EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation
Authors: Chao Song
Zhiyuan Liu
Han Huang
Liang Wang
Qiong Wang
Jian-Yu Shi
Hui Yu
Yihang Zhou
Yang Zhang
Venue: NeurIPS 2025 poster ()
Keywords: enzyme backbone generation
functional guidance
substrate specificity
adapter
Primary area: machine_learning_for_sciences
TL;DR: We propose EnzyControl, a method that integrates a lightweight, modular component into a pretrained motif-scaffolding model to enable functional and substrate-specific control over enzyme backbone generation.

Abstract: Designing enzyme backbones with substrate-specific functionality is a critical challenge in computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specific control, and flexibility for de novo enzyme backbone generation. To address this, we introduce **EnzyBind**, a dataset with 11,100 experimentally validated enzyme-substrate pairs specifically curated from PDBbind. Building on this, we propose **EnzyControl**, a method that enables functional and substrate-specific control in enzyme backbone generation. Our approach generates enzyme backbones conditioned on MSA-annotated catalytic sites and their corresponding substrates, which are automatically extracted from curated enzyme-substrate data. At the core of EnzyControl is **EnzyAdapter**, a lightweight, modular component integrated into a pretrained motif-scaffolding model, allowing it to become substrate-aware. A two-stage training paradigm further refines the model's ability to generate accurate and functional enzyme structures. Experiments show that our EnzyControl achieves the best performance across structural and functional metrics on EnzyBind and EnzyBench benchmarks, with particularly notable improvements of 13% in designability and 13% in catalytic efficiency compared to the baseline models. The code is released at https://github.com/Vecteur-libre/EnzyControl.

EnzyContien Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation
Chao Song'; Zhiyuan Liu
Han Huang', Liang Wang', Yihang Zhou?t, Yang Zhang
Qiong Wang', Jianyu Shi', Hui Yu
Northwestern Polytechnical University,
Nattooall University of Singapore *Institute of Automation at CAS
Thhe Chinese University of Hong Kong,
csong@mail nwpu. edu, en, zhiyuanEnus edu. sg huiyu@nwpu. edu. cn, rihangjoe@ff oomoio. com, zhanggnus. edu. edu.sg
Abstract
Designing enzyme backbones with substrate-specific functionality is critical challenge computational protein engineering. Current generative models excel in protein design but face limitations in binding data, substrate-specine control, and flexibility for de novo enzyme backbone generation. To address this, we introduce EnzyBind, dataset with 11,100 experimentally validated enzyme-substratt pairs specifically curated from PDBbind. Building on this, we propose Enzy Control, a method that enables functional and substrate-specific control in enzyme backbone generation. Our approach generates enzyme backbones conditioned on MSA- annotated catalytic sites and their corresponding substrates, which are automatically extracted from curated enzyme- enzyme-substratee data. At the core of EnzyContre is Enzy dapter, lightweight, modular component integrated into pretrained motif- scaffolding model, allowing it to become substrate- aware. A two-stage training paradigm further refines the model's ability to generate accurate and functional enzyme structures. Experiments show that our EnzyContro achieves the best performance across structural and functional metrics on EnzyBind and EnzyBench benchmarks, with particularly notable improvements 13% in designability and 13% in catalytic efficiency compared the baseline models. The code is released at https: / thub. com/ Veom/Veceur- libre/ EnzyControl.
Introduction
Enzymes are catalysts that drive essential chemical transformations with exceptional selectivity and effectiveness, enabling critical biological processes and industrial applications [1, 2]. Their engineerability allows precise optimization, making enzyme design a vital research area spanning pharmaceuticals [3, 4], specialty chemicals [5], biofuels [6], food production [7], and material synthesis [8]. In enzymatic reactions, binding specific small-molecule substrates is the key [9, 10], yet designing enzymes with precise substrate specificity remains a major challenge.
While enzyme design is often regarded as a subfield of protein design, the two tasks exhibit both similarities and fundamental differrnnces. General protein design has made remarkable progress across several directions, including sequence generation guided by fitness landscapes [11, 12, 13, 14, 15, 16], structure-to-sed to-sequence generation based predefined backbones [17, 18, 19, 20, 21, 22], and co- design of sequence and structure [23, 24, 25]. However, these approaches are not directly applicable
Equual contribution.
Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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to enzyme design, due to unique challenges specific to enzymes. Unlike general proteins, enzymes must satisty strict substrate-specific binding requirements, exhibit evolutionarily conserved functional sites, and maintain catalytic conformations that are highly sensitive to subtle structural variations [26, 27]. Consequently, effective enzyme design must account for substrate interactions, functional site preservation, and conformational constraints, which are often neglected in protein design methods.
Several lines of work have emerged focusing specifically on enzyme design [4, 28, 29, 30, 31, 32]. Nonetheless, existing methods still face significant limitations.
Neglect of functional site conservation. Previous protein generation methods either ignore functional sites or randomly select them during generation, resulting in poor catalytic function and high false positive rates. Although methods like EnzyGen [31] attempt to consider functional sites, they fail to ensure structural designability due to inadequate backbone generation quality.
Ignoring substrate molecules during generation. Most previous works [33, 34, design protein backbones without considering substrate interactions, limiting their applicability to real-world catalytic tasks. While EnzyGen uses the substrate to filter generated enzymes after generation, it does not incorporate substrate information during the generation process, making it unable to tailor enzyme structures for the substrate.
A bsence of high-quality benchmarks. Existing benchmarks [31, 36] mostly synthetic with limited experimental grounding and lack evaluation protocols tailored to enzyme families. Since enzymes classified not by structure but by the chemical reactions they catalyze (i.e., enzyme commission (EC) number, App. C), their utility depends on how effectively they perform these reactions [37]. As a result, meaningful evaluation demands benchmarks designed around enzyme families and their functional roles.
To address the challenges of model design, we develop EnzyControl, framework that extends standard motif-scaffoldiig models (i.e.. FrameFlow [38]) for substrate-aware enzyme backbone generation. EnzyControl consists of three key components: (1) A base network pretrained for motif-scaaffoddigg Specifically, we identify evolutionarily conserved functional motifs through multiple sequence alignments (MSA). These functional sites will be used condition the base network, ensuring that key catalytic features are retained during generation. (2) The Enzy/dappee, lightweight adapter that injects substrate information into the base network. It employs cross- modal projector [39, 40, 41, 42] to bridge the modality gap between substrate and enzyme, and uses cross-attention [43, 44] layers to condition the generation on substrate without altering the base network. (3) EnzyControl includes a two-stage training strategy to facilitate stable and efficient learning. In the first stage, only the EnzyAdapte are trained to align substrate features with enzyme structures, preserving the pretrained parameters. In the second stage, the full model is fine-tuned using a Low-Rank Adaptation (LoRA) approach [45, 46, 47], with continued updates to the adapter guided by the generation loss. Our approach effectively integrates functional site conservation and substrate-awar conditioning, leading to higher fidelity in enzyme backbone generation.
Resolving the absence of high-quality benchmarks. we construct EnzyBind, curated dataset of 11,100 enzyme-substran pairs derived from PDBbind [48]. Each entry is enriched with functional site annotations via MSA. Further, we leverage enzyme family classification for evaluating the consistency of enzyme commission (EC) number between the generated sample and its target native enzyme, thereby providing a more rigorous evaluation framework.
We benckmark EnzyControl on EnzyBind, evaluating the generated enzyme backbones across multiple structural and functional metrics. Experiments show that EnzyControl achieves 0.7166 in designability, a significant 13% relative improvement compared to the second-best model (see Table 1). It also demonstrates significantly improved catalytic efficiency (keat) and functional alignment (EC match rate), achieving 13% and 10% improvements, respectively, over the suboptimal baselines. EnzyControl also achieves 3% improvement of binding affinity than the second-best model on EnzyBench (Table 7). Additional quantitative analyses further highlight its strong residue efficiency (Fig. 9). In particular, EnzyContro consistently generates sequences that are approximately 30 % shorter, while maintaining comparable kcat values across all catalytic efficiency ranges- inddiaatnn its ability to produce compact, functionally robust designs suitable for practical applications.

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2 Related Work
Enzyme Design Applications. Designing effective enzymes remains challenge, particularly in identifying active sites and optimizing functional properties. Common strategies fall into three main categories: semi-rational design [49, 50, 51], rational design [52, 53, 54], and de novo design [55]. Semi-rational design leverages known structures and experimental data to guide site-directed mutagenesis near the catalytic site [56, 57], with residues selected based on structural insights and prior knowledge [58]. Rational design relies more heavily on computational modeling [59], using tools such as molecular dynamics and quantum mechanical simulations to explore enzyme-substrate interactions and reaction mechanisms [60, 61]. Both approaches aim to improve or repurpose natural enzymes. In contrast, de novo design constructs entirely new enzymes by embedding catalytic motif's into synthetic scaffolds, often simplifying structure to focus on function [32]. EnzyContro bridges rational and de novo design with a modular adapter for substrate-awe aware enzyme backbone generation, and leverages MSA to improve the efficiency of active site annotation.
Methods for Motif-Scaffoldin Task. The task is to create proteins with functional properties conferred through a prespecified arrangement of residues known as a motif. The problem is to design the remainder of the protein, called the scaffold, that harbors the motif. Early approaches the motif- scaffolding problem primarily relied on assembling scaffolds from pre-defined protein fragments. These methods are limited by their dependence on finding suitable matches within the Protein Data Bank (PDB) and often struggle to accommodate slight structural mismatches between the scaffold and the motif [62, 63, 64, 65]. More recent models, such as RFdiffusion [66] and FrameFloww [38], represent a shift toward generative modeling. These approaches use diffusion or flow matching models [67, 68, 69] conditioned on the motif's structure and/or sequence, while generating only the surrounding scaffold. However, they cannot incorporate additional design constraints such as substrate specificity. Our method addresses this limitation by enabling scaffold generation conditioned on broader range of inputs, expanding the applicability of notif-scaffolding.
3 EnzyBind: High-Quality Enzyme Dataset
significant limitation of existing datasets, such as EnzymeMap [70] and ReactZyme [7]], is the absence of precise pocket information. These datasets only provide protein sequences and SMILES representations, which are primarily used for predicting EC numbers. Although EnzymeFill [72] addresses this issue by introducing synthetic dataset with precise pocket structures and substrate conformations, its reliability is hindered by the lack of experimental validation in wet-lab settings. To overcome this limitation, present EnzyBind, novel dataset that includes precise pocket structures with substrate conformations, experimentally validated in wet-lab environments. EnzyBind is specifically designed support enzyme catalytic backbone generation tasks.
Data Source. To construct the EnzyBind dataset, we curated enzyme-substrate complexes from the PDBBind database (Fig. 1). Complexes that could not be processed using the RDKit library [73] were excluded. We then cleaned all remaining PDB files following a standardized procedure (detailed in App. D). This preprocessing pipeline resulted in a final dataset of approximately 11,000 subyte-substrate complexes, covering six fundamental catalytic types, as illustrated in Fig. 2.
130%
Raw enzyme complexes
Seq Length annetated
Enzyme complexes
Clean FGB #iles
Enzybind
Percantaga of Bmozknnd (44
25%
10
10
Consenve
42%
BL
33tp
A34a
14,9...
8C
19tu
10a.
Apta
(3,6...)
1124
35%
Query EC nunber
Conduct Haltiple Sequence AL1gmmon=(M5A) using
06.7..1
4%
Sea table with Mare faaily
Conserved anzynes
821
25
224
Engyme Commission casss
Figure 1: Dataset collection and preprocessing.
Figure 2: EC distribution.
Functional Sites Extraction. To guide enzyme backbone generation toward functional outcomes, we first identified functional sites typically associated with conserved sequences. Building prior
3

