “10 to 40 times”)\n10. While it is appreciated that all figures generally include a lot of information, the authors may wish to add some clarifications related to employed abbreviations (e.g., “Relative mean PE” in Fig. 2D). In addition, in some instances (e.g. Fig S7C, text under the graph titles) somewhat larger fonts/increased figure sizes should be used to improve readability and interpretability.", "reviewer_2": "Review of « Computational design of dynamic biosensors for emerging synthetic opioids »\nReviewer understanding\nThe paper shows how mutants of PYR1 that bind to various nitazenes with either good affinity (PYR1^nitav2) or wide spectrum of nitazene species (PAN^nita.1) can be identified by a combination of Deep Mutational Scanning, computational pocket redesign and screening.\nThe authors used replica exchange MD to identify the most frequent conformers of isotonitazene and aligned them to preserve the important nitro group hydrogen bonds. The capacity to bind to a nitazene panel was confirmed using a previously built HMH PYR1 library, and yeast-two-hybrid, identifying initial nitazene binding hits.\nUsing Rosetta fastDesign, the pocket of PYR1 was redesigned in the corresponding conformations (perturbed as allowed by Rosetta dedicated method). A library was built from these designs and experimentally tested with Y2H, identifying 49 responsive sequences for 6 nitazene species. Among them PYR1^nita is selected for further characterization, using Yeast display to perform cell surface titration and later a split luciferase detection system.\nTo optimise it, deep mutational scanning is performed with 2 targets: affinity with a metabolite byproduct of isotonitazene and a less sensitive pan-sensor capable of detecting nitazenes\nin mixture. The 1st one targets 16 residues and relies on FACS+Yeast display+deep sequencing and identifies H159. The second round focused on a restricted library sharing mutations that were beneficial for several nitazene species. The final sequences are further equipped with luminescence detection and tested for cross-reactivity in buffer and urine.\nEvaluation and general comments\nMy background (and associated PhD student reviewer background) is in Computational Protein Design (up to sending sequences to labs that will do the wet lab job). So, the main comments will mostly apply to the computational sections.\nThe paper is well written, properly structured and clear. But it’s now always as detailed as one may hope, especially for computational design. From the title, we expected a paper with a more significant fraction of computational design. This is quite limited here, with the use of replica exchange MD to sample conformers, of Rosetta FastDesign to build a library and the use of LigandMPNN (for some unclear purpose). The main strength of the paper lies in the choice of its targets (nitazenes) which appear to raise significant health issues in the US (at least), the use of PYR1 as a multi-purpose platform for sensor design, a nice mixture of protein engineering (combinatorial libraries, FACS, Yeast display, Y2H, sequencing) and simple computational protein design in the pocket of PYR1 (with a small number of mutable residues) and eventually, an extensive work that ends up in functional sensors, tested also in complex media (urine).\nThe novelty in terms of protocol remains however more moderate. Some of the authors have already published at least one paper that is closely related to this work:\n[A] Beltrán, J., Steiner, P.J., Bedewitz, M. et al. Rapid biosensor development using plant hormone receptors as reprogrammable scaffolds. Nat Biotechnol 40, 1855–1861 (2022). https://doi.org/10.1038/s41587-022-01364-5\nReference [A] modifies PYR1 to develop sensors for a range of small molecules, including structurally diverse natural and synthetic cannabinoids and several organophosphates. On the computational side, it uses Rosetta to predict the stability of PYR1 variants inside a small combinatorial library. The pipeline presented in this paper is very close in substance to the protocol used in reference [A], the main difference being in the use of replica exchange MD to select most frequent conformers of the ligand. The use of Rosetta is quite similar, using FastDesign instead of a simple stability assessment. In itself, the use of replica exchange to sample conformers fairly is not a surprise [B] and the use of FastDesign in a very small pocket is far less computationally challenging that most computational protein design projects.\n[B] Okumura, H., Gallicchio, E. and Levy, R.M., 2010. Conformational populations of ligand‐sized molecules by replica exchange molecular dynamics and temperature reweighting. Journal of computational chemistry, 31(7), pp.1357-1367.\nOverall, the paper reaches a very nice objective, developing two sensors with different detection properties for nitazenes that are tested in a complex matrix (urine), but the importance and novelty in terms of computational protein design remains limited. This contrasts with the title, that brings computational design forward.\nIf the questions detailed in the comments below are answered, my main remaining concern with the paper would be the relative lack of methodological (molecular, experimental or computational) novelty given reference [A] which uses PYR1 as a basis for biosensor development (including cannabinoids), replica exchange for conformer sampling (https://pmc.ncbi.nlm.nih.gov/articles/PMC2848294/) and computational design with Rosetta FastDesign.\nLocal comments and Questions\nThe role of LigandMPNN in the protocol is extremely unclear and a bit confusing. It seems to serve no purpose (it’s not used to design the library) and its only use seems to be motivated by an a posteriori check that the (already experimentally checked as beneficial) mutation A159H is effectively present in a small fraction of LigandMPNN’s output. But the value of this information remains limited. Its main interest would be to compare it with other sequence design tools: does LigandMPNN performs, better, e.g, than Carbonara in this respect (https://www.nature.com/articles/s41467-024-50571-y), or simply than Rosetta FastDesign? If the use of LigandMPNN does not contribute at all to the used pipeline, it’s probably better to remove it to avoid confusion. If this is not removed, please give the sampling temperature.\nThe legend of Fig1. says « This process is iterated over conformer and alignment perturbations and filtered to identify sets of sequences. A library is encoded using position-specific and local residue preferences ». What filtering is performed ? How are the params files built ? Did you use QM/Gaussian to determine partial charges ? It’s unclear how the output of Rosetta FastDesign (a set of sequences) was transformed into the ultramer specifications for NitaZeneLib1 (Table S4). How many sequences were sampled by FastDesign? How/why are they filtered ? What percentage of the set of FastDesign sequences is covered by the theoretical ultramer coverage of the 3 libraries (ABC) of Table S5? Was a computational tool used to build the librairies and cassettes definitions? (such as DeCoDe for example: https://academic.oup.com/bioinformatics/article/36/11/3357/5807608). The precision of the description contrasts here with the extremely detailed description of the SVD usage for conformer docking (which uses a significant amount of space for folklore knowledge. This space could be use to detail how the library is designed instead).\n« We use supervised learning »: why not immediately say « We use regularized linear regressions » ? How were the regularization penalties fixed ?\n« Yielded no sensors when nearly the same panel was used » : why « nearly » ? What changed ?\nDeep sequencing: what type of sequencing technology was used, how long are the reads ? Are they short reads assembled by Quintara or long (eg. ONT/PacBio) reads ?\nLooking into Flash, I understand paired short reads but it’s better if it’s said.\nIn Fig. 1, it would be nice to have the water molecule represented as a dipole (a full H2O) to better visualize the H-bonds.\n« Backbone and side-chains by fast-grd reach » => fast-grid\nFig. S10: not very useful given that LigandMPNN has apparently not been used to design libraries.\nA quick explanation of tables S2 (same for S6) would be welcome. In table S2 for example, with V83VW in the library, how do we get a « M » for N-desethyl etonitazene1?\nTable S3: why mandiprodamid and Win55;212-2 here. I don’t think I have seen these mentioned anywhere in the paper.\nMenitazine -> menitazene ?\nLatin (de novo) could be italicized.\nCode trial: the code executes properly, although one is stopped at some point when the « Ligands.csv » file needs to be filled. Ligands.csv files for the considered system/conformers here would be required for better reproducibility.", "reviewer_3": "Leonard et al. present a computational protocol for redesigning the plant abscisic acid receptor PYR1 to bind diverse nitazenes, an emerging class of synthetic opioids, while preserving its dynamic transduction mechanism. The authors demonstrate that the optimized sensor variant exhibits low nanomolar detection limits in vitro for nitazene and menitazene. Additionally, deep mutational scanning identified sensor variants capable of detecting a range of clinically relevant nitazenes, including their common metabolic byproduct, with limited cross-specificity against unrelated opioids. The authors propose that applying protein design methodologies to versatile receptors such as PYR1 could facilitate the development of generalized biosensors applicable to numerous contexts both in vitro and in vivo.