Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Authors: Jiahe Du
Kaixiong Zhou
Xinyu Hong
Zhaozhuo Xu
Jinbo Xu
Xiao Huang
Venue: ICML 2025 poster (2025)
Keywords: Enzyme design
Diffusion model
Primary area: applications->chemistry_physics_and_earth_sciences

Abstract: Generating novel enzymes for target molecules in zero-shot scenarios is a fundamental challenge in biomaterial synthesis and chemical production. Without known enzymes for a target molecule, training generative models becomes difficult due to the lack of direct supervision. To address this, we propose a retrieval-augmented generation method that uses existing enzyme-substrate data to guide enzyme design. Our method retrieves enzymes with substrates that share structural similarities with the target molecule, leveraging functional similarities in catalytic activity. Since none of the retrieved enzymes directly catalyze the target molecule, we use a conditioned discrete diffusion model to generate new enzymes based on the retrieved examples. An enzyme-substrate relationship classifier guides the generation process to ensure optimal protein sequence distributions. We evaluate our model on enzyme design tasks with diverse real-world substrates and show that it outperforms existing protein generation methods in catalytic capability, foldability, and docking accuracy. Additionally, we define the zero-shot substrate-specified enzyme generation task and introduce a dataset with evaluation benchmarks.

Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Jiahe Du 1 Kaixiong Zhou?
Xinyu Hong
Zhaozhuo Xu*
Jinbo Xu
Xiao Huang'
Abstract
& Nielsen, 2013; Paraschiv al., 2022; Nam et al., 2024). Taking the artificial compound of n.3-rrichloropropanc (TCP) as an example, it is extensively utilized as chemical intermediate and solvent despite its toxicity and resistance to biodegradation (Agency for Toxic Substances and Disease Registry, 2021; Cheremisinoff & Rosenfeld, 2011), which leads to persistent groundwater contaminant. Researchers are actively engaged in discovering engineering enzymes capable of biodegrading TCP (Bogale et al., 2020; Samin & Janssen, 2012). Since there is no existing natural enzymes for TCP, the synthesis paradigm only relies on the expertise of replicating molecular structure of other natural heme- proteins (Zambrano et al., 2022) and lacks the efficiency to discover novel and effective enzymes for the specific substrate.
Generating novel enzymes for target molecules in zero-shot scenarios is a fundamental challenge in biomaterial synthesis and chemical production. Without known enzymes for target molecule, training generative models becomes difficult due to the lack of direct supervision. To address this, we propose a -atrretell- generation method that uses existing enzyme-subee substrate data to guide enzyme design. Our method retrieves enzymes with substrates that share structural sim- ilarities with the target molecule, leveraging func- tional similarities in catalytic activity. Since none of the retrieved enzymes directly catalyze the tar- get molecule, we use a conditioned discrete dif- fusion model to generate new enzymes based on the retrieved examples. An enzyme-substras re- lationship classifier guides the generation process to ensure optimal protein sequence distributions. We evaluate our model on enzyme design tasks with diverse real-world substrates and show that it outperforms existing protein generation methods in catalytic capability, foldability, and docking accuracy. Additionally, we define the zero-shot substrate- seestrate-ppccifeed enzyme generation task and introduce dataset with evaluation benchmarks.
The recent emergence of deep learning based protein gener- ation shows great potential for enzyme design due to their unprecedented accuracy in structure and function prediction. A portion of these methods falls under the category of uncon- ditional generation, such as ProGen2 (Nijkamp et al., 2023) and ProtGPT2 (Ferruz et al., 2022), possessing the capabil- ity generate protein sequences that fold into stable and functional structures and resemble real proteins, without re- lying on the predefined substrate. The other subset of these methods is characterized by conditional generation, con- sisting of ligand-conditioned sequence design and structure generation. The ligand-conditinn sequence design mod- els (Gruver et al., 2023; Martinkus et al., 2023) are proposed to synthesize therapeutic antibodies treating well the anti- gen ligands. On the other hand, the ligand-onnditooned structure generation methods, like LigandMPNN (Dauparas et al., 2025) and RFdiffusionAA (Krishna et al., 2024), gen- erate proteins structurally docking to a given target. By ensuring spatial compatibility, these methods generate effec- tive proteins associated with enhanced biological function and stability in complex cellular environments.
1. Introduction
Substrate-speccffic enzyme generation aims to design new proteins that catalyze reactions to specific new molecules and benefits a wide array of scientific fields, including biomaterials synthesis and chemical production innova- tion (Meghwanshi et al., 2020; Robinson, 2015; Jegannathan
'Dpartmenn of Computing, The Hong Kong Polytechnic Uni- versity, Hung Hom, Hong Kong SAR, China 'Department of Elec- trical and Computer Engineering, North Carolina State Univer- sity, Raleigh, NC. USA Beijing MoleculeMind Co.,Lid., Beijing, China 'Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, USA. Correspondence to: Xiao Huang <xiao. huang @ cdu.u.cdu.hk..
While the unconditional approaches fail match require- ments, existing work of conditional generation cannot be re- purposed directly to generate desired enzymes that catalyze specific substrates represented as small molecules. Partic- ularly, the ligand condition of these models is amino acid sequences of antigens but our substrates are small molecules. The enzyme substrates exhibit a vast chemical space with high structural diversity, including variations in functional
Proceedings of the 4nnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
groups, stereochemistry, and electronic properties, which make it challenging to learn the interactions with enzymes. In addition, the catalytic capability of an enzyme is not solely determined by how it structurally interacts with the substrate molecule, so these models are not yet capable of synthesizing functional enzymes. The label-conditioned generative method, i.e. ZymCTRL (Munsamy et al., 2022), takes an Enzyme Commission (EC) number and outputs a corresponding enzyme sequence. It requires prior knowl- edge about the expected enzyme's EC, which relays human expertise heavily.
We propose substrate-indexed retrieval method to search the functionally-ssmilar enzymes as prompt- ing signals. The key merit is the enzymes associated with the structurally close substrates exhibit similar catalyzing properties. Considering query substrate, we compare the structural closeness with other stored molecules and retrieve the pairwise enzyme data of top-ranking molecules. This approach is distinct from traditional protein retrieval since it retrieves based on substrate similarity instead of protein similarity, tra- ditional protein retrieval does.
In this study, we formally define the task of zero-shot substrate-specifier enzyme generation and identify two pri- mary challenges associated with it. The first challenge is the complete absence of positive samples. For instance, without any effective enzymes for TCP as training data, it is difficult to train or fine-tune model to generate enzymes that catalyze TCP. A potential solution to this challenge is the Retrieval-Augmennt
We employ a discrete diffusion model to generate new enzymes based on the retrieved ones and utilize a substrate-enzyme catalyzing classifier as guidance for the generative process. The classifier transforms the complicated catalytic relationship into continuous and differentiable function for optimizing the generator. With different substrates, it guides the generation to- ward different directions distinct from the whole record data distribution.
Generation (RAG), Specifically, RAG-based methods sample protein sequences as prompts and subsequently instruct models to generate sequences that are structurally and/or functionally similar (Ma al., 2024; Alamdari et al., 2023; Lewis et al., 2020). However, the problem of retrieving proteins without relying emplar enzyme needs to be addressed, as the only input is the target substrate. The second challenge is the generation of proteins that diverge from training data. The generated TCP enzyme must differ from recorded enzymes, as none in the record can effectively catalyze TCP molecules. This divergence requirement extends enzymes for other new substrates. Since the mainstream training methods focus recovering recorded data, new approach is oequired-one that trains models to generate enzymes that are both di- vergent from existing records and capable of catalyzing different target molecules. Furthermore, there currently no comprehensive evaluation framework for zero-shot en- zyme generations. While Johnson et al. (2025) and Song et al. (2024a) introduced certain metrics for computationally designed enzymes, there is lack of refined datasets for zero-shot settings and multiple-perspectte the aubstrate-specifiee enzyme generation task has not yet been fully formulated.
ex-
Experimental results in designing enzymes for particu- lar substrates demonstrate that our model can generate novel enzymes of superior quality. Compared with rule-, unconditioned-, sequence-, and structure-based methods, our framework generates proteins showing high enzymatic capability and high foldability.
2. Substrate-Specified Enzyme Generation Task
We define the substrate-specified enzyme generation task by specifying the model's input and output, along with the training and testing data and evaluation methods.
Problem definition. The task involves generating a pro- tein that serves as the enzyme for the target molecule. Let m denote the Simplified Molecular- tem (SMILES) (Weininger, 1988) string representation of the molecule and let x denote the protein sequence. We have x = (a), 42, ag. a)) E A' where a; is an amino acid and A is the vocabulary of amino acids together with related tokens including gap ("-"). Henceforth, the amino acid a can be represented as a one-hot vector, and we do not differentiate between the protein sequence and the se- quence of one-hot vectors, which means x E A is a matrix with shape 1 x |A]. Let P denote the domain of all protein sequences and let M. denote the domain of all molecular SMILES strings. The function G : M + IP means the task of substrate-speccffed enzyme generation, which can be de- fined as x = G(m; 9) where is the set of G's parameters. If G is machine-learning model, the training process is
evaluations, as
Line-Entry Sys-
To address these two challenges, we propose Substrate- specified enzyme generator (SENZ). Our main contributions follows:
We formally define the task of substrate-speciffed en- zyme generation and present curated dataset. This dataset consists of the substrate-enzym pairs that are extracted from the known enzymes. We further par- tition it into training and test subsets without overlap in terms of proteins and small molecules to secure the zero-shot setting.
2

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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
given by:
where m is optional. If the evaluation focuses solely on the generated protein, is not required. Given different func- tions of feval, the ideal training process should be framed as a multi-objective optimization problem. However, in the substrate-speciffed enzyme generation task, we prioritize catalytic capability above all and thus focus primarily on the corresponding seval-
Xp, 0- = arg min (mG(m Xp, my).
XD and mp are the enzyme and molecule in training set D, respectively, 0* is the optimal parameters, and & is the loss function. The input can include various types of data: Enzyme Commission (EC) label of string SEC Ni. NN..N. three-dimensional conformation structure of the target substrate Cm or an existing enzyme Cx. The generative function can be extended as below:
3. Substrate-Speciffed Enzyme Generator
We present Substrate-speccffed enzyme generator (SENZ), novel approach designed to retrieve enzymes based on a new target substrate and subsequently generate new en- zymes from the retrieved ones with the help of guidance training method.
x= = (mm, SEC+ Cm. Cx),
(2)
where sEc, Cm. Cx are all optional input parameters for G(.), but m is the required input.
Data construction. For this task, we construct dataset of substrate-nnzyme pairwise relationships extracted from public raw data, as illustrated in Fig. 1(a). Each record in raw data comprises the SMILES representations of a chemical reaction with a specific enzyme. To identify the specific substrate in each chemical reaction, we select the least common reactant among all reactants in the database, treating it as the specific substrate for the enzymes involved in that reaction. This approach is grounded in the established observation of substrate specificity (Jackson et al., 2010). relation
3.1. Substrate-Indext Enzyme Retrieval Module
Since there are no existing enzymes for a target substrate in the zero-shot generation setting. is crucial to retrieve the related data record without relying on the ground truth enzyme sequence
anchor. In order retrieve set of related proteins p(m) for the target molecule m, a relational database is constructed and a substrate-similaris based retrieval rule is designed. The superiority is demonstrated by only querying with molecule m, while traditional protein retrieval methods require an anchor sequence to search for similar sequences.
Consequently, we define the cubbstrateenzyyma (m, x) as protein x being the enzyme of molecule m, and the training dataset DD can be defined as follows:
Substrate-nnzyme relational database. We adopt training set D in Eq. (3) as relational database of substrate-enzym pairs (m, x). D contains substrate-indexed enzymes, in which substrates are non-unique indices for corresponding protein sequences as shown in Fig. 1(b) green part.
D = (m, x)}, x is the enzyme of m.
(3)
The substrate-enzyme pair (m, x) is the element of the dataset as in Eq. (3).
Zero-shot data split. All substrate-enzym pairs (m, x) are split into D for training, Dvalid for validation and Drest for testing. To avoid of data leakage, two rules are designed for any two (m);)) and (m2,x), in different subsets: 1. Molecules from different subsets should not be the same, i.e. mı F m2; 2. Any two protein sequences from different subsets, i.e., X1 and X2, should not have an overlap of more than 30% (with an identity exceeding 30%) The split forms a zero-shot setting. Take the target molecule TCP as an example. TCP is in Drest and the model G is generating enzyme for TCP. G has never trained with TCP because TCP is not in D. G has never seen proteins similar to TCP's ground truth enzymes because all of them are only in Drest. and all proteins in D have at least 70% different from them. Therefore generating enzyme for TCP and any molecules in Diest is zero-shot.
Retrieval by substrate-similarity. Based on relational database D, we then retrieve enzymes whose substrates ex- hibit high similarity to the target molecule, with the expec- tation that the generated enzyme will incorporate beneficial features from the retrieved ones. This approach is based on the observation that enzymes catalyzing highly similar substrates may also share some similarities (Goldman et al., 2022). We denote all molecules in D as set Dm, Querying D with molecule m gets a protein set Tp(m) as follows:
(x( x) E D),
m E mEDm- (5)
p(m)_
U pm)) where m, E Di, m & Dm (6)
We consider two cases to retrieve the related enzymes. On one hand, if m is stored in the relational database as shown in Eq. (5), all protein indexed with m, i.e., m's enzymes, are obtained by table-checking: otherwise, if m is not stored (m & Dm) as in Eq. (6), which is the case in the zero- shot enzyme generation, p(m) consists of a number of d enzymes selected from D according to following nules.
Evaluation. Regardless of the input data, models should be evaluated using consistent metrics. An evaluation model feval scores the generated protein x as follows:
y = Seval (x, m),
(4)
3