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work [31], we used MSA to uncover evolutionarily conserved regions across enzymes belonging to the same second-level EC number, using the MAFFT software [74].
4 Proposed Method: EnzyControl
In this section, we present EnzyControl, framework that can extend the motif-scaffolding model for substrate-specifie enzyme backbone generation. We begin by introducing flow matching. Then, present EnzyControl's 's key components: (1) base network pretrained for motif motif-ssaaffoling:: (2) an Adaptr that adapts the base network for substrate-specifie enzyme generation; and (3) a two-stage training strategy effectively combining the base network and EnzyAdapter
4.1 Preliminary: Flow Matching
Flow matching (FM) [75, 76, 34, 77] a generative modeling technique inspired by the strengths of diffusion models, offering a more efficient and stable sampling process. Given access to empirical observations of data distribution X1 ~ P1 and noise distribution X9 ~ Po- the goal of FM is to estimate coupling (po. P1) that describes the evolution between the two distributions. This objective could be formulated as solving an oridinart differential equation (ODE): dx; = v(x,, t)dt, on time t E [0, 1]. where the vector field v: IRd [0, 1] + Rd is set drive the flow from po to P1 and x, lies along the interpolation between and Xı with time l. We can parameterize the drift (vector field) by up (x(x,, with an expressive learner (a neural network, for example) and estimate by solving simple least square regression problem: arg Ex,x, [Iu (xx,t) vo (xe, 1)
8
With this estimation, we can do backward sampling by taking & = fo vel (Xe, t)dt since we have access to the noise X Po. and solve the integration with numerical integrators [78, 79].
4.2 EnzyControl'ss Base Network
Notations and Problem Formulation. An enzyme is a chain amino acids (residues) linked by peptide bonds, which folds into specific 3D structure. We represent the backbone of an enzyme with N residues T T()) ..... [N)] Each frame T (r. x) SE(3) encodes the atom positions of residue, where r E SO(3) is a rotation matrix and x E R is a translation vector (details can be found in App. E.1). Within enzyme backbone, we define subset of residues M {T(())...... , as functional sites, where (i......id is) C (1,..., N). The remaining residues (called scaffold) are denoted as S, such that T MUS. We also represent the enzyme's substrate as a chemical graph g. Given the functional sites M and the substrate 9, our goal is to generate a compatible enzyme backbone by sampling the scaffold S from the conditional distribution (,
Conditional Enzyme Generation. Throughout this section, we use FrameFlow [38] as our base network for motif-scaffolding. We adapt FrameFlow to accept both functional sites M (i.e., motif- scaffolding) [80, 81] and substrate information 9 as conditions, and further conduct enzyme backbone generation. Given the conditions, the goal is to generate an enzyme backbone that preserves the spatial arrangement of M and can G. Formally, the model needs to predict the conditional vector field S., (S, M) 9). Assuming conditional independence, the vector field can be simplified to v(.,, 1 |, 9). As functional sites M provides crucial information about the target structure Si, the predicted vector field is set (S, M g). Following the SE(3) representation of enzyme backbones (SE(3) = SO(3) x RR3 [82]), the training objective minimizes the squared distance between the ground truth and predicted vector fields:
EE [xx, V. (S., (, 9) + |[ (rr, t/ri) vsors) (S,, M, 9 M
(2)
Where Vi and Vsor3) are predicted vector fields of transition vector and rotation matrix.
To support -quivaaani generation, enzyme structures are represented as 3D k-nnaaest-nniighor (k-NN) graphs over residue frames, enabling spatially-aware message passing. Specifically, each node corresponds to a residue and edges connect spatially neighboring residues (ef. Fig. 3). The initial node embeddings ho [h ... Ab61 Nx computed based on residue indices and the

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Feature Initialization
b. Single Layer of Model Architecture
Functional site
Residue in scaffold
Residue indices
Backoones.jdoot
Inttial node
Timestep
Initial embeddings An
fectures
EdgelJpdaate
2a11
Residue indices Distances Timestap
Invoriant Point Attextion
Bray Adapter
Tronsforrer Enneder
Initial edge festures
Initial edge embeddings an
( Prediction Network.
Erayme (RNN grope)
Frume construction
Atom positions)
Tritial frame
(e) Anzpher
Transforner Shooder
Enay/dspper
Enzyme Embedder
FFN
Lineor
Chemical graph
Substrote repr
Cross Attertion
Quary
Enstriant Paint Attenttonn
Subetrate Graph
encoder
Substrote Projector
Pair Fectures
Connectine
Figure 3: EnzyControl is flexible approach for the conditional backbone generation of enzymes. (a) Feature Initialization involves obtaining initial node embeddings and edge embeddings, constructing initial frames for enzyme backbones, and initializing pretrained features for substrates. (b) Single- layer structure prediction network with EnzyAdapter
current timestep. Edge embeddings E NxNNx are initialized with residue indices, timestep, and relative sequence distances. They are further refined via self-conditioning using binned pairwise distance matrix between the model's s C. predictions. Each residue frame TV = (r, E SE(3) is initialized from backbone atoms, following AlphaFold2 (see App. E.1). These initialized embeddings are then processed Invariant Point Attention (IPA) [83] to capture spatial features in an SE(3)- equivariant manner. Transformer layers are interleaved between IPA layers to capture sequence-level dependencies. 'he model updates node embeddings using IPA and propagates these updates to edges through the EdgeUp module, which performs standard message passing. Finally, the BackboneUpdae module applies linear layers to predict translation and rotation updates to each frame. More method details are in App. E.3
4.3 EnzyAdapter and Two-Stage Training Strategy
We introduce Anzytdapter, modular architecture that enhances basic motif-scaffolding models with substrate awareness. EnzyAdapter is model-agnostic and modular, making it compatible with various backbone generation architectures.
Substrate Feature Initialization. In line with the design principles of AtomicFlow [84], we represent the substrate using its chemical graph rather than its 3D conformer, as the binding position of the substrate is typically unknown in advance. To get well-aligned substrate properties, we use pretrained Uni-Mol [85] encoder model extract substrate features. Uni-Mol scalable molecular pretraining model trained on 209 million molecular conformers, endowing it with rich knowledge of chemical structures and interactions. Given that our downstream dataset comprises only approximately 11,000 enzyme- substrate pairs, we freeze the Uni-Mol encoder during training to preserve the generalizable representations learned from large-scale pretraining and avoid overfitting. To effectively decompose the substrate embedding, we use small trainable projector. The projector we used in this study consists two linear layers and a Layer Normalization [86]. This projector maps the Uni-Mol-derived substrate embeddings into the enzyme representation space, enabling the model to incorporate substrate-specific information a flexible and task-aware manner without compromising the robustness of the pretrained encoder. This design strikes principled balance between leveraging broad chemical knowledge and adapting to the specific demands of enzyme- substrae interaction modeling. Formally, given the input graph G. the process is depicted as:
flexible and
Sg = Projector(UniMo|(9)
So E RR
(3)
where D3 denotes the embedding dimensions, and details the projector can be found in App. E.2.
Enzy Adapter Architecture. In the original FrameFlow model, the enzyme features (nodes, edges and frames) are input directly into the IPA layers, without considering substrate interactions. A straightforward method to insert substrate features is to concatenate node features and substrate features and then feed them into the IPA layers [72]. However, we found this approach to be insufficiently effective. Instead, we propose EnzyAdapter where the used cross- cots-ntttntion layers for
5

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substrate features and node features are separate, as depicted in Fig. 3(b). To be specific, we add EnzyAdapter for each layer insert substrate features. Given the substrate features So and the node features in the k-th layer has the output of cross-attention C₁. is computed as follows:
(Q(K)T
Va v). Ck E
C₁= = Atnm Q, K, V) = Softmax
(4)
where Q - h2 Wg
q is the query matrix from the node features, K = So and V = S. are the key, and values matrices from the substrate features. Wg. W₁ and W, are the corresponding weight matrices. Then, Cs is fed into a linear layer before concatenating it to the output of IPA. Hence, the final formulation of Enzy Adapter is defined as:
Linear Lineariinnnneeeee Concat((innant h,)),
C E I Ph.
(5)
Two-stage Training Paradigm. We adopt two-stage training strategy to enhance both stability and efficiency (Fig, 4). In the first stage, we focus on learning substrate- specific features by training only the projector and En- zyAdapter, while keeping the other parts of the prediction network frozen. This allows the model to align substrates with their corresponding enzymes without interference from the backbone. In the second stage, we fine-tune the entire prediction network and the enzyme embedder using LoRA. Simultaneously, we continue updating the projec- tor and Adapter, now guided by the generation loss. This joint optimization ensures that all components remain consistent and aligned with the overall objective.
Stage
Stage
Conserved
Conserved
$
Projector
Ennzyme ambotterr
Projector
MErzyna enbedder
Encyaddster
Ensyldapter Yrust oiner bradee
Predicttion Network
dePrediction ie Network
WA Generated backbene
Genereted bockione
Figure 4: Two-stage training paradigm.
Experiments
5.1 Setup
Implementation Details. We use FrameFlow as the pretrained foundation model and fine-tune on EnzyBind. To improve training efficiency, we group enzymes of the same sequence length into the same batch, minimizing padding overhead. For each input, we generate 20 backbone structures. ProteinMPNN [20] is then used design 5 sequences per backbone, and each sequence's 's structure is predicted using ESMFold [87]. Additional implementation details are provided in App. F.I.
Baseline Models. We compare EnzyControl with EnzyGen, the current state-of-the-ar enzyme generation model, which we retrain on EnzyBind for fair comparison. As models explicitly designed for enzyme backbone generation are limited, we also include several motif sotti-scaafolddng baselines: PROTSEED [24], RFDiffusion [66]. Chroma [88]. FADiff [81], RFDIffusionAA [89], Proteus [90] and Proteina [91]. RFDiffusion, RFDiffusionAA and Chroma are evaluated using their publicly
released checkpoints, as training scripts are not available.
Evaluation Metrics. We evaluate the quality of generated enzyme backbones using both structural and functional metrics. To ensure a fair and consistent comparison across all methods, we adopt a unified evaluation pipeline. Specifically, for every method, the generated backbone structures first processed through inverse folding using ProteinMPNN obtain corresponding sequences. All-atom structures are then predicted from these sequences using ESMFold. All reported metrics are computed on these ESMFold ESMMoodd-redicted structures, ensuring that differences in evaluation outcomes reflect genuine differences in backbone design rather than variations in sequence modeling or structure prediction protocols.
To assess structural consistency, we use two standard measures: TM-score (scTMM, where higher values indicate closer alignment the native structure, and C. CMMDD (scRMSD), where lower values are better. Following FoldFlow [77]. we report the designability rate as the fraction of generated backbones with scRMSD < 2Å. Since Chroma defines designable backbones as those with scTM 0.5, we also include the proportion meeting this threshold.
Because EnzyControl focuses on funetion-aware generation, we include additional metrics to evaluate functional relevance. First, we assess enzymatic function through EC number prediction using