\nOverall, the manuscript is clearly written and presents potentially valuable advancements in sensor engineering, making it a candidate for publication in Nature Communications. However, several major and minor issues need to be addressed prior to publication:\nMajor Issues:\n1. Figures 4C and S16: The authors demonstrate sensor performance in various biological matrices using ratiometric assay data for PYR1 (Fig. S15) and PYR1nitav2 (Fig. 4B) in buffer and urine. However, the sensors PANnita.1 (Fig. 4C) and PANnita (Fig. S16) have been validated only in buffer. It is critical to validate these sensors in urine to fully demonstrate their utility. Additionally, the manuscript does not specify the source of urine used, which should be clearly stated.\n2. Throughout the manuscript, the sensor performance is assessed primarily based on affinity (e.g., EC50) and specificity. However, other critical performance parameters should be investigated, including solubility, kinetics, and thermostability. Given the clinical relevance proposed for these sensors, the authors should experimentally determine the solubility of the PYR1 variants, measure the kinetics of ligand-induced protein dimerization between the PYR1 variant and HAB1, and evaluate the thermostability of the variants at relevant temperatures. The reviewer is concerned that extensive mutations within the ligand-binding pocket may negatively affect these parameters.\n3. Figure 3D: The authors hypothesize the roles of specific mutations in ligand binding by the PYR1 variants. Additionally, earlier in the manuscript (page 3), the authors discuss the significance of a water molecule in the PYR1 biosensor. Would it be feasible to include modeling that incorporates the ligand and the critical water molecule to explain the impact of these mutations on sensor performance? Such modeling would greatly strengthen the mechanistic insights presented.\nMinor Issues:\n1. Figures 3G, S14, S16: The statistical tests employed should be explicitly described in the figure captions.\n2. Reference 51: This study is now published in Science, volume 388, eadr7094 (2025). The authors should update this citation accordingly.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "RESPONSE: Thanks for these comments, which will strengthen the manuscript. We now discuss the more complete body of literature in the introduction and discussion.\nIntroduction (pg 2):\n“While commercial tests exist for fentanyl, including lateral flow assay test strips10-12, there is an immediate need for diagnostic assays that surpass the sensitivity and breadth of current nitazene test strips to detect the range of nitazenes in the drug supply and enable the rapid detection of nitazene metabolites to assist medical providers in treating exposed patients13,41.”\nDiscussion (pg 11-12):\nComputational protein design has advanced towards relevant and pressing societal needs 45. Here\nwe designed and engineered protein biosensors for a range of clinically relevant synthetic\nopioids. These sensors work in urine and can discriminate between different nitazenes and\nstructurally unrelated opioids. Notably, nitazenes cannot currently be differentiated from other\nsynthetic opioids using cell-based assays measuring μ-opioid receptor activation 46. Commercial\ntest strips for nitazenes exist but cannot sense desnitazenes, among other variants. We focused on\ndemonstrating urine efficacy of the PYR1nita sensors because these sensors recognize a R1\nhydroxyl group present in metabolite products of common nitazenes 7,29–31, thus enabling\ndetection of nitazenes post-consumption. The PANnita sensors, which were optimized for binding\nof a wide range of variants, are best deployed to detect nitazenes in environmental samples (e.g.\ndry powders and other forms). Overall, the designed nitazene sensors described in this study are\na strong starting point for additional development into reliable point-of-care diagnostic devices.\nA promising application to pursue is lateral flow assays in which the PYR1-sensing system\nwould replace antibody detection modalities.\nThere are current limitations that must be addressed before real world deployment of this\ntechnology. Full validation of LOD in the appropriate biological matrix is required, including\ndifferent urine samples spiked at different opioid concentrations to evaluate potential for false\npositives. A lower and/or more consistent background signal in urine samples is needed for\nstandardized testing. While we tested the ability of our sensors to detect nitazenes relative to\ndifferent drugs, further testing of more complicated mixtures are necessary. In addition, further\nstudy of the PYR1 binding pocket will help identify sequences that better recognize emerging\ndesigner drugs including the desnitazene and ethylene nitazene variants tested here.\n2. Somewhat related to the above: the authors also ordered ethyleneoxynitazene and\nN-desethyl etonitazene from Cayman (and these compounds were used at some point –\npage 11 of suppl data), but no data are shown on the performance of the final assay for\nthese compounds, which differ from the other nitazenes in that ethyleneoxynitazene has\na deviating R1 functional group (i.e. no ‘conventional’ alkoxy tail) and N-desethyl\netonitazene has a deviating R3 group.\nSimilarly, only ‘intermediate’ results are shown for N-pyrrolidino etonitazene (page\n12 of suppl data) – of note, this compound is not mentioned in the Materials & Methods\nsection on page 1 of Suppl data (can the authors confirm that there is not a mix-up with\nN-pyrrolidino isotonitazene, for which ‘final’ data are available in Fig 4?).\nIn essence, the performance of the final ‘pan’ nitazene sensor was only shown for\na total of 3 nitazenes (isotonitazene, butonitazene and N-pyrrolidino isotonitazene)\n(Figure 4). Recent literature shows that over 40 nitazenes have been functionally\ncharacterized to date. Here, only the performance on 3 nitazenes is shown and, while\npromising, it seems premature to refer to a ‘pan’ nitazene sensor based on the available\ndata. The manuscript would greatly benefit (and it is a specific request of this reviewer) if\nmore (structurally) divergent nitazenes would be tested – including the above-mentioned\nnitazenes (for which the authors already have the standards), as well as a nitazene with a\ndimethylamino (rather than a diethylamino) functional group at R3 (e.g.\nN,N-dimethylamino etonitazene), and an ethylene (rather than a methylene) bridge\ncontaining nitazene (e.g. ethylene etonitazene) – the latter is particularly relevant, as the\nnitazene test strips (which failed to detect ‘des’nitazenes, as mentioned above), also\nfailed to detect that nitazene (perhaps the technology reported in this paper can do better\nthan the test strips in detecting ‘ethylene’ nitazenes? – this is relevant, as several of\nthese newer nitazenes are emerging / have emerged).\nRESPONSE. We concur with most of the comments here. We confirm that N-pyrrolidino\netonitazene was used on page 12 of the supplemental data; it has now been added to the\nMaterials & Methods section on page 1 of the supplemental data.\nTo address those that require additional experiments, we first tested whether our designed\nsensor could recognize ‘des’ nitazenes. We could purchase through Cayman one such pair —\nmetonitazene and metodesnitazene — where the only difference is the presence of a nitro\ngroup at R2. Our results are presented in the SI in Figure S12 and reproduced here.\nFig. S12. Nitro-group dependent binding assessed via yeast surface display. PYR1nita, PYR1nitav2, and PYR1nitav2.1 were titrated against\nmetonitazene and metodesnitazene using 100 nM biotinylated ΔN-HAB1T+. Structures are shown on the right. EC values for metonitazene were\n50\n759 nM (95% c.i. 537-1126 nM), 12.7 nM (95% c.i. 10.0-16.0 nM), and 1133 nM (95% c.i. 536-4409 nM) for PYR1nita, PYR1nitav2, and\nPYR1nitav2.1, respectively. For PYR1nita binding to metodesnitazene was not measurable at the highest concentration tested (25.3 μM). Neither\nPYR1nitav2 nor PYR1nitav2.1 had a saturated binding signal at the highest metodesnitazene concentration tested. Error bars represent 1 s.d. (n=4) and\nin some cases may be smaller than the symbols.\nAs can be appreciated, the loss of a nitro group results in the almost complete loss of binding to\nthe ‘des’ nitazene, which is consistent with the computational design mode. However, there is\nsome binding detected at the highest concentrations, which could be relevant for detection in\nunknown powders. The gate-latch-loop closure mechanism seems to be essential for high\naffinity sensors, but in an unbiased screen we and collaborators have identified sensors for\nmany ligands not containing a hydrogen bond acceptor (doi:10.1101/2025.05.15.654352). We\ndescribe this result in the main results section (page 8)\n“Our underlying hypothesis in the design process was the importance of the nitro group in\nmolecular recognition by the PYR1-based sensors. One prediction resulting from this hypothesis\nis that two nitazenes that differ only by the nitro group should have very different affinities in the\ndesigned and engineered sensors. To examine this, we used yeast display to determine the EC\n50\nfor the PYR1nita, PYR1nitav2, and PYR1nitav2.1 sensors against metonitazene and metodesnitazene.\nMetonitazene and metodesnitazene are identical except the presence or absence of a nitro group\nat R . All three sensors had markedly reduced or no responsiveness to metodesnitazene\n2\ncompared with metonitazene (Figure S12). For example, PYR1nitav2 showed at least a 650-fold\ndifference in EC50 values for metonitazene compared with metodesnitazene. Overall, these\nresults are consistent with the importance of the nitro group in the ligand-induced latch closure,\nconsistent with the design poses. We note that there is small but measurable binding signal\npresent at the higher desnitazene concentrations for the engineered sensors, suggesting that a pan\nsensor may be capable of recognizing even nitazenes lacking a nitro group at R2.”\nTo address the request for additional, structurally divergent nitazenes to be tested, we\nperformed the final luminescence assay using the PANnita and PANnita.1 sensors with the\nrequested nitazenes: three ‘des’nitazenes, metodesnitazene, etodesnitazene, and\nisotodesnitazene, three nitazenes that were tested in initial experiments, N-pyrrolidino\netonitazene, ethyleneoxynitazene, and N-desethyl etonitazene, a dimethylamino nitazene,\nN,N-dimethylamino etonitazene, two ethylene nitazenes, ethylene etonitazene and ethylene\nnitazene, and a nitazene not detected in the nitazene test strip paper, 5-aminoisotonitazene.