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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
(a)
Raw data
Dataset D
Database D
Reoond
desoriytio....
Relation extraction
MST.DK....
Kecone 2
devorpamon...
MRBINHK...
Kecara 3 desermpiee.....
Kocord B NN, deseripton...
MCFDKI E...
MAAASRTX...
(b)
Index
Value
(mm)
Repeat for I steps
MSLADK...
Target m
MSA
Generator
MYRPSK...
[dso][hea [ha
[he (spallhh
MRBINHE...
ANKK...
MMB355 --------K Ki..
[h
MCFDKIE...
MR3 1IHK............................
[ha
MAAASRIK.
Training Pipeline
MSA
Distribution P(x (N?)
Kt --------- EL M- SLL.....
X
Generator G
Reconstruction.
M+
ST. #D#...
X
-K..
M
F...
Loss
MAB-- L.NE.....
Discriminator
Guided
1.0
Loss
Figure 1: (a) Database extraction. Extracting substrate-enzyme relation from records and constructing a relational database indexing with substrates. (b) Sample pipeline. Retrieve enzymes from the database based on their substrates' similarity to the target molecule. Align them in MSA for the generator and insert a fully masked sequence on top. Predict masks of the top sequence every iteration until the full sequence is unmasked. (c) Training pipeline. A partly masked ground truth enzyme sequence is inserted on top of the retrieved sequences' MSA, and the generator outputs the distribution of amino acids on masked positions. The reconstruction loss measures the distribution difference between the generated and ground truth sequence of one timestep before. The guided loss the gap between the score of the generated sequence given by a discriminator and the maximum score of 1.
First, all the substrates m; in D are compared with target molecule m to determine the Tanimoto similarity of their one-hot Morgan fingerprint. The top-d m are selected in descending order based on the similarity to m, represented as mi, ... m.. Finally a number of d enzymes are gathered from p(m:),.. ,..., p(ma)
the top of (m) new row to formulate data point X, at time step t < T in the diffusion model:
X,
X₁ X (m)
E A (d+1)xl and Σ 1 [o,= kt. (7)
p(m.) to form the retrieval result pp(m).
where a; = # means position of x, is masked. 1 (a;=#) = if a, = otherwise 0. There are k f masks in X₁. A is the number of incressing masked positions from x, to X so k T I. Therefore X7 #' is totally noised (masked) sequence, and xo is the finally generated sequence.
3.2. MSsebased Generator Module
With retrieved enzyme sequences, we transform them into Multiple Sequence Alignment (MSA) format as input and employ discrete diffusion model generator derive new enzyme. MSAs matrices of protein sequences aligned to uniform length through strategic gap insertions, facilitating the comparative analysis of homologous posi- tions across related sequences.
Discrete denoising at the generative process. We adopt matrix p E [0, [|x]|A] to represent the probability of select- ing each vocabulary on each position in length / sequence, and p(x:- tribution of x;-1 from unmasking À positions of x,. Appar- ently X,-1 p(xx-1/xx) when x, is fixed. Our generator G is defined as follows:
i|x.) to represent the conditional probability dis-
Discrete noising for enzyme generator. Our generator G, depicted in Fig. i(b), is an order-agnostic autoregressive dif- fusion model (Hoogeboom al., 2022) with MSA trans- former (Rao et al., 2021) backbone. G generates protein quence by gradually denoising from fully noised sequence. To begin with, a number of d enzymes within jp(m) are aligned into MSA matrix by ClustalW algorithm (Thomp- son et al., 1994): ((m) ClustalW (P(ml)) E Adxx A
z = G(X,, m) G(x,, m).
(8)
p(x-1[xx,) = softmax(z).
(9)
The Eq. (8)'s second equation holds because X, [x;; ((m) and (m) is decided by m. z is the model output log lig-liielhhood. Eq. (9) outputs distribution p(x;-1/x.) for sampling by time step. The fully masked sequence Xy can
partly noised sequence x, = (01, 02 01) inserted on

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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
be denoised step by step the final result Xg: XT-1 can be sampled from p(xr-1|xr) and so on Xo can be sampled from (( (x). Those are the denoising steps.
3.3. Guided Training Method
We employ guidance from a catalyzing discriminator to train generator G. The discriminator D evaluates whether molecule and a protein x are a substrate-enzyyn pair with a score y = (xx, m). D is pre-trained on training set 'D and remains frozen during the generator's training.
Molecule and protein representation fusion: To inject the target substrate m into the generative learning process, we adopt a learnable molecule encoder (Ahmad et al., 2023). Specifically, Graph Attention Network (GAT) (Veličković et al., 2018) is used encode the molecule's 's graph struc- ture to embedding hm, which has the same shape token embedding in generative function G. Iim is appended at the end of each row in the MSA representation as an addi- tional token, as illustrated in red in Fig. I(b). This design respects the relative size relationship in terms of atom num- bers between an amino acid and the substrate in the real world. Since the MSA transformer in G performs row-wise attention and tied column-wist attention on the MSA matrix, the integration allows m to influence the generation in G together with the retrieved MSA (m)
Gradient guidance from discriminator. To generate enzyme x containing catalytic capability to a molecule m, the frozen D guides the training of G by constructing guided loss Lg follow:
x" = p(x;-1 = g(2),
(12)
y¹ = D(x ,m),
(13)
Cg = 1 - y²,
(14)
000 = OD(x', ; .) m00
=0D(x", mm/ mm//xx' 00x" 0xx /00G
Vx D(x", m) -0((x /00c. (15)
Training to mimic distribution. With ground truth substrate-enzymer pair (m, in training set, G output dis- tribution pxx;-1|x{") = coftmax((((x)) m)) from x is trained to consist with training set distribution (xx"=|[x")) Ground truth protein xt' is the enzyme of molecule m.
Z is the model output log- ligelikel in Eq. (8), g(-) is Gumbel-sofimax function (Jang et al., 2017) and Og is the parameters of G. The gradients derived from the dis- criminator can decoupled into three steps: soft protein sequence generation, loss construction, and gradient deriva- tion. First, Eq. (12) transforms the output of G into dis- tribution p(x{-1|x associated with differentiable noises. The p(x+-1(x")) can be regarded as a "soft" protein se- quence, i.e., x", at which each token is a continuous amino acid probability instead of one-hot vector. Second, let y" denote the predicted catalyzing score for x* as shown in Eq. (13). We thus construct the guided loss Lg as the dif- ference between y" and maximum score 1. By minimizing loss Z. generator G should be supervised to synthesize soft enzyme sequence x" with score close to 1. Third, when updating generator via 0G + 0g two items needed to be computed according to Eq. (15): Vx- D(x", m) means the gradient direction of xf, to which the soft distribution changes can lead an effective enzyme functioning higher catalyzing probability for target molecule m; dp(x;-1xx")) //0G is the Jacobian matrix describing if the soft sequence changes, how should the parameters within model G correspondingly updates in order to synthe- size proteins adhere to the desired distribution of molecule m's enzymes.
x is partly noised (masked) xm at time step with kt masks. Denoting P = (x."" and Q p(x)-1xx") KI. Ki--diveggence is used to measure the difference:
= DRI(PIIQ) (P. HP)).
(10)
f. = ((P,Q)=-E, E11 log Q1))
= (E(xx_,,sfttmxx(4477 m))).
(11)
DKL is performed on the vocabulary probability dimen- sion of p. The second equation in Eq. (11) is derived from P = p( |x() = x and Q = p(xx-1|x(") ) = softmax(G(x[11 m))) Since (P)) is constant given xm (P,, Q) can measure the difference of our model's distri- bution to the training set and is adopted as reconstruction loss Ly..
aLg/
Variable sequence length; Although x has a fixed length 1, the represented protein sequence may have a different length. MSA inserts gap tokens ("-") into the origin protein sequence of amino acids to align them. x and xX" are masked from sequence in MSA X(m), so there are also many "_" in x Based on Eq. (11), G is learned to out- put the training set sequence distribution p(x"" |x("). As result, the probability of "_" can be high in some posi- tions in G's output p(x;-1 [x("), just as the training target p(x!" (x."). Then "_" will probably be sampled at some position in Xt-1- Gaps "-" in the fully sampled sequence X will be removed and thus Xy is shorter than I.
Therefore, both Cg and L, function by providing a changing direction for the output distribution p(x;-1|x!") ). except they are for different purposes: the former one pursues effective enzyme for m while the later regularize the generative enzymes to be close to training set p[x|"_|xx"") The final loss L is the sum of reconstruction loss L, from Eq. (11) and the guidance loss Lg from Eq. (14), expressed as == L, + L.g. which are used to update the generator.
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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
4. Experiment
Table 2 shows the 10 of different methods' gen- erated enzymes with targets, from which we observe our model generated proteins have the highest catalytic capabil- ity among all. The predicted log1o (Keat)) of Ground Truth enzymes are much higher than those of random protein sequences, suggesting the effectiveness of the evaluation metric. Our model generated enzymes have the highest aver- age turnover number among all the compared methods in the designing tasks. The result shows our model is able to gen- erate enzymes with high turnover numbers when evaluated in silico. Table 2 also suggests that generated enzymes can outperform Ground Truth natural enzymes, which suggests the natural enzymes are possibly not the most efficient.
4.1. Dataset for Substrate-Specified Enzyme Generation
Table 1: Enzyme distribution in the split of Enzyme- Substrate Relation Dataset
#onzyme/mol
dataset
Reatry
#mol
#eazyme
25%
50%
75%
#EC
training
26757
2294
8179
2
4
10
868
819
validation
4279
366
2617
1
3
6
501
381
testing
3946
389
2432
2
7
316
553
total
34982
3049
13228
868
1746
We provide substrate-enzynee relationship dataset tracted from RHEA' database to better evaluate model per- formance on the uubstrate-speciffe enzyme generation task. Statistics of the dataset are shown in Table. 1. The two rules in Sec. 2 are strictly followed to avoid data overlap.
4.3. Protein Properties Evaluation
Research question: Can SENZ generate proteins with good quality as well as catalytic capability? We evaluate the generated sequences for all 389 substrates in the test set with six feval to validate our model's generated sequence in different protein properties. 10 enzymes are generated for each substrate.
4.2. Catalytic Activity Evaluation
Research question: Can SENZ generate proteins with catalytic capability for specified target molecules? This section compares our model with eight baselines and the ground truth enzymes to evaluate the generated proteins' catalytic capability. Ten sequences are generated in each design task.
Metric. Protein property predictors feval are adopted in the evaluation, including: a) the predicted local distance dif- ference test (pLDDT) of ESMFold (Lin et al., 2023), which is the confidence score of protein structure prediction in [1, 100]; b) identity with the nearest different known sequence got by BLASTp² in SwissProt database?; c) the number of clusters with identity over 30%; d) the length of repeat amino acids (Johnson et al., 2025); and e) the successful rate, which quantifies the proportion of successfully generated sequences relative to the total desired number of sequences. Wasserstein distance is used following (Martinkus et al., 2023) in b), and d), and the absolute difference is calculated in c), aiming to describe the distribution difference between the test set and generated enzymes for each target molecule individually.
Baselines. We compare our model with 4 kinds of base- lines. The rule-based methods include: a) the ground truth proteins that are recorded to be the enzymes of target molecule; b) randomly generated amino acids sequences as random proteins; c) single position mutation of the ground truth enzymes; and d) the retrieved enzymes based on our substrate-indee enzyme retrieval method. The unconditional generation models include ProtGPT2 (Ferruz et al., 2022) and ProGen2 (Nijkamp et al., 2023), which generates pro- tein sequences with a distribution like natural ones while having some distance. The Sequence generation models include: ZymCTRL (Munsamy et al., 2022), which takes an Enzyme Commission (EC) number and outputs corre- sponding enzyme; and NOS (Gruver et al., 2023), which is guided diffusion model for antibody infilling with our modi- fied guided function same as our model for enzyme genera- tion. The structure-based model is LigandMPNN (Dauparas et al., 2025), which refines proteins based on the binding of small molecules.
b Table 3 presents the properties of our method--eneratee enzymes, highlighting their superior catalytic capability (o (keat)) and foldability (pLDDT) compared to other neural network methods. Notably, ZymCTRL exhibits sim- ilar properties, but it relies ground truth EC numbers as input. The process of mapping the target substrate to the correct EC number requires more human expertise than our model. The Wasserstein distance with the test set on BLASTp and the difference in cluster number shows that our model can generate new proteins that have a similar dis- tribution with the test set, suggesting our generated proteins cluster properly to be specific for each target substrate, just like natural enzymes.
Metric. We adopt the turnover number of the enzyme (Keat) to measure its catalytic capability. A well-accepted predictor, UniKP (Yu et al., 2023), is used to predict log1o (keat) value for the generated enzyme on the target molecule. UniKP is trained on the dataset of enzyme- substrate reaction keat.
hhups://blastnchh www.ssstnnciciiii....mm alm.nih.goo//do//llatt
help/downloadhllssdatt.
hhtps:/wwww.rhc.ddbborrg
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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Table 2: Average 1o (Keat) generated enzymes towards different targets of 7 tasks.
Type
Model
Sepiap- terin
Propylene oxide
Levo- glu- cosan
cGMP
L-Pro
Pyri- doxine
leukotriene
A4(1-)
Ground Truth
0.247
0.785
0.719
0.132
0.107
0.508
0.371
Random
-0.056
0.076
0.359
-0.203
0.037
0.269
-0.215
Rule
Mutation
0.387
0.752
0.740
0,006
0.030
0,480
0.316
Retrieved
0.139
0.701
0.728
-0.004
0.039
0.234
0.575
ProtGPT2 0.410
0.441
0.491
0.194
0.244
0.432
0.302
Uncond
ProGen2
0.234
0.423
0.529
0.410
0.385
0.517
0.351
ZymCTRL NOS
-0.091
0.444
0.505
0.174
0.109
0.549
0.268
Sequence
0.066
0.331
0.370
-0.071
0.193
0.265
0.229
Structure
LigandMPNN
0.125
0.641
0.707
0.079
0.358
0.333
0.429
Ours
0.705
0.802
0.788
0.464
0.462
0.745
1.288
Table 3: Different properties predicted by their foval of the generated enzymes for test set.
Type
Model
Keat t
PLDDT
WDL (BLASTp)
Absolute difference (#cluster)
WD4 (#re- peat AA)
success ratef(%)
4
Test set
0.363
Random
0.185
20.2
38.3
8.59
Rule
Mutation
0.354
Retrieved
0.351
85.9
19.6
1.87
ProtGPT2
0.322
55.2
31.5
8.58
1.41
100
Uncond
ProGen2
0.352
55.5
26.7
8.47
161.04
100
ZymCTRL
0.375
62.5
23.0
4.12
0.78
99.2
Sequence
NOS
0.224
23.1
36.5
8.59
0.65
100
Structure
LigandMPNN
0.342
31.0
33.6
8.52
3.14
99.5
Ours
0.380
62.8
20.8
1.74
0.90
100
2.0
2.0
1.5
1.5
(Kcat)
1.0
0.5
6:30 0u0d d.
0.39%
PLDDT
60
80
458
28302.55
22.2555
(og())
1.0
0.5
60
8D
0.0
40
-0.5
-0.5
w/o
#Retrieved
#Retrieved
Guidance
Guidance
(c)
(d)
Figure 2: (a) and (b): The distribution of Keat value and pL.DDT of our model with different numbers of retrieved enzymes.
(c) and (d): Those of our model with without discriminator guidance.