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Table I: Evaluation of structural and functional validity of the generated enzyme backbones EnzyBind. The best-performing results are marked in bold, and the second-bes best results are underlined.
Model
TM.5scTM Design
Self Consistency
EC Match Rate
Enzyme Property
kenn
Bind. Aff. ESP Score
Substrate Specificity
AAR
RMSD
Diversity Novelty
Suce.
Rate
Eval Data
NA
NA
-8.6121
NA
NA
NA
NA
EnzyGen
0.0421
0,0263
0,4385
1.4142
-6.7517
0,4724
0.0566
12.6075
0.1226
0.8735
0.0125
PROTSEED
0.4962
0.4071
0.3764
1.5387
-6.3829
0.6244
0.1463
10.5301
0.4113
0.8252
0.0557
RFDiffasion
0.6932
0.5728
0.0812
2.3412
-6.7416
0.6657
0.1083
20.6224
0.6507
0.5834
0.0239
Chroma
0.6546
0.5163
0.4579
2.5325
-6.7258
0.7116
0.2385
9.5328
0,6296
0.6422
0.0968
FADiI
0.6508
0.5351
0.3342
1.9848
-6.5924
0.6571
0.1126
7,8516
0.4376
0.7567
0.0731
RFDiffuusionAA
0.7042
0.5416
0.1134
2 2,508
6.5233
0.6732
0.1257
19.5187
0.6461
0.6149
0.0361
Proteus
0.6944
0.5697
0,3926
2.1407
-6.6113
0.6816
0.1718
10.6912
0.6716
0.6131
0.0753
Proteina
0.7213
0.6328
0.4583
2.4592
6.3322
0,6709
0.1632
7,2409
0.6642
0.5507
0.0955
Ours
0.8848
0.7160
0.5041
2.9168
6,9303
0.7334
0.1861
6,9923
0.4731
10.1
Improv.
+23%
+13%
+10%
+13%
+3%
+3%
+3%
+23/
Table 2: kcat comparison across EC families on EnzyBind.
EC Family
1.1
1.6
1.14
2.1
2.3
2,5
2.7
3.1
3.2
3.4
3.5
3,6
4.1
4.2
5.6
5.99
6.2
Avg.
EnzyGen
3.52
1.63
1,09
1.18
1.36
1.18
1.29
1.29
1.15
1.34
2.34
191
1.47
1.15
2.97
1.52
2.42
1.41
PRÓTSEED
1.14
1,58
2.26
1.16
2.32
2.31
1.17
1,43
2.02
1.82
111
1.61
2.23
1.47
1.74
1.68
1.70
1.54
RFDiftusion
5.12
3,96
2.03
2.13
2.32
1.83
188
2.37
2.86
2.09
3.62
3,00
4.37
2.26
2.36
1.85
2.52
2.34
Chrama
3.79
3.28
2.59
1.79
1.82
2.62
2.76
3.70
1.84
1.70
3.26
2.20
2.92
2.50
3.18
1.48
2.20
2.53
FADiIT'
2.75
2.90
3.20
2.70
2.05
1.19
2.21
1.75
1.76
191
1.21
198
1.52
1.52
2:45
1.39
1.97
1.98
Ours
4.32
6.48
2.09
2.27
1.83
1.52
2.07
2.63
3.19
3.84
3.75
1.97
5.37
2.71
3.22
1.88
2.67
2.92
15.44%
CLEAN [92], sequence- basee model with over 90% accuracy across benchmarks. We compute the EC match rate as the proportion of generated enzymes that share the same EC number as their native counterparts. To further evaluate catalytic performance, we predict the catalytic rate constant (kcat) using UniKP [93], which takes both sequence and substrate as input. We also evaluate substrate specificity through two metrics: binding affinity, calculated via Gnina [94] (lower is better), and the ESP score from EnzyGen [95], where higher values indicate stronger sezyme-substraan interactions. To provide grounded assessments, we report amino acid recovery (AAR) and RMSD relative to native structures. In addition to performance, we also report Diversity and Novelty.
Finally, we define success rate to capture practical multi-objective design goals. A generated enzyme backbone is considered successful if it meets all of the following: (i) matches the native enzyme's s EC number (EC Match Rate), (ii) scTM 0.5, (iii scRMSD < 2Å, and (iv) shows better binding affinity than its native counterpart. Further metric details are provided in App. F.2.
5.2 Main Results
Table I presents the performance of EnzyControl compared to baseline models on both structural and functional metrics. EnzyControl significantly outperforms existing methods in structural quality. Notably, 0.7160 of its generated enzyme backbones are designable, compared to only 0.6328 for the second- best model, Proteina. It also achieves a substantially higher proportion of structures with scTM > 0.5, showing better self-consistency. On functional metrics, EnzyControl achieves an EC match rate of 0.5041 and a catalytic constant (keat) of 2.9168, outperforming the second-best model by 10% and 15%, respectively. This suggests that EnzyControl not only aligns well with the intended enzymatic functions but also generates enzymes with superior catalytic efficiency. Moreover, it consistently produces enzymes with stronger substrate binding affinity and higher ESP score, achieving 2.8% and 3.1% improvements in these metries compared to the second-best method.
Despite its strong performances in structural and functional evaluation, EnzyControl lags behind RFDiffusion and Chroma in diversity and novelty. This is due to the larger and more heterogeneous training sets used by those models, which may inflate diversity and novelty scores relative to our benchmark, as also noted FoldFlow. However, given our primary goal of designing highly specific and functional enzymes, we prioritize performance on structural and functional metrics over diversity and novelty. We further provide evaluation results across different EC families (cf. Table 2 and 3). The enzymes generated by EnzyControl show higher catalytic efficiency and stronger binding to their
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Table 3: Binding affinity comparison across EC families on EnzyBind.
BC Family
1.1
1.6
1.14
2.1
233
2.5
2.7
3.1
3.2
3.4
3.5
366
4.1
4.2
5.6
5.99
6.2
Avg.
EnzyGen
6.43
-6.79
6.85
6.45
5.09
-744
7.62
6.15
6.48
6.94
5.71
6.33
5.93
5.58
6.52
6.72
5.00
66.55
PROTSEED
-5288
6.14
6.36
6,35
6.28
6.81
6.24
6.53
7663
-662
4.55
5.99
.745
-6388
RFDillusion
-7.37
-5.69
-7.14
6.48
-765
55.5
6.56
6.07
6.47
-745
7.11
7221
6.03
-7.1
6.24
6.82
-748
6..74
Chroma
4.68
-7.36
-6.13
8,94
6.62
5544
7663
5.89
6.72
6.35
5.89
.7.3
5.69
6.60
5,92
..97
-6.73
FADiff
-7.44
-7.28
-6.42
-6.09
-686
-6.74
-7.26
46.69
4.15
6.38
-513
-5.6
-7.15
-7.25
5.83
5.14
-5.08
-6.59
Ours
...78
-7.74
-9.26
-6.36
-8.11
-7.12
-7.21
-6,95
-6.66
-6.55
-6.05
-5.72
-5.90
-5.56
-7.39
-7.84
-10.18
-6.93
+..7%
Table Effect of motif residue perturbation on design performance. Perturbation rate indicates the fraction of motif residues replaced with random amino acids.
Perturbation Rate
0.5 scTM
Designability
EC Match Rate
kcat
Binding Affinity
ESP Score
100%
0.8719
0.6863
0.4764
2,4615
-6.4361
0,7183
50%
0.8761
0.7023
0,4918
2.6540
-6,6105
0,7238
0%
0.8848
0.7160
0.5041
2.9168
-6.9303
0.7334
substrates compared to other methods. These results further demonstrate that EnzyControl produces enzymes that are well-aligned with the functional properties of their EC families.
In addition, since EnzyGen is closely related to our work, we conducted a comparison using the EnzyBench dataset from their study. As shown in Table 6 and 7, our model outperforms EnzyGen terms of Binding Affinity (with a 3% improvement) and PLDDT, achieving the best per- formance on these metrics. While our ESP score is slightly lower than that of EnzyGen, the developer of the ESP score metric has noted that a score above 0.6 already indicates strong enzyme-substraae interaction.
Table 7: Comparison with EnzyGen Enzy Benz.Bench.
Binding Affinity
Model
ESP score
pL.DDT
PROTSEED
-7.94
77.07
RFDiffusion
0,53
-8.57
83.43
ESM2+EGNN
0.61
-8.52
85.13
EnzyGen
0.65
-9.44
87.45
Ours
0.63
-9.76
88.37
5.3 Ablation Study and Analysis
Table 5 presents how performance changes when individual components of our model are removed. We first assess the effect of excluding the MSA input by randomly substituting functional sites with unrelated residues. This results in clear decline in sample quality, especially in functional metrics, highlighting MSA's role in preserving the biological relevance of the generated backbones. Removing the Adapter produces similar degradation in performance. Most metrics show reduced scores, with the largest drop in EC match rate. This suggests the EnzyAdapter is critical for generating enzyme backbones that align with their intended functional profiles.
keat Distribution
Binding Affinity Distributioo
Comparison
Enryktaple
8.26
Wilbout Erayhdudter
Brndig Mirrryy Booa mol))
0m
0.4
6110
1/
With Enzphcapter
EngyAdaae
EC Level44
Difieuut Model Configuaaioon
Ensyyre Commisson Casotfiction Level
Figure 5: Comparison of keat distribution.
Figure 6: Comparison of bind- ing affinity distribution.
Figure 7:
Comparison of EC match rate with different levels.
While Table 5 reports average performance across all samples, our primary objective is to generate enzyme backbones with specific functional properties. To better understand the model's behavior at a finer level, we analyze two key functional indicators: binding affinity and kcat, computing their kernel densities (Fig. 5). The x-axis represents keat, and the y-axis shows kernel density estimates forming a continuous curve. A rightward shift shows improved enzymatic efficiency. When Enzy Adapter is removed, the distribution shifts leftward, showing reduced catalytic performance. Fig. 6 illustrates the predicted binding affinity distribution. Here, lower position on the y-axis means stronger binding
8