\nOur results are now shown in SI Figures 18 and 19. Remarkably, our PAN sensors combined to\nrecognize 5 of these 10 new sensors, including two of the three ‘des’ nitazenes and one of the\nthree ethylene nitazenes. Our sensors did not recognize the 5-aminoisotonitazene nor ⅔ of the\nethylene nitazene compounds tested. A more fundamental restructuring of the HAB1 accessory\nprotein would be needed to sense 5-aminoisotonitazene; as well as additional mutations at the\n‘bottom’ of the PYR1 pocket to recognize the two bulkier ethylene nitazenes. While important\ndevelopmental goals, these are outside the scope of the current paper. Representative Figure\nS18 and the main text describing these results (page 9-10) are reproduced below:\nFig. S18. Split luciferase assay data for\nthe PANnita for an extended series of structurally diverse nitazene variants. We utilize 10 µM\nligand (avg. 4,883 ng/mL), 4 nM LgBiT-PYR1, and 4 nM SmBiT-ΔNHAB1T+. Analysis of\nvariance was calculated using an ordinary one-way ANOVA and Dunnett’s multiple comparison\ntest, with a single pooled variance. (**** p<0.0001, ** p<0.01). Error bars are shown for 1 S.E.M.\nfor n=3 replicates.\n“To determine the wider range of efficacy of the PAN sensors, PANnita and PANnita.1 were tested\nagainst ten structurally diverse nitazenes (at 10 μM) not used in sensor engineering. These\nincluded desnitazenes lacking the nitro group, and 5-aminoisotonitazene not yet detected by\nnitazene test strips41. Remarkably, these pan sensors combined to recognize five of these\nnitazenes, including two of the three desnitazenes and one of the three ethylene nitazenes (Fig\nS18-19). Pan sensors failed to recognize 5-aminoisotonitazene which contains an amine at R2\nthat is likely incompatible with the water-mediated latch closure. Additionally, pan sensors did\nnot recognize nitazenes with very bulky R1 groups (ethylene nitazene, ethylene isotonitazene)\nlikely because of steric clashes. Still, the ability to sense diverse nitazenes, including\ndesnitazenes, distinct from those used in the engineering process suggests that these or closely\nrelated engineered sensors may be able to identify the overwhelming majority of nitazenes in the\ndrug supply.”\n3. The authors tested their sensor on urine and state that it “could recognize 4-hydroxy\nnitazene with a LOD of 1 nM in urine”. There are several issues here:\na. First, the data shown in Figure 4B show that the 1 nM LOD holds true for buffer, and not for urine – the signal obtained for urine at 1 nM is essentially the same as the one obtained for urine spiked at a 100-fold lower concentration. Given the absence of matrix controls for non-nitazene opioids, and the fact that there is a matrix effect (urine yielding a different start signal than buffer), this 1 nM LOD for urine is not correct.\nRESPONSE: Here, we calculated LOD as 3 standard deviations above the blank. The blank, at\n0 nM, cannot be plotted, but the data is shown in SOURCE_DATA_FILE. Here, the mean\n450/520 is 1.014 at 1 nM and 0.967 for the 0 nM blank. The standard deviation for the blank is\n0.015, so 1 nM is over 3 standard deviations above the blank mean.\nb. Second, both in the abstract and in the manuscript itself, the authors refer to\n“biological matrices” (plural), whereas only urine was tested. Moreover, only data for a\nsingle urine sample is shown, which served as its own control, which is somewhat\nproblematic, as outlined further.\nRESPONSE. : “Complex biological matrices” was corrected to “a complex biological matrix.” or\n“urine”. Similar edits were made throughout the paper where relevant.\nc. Third, in reality, there is no ‘blank’ correlate of the urine to be tested. In other\nwords: every matrix is different, and it is readily clear that testing of urine yields a\ndifferent background than buffer.\nRESPONSE: We tested a blank correlate of the pooled urine sample. This data is present in the\nSOURCE_DATA_FILE presented as supplementary information to the manuscript.\nd. Fourth, only one urine sample was tested. It has been demonstrated (even in\nnanoluciferase-based assays) that the composition of urine may have a massive impact\non the (background) signal.\nRESPONSE: we used pooled human urine sourced from Innovative Research Inc. (SKU#\nIRHUURE50ML). Urine for laboratory-scale diagnostic assays is often sourced from this\ncompany, and it is appropriate to use pooled human urine for these experiments for fundamental\nresearch. We agree that before further development multiple urine samples would have to be\ntested given the variance in potential background signals; this is out of scope with the paper.\ne. Finally, the other opioids have not been tested in urine as a matrix – most likely\nalso an elevated signal (as compared to buffer) would be obtained for those. How does\nthis impact the chance of scoring false positives, knowing that the concentration of\n4-hydroxy nitazene in urine may be 100 to 1000 times lower than the concentration of\nother opioids?\nBased on all the above, it is imperative that the authors experimentally test:\n(i) the impact of different urine compositions on the sensor – there are\nexamples of how to do this in literature (e.g. in a doping context, looking at\ndifferent urine compositions is standard). Concrete: different urine samples,\nspiked at different concentrations, need to be tested, and based on that a realistic\nLOD (rather than a theoretical one, derived in optimal conditions) should be\nderived. It should be noted that a given urine cannot be used as its own control,\nas in real life, a positive urine needs to be detected ‘as such’ – i.e. there is no\n‘negative’ correlate available. This implies that the actual LOD will most likely be\n(a lot) higher than the one specified in the manuscript.\n(ii) to what extent other opioids spiked in urine may give rise to signals that\nwould overlap with low-4-OH-nitazene-spiked urine samples (i.e. to what extent\nfalse positives may appear owing to a different urinary composition).\nRESPONSE: We agree that development of POC diagnostics at the technical readiness level of\n8-9 would require these experiments, which are thoughtful and carefully laid out. However, this\nis out of scope with the current paper which presents the computational design and engineering\nof these sensors and shows that they are functional in urine. To address these points, we have\nadded the following in the discussion (page 12; here reproduced in full). This discussion\nparagraph describes the limitations of this study, in light of this reviewer’s critiques, more fully.\nComputational protein design has advanced towards relevant and pressing societal needs 45. Here\nwe designed and engineered protein biosensors for a range of clinically relevant synthetic\nopioids. These sensors work in urine and can discriminate between different nitazenes and\nstructurally unrelated opioids. Notably, nitazenes cannot currently be differentiated from other\nsynthetic opioids using cell-based assays measuring μ-opioid receptor activation 46. Commercial\ntest strips for nitazenes exist but cannot sense desnitazenes, among other variants. We focused on\ndemonstrating urine efficacy of the PYR1nita sensors because these sensors recognize a R1\nhydroxyl group present in metabolite products of common nitazenes 7,29–31, thus enabling\ndetection of nitazenes post-consumption. The PANnita sensors, which were optimized for binding\nof a wide range of variants, are best deployed to detect nitazenes in environmental samples (e.g.\ndry powders and other forms). Overall, the designed nitazene sensors described in this study are\na strong starting point for additional development into reliable point-of-care diagnostic devices.\nA promising application to pursue is lateral flow assays in which the PYR1-sensing system\nwould replace antibody detection modalities.\nThere are current limitations that must be addressed before real world deployment of this\ntechnology. Full validation of LOD in the appropriate biological matrix is required, including\ndifferent urine samples spiked at different opioid concentrations to evaluate potential for false\npositives. A lower and/or more consistent background signal in urine samples is needed for\nstandardized testing. While we tested the ability of our sensors to detect nitazenes relative to\ndifferent drugs, further testing of more complicated mixtures are necessary. In addition, further\nstudy of the PYR1 binding pocket will help identify sequences that better recognize emerging\ndesigner drugs including the desnitazene and ethylene nitazene variants tested here.\n4. The title on page 8 and of Figure 4 is misleading, in a way that the ‘pan’ nitazene\nsensor was not applied on biological matrices. Also the abstract can be considered\nmisleading in this context and requires rephrasing. It should be made more clear (also in\nthe abstract) that only the nitav2 sensor was tested in (and may be suitable for) a\nbiological matrix (urine), and that application of the PAN sensors are not to be situated in\nbiological matrix testing (because of insufficient sensitivity; on page 6, the authors\nmention detection of nitazenes in “mixtures” using the PAN sensor – it should be\nspecified what is meant with this). Based on Fig 4 and Fig S13, it is clear that in ideal\nconditions (buffer), concentrations ranging upwards from 500 nM can be detected. In\ncontrast, low-nM or even sub-nM levels are relevant in biological matrices. The PAN\nsensors may still be of value for e.g. pill or powder testing, similar to the nitazene test\nstrips. This is an essential remark.