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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
4.4. Retrieval Effectiveness
by the structure prediction model suggests confidence, and it is low when the evaluated sequence is not very natural-like.
Does the retrieval of enzymes con- tribute to enzyme generation? We modified the number of retrieved enzymes and generated 10 enzymes for each of the 389 target substrates to evaluate the effectiveness of the retrieval method. Results are shown in Fig. 2(a-b).
Research question:
4.6. Case Study Targeting Methyyphhopponnate()))
Research question: Why proteins generated by SENZ are predicted to have the better catalytic capability? We perform docking between substrate, methylphosphonate(( (Gama et al., 2019; von Arx et al., 2023), and generated enzymes with AutoDock-Vine (Eberhardt et al., 2021) to closely examine the generated enzyme's structure and its interaction with the target substrate. The docking result is presented in Fig. 3.
A Comparing generation with 0 and 1 retrieved protein in Fig. 2( and Fig. 2(b), can be concluded that even a single retrieved enzyme is crucial to the generation of enzymes with catalytic capability and foldability. It shows the effectiveness of the retrieval method.
> Comparing generation with I or more retrieved proteins in Fig. 2(a) and Fig. 2(b), it can be concluded that retrieval enhances the generated enzymes' catalytic capability by a small concession of foldability, Fig. 2(a) of Keat shows that the increase in retrieved sequences improves the perfor- mance in terms of catalyzing. In Fig. 2(b) of pLDDT, the foldability decreases with the increase of retrieved enzymes. The reason is that structure prediction examines the full se- quence pattern with existing proteins. The retrieved proteins do not resemble each other in full sequence, making the derived generated sequence less similar to existing proteins. In fact, several short periods (enzymatic active site) in the retrieved sequences dominate the proteins' catalytic capa- bility, which is different from foldability's 's requirement on the full sequence. Therefore, there's trade-off between the enzyme's ss folding stability and catalytic capability. In fact, the trade-off has been reported in other literature (Vanella et al., 2024), which the same case in our generated se- quences. With more retrieved sequences, our model gives up sequence foldability for better catalytic performance.
From Fig. 3(), it is evident that the enzyme generated by our model achieves the lowest AutoDock-Vine score, indicat- ing the highest likelihood of binding between the molecule and the protein. This result is likely due to our generated protein possessing more side chains that extend toward the substrate, resulting in a tighter binding. Although a favor- able docking score does not necessarily ensure catalytic activity, it does demonstrate that our generated enzyme can effectively capture the substrate, which is crucial prerequi- site for the subsequent chemical reaction.
5. Related Work
Unconditional protein generation. Some research fo- cuses on generating proteins that resemble natural ones. Within this scope, the protein language model-based sequence-only approaches include ProGen2 (Nijkamp et al., 2023), ProtGPT2 (Ferruz et al., 2022), and ESM-2 (Lin et al., 2023). These models are trained to predict masked amino acids in natural protein sequences, thus learning to generate proteins that mimic natural ones. The discrete diffusion models approach, aimed at this target, includes EvoDiff (Alamdari et al., 2023), which performs coruup- tion and reconstruction on multiple sequence alignments (MSA), Generative adversarial networks (GAN) approaches, such as ProteinGAN (Repecka et al., 2021), use a discrim- inator to guide the generated protein to resemble natural ones, enabling the generation of natural-like enzymes when template is provided. Structure-baser methods include ProteinMPNN (Dauparas et al., 2022), which seeks to gen- erate protein sequence likely to fold into a given structure. These methods do not target external generation objectives or rely heavily on human-selected input templates to achieve specific functions.
4.5. Guidance Effectiveness
Research question: Does the discriminator guidance con- tribute to enzyme generation? We removed the discrimi- nator in our model and generated 10 enzymes for each of the 389 target substrates to evaluate the guidance effectiveness. The results are shown in Fig. 2(c) and Fig. 2(d).
> Fig. 2(c) shows the necessity of guidance to generate the enzymes with high keat- Fig. 2(c) and Fig. 2(d) also suggest that our model performs the same trade-off in two circumstances with without guidance. Comparing the Keat value of rule-based retrieved sequence in Table. 3 with w/o guidance column in Fig. 2(c), it can be seen that the generated enzymes' Keat is almost the same as the retrieved ones. The reason is that the generator only learns to generate sequences resembling the retrieved ones.
Conditioned protein generation. Non-protein data adopted to guide protein generation. ZymCTRL (Munsamy et al., 2022) uses Enzyme Commission (EC) number as prompt to generate enzymes categorized in the corre- sponding EC. ProGen (Madani et al., 2023) takes natural
It is natural that adopting guidance decreases the foldability of generated enzymes. The discriminator guides the genera- tor to output proteins with high score, which has a different distribution from natural- -ikee proteins. The pLDDT given
'nttps://vina scripps.edh
8

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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
(a) ProtGPT2 Score= 2.607
(b) ProGen2 Score=-2.445
(c) ZymCTRL Score=2.7726
(d) NOS Score=2.5774
(e) LigandMPNN
(f) Ours Score=3.077
(g) Ground Truth Score=22.999
Score=
Figure 3: Docking result and the corresponding AutoDock- Vina scores of 6 neural network generated proteins for methy|phossphonae((1 (1-) and the ground truth. The molecule with 5 atoms in red, orange, and green is methhypphosphonate(. The generated protein is in blue. Proteins' side chains within Á to the substrate are shown. A lower score denotes a better binding position.
language protein labels output corresponding protein se- quence. ProCALM (Yang et al., 2024a) embeds taxonomy and EC number conditions and use the condition embed- ding to generate related proteins. EnzyGen (Song et al., 2024b) integrates EC numbers and alignment- blasdeent-bbseddd iden- tification of functionally important sites, thereby relying on both external label information and intrinsic sequence properties. LigandMPNN (Dauparas al., 2025) and RFd- iffusionAA (Krishna al., 2024) can recover a protein sequence and structure based on a binding molecule, which is derived from their prediction ability on the ligand-protein complex. GENZYME (Hua et al., 2024a) generates pocket structures and subsequently derives the corresponding se- quences through pocket inverse folding, relying on structural training data and the performance of inverse folding models, particularly at functionally important sites.
suited for direct application enzyme generation tasks.
Retrieval method. Retrieval-augmented generation (RAG) is widely adopted in variety of domains (Shi et al., 2024; Shu et al., 2025; Zhang et al., 2025). Some research devel- ops retrieval methods to help with generation or prediction. RetMol (Wang et al., 2023) retrieves molecules based on similarity and desired properties to refine molecules. MSA transformer (Rao et al., 2021) and AlphaFold 2 (Jumper et al., 2021) evolutionary-basee MSA to enhance struc- ture prediction accuracy. They retrieve proteins with pro- teins by sequence similarity only. RSA (Ma et al., 2024) retrieves proteins based on the embedding distance derived from sequences, thereby making it a method that also de- pends on the intrinsic properties of the protein sequences themselves.
Protein guided protein generation. Some research aims to generate new proteins that bind to given protein. The se- quence approach includes NOS (Gruver et al., 2023), which merges antibody and antigen in one sequence and uses the diffusion method to train a transformer, while some prop- erty prediction models can be used in sampling to make the generated protein tend to have certain properties. The structure approach includes AbDiffuser (Martinkus al., 2023), which uses SE(3) equivariant neural network to model residue-to-reside relations. The generation target and output protein are both in the same protein modality.
6. Conclusion
In this paper, we have formally defined the task of zero-shot substrate-speciffed enzyme generation, wherein models are provided solely with a new target molecule and are required to output protein sequence possessing catalytic capabilitiees specific that molecule. To address this task, we introduce the Substrate-specii specified enzyme generator (SEN7., RAG method. SENZ utilizes single molecule as query to retrieve enzymes based on their substrate similarity to the target, thereby enabling the retrieval of known proteins from new molccules. This retrieval strategy capitalizes on the functional similarity of enzymes as indicated by their sub- strates. To generate enzymes from the retrieved sequences, we employ multiple sequence alignment (MSA) on them and introduce a diffusion model generator guided by an enzyme-substrate classifier. This classifier guides the gen- erated protein distribution for different substrates, serving as the objective for the generator during training. In experi- ments involving the generation of enzymes for real-world target molecules. evaluation functions assessed turnover rate and foldability together with other properties, demonstrating the superiority of our model in enzyme generation.
Enzyme evaluation. Enzyme evaluation models can help with enzyme design. ProSmith (Kroll et al., 2024) predicts protein-small molecule interactions. UniKP (Yu al., 2023) predicts the Keat and Km value of enzyme and substrate. NeuralPLexer (Qiao et al., 2024) and AlphaFoold 3 (Abram- son et al., 2024) can predict the grotein-ligand complex structures. Johnson et al. (2025) proposes comprehensive methods for evaluating neural network-generated enzymes but does not include metrics related catalytic activity. Several enzyme-related datasets have been developed (Heid et al., 2023; Hua et al., 2024b; Yang al., 2024b), though their primary focus on reaction prediction makes them less