---

Table 5: Ablating EnzyControl's 's components. Green denotes relative performance change.
Adupter MSA >0.5scTM Designabiiity
EC Match Rate
kcar
Binding Aflinity
ESP Score
/
0.8848
0.7160
0.5041
2.9168
-6.9303
0.7334
0.8748
0.7067
0.471
2.5833
-6.5523
0.7205
x
/
1,32%
1.30%
5.55%
11.4%
5.54%
1.76%
0.8719
0.6863
0,4764
2,4615
664361
0.7183
x
1.46%
4.15%
15.61%
7.13%
2.06%
0.8684
0.6784 5.25%
0.4627
2.4492
-6.9972
0.7168
x
x
1,85%
8.21%
16.03%
7.69%)
2.26%
Table 6: Binding affinity comparison on EnzyBench. The best best-perfonning results are marked in bold.
EC number
1.1.
L.1..3
1.14.14
1.2.1
2.11
2.3.1
2.4.1
2.4.2
2.5.1
26.1
2.7.1
2.7.00
2.7.11
2.7.4
2.7.7
PROTSEED
-6.61
-4.27
-10.22
-6.71
-8.84
-9.58
-7.43
-00.11
-7.44
-5.46
-8.07
-9.68
-10.24
-1168
-8.00
RFDiflusiontII
-7.11
-4.70
-10.74
-7.21
9961
-10.04
-793
10.64
7.84
6.19
-8.5
10.60
10.44
-12.18
-8.50
ESM2+EGNN
6.66
-7.02
9.57
99.8
10,90
10.75
11.31
-8.47
EnzyGen
88.4
-5.10
-10.34
-6.9
-10.08
-9.89
99.65
-1.99
99.8
-8.05
-10.50
-11.65
-12.51
-11.24
-8.86
Ours
.8.58
.5.86
-11.47
.8.29
-10.11
-10166
-7.58
-12.20
8.32
7.72
9.47
-12.21
10:60
-12.85
-9.12
ECC number
3.1.1
3.1.3
3.1.4
3.2.2
3.4.19
3.4.21
3.5.1
3.5.2
3.6.1
3.6.4
3.6.5
4.1.1
4.2.1
4.6.1
Avg
PROTSEED
-6.01 -7.20
-9.16
88.99
-8.67
-5.44
-9.77
-8.12
REDiffusion=1IF
66.11
-7.70
-11.65
-10.08
9.29
99003
5.54
5.94
9.07
-1.12
9.87
11.24
4,88
9.68
8.75
ESM2+BONN
-7.20
-8.1
-9.25
-00.99
-1115
-1134
-9.76
-8.69
EnzyGen
-7.01
8.89
-11.90
10.550
10.60
100.49
6.37
-6.84
9.23
-3.10
-11.35
11.03
-5.51
-10.44
99.61
Ours
-7.53 99.41
-12.20
-11.72
-9.40
-10.89
-6.65
-7.18
-9.86
-12.40
-11.60
-12.35
-6.17
-11.13
19776
1..6%
(i.e., better performance). The removal of Anzytdapter notably alters the curve, particularly around -6, underscoring its importance in modeling binding interactions.
To investigate the sensitivity of our method to motif annotation quality, conduct additional experiments under controlled motif perturbations. As shown Table 4, even moderate misannotation leads consistent performance degradation across all functional metrics. Specifically, compared to the unperturbed baseline (0% perturbation), the 50% perturbation setting yields 2.4% relative decrease in EC match rate, a 9.0% reduction in predicted Kears and measurable drop in binding affinity (from -6.93 to -6.61 kcal/mol). These results confirm two key insights: (1) model performance highly sensitive to motif fidelity, and (2) our MSA-based annotation strategy is essential for high-quality functional enzyme design. This validates both the design rationale and the importance of accurate functional site annotation in generative modeling.
These results confirm that functional site modeling is critical for designing enzymes with desired activities. However, alignment at finer granularity. we evaluate the EC match rate at the third and fourth levels, as shown in Fig. 7. Our method, EnzyControl, achieves state-of-the-art performances across all levels, demonstrating superior functional consistency compared to existing baselines.
current annotations are based on second-level EC categories. To assess
5.4 Quantitative Analysis of Enzyme Function
Zero-shot. To evaluate the generalization capability of EnzyControl, we test it on entirely unseen substrates and second-level enzyme categories excluded from the training set. As shown in Fig. 8, the generated enzymes maintained strong binding affinitics, averaging -7.0125 kcal/mol across new EC categories and -6.7292 kcal/mol across novel substrates. These results suggest that EnzyControl can effectively design enzymes capable of binding unfamiliar targets.
practical wet-lab settings, engineered enzymes should ideally retain func- tional activity while minimizing sequence length, as shorter sequences typically enhance gene expression and reduce synthesis cost. To assess this, we analyze the lengths of generated enzyme sequences across different kcat intervals. The result is shown in Fig. 9. Compared to the suboptimal baseline, RFDiffusion, EnzyControl consistently produces sequences that are apporximately 30% shorter, while maintaining comparable kcat values across all catalytic ranges.
Residue efficiency.

---

Zero-shot Generalization Capability
Performanc Binding Allinty
Bequerice Length Distribution by Range
130
Node Gradamm
10
Real Range
Figure 8: Zero-shot generalization. Figure 9: Sampled sequence length across different kcat range.
Case study. We further validated EnzyControl's performance through targeted case study using PDB ID: 2cv3 (Fig. 10). The enzyme designed by EnzyContra achieved binding affinity of -9.78 kcal/mol and kcat of 9.72 s-1, representing 51 % improvement in binding affinity and nearly 8x higher catalytic efficiency compared to RFDiffusion. Docking simulations also revealed that the enzyme formed more interaction bonds with the substrate.
Binding Affinity -6.46 kcal/mol
kcat: 1.22 s Binding A Affinity: -9.78 kcal/mol keal: 9.72 s-
Sampled RFDiffusion
Sampled by EnzyControl
Figure 10: Comparison of docking results between EnzyControl and RFDiffusion the 2cv3 enzyme. EnzyControl achieves better substrate-specifich than RFDiffusion. Additionally, the EnzyControl- generated sample form more interaction bonds compared to the enzyme generated by RFDiffusion.
Conclusion
In this work, introduce EnzyControl, method for generating enzyme backbones tailored to small molecule substrates. Unlike prior approaches, EnzyControl integrates conserved functional mo- tifs-identified through MSA-into the design process, helping preserve key catalytic features. also uses substrate information to guide backbone refinement, improving binding specificity. To evaluate design quality, constructed new benchmark EnzyBind and compare EnzyControl against existing methods. Our results show that EnzyControl achieves leading performance across both structural and functional metrics. Additional analyses demonstrate EnzyControl's strong generalization capabilities, including zero-shot performance, further highlighting its effectiveness for functional enzyme design.
Acknowledgements
This work was supported in part by the Ministry Education (MOE T1251RES2309 and MOE T2EP201 25-2099,, the Agency for Science, Technology and Research (A*STAR H25J6a0034), the National Natural Science Foundation of China (No. 62372375), the Shaanxi Province Key R&D Program (No.
114) and the Practice and Innovation Funds for Graduate Students of Northwestern Polytechnical University (PF2024080).
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A Limitations
This work focuses exclusively generating enzyme backbones, without modeling the specific conformations these backbones adopt when binding to substrates, which is highlighted by AtomicFlow [84]. Additionally, while method, EnzyControi, produces diverse backhone samples, it struggles to balance diversity with designability. Enhancing designability without sacrificing diversity remains open challenge and direction for future work.
B More Experimental Results
Due to space limitations, Sec. 5.2 only reports overall test results without distinguishing between enzyme families. To provide a more detailed analysis, Table 9 and 10 present evaluation results broken down by EC family. Additionally, we include functional descriptions for each EC family that appears in test set, sourced from Expasy", as shown Table 8.
We further provide additional discussions and exploratory results to highlight the extensibility of our framework more complex biochemical systems, including multimeric assemblies, multi-substrate reactions, and docking-awar aware design strategies.
Discussion 1: Extension Multimeric and Allosteric Systems. Our current framework focuses single-chain enzyme scaffolds, which simplifies sequence strruccuue mapping but limits applicability multimeric complex allosteric systems. To explore potential extensibility, we experimented with a post-hoc multimeric assembly pipeline. Specifically, we first generate a single-chain enzyme using method, and subsequently apply the RFDiffusion binder design module create complementary partner that binds to the designed enzyme surface. This strategy enables the construction of multi- chain complexes without modifying the core architecture.
Original enzyme backbone
Designed binder:
Preliminary results indicate stable complex formation between the designed enzyme and its binder, suggesting that our framework can be naturally extended multimeric contexts through modular assembly.
Discussion 2: Toward Multi-Substrate Enzyme Generation. While our current model conditions enzyme generation on single substrate molecule, many natural enzymes interact with multiple substrates cofactors. To address this. we propose a flexible extension based on substrate uubstrate-guided guided representation aggregation. For each substrate, the EnzyAdapte Adapter generates distinct substrate-awe aware embedding: these embeddings are then aggregated and passed the Transformer backbone generator. This allows the model to capture multiple substrate interaction contexts simultaneously.
Example sequence: SLOVEEDGWLHGYLAEET SUPESUUHAASSSSTTTTTTORRRAASSSSSS
Substrate 1 (binding affinity = -6.62):
COC(-D) c1c (WC/C-C/ c2ccc (cccc)ccc2cccc2)))(()))))).....)
Substrate 2 (binding affinity -6.4):
This multi-substree extension represents promising direction for broadening the model's biochemi-
cal scope, enabling support for multi-step or cofactor-dependent catalytic reactions.
https//eenzmee.xxxasy.rgg

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Docking-Aware Optimization Strategies. In the current version, the substrate included as an embedding rather than through explicit spatial modeling. To improve docking compatibility, we explored two substrate-aware optimization strategies:
Discussion 3:
Sampling- based selection: Multiple enzyme backbones are generated per substrate; docking is performed on all candidates, and the structure with the best predicted binding affinity is selected.
Motif-branching beam search: Starting from the annotated catalytic motif, we stochastically extend short N- and C-terminal fragments to create diverse partial scaffolds. Each candidate is completed and docked with the substrate, and the best-scoring motif variant used as the seed for further generation.
prototype implementation of the sampling- based approach yielded improved docking affinity:
Before optimization (binding affinity -6.92):
KWK. WLLSVPGGLDDKTR ARKETADG EXTTG//DVVVTTTOCCEEEESSSSS//SSSS
WTA.S//S.S...AAEAEEETEEEEEETTTAT CVKDTLVVYNGKIKSSLEE
After optimization (binding affinity -8.38):
XLBAHHLLLLTTTTAARRTEEOEPPPEEEEEE WWW.LKEEAAAA..ITAAAAARAOOLLL... CVKDTLVCYKKKIVVSPEE
LAPPHLLLDDTTTTTTOOLLLLEEEEEEEEED SAA.AAAAALESLAAAAAAAATAAATTEEATE EXPLYWKGDGELLLLNNSDD
These strategies demonstrate that docking-guider optimization can substantially improve substrate compatibility, and integrating such mechanisms into the main generative loop represents an important future direction.
Table 8: EC number and corresponding enzyme functions appeared in the testset.
EC Number
Function
1.1
Acting the CH-OH group of donors
1.6
Acting on NADH or NADPH
1.14
Acting paired donors, with incorporation or reduction of molecular oxygen.
2.1
Transferring one mar-carbon groups
2.3
Acyltransferases
2.5
Transferring alkyl or aryl groups, other than methyl groups
2.7
Transferring phosphorus-contaiiing groups Acting on ester bonds
3.1
3.2
Glycosylases
3,4
Acting peptide bonds (peptidases) Acting on carbon-nitros bonds, other than peptide bonds
3.5
3,6
Acting on acid anhydrides
4.1
Carbon-carbon lyases
4.2
Carbon-oxygen lyases
5.6
Isomerases altering macromolccular conformation
5.99
Other isomerases
6.2
Forming carbon-sulfur bonds
C
Enzyme Commission number
The Enzyme Commission number (EC number) is numerical classification scheme for enzymes, based on the chemical reactions they catalyze. As system of enzyme nomenclature, every EC number is associated with a recommended name for the corresponding enzyme-catalyzed reaction. EC numbers do not specify enzymes but enzyme-catalyzed reactions. If different enzymes (for instance from different organisms) catalyze the same reaction, then they receive the same EC number.
25