\nRESPONSE: Thank you for recommending this important clarification. When designing the\nPYR1nitav2 and PANnita sensors we explicitly considered what matrix each sensor would be most\nuseful in detecting its target ligands. We tested the PYR1nitav2 sensor in urine because it is\noptimized to detect the nitazene metabolic byproduct 4-OH-Nitazene. In contrast, the PANnita\nsensor is most useful for detecting a wider range of structurally distinctive nitazene compounds\nin mixtures before consumption, including your suggestion of pill or powder testing. We have\nupdated the manuscript (page 9) to reflect this reasoning. We also changed the subheading to\nthe below to more accurately describe the experimental results and rationale.\n“Engineered PYR1 sensors can discriminate nitazenes and metabolites in relevant\ndiagnostic scenarios\nTo test whether PYRnitav2.1 and PANnita are capable of sensing nitazenes under relevant conditions,\nwe repurposed our in vitro luminescence assay. Luminescence detection assays are susceptible to\nerrors from varying experimental conditions including fluctuating activity in diverse biological\nmatrices 42. For these reasons, ease of single sample detection is limited by the need for external\ncalibration. A previously described calibrator luciferase 43 enables single-sample ratiometric\nquantitative readouts (Figure 4A). This assay compares the ratio of ligand dependent\nluminescence to background GFP fluorescence to mitigate experimental variance. We tested this\nnew calibrator assay with the parental PYR1 sensor and a range of synthetic cannabinoid sensors\n18. All tested sensors had nearly identical dynamic range, LOD, and EC values between buffer\n50\nand urine (Figure S16). Using this assay, PYR1nitav2.1 could recognize the metabolic byproduct\n4-hydroxy nitazene with a LOD of 1 nM (0.37 ng/mL) in urine, its most clinically relevant\nbiological matrix, and with no cross-reactivity to the unrelated opioids benzylfentanyl, codeine,\nor heroin (Figure 4B).\nAnother diagnostic use for sensors is discriminating nitazenes in unknown powders. To assess\nthis use case, we tested whether PANnita.1 could recognize diverse nitazenes relative to these other\nopioids. PANnita.1 bound the nitazene variants butonitazene, isotonitazene, and N-pyrrolidino\nisotonitazene while exhibiting minimal perception of benzylfentanyl, codeine, and heroin. “\nSecondarily, we tested PANnita and PANnita.1 in urine to demonstrate their efficacy in a complex\nmatrix as well. These results are presented in Figure S23 and described in the main text (page\n10), and reproduced below.\nFig. S23. Split luciferase assay data for PANnita\nsensors in urine. Assay conditions were 2 nM LgBiT-PYR1, 20 nM SmBiT-ΔNHAB1, 10 µM\nligand (3589 ng/mL benzylfentanyl and 6006 ng/mL N-pyrrolidino isotonitazene), and 20 (v/v)\n% urine. Analysis of variance was calculated using an ordinary one-way ANOVA and Tukey’s\nmultiple comparison test, with a single pooled variance. (**** p<0.0001, * p<0.05, ns not\nstatistically significant). Error bars show 1 S.E.M. for n=3 replicates.\nMain text page 10 “Although the PAN sensors are intended for testing unidentified powders in\nvitro, we also tested whether they retain the ability to sense N-pyrrolidino isotonitazene in a\nbiological matrix. In urine, both sensors maintained the ability to discriminate between\nbenzylfentanyl and N-pyrrolidino isotonitazene (Figure S23).”\n5. The discussion only handles the sensor’s design aspect, with zero discussion about\nnitazenes, and the (relevance, possibilities, but also limitations (cfr. critical remarks\nraised above), as well as real-life application potential) of the sensors generated in this\nmanuscript. E.g. a discussion related to the performance of antibodies or activity-based\ntesting (pan-opioid, rather than pan-nitazene) would be relevant.\nResponse: we concur - this paragraph is now in the discussion on page 12. It is reproduced in\nfull above, and incorporates these points as well as the limitations of the technology which we\nwere not able to address in this current manuscript.\nOther comments:\n1. Title: this manuscript specifically focuses on nitazenes and not on synthetic opioids in\ngeneral. Whilst there are technologies (cell-based assays) that look at opioids (and more\nspecifically opioid activity) in general, the topic and specific aim of this manuscript is to\nsolely focus on one specific class of synthetic opioids, i.e. the 2-benzylbenzimidazoles,\ncolloquially referred to as ‘nitazenes’ – hence, a suggestion to rephrase the 2nd part of\nthe title to “… biosensors for ‘nitazene’ synthetic opioids”\nRESPONSE: We will defer to the editor for a final decision. Our preference is to keep the title as\nis.\n2. Page 3 & Figure S1: it is not clear what (biological?) data were used to get to the\n“contribution strength” of nitazene structural features. This requires clarification.\nRESPONSE: The main text on page 4 describes that the data used in figure S1 comes from a\nprevious study of nitazene substitutions on cell-based activation of the μ-opioid receptor\nby De Vrieze et. al. listed as main text reference 7. The text on page 4 has been rewritten to clarify that\nour analysis of these data is shown in figure 1D:\n”We evaluated several regression models, including ordinary least squares, ridge, lasso, and\nelastic net, to assess the relative importance of nitazene substitutions on potency (Figure S1)\nusing a previous study of nitazene substitutions on cell-based activation of the μ-opioid receptor\n7. This analysis showed that a nitro group at R and several larger aliphatic ethers at R instead\n2 1\nof a hydroxyl group increased potency significantly (Figure 1D), consistent with known potent\nsynthetic opioids like isotonitazene 7,28, which contains an isopropoxy branched ether group at\nR .”\n1\n3. Page 6 – it is essential to mention in the manuscript itself how LOD is defined.\nFurthermore, when talking about detection in biological matrices, it is relevant to\nadditionally mention concentrations in ng/mL.\nRESPONSE: thanks for identifying this oversight. We have added to the end of the Split\nLuciferase in vitro Assay Methods in the SI:\n“Limit of detection (LOD) was calculated as the lowest ligand concentration at which the\nmean luminescence measurement exceeded three standard deviations above that of the\ncorresponding blank luminescence measurement.”\nWe have also modified all limits of detection (when in a urine matrix) to units of ng/mL (Figure 4,\nFigure S18).\n4. Page 6: When talking about the “split Nanoluc” system described in reference 39\n(Dixon et al.), it would be good (and actually more accurate) to additionally refer to the\nterm “NanoBiT” (as done elsewhere in the manuscript).\nRESPONSE: Done.\n5. Page 4, line 6 of the 1st paragraph: “A nitro group at R2 and larger aliphatic ethers at\nR1 increased potency significantly”: whilst this holds true for the nitro group, this is not\ntrue for the aliphatic ether. As a matter of fact, the ‘sweet spot’ is to be found in\netonitazene (ethoxy side chain) – both shorter and longer alkoxy tails result in a reduced\npotency (e.g. metonitazene and butonitazene). A more compact tail (e.g. isobutonitazene\nvs butonitazene and isotonitazene vs protonitazene) is also beneficial. Hence, this\nstatement about “larger aliphatic ethers at R1 increased potency” requires rephrasing.\nRESPONSE: The page 4 text describing figure 1D was updated to clarify that certain larger\naliphatic ethers at position R increased potency compared to other functional groups at R such\n1 1\nas the hydroxyl group.\n6. There seem to be some Tables missing: Tables S9 and S10 (listing primers and\nplasmids) and S11 (protein sequence mNeonGreen-NanoBiT) are not present in the\nsupplementary data.\nRESPONSE: These tables are in PYR1 nitazene detection Data S1, which is presented as a\nseparate data file (the file is 117 pages long, which adds length to all but the most dedicated\nreaders).\n7. In the protocol for the split luciferase assay it is not entirely clear what exactly\nhappens with the urine: the text states “urine was added to this mixture such that it\nreaches a final concentration of 20% v/v”. To this reviewer it is not clear what “this\nmixture” refers to. It seems as if urine is added to the 180 µL, but this is unlikely, as\nfurther on it is clear that the entire (180 µL) volume is transferred to the wells containing\nthe recombinant proteins. The authors are requested to make this a bit more clear.\nAs a matter of fact, a ‘final protocol’, combining the information of page 6 with information\nfrom the actual manuscript would be really helpful and is kindly requested, as this\nreviewer can deduce that optimal performance of the assay requires a very dedicated\nprotocol, with optimized concentrations of the different parts of the biosensor and of the\nmNeonGreen-nanoluc fusion ‘calibrator’. Currently, based on the available information, it\nwould be hard to replicate the authors’ findings, even if one would have the constructs or\nrecombinant proteins. Ideally, the information should be presented in a way that allows\nanother researcher to independently replicate the findings.\nRESPONSE: A revised ‘final protocol’ was added that includes some clarifications and\norganization of workflows. We include relevant ranges for protein concentrations while specifics\nare noted in the figure captions themselves. This is now present on pages 6-8 of the SI.\n8. Suppl Fig S14: rather than specifying in the legend “low – mid – max”, specify the\nconcentrations.\nRESPONSE: Done.\n9. Comments related to the text:\na. Page 2: “as well as nitazene metabolic byproducts to assist medical providers\nwith patient care.” : this is a bit a strange phrasing. Likely the authors wish to say that a\nrapid detection of nitazene exposure (e.g. by detecting nitazene metabolites in urine)\nwould aid in patient treatment. Please rephrase.\nRESPONSE: we modified the phrasing on page 2 to read “as well as for rapid detection of\nnitazene metabolites to assist medical providers in treating exposed patients.”