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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Impact Statement
reaction system. Catalysts, 10(1), 2020. ISSN 2073-4344. doi: 10.3390/cata1100100000
This paper presents a method for zero-shot substrate- specified enzyme generation, contributing to advancements in machine learning for protein design. Our approach has potential applications in biomaterial synthesis and industrial biocatalysis, with possible benefits for sustainable chemistry and pharmaceutical development. While generative models in biological design require careful validation and ethical oversight, our work is grounded in established biochemical principles and evaluation frameworks. We do not foresee im- mediate risks associated with misuse, as our method relies on controlled training data.
Cheremisinoff, N.
and Rosenfeld, trichloropoopane (TCP). In Handbook of Pollution Pre- vention and Cleaner Production: Best Practices in the Agrochemical Industry, pp. 233-246. William Andrew Publishing, Oxford, 2011. ISBN 978-1-4377--7825-0 doi: htps://doi.orr/10..
5 1,2,3-
Dauparas, J., Anishchenko, I., Bennett, N., Bai, H., Ragotte, R. J., Milles, L. F., Wicky, B. I., Courbet, A., de Haas, R. J.. Bethel, N., et al. Robust deep learning- pro- tein sequence design using ProteinMPNN. Science, 378 (6615):49-56, 2022.
References
Dauparas, J., Lee, G. R., Pecoraro, R., An, L.,, Anishchenko, I. Glasscock, C., and Baker, D. Atomic context- conditioned protein sequence design using LigandMPNN. Nature Methods, 22(4):717-723, April 2025. ISSN 1548-
Abramson, J.. Adler, Dunger, J., Evans, R., Green, T.,
Pritzel, A., Ronneberger, O., Willmore, L.,, Ballard, A. J., Bambrick, J., Bodenstein, S. W., Evans, D. A., Hung. C.- C., O'ill,, M., Reiman, D., Tunyasuvunakool, K., Wu, Z., Zemgulyté, A., Arvaniti, E., Beattie, C., Bertolli, O., Bridgland, A., Cherepanov, A., Congreve, M., Cowen- Rivers, A. I., Cowie, A., Figurnov, M., Fuchs, F. B., Gladman, H., Jain, R., Khan, Y. A., Low, C. M. R., Per- lin, K., Potapenko, A., Savy, P., Singh, S., Stecula, A., Thillassundaram, A., Tong, C., Yakneen, S., Zhong, E. D., Zielinski, M., Zídek, A., Bapst, V., Kohli, P., Jaderberg, M., Hassabis, D., and Jumper, J. M. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016):493-500 June 2024. ISSN 1476-4687. doi: 10. 1033/0011866661 111113//445566-00-744777---
7105. doi: 10. 10.1038644152--05-006666
de Vries, E. J. and Janssen, D. B. Biocatalytic conversion epoxides. Current Opinion in Biotechnology. 14(4); 414 414-420, 2003.
Eberhardt, J., Santos-Martins, D., Tillack, A. F., and Forli, S. Autodock vina 1.2.0: New docking methods, expanded force field, and python bindings. Journal of Chemical Information and Modeling, 61(8):38113389 2021. doi: 10. //acs.jcimm.mmm..... 1c00203, PMID: 34278794.
Fang, X., Liu, L., Lei, J., He, D., Zhang, S., Zhou, J., Wang, F. Wu, H., and Wang. H. Geometry-enhaneed molecu- lar representation learning for property prediction. Na- ture Machine Intelligence, 4(2):127-134, February 2022. ISSN 2522-5839. doi: 10. 00088862225-001--044333
Agency for Toxic Substances and Disease Registry. Tox- icological profile for 1223-rrichlomoproppaee,, 2021. Ac- cessed: (2024.04.20). https:/ https://wwwwt htwp///wwttt...//.o atsdr edc. gov/ToxProfiles/ttes.
Ahmad, W., Tayara, H., and Chong, K. T. Attention- based graph neural network for molecular solubility prediction. ACS Omega, 8(3):323633244, 2023. doi: 10.102//acsomoga.22e0100
Ferruz, N., Schmidt, S., and Höcker, B. ProtGPT2 is deep unsupervised language model for protein design. Nature Communications, 13(1):4348, July 2022. ISSN 2041-1723. doi: 10.1038/s4
Alamdari, S., Thakkar, N., van den Berg, R., Lu, A., Fusi, N., Amini, A., and Yang, K. Protein generation with evo- lutionary diffusion: sequence all you need. In NeurIPS 2023 Generative Al and Biology (GenBio) Workshop, 2023.
Gama, S. R., Vogt, M., Kalina, T., Hupp, K., Hammer- schmidt, F., Pallitsch, K., and Zechel, D. L. An oxidative pathway for microbial utilization of methylphosphonic acid as a phosphate source. ACS Chemical Biology, 14 (4):735 741,, 2019. doi: 10.1021/acscce http:///ccchmbbb...90000 PMID: 30810303
Bilski, P., Li, Ehrenshff,,, M., Daub, M., and Chignell, C. Vitamin b6 (pyridoxine) and its derivatives efficient singlet oxygen quenchers and potential fungal antioxi- dants. Photochemistry and photobiology, 1292114 2000.
Goldman, S., Das, R., Yang, K. K., and Coley, C. W. Ma- chine learning modeling of family wide enzyme-substrate specificity screens. PLOS Computational Biology, 18(2); 1-20, 02 2022. doi: 10. 10.133 10.1377/jouuui wtj.urnll..cb..1009900... 1009853.
Bogale, T. F., Gul, I., Wang. L., Deng, J., Chen, Y., Majerié- Elenkov, M., and Tang, L. Biodegradation of 1,2,3- trichloropropane to valuable (s)-2,3-dep using a one-pot
Gruver, N., Stanton, S., Frey, N. C., Rudner, T. G.G. J., Hötzel, I., Lafrance- Vanasse, J., Rajpal, A., Cho, K., and Wilson, G. Protein design with guided discrete diffusion. In
10

---

Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurlPS 2023, New Orleans, LA, USA, December 10 16, 2023, 2023.
2013. ISSN 0959-6526. doi: http:://bbiorg//10000166/ jclepro.2012.11.005.
Johnson, S. R., Fu, X., Viknander, S., Goldin, C., Monaco, S., Zelezniak, A., and Yang, K. K. Computational scoring and experimental evaluation of enzymes gen- erated by neural networks. Nature Biotechnology, 43 (3):396-405, March 2025. ISSN 1546-1696. doi: 10.10388/411577--24-02244444
Guo, Z., Guo, K., Nan, B., Tian, Y., Iyer, R. G., Ma, Y., Wiest, O., Zhang. X., Wang. W., Zhang, C., and Chawla, N. V. Graph-based molecular representation learning. In Elkind, E. (ed.), Proceedings of the Thirty-Second In- ternational Joint Conference on Artificial Intelligence, IJCAI-23, pp. 6638-6646. International Joint Confer- ences on Artificial Intelligence Organization, 8 2023. doi: 00044966/icaai.2222373 Survey Track.
Jumper. J., Evans, R., Pritzel, A., Green, T., Figurnov, M. Ronneberger, O., Tunyasuvunakool, K., Bates, R., Zidek, A., Potapenko, A., Bridgland, A., Meyer, C., Kohl, S. A. A., Ballard, A. J., Cowic, A., Romera-Paredes, B., Nikolov, S., Jain, R., Adler, J., Back, T., Petersen, S., Reiman, D., Clancy, E., Zielinski, M., Steinegger, M. Pacholska, M., Berghammer, T., Bodenstein, S., Silver, D., Vinyals, O., Senior, A. W., Kavukcuoglu, K., Kohli, P., and Hassabis, D. Highly accurate pro- tein structure prediction with AlphaFold. Nature, 596 (7873)5583-89, August 2021. ISSN 1476-4687. doi: 10. 10.10388/41186-021--000
Haeggstrom, J. Z. Structure, function, and regulation of leukotriene a4 hydrolase. American journal of respiratory and critical care medicine, 16l[supplemenn_11S225-5333 2000.
Heid, E.,, Probst, D., Green, W. H., and Madsen, G. K. En- zymemap: curation, validation and data-driven prediction of enzymatic reactions. Chemical Science, 14(48):14229 14242, 2023.
Kipf, T. N. and Welling, M. Semi-supervised classification with graph convolutional networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net, 2017.
Hoogeboom, E., Gritsenko, A. A., Bastings, J., Poole, B., van den Berg, R., and Salimans, T. Autoregressive dif- fusion models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022.
Krishna, R., Wang, J., Ahern, W., Sturmfels, P., Venkatesh, P., Kalvet, I., Lee, G. R., ishchenko, I, Humphreys, I. R., McHugh, R., Vafeados, D., Li, X., Sutherland, G. A., Hitchcock, A., Hunter, C. N., Kang, A., Brackenbrough, E., Bera, A. K., Baek, M., DiMaio, F., and Baker, D. Generalized biomolecular modeling and design with ence, 384(6693)) 2024. doi: 10. 126/science. ad12528.
Hua, C., Lu, J., Liu, Y., Zhang, O., Tang, J., Ying, R., Jin, W., Wolf, G., Precup, D., and Zheng, S. crantion-conditioned de novo enzyme design with GENzyme. arXiv preprint arXiv:2411.16694 2024a.
Burrows, S., An-
Hua, C., Zhong, B., Luan, S., Hong, L., Wolf, G., Precup, D., and Zheng, S. Reactzyme: A benchmark for enzyme- reaction prediction. In Advances Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurlPS 2024, Vancouver, BC, Canada, December 10- 15, 2024, 2024b.
All-Atom. Sci-
Kroll, A., Ranjan, S., and Lercher, M. A multimodal trans- former network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme- substrate relationships. PLOS Computational Biology, 20 (5):10012000, 2024.
Jackson, C. J., Gillam, E. M., and Ollis, D. L. 4.26 directed evolution of enzymes. Liu, H.-W. B. and Begley, T.
(eds.). Comprehensive Natural Products III. pp. 654-673. Elsevier, Oxford, 2010. ISBN 978-0-98--022611-5 doi: 0-00777800000000620--000007-3330
Layton, D. S., Ajjarapu, A., Choi, D. W., and Jarboe, L. Engineering ethanologenic escherichia coli for levoglu- cosan utilization. Bioresource technology, 102(17):8318- 8322, 2011.
https://doi.or//100
Jang, E., Gu, S., and Poole, B. Categorical reparameteriza-- tion with gumbel-softtaxx. In 5th International Confer- ence on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceed- ings. OpenReview.net, 2017.
Lewis, P., Perez, E.,, Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., and Kiela, D. aerrieval-augmented genera- tion for knowledge-inteniive NLP tasks. In Advances in Neural Information Processing Systems 33: Annual Con- ference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6- 2020, virtual, 2020.
Jegannathan, K. R. and Nielsen, P. H. Environmental assess- ment of enzyme use in industrial production - litera- ture review. Journal of Cleaner Production, 42:228-244 240,