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Table 9: pLDDT comparison on EnzyBench. The best- pest-performing results are marked in bold.
E.C. number
1.1.1
1.14.13
1.14.14
1.2.1
211.1
2.3.1
24.1
24.2
2.5.1
26.1
2.7.1
2.7.10
2.7111
2.74
2.7.7
PROTSBED
77.10
71.19
74.24
78.67
77.40
74.54
75.18
77.11
74.79
75.55
75.90
81.05
74.05
76.50
78.00
RFDiffusian-IF
89.32
82.04
85.1
85.61
81.13
87.51
85.91
ESM2+EGNN
90.67
90.93
90.30
87.67
79.40
84.78
84,80
84.56
90.21
87.47
83.52
88.92
85,59
90.09
81.80
FazyGen
91.86
92.70
91.99
87.71
92.81
87.02
89.69
89.20
85.19
87.64
Ours
91.64
90.33
93.85
89.63
85.46
88.75
93.92
88.94
91.22
90.51
88.76
89.03
88.39
92.45
86.62
EC number
3.1.1
3.1.3
3.1.4
3.2.2
3.4.19
3.4.21
3.5.1
3.5.2
3.6.1
3.6.4
3.6.5
4.1.1
4.2.1
4.6.1
Avg
PROTSBED
76,29
77.89
15.75
78.76
73.56
82.40
76.70
75 90
75.16
74.62
83.46
76.36
78.87
83,31
76.91
RFDilfusion-11
92.04
71.35
82.87
79.02
ESM2+EGNN
87.27
87.05
85.50
72.23
71.31
82.62
83.48
88.69
84,96
73.34
80.77
87.72
89.70
85.48
84,86
EnayGen
77.14
76.31
Ours
90.75
82.33
83.02
87.56
72. 19
90.62
87.47
90.06
87.29
84.16
86.33
88.59
90.48
89.91
88.28
Table 10: ESP score comparison on EnzyBench. Bench. The best- best-performing results are marked in bold.
EC number
L.I.1
1.14.13
1,14.14
1.2.1
2.1.1
2.3.1
2.4.1
2.4.2
2.5.1
2.6.1
2,7.1
2.7.10
2.7.1
2.7.4
2.7.7
PROTSEED
0.54
0.24
0.39
0.57
0.83
0.52
0.29
0.75
0.58
0.45
0.77
0.88
0.81
0.78
0.69
RFDiffusion+IF
0.45
0.54
0.39
0.47
0.43
0.48
0.39
0.52
0.46
0.53
0.50
0.51
0.60
0.55
0.53
ESM2-EGNN
0.58
0.35
0.35
0.63
0.79
0.53
0.32
0.80
0.51
0.76
0.88
0.88
0.77
0.70
EnzyGen
0.64
0.38
0.42
0.72
0.80
0.61
0.38
0.86
0.66
0.53
0.76
0.92
0.93
0.80
0.79
Ours
0.67
0.56
0.51
0.65
0.84
0.59
0.47
0.79
0.72
0.65
0.68
0.53
0.55
0.82
0.61
EC number
3.1.1
3.1.3
3.1.4
3.2.2
3.4.19
3.4.21
3.5.1
3.5.2
3.6.
3.6.4
3.6.5
4.1.1
4.2.1
4.6.1
Avg
PROTSEED
0.70
0.90
0.84
0.48
0.29
0.69
0.31
0.10
0.50
0.57
0.37
0.84
0.83
0.42
0.58
RFDiITusion1IF
0.33
0.61
0.62
0.49
0.62
0.45
0.47
0.44
0.55
0.63
0.59
0.59
0.84
0.45
0.52
ESM2+EGNN
0.71
0.78
0.82
0.43
0.22
0.56
0.35
0.11
0.61
0.73
0.37
0.81
0.89
0.54
0.60
EnzyGon
0.76
0.62
0.88
0.47
0.26
0.73
0.40
0.14
0.66
0.78
0.40
0.80
0.93
0.57
0.64
Ours
0.41
0.44
0.65
0.51
0.63
0.77
0.59
0.53
0.69
0.75
0.66
0.84
0.56
0.60
0.63
We also provide an illustration of EC number in Fig. 11. These categories applied by our EnzyControl to guide the enzyme backbone generation with specific functions.
2.1.2 Hydroxymethyl-. formyl- and related transferases.
1 Oxidoreductases
2.1 Transferring carbon groups.
2.1.2.1 glycine hydroxymetthylranssfrras
2 Transferases
2.2 Transferring aldehyde ketonic groups.
3 Hydrolases
Lyases
5.1 Racemases and epimeroses.
5.1.1 Acting on amino acids and derivatives
5.1.1. alanine racemase
5 Isomerases
5.2 Cis-trans- isomerases
6 Ligases
7 Translocases
Figure 11: Enzyme Commission (EC) number in BRENDA.
D
More Details on the Dataset
D.I
Data Licenses
EnzyBind is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0). This license allows users to copy, redistribute, remix, transform, and build upon the dataset for any purpose, including commercial use, provided appropriate credit is given to the creators. A copy of
26

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the license is available at derived from the PDBbind database. PDBbind is a curated database of protein-ligande complexes derived from the Protein Data Bank (PDB). Users must also comply with the licensing terms of the original PDB and PDBbind datasets.
https//oroo///ooi///oooooo////// bttps////////////..//// 0/. This dataset is
D.2 Complex Preprocessing
Traditional data-splitting strategies for enzyme datasets often rely on chronological order-traand trainnn on complexes published before certain date and testing on those afterward. However, since our objective is to generate enzyme backbones conditioned on desired functions, we adopt functionally meaningful split based on sequence similarity. Specifically, we use CD-HIT [96] to cluster enzyme sequences and ensure that enzymes the training and test sets are disjoint. Clusters are then randomly assigned to either training or testing, and enzyme-substrate pairs are sampled accordingly.
Our dataset consists of 11,100 enzyme-substrate complexes, which are first filtered and standardized. We begin by removing complexes that cannot be parsed by the RDKit library [73]. Following the preprocessing pipeline from EquiBind [97], we standardize each molecule using Open Babel [98], correct hydrogen placements on enzymes, and add missing hydrogens with the reduce tool..
One remaining challenge is that model cannot process multi-chain enzymes symmetric complexes containing repeated enzyme units, as illustrated in Fig. 12. To address this, we retain only the substrate atoms that are within 10Å of any enzyme atom, ensuring that each sample represents a physically relevant interaction while excluding redundant ambiguous structural data.
Figure 12: Examples of multi-chain structures and symmetric enzyme complexes.
D.3 Multiple Sequence Alignment
We identify functional sites in protein sequences using multiple sequence alignment (MSA). As illustrated Fig. 13, each row represents enzyme sequence from the same enzyme family, based on the second-level classification in the BRENDA database.
A
F
M
L E
C O
We align these sequences using MAFFT and identify conserved residues by applying an identity threshold т. Residues that appear consistently across all aligned se- quences sucnces-such&ee as F, L, and E in our example- are consid- ered functional sites. Following the EnzyGen approach, we set 7 0.3 in our experiments.
E
functional sites
binery mask vector (1
meons functional site)
Once functional sites determined, encode them binary vector with the same length as the original sequence. Each position in the vector set to if the corresponding amino acid is a functional site, and 0 otherwise.
Figure 13: Multiple sequence alignment.
27

---

E More Methodology Details
E.1 Backbone Frame Representation
We represent each residue's backbone atoms using local reference frame Fig. 14. As noted in Sec. 4.1, we assume idealized atomic coordinates for the backbone atoms-N, Co C. O-baed on standard chemical bond lengths and angles. To construct local frame for each residue, follow the rigid3Point procedure used in AlphaFold2. This method defines a coordinate frame from the backbone atoms using the following steps:
= C Ca:
N - Ca
U1 / /||l|]
U2 02 e2(e] v2)
e2 = U2 U2
C3 E1 e2
R = concat(e1, 63)
Ca
T (R,
where the first four lines follow from Gram-SShhidt. The operation of going from coordinates to frames is called atom2frame.
R
r == GramSchmidt((1, v2)
Cu
V2
V1
x = Ca E B
V
(rx) SE(3) SO(2)
C
R
Figure 14: Backbone frame representation.
E.2 Model Architecture of Projector
The projector consists of two linear layers and a layer normalization (Fig. 15), it can decompose the substrate embedding from the pretrained Uni-Mol encoder and output well-aligned substrate features.
EnzyAdapter
Linear & Norm
Key
Value
Query
SiLU
Projector
Linear
Substrate Embedding
Enzyme Embedding
Figure 15: Model architecture of projector.
E.3
Edge and Backbone Update
Edge Update. The edge update step is a critical component the message-passing mechanism in the Evoformer architecture used by AlphaFold 2. It updates the pairwise edge features by integrating
28

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information from the source and target node embeddings. This process is formally defined as follows:
hown
Linear( Linear(h:-))
hownn E Dr,/2
in
concat (h Jown: down: 2k =bm))
2 E R
Pn+D:
(6)
2 FLT
LayerNormMML.((ii
zm E R:D:
We elaborate each step below:
At layer & + 1, each node embedding hx+1 IR. is projected to lower-dimensional representa- tion of size D2 using a learned linear transformation. This projection reduces the computational cost of subsequent operations and serves bottleneck that encourages the model to extract the most salient features for edge-level reasoning. Notably, this transformation does not include non non-linear activation function.
To construct the input to the edge update MLP for the residue pair (n, m). the model concatenates three components: the down-projected source node embedding down the down-projected target node embedding down and the current edge feature vector " This combined representation "n lies in IR Pi+D: and captures both contextual and pairwise information relevant to the interaction between residues n and m.
The combined vector = is passed through multi-layer perceptron (MLP), which typically consists of multiple fully connected layers with non-linear activation functions such as ReLU GELU, The MLP outputs an updated edge embedding *k+1 E RD., A Layer Normalization operation applied afterward to stabilize the learning dynamics and ensure consistent feature scaling across the embedding dimension.
Backbone Update. The backbone update step in AlphaFold2 is responsible for refining the 3D position and orientation of each residuc's local frame using a learnable figidbboyy transformation. This transformation modeled element of the special Euclidean group SE(3), combining both rotation and translation. The update proceeds through the following sequence of operations:
b, c, d, Tupdate = Linear(hs)
(a, b, c.
= (1, b, d)/ V 12
e² +
aa + b c² d2
2bc 2nd
2bd + 2ac
Rupdate
2bc + 2ad
a + α d2
2cd - 2ab
(7)
2bd 2ac
2nd + 2ab
a² 122 2 d2
T Tupdase- (Rupdate: Fupdate
Tx+1 Tj- Tupdate
We elaborate each step below:
A linear transformation is applied to the node embedding h4 E IR Dh, producing three scalar values 3 These values parameterize spatial transformation b, c, d and translation vector Zupsate to be applied to the current residue frame.
The scalar 1 is prepended to (b, c, d) to construct a 4-dimensional vector (a, b, c, d), which is then normalized to unit norm. This vector forms unit quaternion, robust and differentiable representation 3D rotation.
The unit quaternion is converted into 3 rotation matrix Rupdate E SO(3) using closed-form expression. This guarantees orthonormality and preserves the group structure of the transformation.
The rotation Rupdate is combined with the translation vector Vupdate to form rigid-body transforma- tion Tupiate E SF(3), representing a learned update in 3D space.
Finally, the transformation Tiptate applied to the current residue frame Ti by composition, yielding the updated frame Tk+1. This results in new position and orientation for the residue, enabling progressive refinement backbone geometry across Evoformer layers.
29