\nb. Page 2: The existence of nitazene test strips is particularly relevant in the\ncontext of this manuscript (see also previous comments), and can readily be mentioned\nwhen talking about the fentanyl strips.\nRESPONSE: We have now included the existence of nitazene test strips in the introduction as\nwell as the discussion.\nIntroduction (page 2):\n“While commercial tests exist for fentanyl, including lateral flow assay test strips10-12, there is an\nimmediate need for diagnostic assays that surpass the sensitivity and breadth of current\nnitazene test strips to detect the range of nitazenes in the drug supply and enable the rapid\ndetection of nitazene metabolites to assist medical providers in treating exposed patients13,41.”\nDiscussion (page 12): reproduced above.\nc. Page 8, first line: should “benzylimidazole” be “benzylbenzimidazole”?\nRESPONSE: Yes. “Benzylimidizole” was corrected to “benzylbenzimidizole”\n12\nd. Page 9: “these other synthetic opioids” – delete “other”, as this part of the\nsentence also refers to codeine and heroin, which are natural and semi-synthetic opioids.\nRESPONSE: “Other” was deleted.\ne. Page 9, next line and second-to-last line: “N-pyrrolidino nitazene” should be\n“N-pyrrolidino isotonitazene”\nRESPONSE: Agreed. “N-pyrrolidino nitazene” has been changed to “N-pyrrolidino\nisotonitazene.”\nf. Page1 (suppl): “dymanics” should be “dynamics”\nRESPONSE: Done\ng. Page 5 (suppl): rewrite sentence containing « … and samples run maintained\n…”\nRESPONSE. “And samples run maintained at 1% v/v% of solvent.” was replaced by “the\nfinal volume of solvent was maintained at 1% v/v% in all samples.”\nh. Page 8 (suppl): “indicates the relative contribution”\nRESPONSE: Done.\ni. Page 24 (suppl, Fig. S15, legend): “error bars show 1 s.e.m.” (instead of “are\nshown”)\nRESPONSE: Done.\nj. Please note that some known nitazene analogues (e.g., 4’-OH nitazene) are less\npotent than fentanyl – the authors are asked to add this nuance to their manuscript, for\nexample in the following instances:\ni. Abstract, first sentence: “… class of synthetic opioids that often rival or\nexceed fentanyl…” (add e.g. “often”)\nRESPONSE: Done.\nii. Introduction, first paragraph: “First identified in the US and Canadian\ndrug supply in 2019, different nitazenes are considered more potent than fentanyl”\n(add e.g. “different”; suggestion to remove “10 to 40 times”)\nRESPONSE: Instead of “different nitazenes are considered more potent than fentanyl,” we state\n“nitazenes are up to 40 times more potent than fentanyl.”\n13\n10. While it is appreciated that all figures generally include a lot of information, the\nauthors may wish to add some clarifications related to employed abbreviations (e.g.,\n“Relative mean PE” in Fig. 2D). In addition, in some instances (e.g. Fig S7C, text under\nthe graph titles) somewhat larger fonts/increased figure sizes should be used to improve\nreadability and interpretability.\nRESPONSE: In Fig. 2D, “Relative mean PE” was clarified and now reads “Relative Mean PE =\nmean phycoerythrin (PE) fluorescence normalized to the maximum mean PE value.”\nAdditionally, we moved the information below the graph titles in Fig. S7C to the figure caption:\n“We utilize 100 nM LgBiT-PYR1 and 10 nM SmBiT-ΔNHAB1T+.” The subheadings were\nconsistent across all 4 panels previously and we decided they were not necessary on the figure\nitself.\nReviewer #1 (Remarks on code availability):\nnot applicable\nReviewer #2 (Remarks to the Author):\nReview of « Computational design of dynamic biosensors for emerging synthetic opioids\n»\nReviewer understanding\nThe paper shows how mutants of PYR1 that bind to various nitazenes with either good\naffinity (PYR1^nitav2) or wide spectrum of nitazene species (PAN^nita.1) can be\nidentified by a combination of Deep Mutational Scanning, computational pocket redesign\nand screening.\nThe authors used replica exchange MD to identify the most frequent conformers of\nisotonitazene and aligned them to preserve the important nitro group hydrogen bonds.\nThe capacity to bind to a nitazene panel was confirmed using a previously built HMH\nPYR1 library, and yeast-two-hybrid, identifying initial nitazene binding hits.\nUsing Rosetta fastDesign, the pocket of PYR1 was redesigned in the corresponding\nconformations (perturbed as allowed by Rosetta dedicated method). A library was built\nfrom these designs and experimentally tested with Y2H, identifying 49 responsive\nsequences for 6 nitazene species. Among them PYR1^nita is selected for further\ncharacterization, using Yeast display to perform cell surface titration and later a split\nluciferase detection system.\nTo optimize it, deep mutational scanning is performed with 2 targets: affinity with a\nmetabolite byproduct of isotonitazene and a less sensitive pan-sensor capable of\n14\ndetecting nitazenes in mixture. The 1st one targets 16 residues and relies on FACS+Yeast\ndisplay+deep sequencing and identifies H159. The second round focused on a restricted\nlibrary sharing mutations that were beneficial for several nitazene species. The final\nsequences are further equipped with luminescence detection and tested for\ncross-reactivity in buffer and urine.\nEvaluation and general comments\nMy background (and associated PhD student reviewer background) is in Computational\nProtein Design (up to sending sequences to labs that will do the wet lab job). So, the\nmain comments will mostly apply to the computational sections.\nThe paper is well written, properly structured and clear. But it’s not always as detailed as\none may hope, especially for computational design. From the title, we expected a paper\nwith a more significant fraction of computational design. This is quite limited here, with\nthe use of replica exchange MD to sample conformers, of Rosetta FastDesign to build a\nlibrary and the use of LigandMPNN (for some unclear purpose). The main strength of the\npaper lies in the choice of its targets (nitazenes) which appear to raise significant health\nissues in the US (at least), the use of PYR1 as a multi-purpose platform for sensor\ndesign, a nice mixture of protein engineering (combinatorial libraries, FACS, Yeast\ndisplay, Y2H, sequencing) and simple computational protein design in the pocket of\nPYR1 (with a small number of mutable residues) and eventually, an extensive work that\nends up in functional sensors, tested also in complex media (urine).\nThe novelty in terms of protocol remains however more moderate. Some of the authors\nhave already published at least one paper that is closely related to this work:\n[A] Beltrán, J., Steiner, P.J., Bedewitz, M. et al. Rapid biosensor development using plant\nhormone receptors as reprogrammable scaffolds. Nat Biotechnol 40, 1855–1861\n(2022).https://doi.org/10.1038/s41587-022-01364-5\nReference [A] modifies PYR1 to develop sensors for a range of small molecules,\nincluding structurally diverse natural and synthetic cannabinoids and several\norganophosphates. On the computational side, it uses Rosetta to predict the stability of\nPYR1 variants inside a small combinatorial library. The pipeline presented in this paper is\nvery close in substance to the protocol used in reference [A], the main difference being\nin the use of replica exchange MD to select most frequent conformers of the ligand. The\nuse of Rosetta is quite similar, using FastDesign instead of a simple stability\nassessment. In itself, the use of replica exchange to sample conformers fairly is not a\nsurprise [B] and the use of FastDesign in a very small pocket is far less computationally\nchallenging that most computational protein design projects.\n15\n[B] Okumura, H., Gallicchio, E. and Levy, R.M., 2010. Conformational populations of\nligand‐sized molecules by replica exchange molecular dynamics and temperature\nreweighting. Journal of computational chemistry, 31(7), pp.1357-1367.\nOverall, the paper reaches a very nice objective, developing two sensors with different\ndetection properties for nitazenes that are tested in a complex matrix (urine), but the\nimportance and novelty in terms of computational protein design remains limited. This\ncontrasts with the title, that brings computational design forward.\nIf the questions detailed in the comments below are answered, my main remaining\nconcern with the paper would be the relative lack of methodological (molecular,\nexperimental or computational) novelty given reference [A] which uses PYR1 as a basis\nfor biosensor development (including cannabinoids), replica exchange for conformer\nsampling (https://pmc.ncbi.nlm.nih.gov/articles/PMC2848294/) and computational design\nwith Rosetta FastDesign.\nRESPONSE: Thank you for the comments, which were very helpful in pointing out where our\nmethod requires additional explanation. While we did use Rosetta FastDesign to inform some\naspects of mutational library design, the more important computational design method was in\nligand conformer docking along the known hydrogen bond acceptor, sampling a diversity of\nslight variations of these alignments, and modeling binding to real Y2H hit sequences. Where\nnoted below, we have clarified where the method is innovative, and where we can improve by\nusing more modern sequence generation and filtering algorithms.\nWe agree that temperature replica exchange is well known as a way to accelerate sampling of\nthe conformational ensemble, and can be used to calculate the free energy required to\npreorganize a ligand into a conformation found in a protein-ligand complex, as described by\nOkamura et al. which the reviewer cites. They key novelty in this effort is that we find that with\ncurrent force fields, including explicit solvent (unlike the cited reference, which used implicit\nsolvent) we can reliably use MD simulation to identify a small number (<5) of low free energy\nconformers (< 1 kcal/mol from the minimum free energy conformer observed in simulation).