---

Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., and Rives, A. Evolutionary-scall predic- tion of atomic-level protein structure with a language model. Science, 379(6637):1223-1133 2023. doi: 10.1 1626/ccenneeade225744
generation of artificial enzymes. In Machine Learning for Structural Biology Workshop. NeurlPS 2022, 2022.
Nam, K., Shao, Y., Major, D. T., and Wolf- Watz, M. Perspec- tives on computational enzyme modeling: From mecha- nisms to design and drug development. ACS Omega, 9 (7):3333-7412, 2024. doi: 00.102//asomeeg..3c000444
Nijkamp, E., Ruffolo, J. A., Weinstein, E. N., Naik, N., and Madani, A. ProGen2: exploring the boundaries of protein language models. Cell systems, 14(11):968 -988, 2023.
Liu, Z., Chen, S., Zhou, K., Zha, D., Huang, X., and Hu, X. RSC: Accelerate graph neural networks training via ran- domized sparse computations. In Krause, A., Brunskill, E., Cho, K., Engelhardt, B., Sabato, S., and Scarlett, J. (eds.), Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 21951-21968. PMLR, 29-29 Jul 2023.
Paraschiv, G., Ferdes, M., Ionescu, M., Moiceanu, G., Zabava, B. S., and Dinca, M. N. prccasee--ersiaee en- zymes used to reduce environmental pollution. Energies, 15(5), 2022. ISSN 1996-1073, doi: 10.3390/cn15051833.
Qiao, Z. Nie, W., Vahdat, A., Miller, T. F., and Anandku- mar, A. State-specific protein-ligand complex structure prediction with a multiscale deep generative model. Na- ture Machine Intelligence. 6(2): February 2024. ISSN 2522-5839. doi: 10.
Lucas, K. A., Pitari, G. M., Kazerounian, S., Ruiz-Stewart, I., Park, J., Schulz, S., Chepenik, K. P., and Waldman, S. A. Guanylyl cyclases and signaling by cyclic gmp. Pharmacological reviews, 52(3):375-413, 2000.
Rao, R. M., Liu, J., Verkuil, R., Meier, J., Canny, J.,, Abbeel, P., Sercu, T., and Rives, A. Msa transformer. In Meila, M. and Zhang, T. (eds.), Proceedings of the 38th Inter- national Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pp. 8844- 8856. PMLR, 18-2 24 Jul 2021.
Ma, C., Zhao, H., Zheng. L., Xin, J., Li, Q., Wu, L., Deng, Z., Lu, Y. Y., Liu, Q. Wang, S., and Kong, L. Re- trieved sequence augmentation for protein representa- tion learning. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Process- ing, pp. 1738-1767, Miami, Florida, USA, November 2024. Association for Computational Linguistics. doi: 10.18663/v122224.mmml
Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A.. Viknander, S., Abuajwa, W., Savolainen, O., Meskys, R., Engqvist, M. K. M., and Zelezniak, A. Expand- ing functional protein sequence spaces using genera- tive adversarial networks. Nature Machine Intelligence, 3(4):324-333, April 2021. ISSN 2522-5839. doi: 10. 10.1038//42256-021-00000
104.
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., Fraser, J. S., and Naik, N. Large language models generate functional protein se- quences across diverse families. Nature Biotechnology, 41(8):1099-1100 106, August 2023. ISSN 1546-1696. doi: 10. 10.1038/441587-00-0001133
Robinson, P. K. Enzymes: principles and biotechnological applications. Essays in Biochemistry, 59:1-41, 10 2015. ISSN 0071-1365. doi: 10.1042/bse0590000.
Martinkus, K., Ludwiczak, J., Liang, W., Lafrance- Vanasse, J. Hötzel, I., Rajpal, A., Wu, Y., Cho, K., Bonneau, R., Gligorijevic, V., and Loukas, A. Abdiffuser: full-atom generation of vntvo functioning antibodies. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurlPS 2023, New Orleans, LA, USA, December 10 16, 2023, 2023.
Samin, G. and Janssen, D. B. Transformation and biodegra- dation of 1, 2, 3-rrichloroproppan (tcp). Environmental science and pollution research, 19:3067-3078, 2012.
Shi, Y., Tan, Q., Wu, X., Zhong, S., Zhou, K., and Liu, N.
knowledge editing in language models for multi-hop question answering. In Proceed- ings of the 33rd ACM International Conference on In- formation and Knowledge Management, CIKM '24, pp. 2056-2066, New York, NY, USA, 2024. Association for Computing Machinery. ISBN 9798400704369. doi: 10. 145/362767333677722
Meghwanshi, G. K., Kaur, N., Verma, S., Dabi, N. K., Vashishtha, A., Charan, P., Purohit, P., Bhandari, H., Bho- jak, N., and Kumar, R. Enzymes for pharmaceutical and therapeutic applications. Biotechnology and applied biochemistry, 67646658,6 2020.
Shu, D., Duan, B., Guo, K., Zhou, K., Tang, J., and Du, M. Aligning large language models and geometric deep models for protein representation. Patterns, 6(5), 2025.
Munsamy, G., Lindner, S., Lorenz, P., and Ferruz, N. Zym- CTRL: a conditional language model for the controllable
12

---

Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
Song, Z.., Huang, T., Li, L.,, and Jin, W. SurfPro: functional protein design based continuous surface. In Proceed- ings of the 41st International Conference on Machine Learning, ICML'24. JMLR.org, 2024a.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. Graph attention networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 May 3, 2018, Conference Track Proceedings. OpenReview.net, 2018.
Song, Z., Zhao, Y., Shi, W., Jin, W. Yang, Y., and Li, L. Gen- erative enzyme design guided by functionally important sites and moall-molecule substrates. In Proceedings of the s1 International Conference Machine Learning, ICML'22. JorR.org, 2024b.
von Arx, N., Kidane, A. T., Philippi, M., Mohr, W. Lavik, G., Schorn, S., Kuypers, M. M. M., and Milucka, Methyyphosspoonat--rii
methane for- mation and its link to primary production in the olig- otrophic North Atlantic. Nuture Communications, 14(1): 6529, October 2023. ISSN 2041-1723. doi: 10.1038/ 41147-023-42304-4
Sun, M., Zhou, K., He, X., Wang, Y., and Wang, X. Gppt: Graph pre-training and prompt tuning to gener- alize graph neural networks. In Proceedings the 28th ACM SIGKDD Conference Knowledge Discovery and Data Mining, KDD '22, pp. 1717-1727, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781 118143333850, doi: 10. 145/3534678.353999
Wang. Z., Nie, W., Qiao, Z., Xiao, C., BaBanirnn,, R. G., and Anandkumar, A. Retrieval- controllable molecule generation. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net, 2023.
Tan, Q. Zhang. X., Du, J., and Huang, X. Graph Neural Networks with Non-Recursive Message Passing. In 2023 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 506-514, Los Alamitos, CA, USA, December 2023. IEEE Computer Society. doi: 10. D0.110001CDDW0684772222233
Weininger, D. Smiles, chemical language and information system. 1. introduction to methodology and encoding rules. Journal of chemical information and computer sciences, 28( 18)33,,-3,,, 1988.
Tanner, J. Structural biology of proline catabolism. Amino acids, 35:719-730, 2008.
Yang. J., Bhatnagar, A., Ruffolo. J. A., and Madani, A. Conditional enzyme generation using protein language models with adapters. arXiv preprini arXiv:2410.03634, 2024a
Therrien, F., Sargent, E. H., and Voznyy, O. Using GNN property predictors as molecule generators. Nature Com- munications, 16 16(1):4301, May 2025. ISSN 2041-1723. doi: 10.1038//41480 44553044114--02555949394
Yang. J., Mora, A., Liu, S., Wittmann, B. J., Anandkumar, A., Arnold, F. H., and Yue, Y. CARE: a benchmark suite for the classification and retrieval of enzymes. In Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, NeurIPS 2024, Vancouver, BC, Canada, December 10 15, 2024, 2024b.
Thompson, J. D., Higgins, D. G., and Gibson, T. J. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weight- ing, position-specific gap penalties and weight matrix
choice. Nucleic Acids Research, 22(22)4473-44680 I1 1994. ISSN 0305-1048. doi: 10. 00.11993/aar/2222..673..
Yu, H., Deng, H., He, J., Keasling, J. D., and Luo, X. UniKP: unified framework for the prediction of en- zyme kinetic parameters. Nature Communications, 14 (1):8211, December 2023. ISSN 2041-1723. doi: 10.103885414477-03-44111
Thöny, B., Auerbach, G., and Blau, N. Tetrahydrobiopterin biosynthesis, regeneration and functions. Biochemical Journal, 347(1): (1):11111111 2000.
Tsubaki, M. and Mizoguchi, T.
On the equivalence of molecular graph convolution and molecular wave func- tion with poor basis set. In Advances in Neural Infor- mation Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
Zambrano, G., Sekretareva, A., D' Alonno D., Leone, L., Pavone, V., Lombardi, A., and Nastri, F, Oxidative de- halogenation of trichlorophenol catalyzed by promiscu- artificial heme-enzyme. RSC Adv. 12:12947-12956, 2022. doi: 10. (0.1039/22AA001 ID.
Vanella, R., Küng, C., Schoepfer, A. A., Doffini, V., Ren, J., and Nash, M. A. Understanding activity-stability trade- offs biocatalysts by enzyme proximity sequencing. Na- ture Communications, 1501),8807, February 2024. ISSN 2041-1723. doi: 10.1038//411 440184444447-02445555000
Zhang, Q., Chen, S., Bei, Y., Yuan, Z.., Zhou, H., Hong, Z., Dong, J., Chen, H., Chang, Y., and Huang, X. A survey of graph retrieval-augmente generation for customized large language models. arXiv preprint arXiv:2501.13955 2025.
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Zhou, H., Tan, Q. Huang, X., Zhou, K., and Wang, X. Temporal augmented graph neural networks for session- based recommendations. In Proceedings of the 44th In- ternational ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '21, pp- 1798-1802, New York, NY, USA, 2021a. Association for Computing Machinery. ISBN 97819971180 450380377, doi: 00.144574044333443312.
Zhou, K., Huang, X., Li, Y., Zha, D., Chen, R., and Hu, X. Towards deeper graph neural networks with differentiable group normalization. Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020a.
Zhou, K., Song. Q. Huang, X., Zha, D., Zou, N., and Hu, X. Multi-channel graph neural networks. In Bessiere, C. (ed.), Proceedings of the Twent--Nnnth International Joint Conference Artificial Intelligence, IJCAI-20, pp. 1552-1355 International Joint Conferences on Artificial Intelligence Organization, 7 2020b. doi: 10.24963/ijcai. 2020/188. Main track.
Zhou, K., Huang, X., Zha, D., Chen, R., Li, L., Choi, S., and Hu, X. Dirichlet energy constrained learning for deep graph neural networks. In Advances in Neural In- formation Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6- 2021, virtual, 2021b.
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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
A. Experiment Details
A.I. Source the substrates used in the experiments and reason of selection
The seven substrates presented in Table 2 were chosen for their commonality and importance in enzymatic processes, highlighting their relevance as candidates for novel enzyme design.
Sepiapterin (Thöny al., 2000). Reason for Designing Enzymes: Enhancing the efficiency and specificity of sepiapterin reductase can improve tetrahydrobiopterin (BH4) production, which is crucial for neurotransmitter synthesis and nitric oxide production.
Propylene Oxide (de Vries & Janssen, 2003). Reason for Designing Enzymes: Engineering epoxide hydrolases or monooxygenases can provide higher enantioselectivity and stability under industrial conditions for the production of chiral intermediates in pharmaceuticals.
Levoglucosan (Layton et al., 2011). Reason for Designing Enzymes: Developing specific glucosidases can enable efficient hydrolysis of levoglucosan into fermentable sugars, facilitating biofuel production from biomass pyrolysis products.
cGMP (Lucas et al., 2000). Reason for Designing Enzymes: Developing specific glucosidases can enable efficient hydrolysis of levoglucosan into fermentable sugars, facilitating biofuel production from biomass pyrolysis products.
L-Proline (L-Pro) (Tanner, 2008). Reason for Designing Enzymes: Engineering proline racemase or proline dehydrogenase can enhance the production of D-proline, a valuable chiral building block in pharmaceutical synthesis.
Designing enzymes for pyridoxine is important for enhancing its role in oxidative stress resistance by modulating its singlet oxygen quenching properties, which can be applied to improving fungal resilience, developing antioxidant therapies, and advancing fluorescence- based imaging techniques.
Pyridoxine (Bilski et al., 2000). Reason for Designing Enzymes:
Leukotriene A4(1-) (Haeggstrom, 2000). Reason for Designing Enzymes: Developing selective leukotriene A4 hydrolase inhibitors can lead to new antiminflammatory drugs with fewer side effects.
A.2. More Details about baselines
ProGen2 (Nijkamp et al., 2023) and Pro1GPT2 (Ferruz et al., 2022): We utilized the pre-trained weights for both models to generate sequences with a maximum length of 1024. These models serve as benchmarks for the capability of protein language models to generate sequences without specific functional guidance.
ZymCTRL (Munsamy al., 2022): This model employs pre-trained weights and uses the Enzyme Commission (EC) number a prompt for the autoregressive generation process. It is worth noting that the EC number provides more detailed information about enzymatic function compared to the substrate alone, offering this baseline an advantage in generating enzyme sequences for the given tasks.
NOS (Gruver et al., 2023): We trained NOS following the methodology of its original paper. The original NOS framework uses a discriminator to score the binding affinity between an antibody and an antigen (two protein sequences). We replaced the original discriminator with our cezyme-substrate probability scoring model in our adaptation. Furthermore, we replaced the target protein sequence input with the target substrate molecule. During inference, the NOS generator is updated iteratively for 10 steps using the test set input before sampling, following discrete diffusion model for sequence generation, as described in the original paper. These adjustments allow NOS to generate enzymes in our setting while preserving its original generative framework.
LigandMPNN (Dauparas et al., 2025): This reverse folding model generates a protein sequence based on a grotein-ligant complex structure. To adapt it for our task, we randomly generated protein sequences (length: 1024) and predicted their structures using ESMFold (Lin et al., 2023). Using RDKit, we generated the structure of the target substrate, and NeuralPL.exer (Qiao et al., 2024) was employed dock the substrate with the predicted protein structure, creating complex structure. The resulting complex was then input into LigandMPNN for sequence redesign.
A.3. Computation Resources
All the experiments conducted within 200 GB memory, 2 Intel Xeon Gold 6426Y CPUs, and 4 NVIDIA 4090D GPUs with 24 GB memory each. All used data in the experiment requires storage of less than 500 GB.
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Retrieval Augmented Zero-Shot Enzyme Generation for Specified Substrate
The total training time of the models is less than 40 hours.
B. Additional Related Work
Graph Neural Networks for Molecular Modeling. Graph Neural Networks (GNNs) have been extensively applied across various domains (Kipf & Welling, 2017; Veličković et al., 2018; Zhou et al., 2020a; 2021b; 2020b; Sun et al., 2022: Tan et al., 2023; Zhou et al., 2021a; la; Liu et al., 2023), and have shown particular promise in molecular representation learning. Guo et al. (2023) provide a comprehensive review of molecular graph modeling approaches, including 2D-based and 3D-based graph modeling methods. Fang al. (2022) incorporate three-dimensional molecular geometry by constructing graphs informed by spatial structure and applying GNNs to capture geometric dependencies effectively. In the context of enhancing molecular representations, Tsubaki & Mizoguchi (2020) integrate atomic orbital features with graph convolutional networks (Kipf & Welling, 2017) to improve performance on downstream tasks, Beyond representation, GNNs have also been utilized for molecule generation; for instance, Therrien et al. (2025) employ gradient ascent on a trained GNN- molecular property predictor to design novel molecules.
C. Limitation
Currently, the implementation of our method can only deal with small molecule substrates. If users want to generate enzymes for polymer substrates like DNA, RNA, protein, or polysaccharides with our model, they have to derive the SMILES of the corresponding monomer or dimer manually for input.
16