---

During generation, we condition the generation process on known struc- tural motifs which are provided are treated as fixed anchors and stored as 21 | A binary mask
Algorithm 1 Inference
Require:
annotated motifs 21 {trans1, rot, }. model pa- rameters 8, schedule t, motif mask E {0, 1 number
of steps
determines which parts of the struc- ture are generated and which parts are clamped the known motif. each
1: Initialize noisy sample zo < ((x), egg. Gaussian translation and IGSO(3) rotation
2: 3y +- 20
denoising step, overwrite the motif region in the predicted structure with its true value from Tı, ensuring that motif geometry remains unchanged throughout the sampling process. This design enables consistent integration of known substructures while flexibly generating surrounding regions. The pesudocode shown in Alg. 1.
3: for i 0 m 1
4: Zt. ti, I1, motif motiffmask, substrate + DataLoader
5: Predict vector field: Ar + model( t;; 0)
6: Euler step updating: E++1 t + At Ar
7: Overwrite motif structure: I++1 + #141 motif mask + (1 motif mask)
8: end for
9: return Trajectory zo - 211
F
More Details on Experimental Settings
F.
Additional Training Details
We adopt Low-Rank Adaptation (LoRA) with rank of r 16 and scaling factor 32, targeting key linear projection modules across attention and embedding components, as specified in Table 11. The node and edge embeddings are configured with dimensionalities of 256 and 128, respectively.
Our model supports a maximum of 2000 residues and embeds 1000 discrete timesteps using both sinusoidal and learned positional encodings. Node-level features include spatial coordinates, timestep embeddings, and optional chain-level signals. For edge features, we employ relative position encoding, discretized into 22 bins, and include diffusion-specific masks and self-conditioning mechanisms to enhance robustness.
The IPA module comprises six stacked blocks with multi-head attention (8 heads), point-based QK and V projections, and a lightweight sequence-leve Transformer consisting of layers with heads each. These configurations were selected based on empirical validation to balance computational efficiency with modeling capacity.
F.2 Evaluation Details
This section details the computation of each evaluation metric used in our study.
To evaluate self-consistency, we measure the structural similarity between the generated protein backbones and the all-atom structures predicted by ESMFold. Specifically, we compute the TM-score and RMSD between the two. TM-scores are calculated using the tmtools, while RMSD is computed after structural alignment following the procedure described in FrameFlow.
We assess two aspects of enzyme properties: EC number classification and catalytic efficiency (keat). Both are predicted using pretrained models. For EC number prediction, we use CLEAN, which takes only the amino acid sequence input. For keat prediction, we input both the sequence and the substrate's 's SMILES representation into a separate predictive model. We define the HC match rate as the proportion of samples whose predicted EC number matches that of the corresponding native enzyme. For kcat, we report the average predicted value across all samples.
Substrate binding is evaluated using two methods. First, we perform docking simulations with GNINA to assess how well each enzyme binds to a given molecule. We treat the entire enzyme structure as the search space for docking. Second, compute the ESP score using the pretrained ESP model, which takes both the sequence and substrate as inputs. The final ESP score is the mean value across all samples.
Diversity measures the proportion of unique structural clusters among generated enzymes, while Novelty reflects how dissimilar the generated structures are from native ones, computed as the average
30

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Table 11: Model configuration and hyperparameter settings.
Module
Parameter
Value
Description
lora_r
16
Rank of low low-rank matrices in LoRA.
lora_alpha
32
Scaling factor for LoRA A adoptation.
LoRA Settings
_ora_drepout
0.0
Dropout applied LoRA layers.
lora_bias
"none"
Whether LoRA includes bias temms.
List modules
Target modules for LoRA adaptation.
jode_enbed_size
256
Dimensionality of node embeddings.
Fibeeding Dimensions
edge_enbad_stae
128
Dimensionality of edge emheddings.
6_8
256
Size node feature representation.
c_pos_emb
128
Positional embedding dimensionality.
Node Features
max_num_res
c_tinestep_cnt
2000
128
Maximum number of residues.
Timestep embedding dimensionality.
tinestep_int
1000
Number of discrete time intervals.
eubed_chain
False
Whether embed chain-level info.
single_bias a c_s
2
Number of single bias transitions.
256
Node representation dimension.
c_p
128
Edge embedding dimensionality. Relative positional embedding size.
Edge Features
relpos_k
64
feat_dim
64
Feature dimensionality. Number of distance bins.
aun bins
22
self_condition
Whether apply conditionng.
c_a
256
Input node feature dimension.
c_2
128
Input edge feature dimension.
c_bidden
128
Hidden dimension IPA module.
no_ bo_heads
8
Number of attention heads.
IPA Module
no_qk--pointa
Number QK reference points.
_o_V_points
12
Number of reference points.
seq_tifmr
hog_ttfr__uu__eeee muuu_hedde
Heads in sequence-level Transformer.
2
Layers sequence level Transformer.
nun bln_blocks
Number of IPA module blocks.
of the maximum TM-scores between each designable enzyme and all native proteins-lower values imply more novel designs. We use Foldseek. Diversity is measured by clustering the generated proteins with Foldseek and calculating the ratio of the number of clusters to the total number of samples. Novelty is defined as the average of the maximum TM-scores between each generated enzyme and all native proteins. Lower scores indicate greater novelty, as they reflect less structural similarity to known proteins.
While these metrics are model-based, they are grounded in experimentally validated frameworks and have demonstrated predictive fidelity in wet-lab settings:
EC number prediction is performed using CLEAN [92], a sequence-based model trained and benchmarked on large-scale enzymatic datasets. CLEAN achieves over 90% accuracy on standard benchmarks and has been experimentally validated on real enzymes such as MJ1651 and SsFIA, with demonstrated capability predict novel EC assignments.
Catalytic rate constant (Keat) is estimated via UniKP [93], which is trained on experimentally measured kinetic data. Its predictions have been corroborated by wet-lab validation on tyrosine ammonia lyase (TAL), confirming its reliability in capturing catalytic efficiency.
Euzyme-substrate interaction strength is quantified using the ESP score from EnzyGen [95], which incorporates statistical testing to ensure interpretability and confidence in its predictions.
Binding affinity is computed using Gnina [94], physics- based molecular docking tool. This provides an orthogonal, structure-driven validation of substrate compatibility that complements sequence- and learning-basee functional metrics.
F.3
Computational Resource
All experiments were conducted on a high-performance computing node equipped with 4x NVIDIA A100 GPUs (80GB) and dual Intel(R) Xeon(R) Gold 6348 CPUs (2.60GHz, 2 sockets, 28 cores per socket, 1 112 threads in total).
31