\nThese conformations are sufficiently close to the experimentally populated conformers that a\nRosetta design of the biosensor to bind a ligand is generally successful using this small number\nof configurations, whereas other ways of generating a conformational ensemble, even with a\nmuch larger ensemble, were unsuccessful. Additionally, with replica exchange MD, these\nsimulations can be performed in a matter of hours, making them feasible for high-throughput\ndesign. Okumura et. al., it has been added as a reference to the TREMD method section.\nTo better clarify the method, we have improved the description of the method in the main text\n(page 4):\n“Repurposing an existing pocket to sense nitazenes requires a way to down-select the\nastronomical number of possible ligand-protein conformations. We separated the design problem\ninto two components: (1.) defining the structural pose (the rigid body orientation of the ligand\n16\nrelative to the PYR1 backbone); and (2.) identifying a likely PYR1 sequence for that pose. To\ndefine the structural pose, we developed a new computational design protocol to identify likely\nligand-protein conformations and ligand orientations in the receptor binding pocket that maintain\nthe transduction mechanism (Figure 1B,E). Sequences for these poses are then designed using\nthe physically based algorithm Rosetta FastDesign 32 followed by physics-based filtering metrics.\n“\nLocal comments and Questions\nThe role of LigandMPNN in the protocol is extremely unclear and a bit confusing. It\nseems to serve no purpose (it’s not used to design the library) and its only use seems to\nbe motivated by an a posteriori check that the (already experimentally checked as\nbeneficial) mutation A159H is effectively present in a small fraction of LigandMPNN’s\noutput. But the value of this information remains limited. Its main interest would be to\ncompare it with other sequence design tools: does LigandMPNN performs, better, e.g,\nthan Carbonara in this respect (https://www.nature.com/articles/s41467-024-50571-y), or\nsimply than Rosetta FastDesign? If the use of LigandMPNN does not contribute at all to\nthe used pipeline, it’s probably better to remove it to avoid confusion. If this is not\nremoved, please give the sampling temperature.\nRESPONSE: Thanks for identifying that oversight; we have added the sampling temperature\n(0.2) in the SI methods (SI page 3).\nLigandMPNN redesign was performed for two reasons: 1. Rosetta FastDesign is well known to\nmiss productive polar interactions between ligands and residues. We wanted to test\nLigandMPNN to determine whether the A159H mutation would be identified using this DL\npacking method. 2. We use LigandMPNN as a demonstration of the separability of the design\nprocess between identification of the ligand-PYR1 backbone pose and the design of likely PYR1\nsequences compatible with the pose. We have included the following description describing\nthese reasons in the main text (page 8)\n“The largest enriched mutation for both ligands was A159H. Our original design models did not\ninclude a residue which delocalizes the partial charge on the nitro group; we hypothesize that\nH159 satisfies this requirement (Figure 3D). Indeed, redesigning the PYR1 sequence using the\ndeep learning algorithm LigandMPNN 41 identifies H159 in 8% of designs (Figure S10). This\nfinding by an orthogonal design method is consistent with the proposed binding pose;\nadditionally, the incorporation of a newer, deep learning packing algorithm for sequence design\nhints at future avenues to build on our ligand docking method to improve the computational\ndesign process. Overall, the mutational profile is largely consistent with the designed binding\nmode.”\nperturbations and filtered to identify sets of sequences. A library is encoded using\nposition-specific and local residue preferences ». What filtering is performed ? How are\nthe params files built ? Did you use QM/Gaussian to determine partial charges ? It’s\nunclear how the output of Rosetta FastDesign (a set of sequences) was transformed into\nthe ultramer specifications for NitaZeneLib1 (Table S4). How many sequences were\nsampled by FastDesign? How/why are they filtered ? What percentage of the set of\nFastDesign sequences is covered by the theoretical ultramer coverage of the 3 libraries\n(ABC) of Table S5? Was a computational tool used to build the libraries and cassettes\ndefinitions? (such as DeCoDe for example:\nhttps://academic.oup.com/bioinformatics/article/36/11/3357/5807608). The precision of\nthe description contrasts here with the extremely detailed description of the SVD usage\nfor conformer docking (which uses a significant amount of space for folklore knowledge.\nThis space could be used to detail how the library is designed instead).\nRESULTS: We have updated the computational design protocol in the SI to include more\ndetailed answers to these questions. Briefly, our “dock to sequence” alignment process available\nin our Structural Replacement Biosensor Design repository samples a specified number of\nrandom ligand alignments to identify ligand conformer alignments that do not clash in space with\nthe protein backbone and sidechains of an input amino acid sequence known or suspected to\nbind that ligand. Conformer energy cutoffs and the number of alignments tested per conformer\nand total per menitazene and isotonitazene ligands are documented in Table S3. These\nalignments are then filtered by whether they pass the clash check described in the methods\nsection, with passing alignments then used as inputs for FastDesign (Table S3). NitazeneLib1\nwas designed using both the FastDesign output and rational design guided by the passing\nalignment structures and our knowledge of PYR1 binding. For example, mutations to large\naliphatic or aromatic residues at positions 83 & 87 lead to loop closure and constitutive\n(non-ligand activated) binding.\n« We use supervised learning »: why not immediately say « We use regularized linear\nregressions » ? How were the regularization penalties fixed ?\nRESULTS: We have revised the text to explicitly state that we applied linear regression models\nwith and without regularization (ordinary least squares, ridge, lasso, and elastic net) rather than\nthe broader “supervised learning” description. Ridge regression used the default ⍺=1.0. For\nlasso and elastic net, we set ⍺=0.1 to avoid excessive coefficient shrinkage and preserve\ninterpretable feature contributions.\n(For main text page 4): “We evaluated several regression models, including ordinary least\nsquares, Ridge, Lasso, and elastic net, to assess the relative importance of nitazene substitutions on potency (Figure S1).”\n18\n(For SI1 legend): Fig. S1. Summary of supervised learning of nitazene structural features.\n(a) One-hot encoding representation of R groups used as features in the regression models. (b)\nModel comparison showing standardized 𝑅2 scores (orange) and mean squared error (MSE,\nblue) for various regression models, with higher 𝑅2 and lower MSE indicating better model\nperformance. (c-f) Feature importance for the top four performing models: (c) ordinary least\nsquares linear regression, (d) elastic net regression, (e) ridge regression, and (f) lasso\nregression. Ridge regression used the default L2 penalty (⍺=1.0). Lasso and elastic net used\n⍺=0.1 to moderate L1-based shrinkage and retain interpretable coefficients. The absolute\nmagnitude of the learned regression coefficients indicate the relative contribution of each R\ngroup feature to the predicted 𝑙𝑜𝑔𝐸𝐶50 value (described here as the coefficient magnitude).\n« Yielded no sensors when nearly the same panel was used » : why « nearly » ? What\nchanged ?\nRESPONSE: In the original submission, the DSM screen excluded metonitazene and\nprotonitazene as we had not yet purchased those chemicals. We have now gone back and\nscreened them against the DSM library to complete the data for table S1. As expected, there\nwere no sensors from the DSM library. We have updated the main text to remove the adverb\n‘nearly’ and have updated Table S1 with the new experimental data.\nDeep sequencing: what type of sequencing technology was used, how long are the reads\n? Are they short reads assembled by Quintara or long (eg. ONT/PacBio) reads ?\nLooking into Flash, I understand paired short reads but it’s better if it’s said.\nRESPONSE: We have now added ‘short-read’ to deep sequencing in Page 7 of the main text. In\nthe methods of the original submission, we describe the sequencing used. Briefly, Azenta\nAmplicon-EZ was used for evaluation of the computationally designed library, and Quintara\nAmpExpress services were used for the deep mutational scanning experiments presented in\nFigure 3 and in the SI.\nIn Fig. 1, it would be nice to have the water molecule represented as a dipole (a full H2O)\nto better visualize the H-bonds.\nRESPONSE: We have updated Figure 1B to show the water molecule as a dipole with\nhydrogens visible.\n« Backbone and side-chains by fast-grd reach » => fast-grid\nRESPONSE: fixed.\nFig. S10: not very useful given that LigandMPNN has apparently not been used to design\nlibraries.\nabove.\nA quick explanation of tables S2 (same for S6) would be welcome. In table S2 for\nexample, with V83VW in the library, how do we get a « M » for N-desethyl etonitazene1?\nRESPONSE: Thanks for the suggestion. We have now improved the Table legends in S2 and\nS6. S2 is reprinted below\n“Table S2. HMH library input and nitazene screen output. The top row shows per-position\nresidue for the wild-type PYR1 sequences. The second row sets the allowable per-position\nresidues in the computationally designed HMH library. All remaining rows show either a\nmutation from WT or a blank cell (if WT residue) at the indicated position. Some non\nlibrary-encoded mutations are present; these can occur due to unintended sequence changes\nintroduced during library assembly, for example from PCR errors by the polymerase.”\nTable S3: why mandipropamid and WIN-55;212-2 here. I don’t think I have seen these\nmentioned anywhere in the paper.