Reviews:
[{"review_id": "q8sasPOiKH", "reviewer": "Reviewer_MkMZ", "summary": "This manuscript proposes the SENZ method for zero-shot substrate-specified enzyme generation. Its key points include: (1) defining the task and constructing a substrate-enzyme dataset; (2) retrieving relevant enzymes based on substrate similarity; (3) generating new enzymes via a diffusion model guided by a classifier for optimization; and (4) validating on diverse substrates with superior performance over existing methods.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Area Chair and Reviewers,\n\nWe sincerely thank you for your thoughtful and thorough evaluation of our paper. Below are our detailed responses to your comments:\n\n## **`Source of the 7 substrates used in the experiments and reason of selection`**\n\nThanks for your comment. We selected these substrates because they are common and important in enzymatic reactions. Then, it is worthy to design new enzymes for these substrates.\n\n1. Sepiapterin [1]\n\n   **Reason for Designing Enzymes**: Enhancing the efficiency and specificity of sepiapterin reductase can improve tetrahydrobiopterin (BH4) production, which is crucial for neurotransmitter synthesis and nitric oxide production. [1]\n\n   [1] Thöny B, Auerbach G, Blau N. Tetrahydrobiopterin biosynthesis, regeneration and functions. Biochem J. 2000 Apr 1;347 Pt 1(Pt 1):1-16.\n\n2. Propylene Oxide [2]\n\n   **Reason for Designing Enzymes**: Engineering epoxide hydrolases or monooxygenases can provide higher enantioselectivity and stability under industrial conditions for the production of chiral intermediates in pharmaceuticals. [2]\n\n   [2] Erik J de Vries, Dick B Janssen, Biocatalytic conversion of epoxides. Current Opinion in Biotechnology. Volume 14, Issue 4, 2003, Pages 414-420.\n\n3. Levoglucosan [3]\n\n   **Reason for Designing Enzymes**: Developing specific glucosidases can enable efficient hydrolysis of levoglucosan into fermentable sugars, facilitating biofuel production from biomass pyrolysis products. [3]\n\n   [3] Donovan S. Layton, Avanthi Ajjarapu, Dong Won Choi, Laura R. Jarboe. Engineering ethanologenic Escherichia coli for levoglucosan utilization. Bioresource Technology,\n   Volume 102, Issue 17, 2011, Pages 8318-8322.\n\n4. cGMP [4]\n\n   **Reason for Designing Enzymes**: Developing specific glucosidases can enable efficient hydrolysis of levoglucosan into fermentable sugars, facilitating biofuel production from biomass pyrolysis products. [4]\n\n   [4] Lucas KA, Pitari GM, Kazerounian S, Ruiz-Stewart I, Park J, Schulz S, Chepenik KP, Waldman SA. Guanylyl cyclases and signaling by cyclic GMP. Pharmacol Rev. 2000 Sep;52(3):375-414.\n\n5. L-Proline (L-Pro) [5]\n\n   **Reason for Designing Enzymes**: Engineering proline racemase or proline dehydrogenase can enhance the production of D-proline, a valuable chiral building block in pharmaceutical synthesis. [5]\n\n   [5] Tanner JJ. Structural biology of proline catabolism. Amino Acids. 2008 Nov;35(4):719-30.\n\n6. Pyridoxine [6]\n\n   **Reason for Designing Enzymes**:  Designing enzymes for pyridoxine is important for enhancing its role in oxidative stress resistance by modulating its singlet oxygen quenching properties, which can be applied to improving fungal resilience, developing antioxidant therapies, and advancing fluorescence-based imaging techniques. [6]\n\n   [6] Bilski P, Li MY, Ehrenshaft M, Daub ME, Chignell CF. Vitamin B6 (pyridoxine) and its derivatives are efficient singlet oxygen quenchers and potential fungal antioxidants. Photochem Photobiol. 2000 Feb;71(2):129-34.\n\n7. leukotriene A4(1-) [7]\n\n   **Reason for Designing Enzymes**: Developing selective leukotriene A4 hydrolase inhibitors can lead to new anti-inflammatory drugs with fewer side effects.\n\n   [7] Haeggström JZ. Structure, function, and regulation of leukotriene A4 hydrolase. Am J Respir Crit Care Med. 2000 Feb;161(2 Pt 2):S25-31. doi: 10.1164/ajrccm.161.supplement_1.ltta-6.\n\n## **`To remove structural elements such as signal peptides and transmembrane domains`**\n\nThanks for your suggestion. This process can provide the model with clearer sequence patterns by eliminating undetermined sections. In the future version, we will test the performance after removing signal peptides and transmembrane domains in the whole dataset. \n\n## **`Detail of unconditional generation baselines`**\n\nProGen2 and ProtGPT2: We utilized the pre-trained weights for both ProGen2 and ProtGPT2 to directly generate sequences with a maximum length of 1024. No information related to target substrates is provided to either model. These models serve as baselines for the capability of protein language models to generate sequences without specific functional guidance.", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "Yes", "experimental_designs_or_analyses": "Yes", "relation_to_broader_scientific_literature": "All good", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "This manuscript has several notable strengths:\n\n1.The SENZ method achieves zero-shot substrate-specified enzyme generation, innovatively proposing a way to generate novel enzymes for specific target substrates without direct supervision data.\n2.The method integrates retrieval-augmented generation technology, addressing the lack of positive samples in zero-shot scenarios by retrieving enzymes with substrates structurally similar to the target as prompting signals.\n3.The generated enzymes are comprehensively evaluated across multiple dimensions, including catalytic capability (kcat), foldability (pLDDT), and similarity to known enzymes (BLASTp), with experimental results demonstrating the effectiveness of the method.\n\nHowever, the manuscript has the following issues:\n\n1.The 7 substrates used in the experiments (Sepiapterin, Propylene oxide, Levo-glucosan, cGMP, L-Pro, Pyridoxine, leukotriene A4(1-)) listed in Table 1—where do they originate from? Please specify the source and explain why these particular substrates were selected for the experiments.\n\n2. It is suggested that the authors consider removing structural elements such as signal peptides and transmembrane domains, which may affect expression and activity. This would enhance the feasibility and efficiency of the generated enzymes in practical applications, particularly in in vitro expression systems.", "other_comments_or_suggestions": "It is recommended that when the \"Uncond\" class methods are first introduced in the experimental section, a brief explanation of how unconditional generation models (such as ProtGPT2 and ProGen2) are utilized to generate enzyme sequences for specific substrates should be provided.", "questions_for_authors": "Same to Other Strengths And Weaknesses", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "No"}}, {"review_id": "DpLzQZvsxi", "reviewer": "Reviewer_aDZN", "summary": "This paper presents a retrieval-augmented approach for enzyme sequence generation, conditioned on substrates. The authors introduce a novel benchmark designed to address the zero-shot setting, which is aligned with downstream generation tasks. The proposed method utilizes a diffusion model to iteratively generate enzyme sequences, conditioned on multiple sequence alignment results and the target substrate. Empirical evaluations demonstrate that their method outperforms existing baselines in terms of generation quality.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Area Chair and Reviewers,\n\nWe sincerely thank you for your thoughtful and thorough evaluation of our paper.\n\n# `Mentioned reference`\n\nWe will include these six pieces of literature in the related work section in the final version of this paper since modification to the manuscript is not allowed currently. We plan to test our model on the mentioned dataset.\n\n# `Complexity of catalysis`\n\nOur approach to capture the complexity of enzyme catalysis beyond just the structural interactions is **by generation based on reference enzymes**. Catalytic capability is not fully decided by binding affinity or docking position but also by some unclear features that cannot modeled directly. The reason to use similar molecules' enzymes as references is these enzymes are known to function for similar molecules and thus contain necessary features even if the feature itself is unclear. The discriminator guidance provides clues to necessary features by serving as a loss.\n\n# `Predictive model and Binding affinity`\n\nThe reliability of the predictive model can be checked from its original paper. In our paper, the rule-based method generated enzymes' predicted $k_{cat}$ is reasonable in terms of order, since the totally random generated protein << retrieved proteins $\\approx$ mutation of ground truth < ground truth. The rule-based baselines are to evaluate if the predictive model can predict values in reasonable order.\n\nTo reflect binding affinity, Vina score is provided in the case study. We are still implementing massive automated evaluation by AutoDock-Vina, since there are frequent cases unsuitable for the input check.\n\n# `Novelty`\n\nThe approach of expressing substrate conditions to the model is novel, which includes two novel parts.\n\n1. **Retrieval based on substrates**\n\n   The idea of using a substrate to find related enzymes and using related emzyes to facilitate new enzyme generation is novel. The approach to align related enzymes and serve as the base of generation is novel. This approach makes zero-shot generation practical.\n\n2. **Adopt discriminator in training**\n\n   The approach of using a discriminative mode to describe the generation target of catalytic capability is novel.\n\n# `Dataset`\n\nOur dataset is created based on Rhea, which is an expert-curated knowledgebase of chemical and transport reactions of biological interest - and the standard for enzyme and transporter annotation in UniProtKB. Our dataset inherits the quality and coverage from Rhea.\n\n To control the quality, we carefully designed the **dataset split**.\n\n1. Sequence identity threshold\n\n   EnzyBench partitioned training, validation, and testing sets based on a sequence identity threshold of 50%, but ours is 30%. \n\n2. Distribution of enzymes in split\n\n   Unlike EnzyBench split enzymes based on their third-level categories, we split the enzymes by substrates. We guarantee that no two enzymes in different splits share the same substrate.\n\nThere are 34982 entries, so it cannot be regarded as limited, given the fact that high-quality annotated enzyme data is precious.\n\n# ` Substrate-enzyme interaction complexity`\n\nOur model does not aim to model the substrate-enzyme interactions directly, but to use retrieved reference enzymes to facilitate generation. We use the target substrate to retrieve enzymes, which contain the desired enzyme's properties. The model does not need to model substrate-enzyme interactions directly but needs to learn generation from references.\n\n# `Vina score`\n\nWe provide the **docking results with the ground truth** here. The Vina score of the ground truth enzyme is **-3.036**, which is very close to our generated enzyme. It means that our generated enzymes bind the substrate slightly better than the ground truth with no discrepancy. We will add a subfigure in Figure 3.\n\nAbout the value -3.075 and -3.036. The ligand only has five atoms and thus is small. It is hard for a small ligand to get a high score.\n\n# $k_{cat}$`  for valid sequences`\n\nThanks for your suggestion. When setting plddt threshold as 70%, our model-generated protein above the threshold has an average predicted $k_{cat}$ of **0.362**.\n\n# `The significance of retrieval`\n\nThank you for identifying our novelty. This design aims to provide indirect substrate information in the format of protein sequences. Substrate-based retrieval is adopted for two significant reasons.\n\n1. Without any protein as input, there is no anchor protein sequence to search for similar sequences.\n2. With only a target substrate as input, the retrieval method has to use this molecule as the key.\n\n# `Substrate concatenation`\n\nIt provides direct information on the target substrate by serving as a single token prompt persistent in the diffusion process. The sequence to generate has known positions from two dimensions then:\n\n1. The rows of aligned sequences.\n2. The column of the target molecule's embedding.