Reviews:
[{"review_id": "QnWIATamu8", "reviewer": "Reviewer_DtbU", "summary": "This paper presents EnzyControl, a method for generating enzyme backbones conditioned on both functional catalytic motifs (extracted via MSA) and substrate molecules (represented as chemical graphs). The approach introduces EnzyAdapter, a modular component that injects substrate awareness using cross-attention, and employs a two-stage training strategy for stability and alignment. EnzyControl outperforms existing methods on structural and functional metrics, using a new benchmark dataset called EnzyBind curated from experimentally validated enzyme–substrate complexes.", "questions": "Have the authors tested the effect of motif misannotation?\nWhy is the substrate encoder (Uni-Mol) frozen?", "limitations": "yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: The method introduces a novel approach to enzyme design by incorporating substrate-aware generation using the modular EnzyAdapter architecture. The paper also provides a comprehensive evaluation, including ablation studies, comparison against strong baselines. The paper introduces EnzyBind, a curated dataset of 11k enzyme–substrate pairs, which will serve as a useful benchmark for future work.\n\nWeaknesses: The method is currently limited to single-chain enzyme scaffolds, with no support for modeling multimeric or complex allosteric systems, which are common in many natural enzymes. This constraint reduces the method’s applicability to broader classes of enzymes.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns during the rebuttal period.", "rebuttal": "We sincerely appreciate the reviewers’ thoughtful comments and constructive suggestions. These insights have been invaluable in helping us improve the clarity, rigor, and scope of our manuscript. In response, we have carefully revised the paper and provide detailed point-by-point replies to each of the comments below.\n\n> **Q1:** The method is currently limited to single-chain enzyme scaffolds, with no support for modeling multimeric or complex allosteric systems, which are common in many natural enzymes. This constraint reduces the method’s applicability to broader classes of enzymes.\n\n**Response:** We appreciate your concern regarding the complexity of allosteric systems and multimeric enzymes. Our decision to focus on single-chain backbone design is based on the following considerations:\n\n- **Simplification and practicality**: From the substrate’s point of view, what matters is the immediate local environment it interacts with—namely, the binding pocket. This local region defines the key physicochemical interactions, regardless of whether it is formed by a single chain or assembled from multiple chains. Therefore, in our approach, the pocket is treated as a self-contained structural fragment, abstracted from the broader oligomeric context. Designing within a single-chain framework simplifies the problem while still capturing the essential features of the pocket environment. This makes the method more tractable and broadly applicable in practice.\n- **Data availability**: There is a significant imbalance in data: we identified only ~2,659 multimeric enzyme–substrate complexes with sufficient functional annotations in the PDB. In contrast, high-quality single-chain data is far more abundant, enabling more reliable training and evaluation.\n- **Extensibility of our approach**: In response to your suggestion, we explored an extension to multimeric assemblies via post-hoc binder design. Specifically, we first generate a single-chain enzyme using our method, and then apply RFDiffusion’s binder design module to create a complementary chain that binds to the enzyme surface. This results in a multimeric complex composed of the designed enzyme and a newly generated binding partner.\n\nWe experimented with this pipeline and present preliminary results on multimeric complex formation below.\n\n**original backbone:** MELPKRRIRLLVLYTPEVEAGPLADPAKREAHIREVVAKVNELLKPFNIEIVLVDIISIGSNYDVDFSAPCEALRAQLEALVATKLKKEIDFDMAVVFGGESLAPCIEGFAALGADISTGRGVALAVLDPSDAEADARAVAAQILRLLGVTAPPERRVGPNGGDEDGVVWGEDGVEESLAWSLEQLRRYFEEHQPAEYLLPP\n\n**binder:** SGLERWKEIDENNQWEELTKELLAKQVYRPETNAATGATIIATGPAGAELGAALRAAYGPDPATLVGGVLPRPTTTGIGYAFLGGVQTPEELARIARLLVSDPTAAVAAYVMTAEDGRIHWDEAAGRAWLAE\n\n\n\n> **Q2:** Have the authors tested the effect of motif misannotation?\n\n**Response:** In ablation study (Table 4, w/o MSA), we have a similar experiment, where the motif (*i.e.*, MSA) is completely random. We can observe that this ablation shows sigificant performance drop.\n\nFollowing your instruction, we now include a new experiment where only 50% of motif residues are randomly perturbed. The results (shown below) demonstrate that (1) motif misannotation substantially affects model performance, and (2) our MSA-based annotation strategy is important for functional enzyme design.\n\n| perturbed rate | >0.5scTM | Designability | EC Match Rate | kcat   | Binding Affinity | ESP score |\n| -------------- | -------- | ------------- | ------------- | ------ | ---------------- | --------- |\n| 100%           | 0.8719   | 0.6863        | 0.4764        | 2.4615 | -6.4361          | 0.7183    |\n| 50%            | 0.8761   | 0.7023        | 0.4918        | 2.6540 | -6.6105          | 0.7238    |\n| 0%             | 0.8848   | 0.7160        | 0.5041        | 2.9168 | -6.9303          | 0.7334    |\n\n\n\n> **Q3:** Why is the substrate encoder (Uni-Mol) frozen?\n\n**Response:** Our decision to freeze the encoder was carefully considered based on several factors:\n\n- **Preserving generalization from large-scale pretraining.** Uni-Mol was pretrained on **209 million** molecular conformers, capturing extensive chemical structure and interaction knowledge. In contrast, our downstream dataset contains only about **11,000 enzyme–substrate pairs**, which is several orders of magnitude smaller. Fine-tuning such a large pretrained model on limited data would risk overfitting and degradation of its generalizable representations.\n- **Adaptation via a learnable projector.** To bridge the domain gap without modifying the frozen encoder, we introduce a learnable projector that maps Uni-Mol’s substrate embeddings into the enzyme representation space. This enables our model to incorporate substrate-specific information in a trainable and flexible way, while retaining the pretrained encoder’s robustness.\n\nWe believe this approach offers a strong trade-off between leveraging large-scale chemical knowledge and adapting to the specificity of the enzyme generation task. We have clarified this rationale in the revised manuscript."}, {"review_id": "7qQiN0fYew", "reviewer": "Reviewer_GqqU", "summary": "This paper proposes EnzyControl, a flow-matching method to design enzyme backbone structures conditioned on automatically mined functional sites and corresponding substrates. The proposed method shows good performance on many metrics, demonstrating its good performance in designing high-quality enzyme backbone structures.", "questions": "Please see above weaknesses.", "limitations": "Please see above weaknesses.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. The paper curates an enzyme-substrate complex benchmark EnzyBind from PDBbind, which is useful for either designing enzyme-substrate complexes  or substrate-binding enzyme design.\n\n2. The proposed method shows good performance compared to strong baselines.\n\n3. The writing of this paper is pretty good, making this paper easy to follow.\n\nWeaknesses:\n\n1. In line 47, the authors mentioned \"existing benchmarks are mostly synthetic with limited experimental grounding and lacking evaluation protocols tailed to enzyme families\". According to my best knowledge, EnzyBench is curated from PDB, which are all experimentally confirmed structures, and it is classified into fourth-level EC categories. So this statement is not right. However, I do think EnzyBind is meaningful as EnzyBench are composed of only enzymes while EnzyBind are enzyme-substrate complexes. The problem is in this paper, the authors didn't design enzyme-substrate complexes or designing substrate-binding enzyme backbones. Instead, they just model the substrate representation and then design an enzyme without modeling enzyme-substrate interactions. This way makes EnzyBind meaningless. As the way this paper used, EnzyBench can achieve the same goal. \n\n2. In experiments, there are many metrics evaluated on the designed enzyme sequences. While this paper targets at designing enzyme backbone structures, they subsequently applied ProteinMPNN to generate an enzyme sequence to achieve evaluation. This is actually unfair to other baselines models that directly design enzyme sequences.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for the extensive efforts on reviewing our submission and the valuable suggestion. Here we present a detailed response to address your concern.\n\n> **Q1:** In line 47, the authors mentioned \"existing benchmarks are mostly synthetic with limited experimental grounding and lacking evaluation protocols tailed to enzyme families\". According to my best knowledge, EnzyBench is curated from PDB, which are all experimentally confirmed structures, and it is classified into fourth-level EC categories. So this statement is not right.\n\n**Response:** We thank for your valuable feedback and clarify our intention and provide a more precise explanation below:\n\n- **Clarification on synthetic and predicted structures**: Our original statement aimed to highlight that many recent enzyme-related benchmarks—such as EnzymeFill [1] and ClipZyme [2]-include structures predicted by AlphaFold2 rather than experimentally resolved ones. These datasets, though useful, may not offer the level of structural accuracy needed for generative tasks that are highly sensitive to atomic detail.\n- **Specific limitation of EnzyBench for enzyme backbone generation**: While EnzyBench is indeed experimentally grounded and EC-classified, it provides only $C_\\alpha$ coordinates, not full atomic backbone structures (e.g. $C_\\beta$, $N$, $O$). This makes it less suitable for enzyme backbone generation tasks, which require complete and precise 3D geometries.\n- **Design considerations in EnzyBind**: To address these limitations, EnzyBind includes only experimentally determined structures. All entries are supported by literature references, and structures were resolved via X-ray crystallography or cryo-EM. We also applied a resolution filter during data curation to ensure structural quality.\n\n\n\n> **Q2:** The authors didn't design enzyme-substrate complexes or designing substrate-binding enzyme backbones. Instead, they just model the substrate representation and then design an enzyme without modeling enzyme-substrate interactions. This way makes EnzyBind meaningless. As the way this paper used, EnzyBench can achieve the same goal.\n\n**Response:** We thank the reviewer for the thoughtful comment. While EnzyBench provides a valuable evaluation protocol by filtering generated enzymes using substrate information post hoc, our method integrates enzyme-substrate interaction modeling directly into the generation process. Our response is as follows:\n\n- **Explicit interaction modeling during generation**: Our method integrates interaction signals directly into the generative process. Specifically, we jointly input the **substrate representation** and the **annotation motif**—which captures reaction-specific and functional context—into the model. These two sources of information are fused through the **EnzyAdapter** module, which facilitates **cross-modal information exchange** during backbone generation. This design allows the model to condition enzyme structure generation on both substrate characteristics and catalytic context. As a result, our task explicitly targets **substrate-binding enzyme backbone generation**, rather than generic scaffold generation.\n- **Interaction-aware evaluation**: To assess how well the generated enzymes bind the given substrates, we evaluated multiple interaction-specific metrics, including kcat, Binding affinity and ESP score. These metrics directly reflect the compatibility between enzyme and substrate.\n- **Empirical support via ablation studies**: Our ablation experiments (Table 4) demonstrate that removing either the **annotated motif** or the **EnzyAdapter** significantly reduces performance on the above interaction-related metrics. This confirms the importance of these components for learning enzyme-substrate interactions during generation.\n\n\n\n> **Q3:** In experiments, there are many metrics evaluated on the designed enzyme sequences. While this paper targets at designing enzyme backbone structures, they subsequently applied ProteinMPNN to generate an enzyme sequence to achieve evaluation. This is actually unfair to other baselines models that directly design enzyme sequences.\n\n**Response**: Thank you for the valuable comment. We apologize for the lack of clarity in the original manuscript. To ensure a fair comparison, **all baseline models were evaluated using the same pipeline**. We have revised the manuscript to make this evaluation strategy explicit. Below, we provide further clarification:\n\n- For all motif-scaffolding baselines, including those not originally designed for structure generation, we followed a unified evaluation protocol: the generated backbones were first passed through **inverse folding using ProteinMPNN**, then **all-atom structures were predicted with ESMFold**, and finally, all evaluation metrics were computed on the predicted structures.\n- For *EnzyGen*, which performs backbone–sequence co-design, we extracted the generated backbone structures and evaluated them using the **same pipeline**—inverse folding, followed by ESMFold-based structure prediction and metric computation—to maintain consistency across all methods.\n\nWe again apologize for the earlier ambiguity and have updated the manuscript to clearly describe this standardized evaluation approach.\n\n\n\n**Reference:**\n\n[1] Hua C, Liu Y, Zhang D, et al. Enzymeflow: Generating reaction-specific enzyme catalytic pockets through flow matching and co-evolutionary dynamics[J]. arXiv preprint arXiv:2410.00327, 2024.\n\n[2] Mikhael P G, Chinn I, Barzilay R. Clipzyme: Reaction-conditioned virtual screening of enzymes[J]. arXiv preprint arXiv:2402.06748, 2024."}, {"review_id": "uHYg907P93", "reviewer": "Reviewer_F4KC", "summary": "The paper presents **EnzyControl**, a method for generating enzyme backbones with functional and substrate-specific control. It introduces **EnzyBind**, a curated dataset of 11,100 experimentally validated enzyme-substrate pairs with annotated catalytic sites. EnzyControl builds on a motif-scaffolding backbone (FrameFlow), augmented with a novel **EnzyAdapter** that incorporates substrate features via cross-attention, and a two-stage training scheme that first aligns substrate-enzyme representations and then fine-tunes the full model. The method achieves significant improvements over baselines in structural and functional metrics, including a 25% gain in designability, 15% in catalytic efficiency, and 10% in EC number match. It also shows strong generalization to unseen substrates and generates compact, functional enzyme designs, advancing the state of enzyme generation for practical applications.", "questions": "- The paper emphasizes the critical role of evolutionarily conserved catalytic motifs derived from MSA, but it remains unclear precisely how these conserved residues are represented and fed into the generative model. Could you provide more explicit detail (perhaps a figure or algorithm pseudocode) describing how functional sites identified by MSA annotations are integrated into the input representation?