\nRESPONSE: We have removed mandipropamid and WIN-55,212-2 from Table S3. In the\noriginal submission, we included docking results from mandipropamid and WIN-55,212-2\n(ligands where the engineered sensor has an experimentally determined structure) as\nbenchmarks to inform how many alignments per conformer were needed to properly sample\nligand alignments for nitazenes. However, since we do not use these crystal structures or\notherwise refer to the analysis for these ligands, we decided to remove mandipropamid and\nWIN-55,212-2 from the manuscript.\nMenitazine -> menitazene ?\nRESPONSE: fixed.\nLatin (de novo) could be italicized.\nRESPONSE: We have corrected this in the discussion (page 12).\nReviewer #2 (Remarks on code availability): Code trial: the code executes properly,\nalthough one is stopped at some point when the « Ligands.csv » file needs to be filled.\nLigands.csv files for the considered system/conformers here would be required for better\nreproducibility. The code seems to perform as expected but it would be easier to test if\nsuitable csv files were provided.\nRESPONSE: We have updated the README file in the repository to provide clearer instructions\nfor how to fill in the spreadsheet Ligands.csv that is generated by create_table.py and have\nincluded an example file for reference.\nLeonard et al. present a computational protocol for redesigning the plant abscisic\nacid receptor PYR1 to bind diverse nitazenes, an emerging class of synthetic opioids,\nwhile preserving its dynamic transduction mechanism. The authors demonstrate that the\noptimized sensor variant exhibits low nanomolar detection limits in vitro for nitazene and\nmenitazene. Additionally, deep mutational scanning identified sensor variants capable of\ndetecting a range of clinically relevant nitazenes, including their common metabolic\nbyproduct, with limited cross-specificity against unrelated opioids. The authors\npropose that applying protein design methodologies to versatile receptors such as PYR1\ncould facilitate the development of generalized biosensors applicable to numerous contexts\nboth in vitro and in vivo.\nOverall, the manuscript is clearly written and presents potentially valuable\nadvancements in sensor engineering, making it a candidate for publication in Nature\nCommunications.\nRESPONSE: Thank you for the positive assessment.\nHowever, several major and minor issues need to be addressed prior to\npublication:\nMajor Issues:\n1. Figures 4C and S16: The authors demonstrate sensor performance in various\nbiological matrices using ratiometric assay data for PYR1 (Fig. S15) and PYR1nitav2 (Fig.\n4B) in buffer and urine. However, the sensors PANnita.1 (Fig. 4C) and PANnita (Fig. S16)\nhave been validated only in buffer. It is critical to validate these sensors in urine to fully\ndemonstrate their utility. Additionally, the manuscript does not specify the source of\nurine used, which should be clearly stated.\nRESPONSE: With respect to the urine source, we now list in the SI “Pooled human urine was\nsourced from Innovative Research Inc. (SKU# IRHUURE50ML) and was sterile filtered before\nuse to remove any aggregates.” (SI Page 6). Urine for laboratory-scale diagnostic assays is\noften sourced from this company, and it is appropriate to use pooled human urine for these\nexperiments.\nWith regards to PANnita sensors demonstrated in urine, we have now run experiments in urine\nwith N-pyrrolidino isotonitazene and benzylfentanyl; these results are now reported in SI Figure\n23 (reprinted in response to reviewer #1 above). These results show that both PANnita and\nPANnita.1 retain specificity relative to other opioids in a urine matrix.\nNote that these sensors are intended for use in discriminating nitazenes in mixed white\npowders, not as metabolites found in biological matrices like urine. The fault lies with the main\n21\ntext in the original manuscript, which was written ambiguously. To clarify this intended purpose,\nwe now state in the main text “”Another diagnostic use for sensors is discriminating nitazenes in\nunknown powders. To assess this use case, we next tested whether PANnita.1 could recognize\ndiverse nitazenes relative to these other opioids. PANnita.1 bound the nitazene variants\nbutonitazene, isotonitazene, and N-pyrrolidino isotonitazene while exhibiting minimal perception\nof benzylfentanyl, codeine, and heroin.” (Page 9).\n2. Throughout the manuscript, the sensor performance is assessed primarily based on\naffinity (e.g., EC50) and specificity. However, other critical performance parameters\nshould be investigated, including solubility, kinetics, and thermostability. Given the\nclinical relevance proposed for these sensors, the authors should experimentally\ndetermine the solubility of the PYR1 variants, measure the kinetics of ligand-induced\nprotein dimerization between the PYR1 variant and HAB1, and evaluate the\nthermostability of the variants at relevant temperatures. The reviewer is concerned that\nextensive mutations within the ligand-binding pocket may negatively affect these\nparameters.\nRESPONSE: These are excellent and reasonable requests, and we have performed several\nexperiments to address these. With respect to solubility, what is typically described in the\nliterature is recombinant expression yields, as these integrate differences in folding kinetics,\nsolubility, and thermodynamic stability when comparing between variants. Expression yields for\nall LgBiT-PYR1-MBP tagged constructs are 19 mg/L & they are soluble at least at a\nconcentration of 3 mg/mL in the standard desalting buffer. We supply example purified protein\nby gel electrophoresis/simply Blue staining. These results are now shown in SI Figure 20 and\nreproduced below.\nchromatography and visualized by SDS-PAGE gel and SimplyBlueTM staining. The molecular\nweight of each protein is 82 kDa. The weaker band above 37 kDa likely corresponds to a\ntruncation product of His-tagged maltose binding protein, which is approx. 44 kDa. The\nconcentration of each protein is assessed by the Edelhoch method 16 using a predicted extinction\ncoefficient from the primary sequence of the full protein. Yields for each variant were converted\nto a per liter of culture basis.\nFor thermostability, we developed an thermal inactivation assay similar to Daffern…Whitehead\nJMolBiol 2024. The differences involve using light output from the split luciferase system rather\nthan a phosphatase inactivation assay reported in the Daffern et al paper. We included a\npositive control of the most stable engineered PYR1 sensor (HOT5mandi), which we utilized as a\ncontrol to compare our designs with. Our results are now shown in SI Figure 21.\nFig. S21. Thermal inactivation assay for nitazene variants and HOT5mandi. The split\nluciferase assay is used to measure normalized luminescence intensity as a function of incubation\ntemperature. In a method conceptually similar to Daffern et al. 19, LgBiT-PYR1 variants are\nincubated for 20 minutes at varying temperatures and then brought to 4℃ and placed on ice\nbefore incubation with SmBiT-HAB1 and their respective ligands. Final concentrations are 2 nM\nmandipropamid, PYR1nitav2.1 is 4-hydroxy nitazene, and both PANnita and PANnita.1 are used in\nconjugation with N-pyrrolidino isotonitazene. All measurements were collected in triplicates\n(n=3) and error bars show 1 S.E.M, although in some cases, error bars are smaller than the\nsymbols. We fit a curve to the data using nonlinear least-squares regression with a Boltzmann\nsigmoidal equation. The shaded region represents a 95% confidence interval of this fit. We used\nthis midpoint of this fit to determine the temperature at which a 20 minute incubation resulted in\n20\n50% loss in activity (𝑇 ). The normalized RLU/sec values plateaus above zero likely because of\n50\n20\nresidual reconstituted split luciferase activity. The HOT5mandi 𝑇 found in this assay is very\n50\nsimilar to that found by the Daffern et al. 19 assay.\nFor the kinetics of activation of the sensor, the individual kinetics of the ternary complex\nformation are difficult to assess because PYR1 first binds ligand and then the PYR1-ligand\ncomplex is recognized by HAB1 to form the ternary complex (for exact details, see the\nmathematical formulations in Steiner, Swift, …. Whitehead Biochemistry 2022). Furthermore, the\nternary complex places SmBiT and LgBiT in local proximity, and they must bind to reconstitute\nactive luciferase. For the purposes of a diagnostic assay, what matters is the observed kinetic\nrate (k ) of activation and the approximate time to reach 90% of equilibrium, 𝝉 . We\nobs 90%\ndemonstrate this k for the PYR1nitav2 sensor compared with HOT5mandi using a luciferase assay,\nobs\nwhere the PYR1 sensor is limiting. Our results are now shown in SI Figure 22 and reproduced\nbelow.\nFig. S22. A split luciferase assay for assessment of equilibration time for engineered PYR1\nsensors. The concentrations used in this assay are 2 nM LgBiT-PYR1, 20 nM SmBiT-HAB1,\nand 10 µM ligand. The ligand used with HOT5mandi is mandipropamid, PYR1nitav2.1is 4-hydroxy\nnitazene, and both PANnitaand PANnita.1 use N-pyrrolidino isotonitazene. All measurements were\ncollected in triplicates (n=3). We fit a curve to the data using nonlinear least-squares regression\nwith a one-phase association equation shown in the panel inset. The shaded region represents a\n24\n95% confidence interval of this fit. We used this fit to determine the observed rate constant, k ,\nobs\nand the approximate time to reach 90% of steady-state luminescence, τ (2.3/k ).\n90% obs\nCombined, the results show minimal changes to expression yields, stability against thermal\nchallenge, or kinetics of activation. We include a new paragraph in the main text results (page\n10):\n“In directed evolution, it is well established that optimizing for affinity and breadth can impact\nother properties like protein expression yields, stability against thermal challenge, and kinetic\nactivation 44. We assessed these properties in the pan-nitazene sensors and PYR1nitav2.1 compared\nwith HOT5mandi 37, the most stable engineered PYR1-based sensor developed thus far. All\nproteins had identical expression yields of 19 mg/L under standard E. coli expression conditions\nwhen expressed as LgBiT and MBP-tagged fusion proteins (Fig S20). To test stability against\nthermal challenge, we developed a luminescence-based thermal inactivation assay. All sensors\nshowed a 44.8oC or higher temperature at which a 20 minute incubation resulted in 50% loss in\n20 20\nactivity (𝑇 ). The 𝑇 for PYR1nitav2.1 was 48.0oC (95% c.i. 47.3-48.6oC), which was not\n50 50\n20\nsignificantly different (p-value = 0.13) than that for HOT5mandi (𝑇 =48.5, 95% c.i.