\n\n# `7 tasks`\n\n Please refer to the response to Reviewer MkMZ.", "claims_and_evidence": "The authors review existing protein generation methods and assert that \"existing work on conditional generation cannot be repurposed directly to generate desired enzymes that catalyze specific substrates represented as small molecules\" (lines 42-45). However, this claim overlooks several existing approaches that address enzyme generation with a focus on catalysis, such as ProCALM[1], GENZYME[2], and EnzyGEN[3]. Additionally, in the context of enzyme sequence generation, there are notable datasets and benchmarks that should be considered, including ReactZyme[4], CARE[5], EnzymeMap[6], and EnzyBench[3].\n\n**Reference:**\n\n[1] Yang J, Bhatnagar A, Ruffolo J A, et al. Conditional enzyme generation using protein language models with adapters[J]. arXiv preprint arXiv:2410.03634, 2024.\n\n[2] Hua C, Lu J, Liu Y, et al. Reaction-conditioned De Novo Enzyme Design with GENzyme[J]. arXiv preprint arXiv:2411.16694, 2024.\n\n[3] Song Z, Zhao Y, Shi W, et al. Generative enzyme design guided by functionally important sites and small-molecule substrates[J]. arXiv preprint arXiv:2405.08205, 2024.\n\n[4] Hua C, Zhong B, Luan S, et al. Reactzyme: A benchmark for enzyme-reaction prediction[J]. Advances in Neural Information Processing Systems, 2025, 37: 26415-26442.\n\n[5] Yang J, Mora A, Liu S, et al. CARE: a Benchmark Suite for the Classification and Retrieval of Enzymes[J]. arXiv preprint arXiv:2406.15669, 2024.\n\n[6] Heid E, Probst D, Green W H, et al. EnzymeMap: curation, validation and data-driven prediction of enzymatic reactions[J]. Chemical Science, 2023, 14(48): 14229-14242.", "methods_and_evaluation_criteria": "In the \"Problem definition\" part, the authors formalize the generative function as $\\textbf{x}=G(\\textbf{m},s_{EC},\\textbf{C}_m,\\textbf{C}_x)$, where they consider $\\textbf{m}$ as a single condition during training. However, this raises the question of whether their approach effectively addresses the problem outlined in the Introduction, namely that \"the catalytic capability of an enzyme is not solely determined by how it structurally interacts with the substrate molecule.\" Does the current setting fully capture the complexity of enzyme catalysis beyond just the structural interactions?\n\nThe authors also use $k_{cat}$ as an evaluation criterion, which is derived from a predictive model known to have significant limitations. This raises concerns about the reliability of the results. The authors should provide a justification for the reliability of the selected predictive model. Additionally, since the model is designed to generate enzyme sequences that can bind to the target substrates, it would be important to also compute the binding affinity as part of the evaluation.\n\nPlease clearly indicate what novel contributions come from the authors versus have been taken from other papers and combined. For example, it seems that most of the generative model is based on diffusion model and not introduced by the authors. Is the approach for substrate-conditioning developed by you or by others? The substrate-indexed retrieval method for example (to the best of my knowledge) is a novel contribution of the authors.\n\nAs discussed in Section 2, there are several enzyme-substrate datasets available. What differentiates your dataset from others in terms of quality and coverage? For example, EnzyBench contains over 100,000 enzyme-substrate pairs, which is a large and diverse dataset. Given that sequences are typically much easier to gather than structures for proteins or enzymes, can the authors ensure that their model is capable of learning meaningful patterns with a more limited dataset?\n\nAdditionally, the authors claim in line 50 that learning the interactions between substrates and enzymes is challenging. However, their method simply concatenates the embedding of the target substrate with the noisy enzyme sequence during training. Does this approach effectively address the complexity of learning substrate-enzyme interactions, or does it oversimplify the problem?", "theoretical_claims": "There is no theoretical proof.", "experimental_designs_or_analyses": "I have some concerns regarding the validity of the $k_{cat}$ results. The authors claim that their model performs better than the baselines, and even outperforms the ground truth. However, in the case study, the reported Vina score is -3.075, which indicates that binding with the target substrate is unlikely. How can the authors reconcile these discrepancies in their results? Additionally, they should compare their docking results with the ground truth for a more reliable assessment.\n\nThe authors use pLDDT to evaluate sequence validity, which is an appropriate initial step. However, once the validity of the generated sequences is confirmed, further evaluations should follow, such as computing $k_{cat}$ and binding affinity. It would be helpful if the authors reported the $k_{cat}$ for the proportion of valid sequences as part of this process.\n\nThe proposed approach retrieves sequences based on substrate similarity rather than protein similarity, which distinguishes it from other methods. Can the authors provide evidence to demonstrate the significance of this design choice?\n\nFinally, the authors only assess the influence of substrate guidance as part of the contrastive learning loss. However, the role of the concatenation operation for the generator remains unclear. Could the authors elaborate on the rationale behind this design choice and its impact on model performance?", "relation_to_broader_scientific_literature": "This paper introduces a method for zero-shot substrate-specific enzyme generation, making a contribution to the field of machine learning for protein design. The proposed approach has potential applications in biomaterial synthesis and industrial biocatalysis, with possible implications for sustainable chemistry and pharmaceutical development.", "essential_references_not_discussed": "Please see the section of **Claims And Evidence**.", "other_strengths_and_weaknesses": "None", "other_comments_or_suggestions": "They should provide more details for the selected seven tasks.", "questions_for_authors": "None.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "45PGF7PgN0", "reviewer": "Reviewer_Xwvh", "summary": "The paper introduces SENZ, a substrate-specified enzyme generator, a RAG-based method to retrieve known enzymes and generate new enzymes based on a substrate. The authors define the task as generating a protein that serves as an enzyme for a given small molecule target. As a first step, the authors design a retrieval algorithm that retrieves a protein for a given query molecule based on catalyzing properties. Then a discrete diffusion model generates new enzymes conditioned on the retrieved enzymes and substrate.\n\n## Update After Rebuttal\n\nI thank the authors for addressing my review. I have decided to stay with my score of weak accept.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Area Chair and Reviewers,\n\nWe sincerely thank you for your thoughtful evaluation.\n\n# `Retrieval quality`\n\nThe quality of retrieved enzymes is reflected in the baseline method named '**Retrieved**' in Table 2. That row is as follows:\n\n| Method    | $k_{cat}$ | pLDDT |\n| --------- | --------- | ----- |\n| Retrieved | 0.351     | 85.9  |\n\n# `Random versus unconditionally generated enzymes`\n\n1. 'Random' are randomly generated amino acid sequences. They are not randomly selected enzymes.\n2. Unconditional generated proteins reflect the distribution of the training set of the baseline method. These models are trained to generate natural-like proteins. \n\nThe comparison reflects the features of $k_{cat}$ prediction model in evaluation. A more natural-like protein obtains a higher score than a pure random amino acid sequence.\n\n# `Retrieved enzymes versus ground truth`\n\nThe retrieval is from a different set which contains no ground truth enzymes. The retrieval module can never see the ground truth proteins.\n\n# `Line 297: Natural versus unconditionally generated enzymes`\n\n1. Natural enzymes are ground truth, which are the enzymes for target substrates.\n2. Unconditionally generated enzymes are generated by baseline models. They do not exist in nature.\n\n# `Novelty of retrieval and substrate 3D information`\n\nOur novelty is searching similar substrates and using their enzymes as reference for the generation model, not the metric about molecule similarity. We use a well-recognized method to calculate the similarity between molecules in the retrieval database. Morgan Fingerprint (MF) is an easy and recognized method to map molecules into the same vector space as representation. Tanimoto distance is designed to measure the similarity between vectors like MF.\n\nMF represents structure without relying on 3D coordinates. It encodes atom environments in a molecule, capturing local atomic neighborhoods. It can indicate whether certain functional groups or motifs are present in a molecule. It allows the comparison of molecules based on shared substructures.\n\n# `Identity exceeding 30%`\n\nIt means using `mmseqs2` with `--min-seq-id 0.3`. In Line 143, it means any two protein sequences from different subsets (training set, validation set, or testing set) have a maximum of 30% position overlap. This restriction is to avoid data leakage in protein sequence dataset split. Proteins are not relevant under the threshold.\n\n# `Eq 5,6 and Line [157-163 Col 2]`\n\nNotation for protein:\n\n1. $x$ is a single protein.\n2. $\\mathbb{P}^{(m)}$ is a set of proteins related to molecule $m$. If the molecule is in the retrieval database, these proteins are the enzymes of the molecule; If the molecule is not in the retrieval database,  these proteins are the enzymes of similar molecules.\n\nEquation 6 is a definition relying on equation 5: if the target molecule is not in the retrieval dataset, the proteins for some molecules in the dataset will be collected and serve as the proteins for the target molecule.\n\nLine [157-163 Col 2: ] is the process of retrieving proteins for a target molecule.\n\n1. Calculate the similarity between the target molecule and molecules in the database.\n2. Order molecules in the database descending by similarity.\n3. Get all the enzymes of each molecule in database in order until a desired amount is obtained.\n4. If a molecule has too many enzymes, only part of them is selected. It is to prevent all retrieved proteins from being enzymes of a single molecule.\n\n# `Diversity and novelty`\n\n1. The **diversity** of generated enzymes for the same substrate.\n\n   It is measured by the metric **#clusters**. Enzymes for the same target substrate are generated at first. Then they are clustered by a threshold of 30% identity, which means every two proteins from different clusters do not have an overlap of 30%. The number of clusters is recorded. Ground truth enzymes of the same substrate are also clustered, and the number of clusters is obtained. The absolute difference between clusters is the resemblance of diversity to natural distribution. If the generated enzymes have approximately the same number of clusters as natural ones, they have a natural distribution in terms of diversity.\n\n2. The **novelty** of generated enzymes.\n\n   It is measured by the metric **BLASTp**. It can be regarded as the distance from a protein to the nearest in all other natural proteins. It is the identity of the generated enzyme with the nearest different known protein, which is obtained by BLASTp2 in the SwissProt database. We also get this value of natural enzymes, and the absolute difference between it and of generated proteins is calculated. If the distance of generated enzymes is similar to that of natural ones, they have a natural distribution in novelty.\n\n\n# `Average docking score `\n\nWe will calculate the docking score in the next version of the manuscript. Runnig docking needs to manually adjust the protein structure file with the substrates'.", "claims_and_evidence": "* The authors claim that SENZ can generate enzymes for a particular substrate. \n* The retrieval quality is not measured. \n\nThe claim of the model being superior to any others is slightly not convincing. There’s essentially only 3 comparisons across the models: The average magnitude of the turnover enzyme number over 7 tasks, the average property scores, and the docking score of one target. The second one is fine. The first one only reports on the average performance of each model’s generation, but doesn't include the generation performance comparison, which I think is equally important. The third one seems to be cherry-picking as there’s only one target being compared. I have detailed what I’d like to see in the question part below.", "methods_and_evaluation_criteria": "Section 4.2: The authors assess the capability of SENZ to generate new proteins by generating new enzymes given a substrate and computing the turnover number\nThe values in Table 1 are a little strange. Random enzymes are significantly worse than unconditionally generated enzymes, but they are also randomly generated. Standard deviations are not provided along with the mean scores for each task, but possibly more than 10 sequences are needed to get valid evaluation results\nShouldn’t the retrieved enzymes match the ground truth proteins? Is the retrieval from a different set of enzymes?