\n50\n47.3-48.6oC)37 (Fig S21). We also tested the time the luminescence assay takes to equilibrate. At\n10 μM ligand and at room temperature, all sensors reached 90% of steady-state luminescence\nvalues within 17 minutes. Notably, PYR1nitav2.1 reached equilibrium faster than HOT5mandi (11 vs.\n14 minutes, p-value 0.0458) (Fig S22). Although the PAN sensors are intended for testing\nunidentified powders in vitro, we also tested whether they retain the ability to sense\nN-pyrrolidino isotonitazene in a biological matrix. In urine, both sensors maintained the ability\nto discriminate between benzylfentanyl and N-pyrrolidino isotonitazene (Figure S23). Thus, the\ntested sensors increased breadth and affinity for nitazenes without appreciably changing their\nunderlying kinetic activation, expression yields, or stability against thermal challenge. “\n3. Figure 3D: The authors hypothesize the roles of specific mutations in ligand binding by\nthe PYR1 variants. Additionally, earlier in the manuscript (page 3), the authors discuss\nthe significance of a water molecule in the PYR1 biosensor. Would it be feasible to\ninclude modeling that incorporates the ligand and the critical water molecule to explain\nthe impact of these mutations on sensor performance? Such modeling would greatly\nstrengthen the mechanistic insights presented.\nRESPONSE: In addition to panel 3D, which includes the water molecule as a dipole shown as\nsticks, we also updated panel 1B to more clearly show the water molecule as a dipole shown\nwith atoms represented as spheres.\nMinor Issues:\n1. Figures 3G, S14, S16: The statistical tests employed should be explicitly described in\nthe figure captions.\noriginal submission. These figure captions now include these tests, which were “Analysis of\nvariance was calculated using an ordinary 2-way ANOVA and Dunnett’s multiple comparison\ntest, with a single pooled variance.” We have included this information as well for new figures\nadded to the SI.\n2. Reference 51: This study is now published in Science, volume 388, eadr7094 (2025).\nThe authors should update this citation accordingly.\nRESPONSE: The citation for reference 51 has been updated.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #4 (Remarks on code availability):\nnot applicable\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports.\nThis is part of the Nature Communications initiative to facilitate training in peer review\nand to provide appropriate recognition for Early Career Researchers who co-review\nmanuscripts.\nReviewer #1 (Remarks to the Author):\nThe authors have addressed the majority of the comments of these reviewers – and the\nlimitations of the sensor (poor to no recognition of ‘des’nitazenes and ethylene nitazenes) are\nnow more apparent. The authors have not performed the requested experiment on urine\nsamples, and this remains a key limitation of this manuscript. Nevertheless, pending correction\nof some mistakes (see below), these reviewers consider this manuscript of sufficient novelty\nand promise to warrant publication in Nature Communications.\nThere are a couple of remaining issues that need to be addressed:\n1. on page 9 the authors refer to recognition of “one of the three ethylene nitazenes”. With this\nthey refer to recognition of ethyleneoxynitazene. However, this is not an ethylene bridge-\ncontaining nitazene (the ‘ethylene’ in the name of ethylenoxynitazene refers to the closed-ring\nderivative of etonitazene (the ethoxy group of etonitazene being contained in a ring structure in\nethyleneoxynitazene)). As it is written now, it is misleading, as if a ‘real’ ethylene-linker nitazene\nwould be recognized by one of the sensors. This is not the case and remains a limitation. It is\nessential that this is rectified.\n“One of the three ethylene nitazenes” was changed to “ethyleneoxynitazene”.\n2. At the top of page 10 the authors specify that the pan sensors do not recognize nitazenes\nwith bulky R1 groups, with referral to ethylene nitazenes. This is incorrect. The ethylene\nnitazenes specified here have a different linker (ethylene rather than methylene in ‘conventional’\nnitazenes); the R1 group corresponds to the alkoxy group. This needs to be rectified. Moreover,\nit is overtly incorrect that a bulky R1 group would lead to non-recognition by the sensors, as\nexemplified by the recognition of butonitazene.\nThis sentence was changed to instead say “Additionally, pan sensors did not recognize some\nnitazenes (ethylene nitazene, ethylene isotonitazene) likely because of steric clashes.”\n3. On page 11, in the legend of Fig 4, there are two different confidence intervals specified for\nthe EC50 in buffer.\nWe have corrected this oversight.\n4. Line 370: “These sensors work in urine”: this is misleading. A proof of concept that *one* of\nthe sensors works in urine was provided, pending the same urine sample being available as a\ncontrol. It is essential that the authors avoid ‘overselling’ their paper, as is clearly done here. As\nthe authors have opted to *not* perform a simple experiment in which multiple urine samples\nwere tested, they have to tune down the statements about the detection in urine. A proof of\nconcept was provided (it was explicitly the authors’ choice not to test more samples, or not to\nshow the results of possibly tested other urine samples), and the manuscript needs to reflect\nthis.\nThis has been corrected to now state “These sensors work in urine and can discriminate\nbetween different nitazenes and structurally unrelated opioids; one of these is functional in\npooled urine sample.”\n5. Line 385: in addition to false positives, also add “false negatives” (because it may not be able\nto distinguish a signal from background).\nWe have updated this to include “false negatives”.\n6. Discussion line 417-421 is not really correct. The ethylene bridge-containing nitazenes are\nnot larger than e.g. butonitazene, which is recognized. Most likely the orientation, rather than\nthe size as such, poses an issue (the hypothesis that this could lead to steric clashes can\nremain). This should be corrected (i.e. currently there is 3 times a mentioning of “larger”,\nwhereas the data do not support the hypothesis that size per se is the key problem).\nText: line 387: “is necessary” (instead of “are necessary”)\nLanguage suggesting that the PAN sensors did not bind “larger” ligands was eliminated to place\nmore emphasis on the ethylene nitazenes not binding due to steric clashes. “Are necessary was\nchanged to “is necessary”.\nReviewer #1 (Remarks on code availability):\n(not applicable for these reviewers)\nReviewer #2 (Remarks to the Author):\nI have read the rebuttal and I'd like to thanks the authors for the answers to the very precise\nquestions I asked. Most of these are now solved, but I'm happy to still add two new questions:\nPlease give the noise level of the LigandMPNN model used for inference (this is different from\nthe sampling temperature, please report both). I personally don't agree with the authors that\nPMPNN/LigandMPNN have supplanted physical-based scoring functions. While PMPNN is\nuseful for stabilization/enhancing expression, it's not necessarily best for designing\ninterfaces. I haven't used ligandMPNN for design yet, but it's likely suffering from related\nweaknesses (also caused by its autoregressive decoding, probably with worse side-effect in\nLigandMPNN than PMPNN). I still find the use of LigandMPNN somewhat artificial/unnecessary in this paper but\nthe extra-explanations by the authors makes the pill easier to swallow :-)\nWe have added the noise level of 0.1 Angstrom Gaussian noise in the SI methods.\nSimilarly, if you tried elastic net, please give the 2 penalties (the L1 and L2) for it.\nFor the elastic net model, we used the default l1_ratio = 0.5, which corresponds to the penalty\nbeing equally partitioned between the L1 and L2 components. Typically, only the l1_ratio is\nreported, but we can derive the explicit L1 and L2 contributions (both are 0.05 given ⍺ = 0.1).\nWe have updated the S1 figure caption to include: \"The default l1_ratio = 0.5 was used for the\nelastic net model, which yields L1 and L2 penalty weights of 0.05 each.\"\nI understand from the authors' rebuttal that the process that goes from Rosetta Fastdesign'd\nsequences to Ntazenlib1 is not fully automated and includes human intervention and expertise.\nPlease explicitly mention this.\nWe have now mentioned this in the main text as follows (blue text): “After manual curation, the\nlibrary was ordered as an oligo pool….”\nOverall, this round of reviewing has led to a clearer and more precise paper, but our main\ncomments seems still valid: the methodological computational protein design contributions look\nminor while the extensive experimental work and positive results obtained by the authors are\nprobably the main contributions here.\nThank you for your positive assessment.\nReviewer #2 (Remarks on code availability):\nI tested the code on the first round of review and was stopped by missing explanation on\nrequired csv files and lack of examples.\nI noticed the authors have updated the README and provided an example csv file.\nI haven't retested after this change and assume the authors have done their job properly.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed all of the concerns from Reviewer #3.\nReviewer #4 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nReviewer #5 (Remarks to the Author):\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts."}]

Decision letter