\nLine 297: How are unconditionally generated enzymes different from natural enzymes?\nIn general, the proposed metric is not a strong indicator of the efficacy of the metric.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "See Evaluation Criteria.", "relation_to_broader_scientific_literature": "N/A", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "The retrieval component of the pipeline is very similar to fuzzy search algorithms and semantic search algorithms and is not particularly novel. \nSubstructure similarity is measured with Tanimoto distance of Morgan fingerprints which does not capture 3D information.", "other_comments_or_suggestions": "Line 143 Col 1: Unclear what “(with an identity exceeding 30%)” means \nThe notation in equation 6 is non-standard and a bit confusing. In equation 5, enzymes are presented as x but then enzymes are represented as P(m_i). Is P(m_i) a set of enzymes as there may be multiple protein matches given a substrate? \nLine [157-163 Col 2: ]The following paragraph is difficult to parse.", "questions_for_authors": "How is the diversity and novelty of your model’s generation? For example, for a given substrate generated enzymes, how different are they, and how many are previously not seen in the database or from the testing set?\nFor the generation results you have in Experiment 4.1, what’s the average docking score for each to its target?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wkJvt6H8YM", "reviewer": "Reviewer_dRSM", "summary": "This paper introduces a novel method for de novo enzyme design using retrieval augmentation for the generative process. The core of this method is to take a given substrate for the enzyme, search in an enzyme database for protein sequences that are enzymes of similar substrates, align these protein sequences together, and provide these multiple sequence alignments to the generator model, which designs a sequence. Finally, a discriminator will predict how well the substrate and the generated sequence fit together. In addition, the authors construct a new enzyme-substrate database for training and retrieval.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Area Chair and Reviewers,\n\nWe sincerely thank you for your thoughtful and thorough evaluation of our paper.\n\n# `UniKP for evaluation`\n\nThanks for your comment. Using the predictive model for scoring is what we do in practice to filter better proteins. Before the wet lab experiment, proteins are scored by the predictive model, and proteins with a higher score are tested with higher priority. The advances in predicted scores suggest higher priority for wet lab experiments. We plan to test the designed enzymes in the wet lab, and the selection is based on the score.\n\nThe evaluation of protein properties depends on machine learning model predictions, particularly when no traditional methods are available as alternatives. The direct *in silico* evaluation of catalytic capability is by adopting the predictive model, and it is also a necessary step for filtering before wet lab experiments. This process is also adopted in protein design tasks for different features with predictive models for different properties.\n\n# `Guidance for kcat improvement`\n\nIn this manuscript, we want to show how far the $k_{cat}$ can achieve,  because a higher predicted $k_{cat}$ value makes designed proteins a higher priority for wet lab experiments. In real-world enzyme design, experts can decide the preference for predicted $k_{cat}$ or plddt, and then use a version of the model trained with or without guidance. We show the comprehensive effect of the module in facilitating experts' selection of model versions.\n\n# `AlphaFold3’s support for ligands to filter`\n\nThank you for your suggestion. We will use more structure prediction models in evaluation in future versions of this manuscript.\n\n# ` Sequence similarity of the binding pocket in retrieved and the catalytic site`\n\nThanks for your suggestion. The binding sites are usually not adjacent in sequence, so the sequence's similarity cannot be compared. The **binding sites** of an enzyme in the Uniprot annotation are usually discrete positions, which are not adjacent to each other in the protein sequence. Therefore, the sequence similarity cannot be compared based on several unconnected positions.\n\nThere is no enough annotation related to every enzyme's **catalytic site**. Our dataset is created based on Rhea, which is an expert-curated knowledgebase of chemical and transport reactions of biological interest. However, there is no pocket or catalytic site annotation on the enzymes.\n\n# `Influence of database size`\n\nThanks for your suggestion. We will use the full enzyme dataset in real-world new enzyme designing tasks. Of course, the number of substrates in the database decides the quality of retrieved proteins. Therefore, using the largest reliable enzyme knowledge base as the retrieval database is required in real-world tasks.\n\n# `Max kcat for Table 1`\n\nThe max $k_{cat}$ value in Table 1 is in the table below.\n\n| Model        | Sepiap-terin | Propylene oxide | Levo- glucosan | cGMP  | L-Pro | Pyridoxine | leukotriene A4(1-) |\n| ------------ | ------------ | --------------- | -------------- | ----- | ----- | ---------- | ------------------ |\n| Ground Truth | 0.703        | 0.785           | 0.736          | 0.288 | 0.159 | 0.573      | 0.676              |\n| Random       | 0.050        | 0.303           | 0.734          | 0.072 | 0.311 | 0.616      | 0.056              |\n| Mutation     | 0.898        | 0.838           | 0.837          | 0.452 | 0.155 | 0.527      | 0.666              |\n| Retrieved    | 0.409        | 0.955           | 0.981          | 0.401 | 0.156 | 0.639      | 0.856              |\n| ProtGPT2     | 0.816        | 0.910           | 1.028          | 0.621 | 0.724 | 0.769      | 0.555              |\n| ProGen2      | 1.244        | 0.771           | 0.965          | 0.833 | 0.789 | 0.810      | 0.800              |\n| ZymCTRL      | 0.004        | 0.604           | 0.774          | 0.348 | 0.454 | 0.728      | 0.538              |\n| NOS          | 0.349        | 0.590           | 0.488          | 0.341 | 0.412 | 0.711      | 0.447              |\n| LigandMPNN   | 0.653        | 0.778           | 1.174          | 0.251 | 0.497 | 0.786      | 0.976              |\n| Ours         | 1.102        | 1.117           | 1.065          | 0.642 | 0.909 | 0.934      | 1.470              |\n\n# `Distillation of the UniKP into the discriminator`\n\nThanks for your suggestion. In real-world tasks, we will consider utilizing other predictive models, including UniKP, as the discriminator as long as it is end-to-end and preserves the gradient. We will also try to use predictions for further rounds of training. The reason we do not adopt it in the current manuscript is that the evaluation relies on UniKP, which should not be seen by modules in training and generation.", "claims_and_evidence": "This method generates enzyme protein sequences that it claims are superior to that of other methods. This is done by comparing the kcat predicted by UniKP of their generated sequences compared to other methods. However, this is not a reliable metric as UniKP is a simple regression model trained on protein language model embeddings, and although it is state of the art, it is not accurate enough to deem one model better than the other. This is doubly true in this case since the difference between the kcat’s of this model compared to other methods is small. \n \nThere are also claims made that do not seem to be true even if the UniKP model’s outputs are treated as ground truth. For example, the results in Figure 2. It is claimed in section 4.5 that “Fig. 2(c) shows the necessity of guidance to generate the enzymes with high kcat.” I do not agree with this interpretation of the figure’s data. In Fig. 2c, one can only see a slight improvement in the kcat, which is unremarkable when taking into account the distribution of the predicted kcat’s. On the other hand, the only significant result of the guidance seems to be decreasing the pLDDT by a large amount in Fig. 2d.", "methods_and_evaluation_criteria": "It is difficult to evaluate whether generated enzymes achieve high catalytic efficiency in silico, which is the main issue of this paper. Even if this model is very performant, the evaluation criteria is not predictive or sensitive enough to make the claims that are underpin this paper. \n \nOn the other hand, I do like the use of AutoDock Vina for the docking results, as that is a structure-based method that has been widely validated. This could be interesting for further exploration, although the low confidence AlphaFold2 structures generated by this model currently will make docking unreliable as well.", "theoretical_claims": "The paper does not focus on formal theoretical claims.", "experimental_designs_or_analyses": "The experiments regarding foldability and designability of the generated sequences are conventional and have no issues. However, the central claims of this paper rely upon prediction of enzymatic kcat’s by UniKP. This is a computational model, relying upon Prot-T5 embeddings. Since these are only predictions, driven only by the sequence, it is hard to interpret the differences in kcat, especially minor ones such as those in this paper. In other words, the claims of this paper are too strong to merely be based on the kcat predictions of UniKP.", "relation_to_broader_scientific_literature": "This paper has designed and implemented a novel, creative, and highly promising method for protein sequence generation using retrieval augmentation. I think that this type of method will be very important for the advancement of the field. Furthermore, development of the substrate-enzyme database will accelerate enzyme research on its own.", "essential_references_not_discussed": "NA", "other_strengths_and_weaknesses": "See above", "other_comments_or_suggestions": "NA", "questions_for_authors": "Have the authors considered using AlphaFold3’s support for ligands to filter their predictions and score them against other models?\n\nHow does the sequence similarity of the binding pocket of the ligand look compared to the retrieved sequences from the database? Is the catalytic site stable or does the model make mutations here?\n\nHow sensitive are the model’s generations to the size of the database? Would a smaller database, particularly with less ligands available, make the outputs noticeably worse? How much more data is needed for the database to improve the model’s outputs?\n\nThe average log kcat is reported for Table 1. However, when designing enzymes de novo, it is likely that the sequences with max kcat will be tested. Could you also report max kcat of different methods for these tasks so that the shape of the distribution of the outputs becomes clear?\n\n Have the authors considered distillation of the UniKP model into the discriminator used here, letting the predictions of UniKP for the model’s designs train the discriminator further? Would this improve results?", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper proposes SENZ, a novel retrieval-augmented generative framework for zero-shot enzyme design, where the goal is to generate enzymes that catalyze substrates for which no known enzyme exists. The method retrieves structurally similar substrates and their associated enzymes from a database, aligns them, and uses a discrete diffusion model to generate new enzyme sequences.

Pros: 
- Several authors recognized the novelty of the approach. The idea of using retrieval augmentaion in enzyme design with diffusion models is novel.
- Promising performance across several metrics
- Diversity, novelty and binding score measurements are commonly used and trusted in the field. 

Cons:
- Several key publications and datasets were not included.
- k_cat predicted by UniKP is used for evaluation, which is not robust.
- retriveal quality is not studied 

While there are valid critisms on the evaluation protocol in this submission, given the novelty of this submission, I believe the merits of this submission outweighs its weaknesses.
Decision: Accept (poster)