\n- Your current approach conditions enzyme generation on a single substrate molecule, limiting the model’s applicability to single-substrate enzyme reactions. Many practical enzymes, however, bind multiple substrates or cofactors simultaneously. How might you extend your current framework to handle multi-substrate scenarios or enzymes catalyzing multi-step reactions?\n- The evaluation relies heavily on predicted functional metrics (e.g., predicted kcat, binding affinity, ESP score). How confident are you that these predicted metrics translate to real-world enzyme functionality? Have you considered evaluating or cross-validating your generated designs using orthogonal computational methods or literature-based validation?\n- The EnzyAdapter component and the two-stage training scheme are central to your method. However, similar adapter-based mechanisms and multi-stage fine-tuning procedures are increasingly common in generative modeling literature. Could you clarify explicitly the unique contributions or adaptations made to these elements specifically tailored for enzyme backbone generation?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n* **Quality**: The paper proposes a well-designed and technically solid framework, EnzyControl, that integrates conserved catalytic motifs and substrate-specific control into enzyme backbone generation. The architecture is systematically validated through comprehensive benchmarks and ablation studies, showing clear improvements over state-of-the-art baselines in both structure and function-related metrics.\n\n* **Clarity**: Overall, the paper is clearly structured, and its core contributions are well explained. Evaluation metrics are well chosen, and the use of visual aids such as diagrams and tables enhances readability. The two-stage training strategy is described in a transparent and reproducible manner.\n\n* **Significance**: The work addresses a central challenge in computational enzyme design: generating functionally valid enzyme backbones conditioned on both conserved motifs and substrate molecules. However, the current method is limited to **single-substrate scenarios**, which restricts its applicability to more complex or realistic biochemical tasks where enzymes interact with multiple substrates or cofactors. Extending the framework to handle such cases would greatly improve its practical relevance.\n\n* **Originality**: The paper introduces several novel ideas, including the EnzyAdapter module for cross-modal conditioning and the curated EnzyBind dataset with MSA-based functional site annotations. The combination of motif preservation and substrate-awareness in a unified generative model is a clear advancement over prior enzyme design approaches.\n\n---\n\n**Weaknesses:**\n\n* **Quality**: The method lacks experimental (wet-lab) validation, and while not strictly necessary at this stage, it does limit confidence in the model’s downstream utility. In addition, comparisons against newer or more diverse generative enzyme design baselines (e.g. RFdiffusion2) could strengthen the empirical case.\n\n* **Clarity**: One notable gap is the insufficient explanation of how conserved sites derived from MSA are actually incorporated into the model. Although functional site preservation is central to the method’s design, the representation, encoding, or usage of these motifs in the model pipeline is not clearly detailed, which could hinder understanding and replication.\n\n* **Significance**: While the model performs well on benchmarks, its restriction to single-substrate input limits its generalizability to more complex enzymatic reactions. Many real-world enzymes catalyze multi-substrate or multi-step reactions, which are currently beyond the scope of this framework.\n\n* **Originality**: Although EnzyControl’s integration of components is innovative, many of its building blocks—such as flow matching, cross-attention, and adapter layers—are borrowed from existing techniques in protein design or molecular modeling. The novelty lies more in the synthesis of these ideas than in any single algorithmic breakthrough.", "was_revised": true, "final_justification": "The authors have addressed most of my concerns during the rebuttal period.", "rebuttal": "Thank you for your feedback. Your suggestions have enhanced the reliability of our work and the readability of the paper. Below are responses to your questions one by one.\n\n> **Q1:** The method lacks experimental (wet-lab) validation, and while not strictly necessary at this stage, it does limit confidence in the model’s downstream utility.\n\n**Response:** Thank you for the suggestion. While our study does not include direct wet-lab experiments, we address this concern from three angles:\n\n- **Standard evaluation protocol**: We follow widely adopted evaluation practices in protein design (e.g., Proteina [1]), which rely on computational predictors due to the high cost of wet-lab testing.\n- **Metrics grounded in experimental validation:** Our functional metrics (e.g., kcat) are computed using models validated against real biological assays.\n- Additional metrics: We also report AAR and RMSD relative to native structures, which provide grounded assessments (see table below).\n\n| Metrics | EnzyGen | PROTSEED | RFDiffusion | Chroma | FADiff | Ours   |\n| ------- | ------- | -------- | ----------- | ------ | ------ | ------ |\n| AAR     | 0.0566  | 0.1463   | 0.1083      | 0.2385 | 0.1126 | 0.1861 |\n| RMSD    | 12.6075 | 10.5301  | 20.6224     | 9.5328 | 7.8516 | 6.9923 |\n\nWe agree that future experimental synthesis and testing are important and plan to explore this direction in follow-up work.\n\n\n\n> **Q2:** Comparisons against newer or more diverse generative enzyme design baselines (e.g. RFdiffusion2,) could strengthen the empirical case.\n\n**Response:** Following your instruction, we add RFdiffusion-AA [5], Proteus [4], and Proteina [1] as additional baselines, while RFDiffusion2 is not publicly available. The results are presented below:\n\n| Model        | >0.5scTM | Designability | EC Match Rate | kcat   | Binding Affinity | ESP Score | AAR    | RMSD    |\n| ------------ | -------- | ------------- | ------------- | ------ | ---------------- | --------- | ------ | ------- |\n| RFDiffusonAA | 0.7042   | 0.5416        | 0.1134        | 2.5808 | -6.5233          | 0.6732    | 0.1257 | 19.5187 |\n| Proteus      | 0.6944   | 0.5697        | 0.3926        | 2.1407 | -6.4613          | 0.6816    | 0.1718 | 10.6912 |\n| Proteina     | 0.7213   | 0.6328        | 0.4583        | 2.4592 | -6.3522          | 0.6709    | 0.1632 | 7.2409  |\n\nWe appreciate your suggestion, which has helped improve the completeness and robustness of our experimental comparisons.\n\n\n\n> **Q3:** Although EnzyControl’s integration of components is innovative, many of its building blocks are borrowed from existing techniques in protein design or molecular modeling. The novelty lies more in the synthesis of these ideas than in any single algorithmic breakthrough.\n\n**Response:** We appreciate your observation. However, we respectfully emphasize that the core novelty of our work lies in **the principled integration of enzymology-specific insights** into the modeling pipeline, rather than in isolated algorithmic innovations:\n\n- **EC-based Motif Annotation:** We extract conserved motifs by clustering enzymes by EC number, capturing key functional regions to guide backbone generation.\n- **Substrate Conditioning via EnzyAdapter:** EnzyAdapter enables substrate-conditioned backbone generation by aligning enzyme and substrate representations. It injects substrate-specific signals, allowing scaffold design tailored to the target substrate.\n\nAdditionally, we contribute **functional evaluation metrics beyond structure** (e.g., kcat, ESP score) and **a high-quality dataset** for structure-aware design tasks.\n\nWe will revise the manuscript to better emphasize this integration and kindly ask the reviewer to consider the contribution in this context.\n\n\n\n> **Q4:** It remains unclear precisely how conserved residues are represented and fed into the generative model. Could you provide more explicit detail (perhaps a figure or algorithm pseudocode) describing how functional sites identified by MSA annotations are integrated into the input representation?\n\n**Response:** Thank you for raising this important point. Below, we provide a more explicit explanation of the process.\n\nDuring generation, known structural motifs are treated as fixed anchors, and a binary mask specifies which regions to generate. At each denoising step, we overwrite the motif region with its true value to preserve its geometry, allowing flexible generation around a fixed functional core. The pesudocode is shown below:\n\n---\n**Require:** Annotated motifs: $\\mathbf{x_1}$, Model output: $\\mathbf{x_t}$, Motif mask: $\\mathbf{m} \\in \\{0, 1\\}^n$, Number of timesteps: $t$\n\n---\n\n1. Generate a binary mask $\\mathbf{m} \\in \\{0, 1\\}^n$.\n2. Center the motif coordinates: $\\mathbf{x_1} \\leftarrow \\mathbf{x_1} - \\bar{\\mathbf{x}}_1$\n3. **for** $i = 0$ to $t - 1$ **do**  \n   &emsp;1. *# diffusion procedure*  \n   &emsp;2. *Overwrite the motif region: $\\mathbf{x_t} \\leftarrow \\mathbf{x_t} \\cdot \\mathbf{m} + \\mathbf{x_1} \\cdot (1 - \\mathbf{m})$*\n\n   &emsp;3. *# compute loss*\n4. **end for**\n---\n\nWe will include this explanation in the revised manuscript to clarify the pipeline for motif integration.\n\n\n\n> **Q5:** Your current approach conditions enzyme generation on a single substrate molecule. Many practical enzymes, however, bind multiple substrates or cofactors simultaneously. How might you extend your current framework to handle multi-substrate scenarios or enzymes catalyzing multi-step reactions?\n\n**Response:** Thank you for the insightful comment. We agree that supporting multi-substrate reactions would broaden the model’s applicability. While our current design focuses on single substrate, our architecture is flexible and can be readily extended to multi-substrate scenarios.\n\nAs a preliminary idea, we suggest using the same annotated motifs for each substrate, obtaining individual substrate-guided representations via EnzyAdapter, and aggregating them before passing them to the Transformer generator. This allows the model to capture multiple interaction contexts. A case study is shown below.\n\n**sequence:** LSPEEIEEIKANNQWAERTAALDKTVTLNPSLTLGDWTVDNTGGLDDPDAATRLCRGTIDLATGKIGSGGSVGEKDGGVTIGGLSLGVEEDGVLHGYLAEISASGATVRVPVRPDDTYRDLAARAQAQLGTSSDAATGATLTLTDIEVRNVGFIITASSA\n\n**substrate1 (binding affinity = -6.62):** COC(=O)c1c(OC/C=C/c2ccc(c(c2)c2onc(c2)C(=O)O)F)cccc1O\n\n**substrate2 (binding affinity = -6.4):** OCc1ccc(c(c1)N(c1ccnc(n1)Nc1cc(cc(c1)S(=O)(=O)C)N1CCOCC1)C)C\n\nWe appreciate the suggestion and view flexible support for multi-substrate inputs as a valuable direction for future work.\n\n\n\n> **Q6:** The evaluation relies heavily on predicted functional metrics. How confident are you that these predicted metrics translate to real-world enzyme functionality? Have you considered evaluating or cross-validating your generated designs using orthogonal computational methods or literature-based validation?\n\n**Response:** Thank you for the thoughtful question. We agree that the reliability of predicted functional metrics is essential for evaluating the practical utility of generated enzymes. While our evaluation relies on predictive models, these metrics are grounded in experimentally validated methods:\n\n- kcat, EC Match Rate, and ESP Score are computed using deep learning models benchmarked against wet-lab data:\n  - CLEAN [2] (EC classification) has been validated on real enzymes like MJ1651 and SsFIA, and can predict novel EC numbers.\n  - UniKP [3] (kcat prediction) is trained on experimental kinetics data and has been validated with wet-lab results on tyrosine ammonia lyase (TAL).\n  - ESP score [6] quantifies enzyme–substrate interaction strength, with statistical testing for interpretability and confidence.\n- Binding affinity is computed using docking, which is physics-based, offering an independent, orthogonal validation of structural compatibility.\n\nIn response to your suggestion, we also evaluated similarity between generated and native scaffolds using AAR and RMSD (see Q1). These additional metrics further support the plausibility of our results.\n\nWe will include this discussion and the corresponding table in the revised manuscript, and thank you for encouraging a more comprehensive assessment.\n\n\n\n> **Q7:** The EnzyAdapter component and the two-stage training scheme are central to your method. However, similar adapter-based mechanisms and multi-stage fine-tuning procedures are increasingly common in generative modeling literature. Could you clarify explicitly the unique contributions or adaptations made to these elements specifically tailored for enzyme backbone generation?\n\n**Response:** Thank you for the insightful question. While we acknowledge that these elements are increasingly used in the generative modeling literature, our contribution lies in **how these components are adapted and integrated with enzyme-specific biological context**. We also contribute a suit of functional evaluation metrics and a benchmark. For a detailed explanation, please refer to our response to **Q3**.\n\n\n\n**Reference:**\n\n[1] Geffner T, Didi K, Zhang Z, et al. Proteina: Scaling flow-based protein structure generative models[J]. arXiv preprint arXiv:2503.00710, 2025.\n\n[2] Yu T, Cui H, Li J C, et al. Enzyme function prediction using contrastive learning[J]. Science, 2023, 379(6639): 1358-1363.\n\n[3] Yu H, Deng H, He J, et al. UniKP: a unified framework for the prediction of enzyme kinetic parameters[J]. Nature communications, 2023, 14(1): 8211.\n\n[4] Wang C, Qu Y, Peng Z, et al. Proteus: exploring protein structure generation for enhanced designability and efficiency[J]. bioRxiv, 2024: 2024.02. 10.579791.\n\n[5] Krishna R, Wang J, Ahern W, et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom[J]. Science, 2024, 384(6693): eadl2528.\n\n[6] Kroll A, Ranjan S, Engqvist M K M, et al. A general model to predict small molecule substrates of enzymes based on machine and deep learning[J]. Nature communications, 2023, 14(1): 2787."}, {"review_id": "hio0P0qd5L", "reviewer": "Reviewer_YKVy", "summary": "This paper introduces EnzyControl, a framework for enzyme backbone generation with substrate-specific and functional-site conditioning. The model integrates an adapter mechanism (\"EnzyAdapter\") into a pre-trained motif-scaffolding model (FrameFlow) and incorporates multiple sequence alignment (MSA) data and substrate information. The authors also contribute a curated dataset (EnzyBind) and demonstrate quantitative improvements over prior methods in structure quality and catalytic function.", "questions": "N.A.", "limitations": "N.A.", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n1. Enzyme design conditioned on both functional sites and small molecule substrates is a meaningful and underexplored challenge in protein generation.\n\n2. Dataset Contribution: EnzyBind is a valuable addition, with ~11k experimentally validated enzyme–substrate complexes and MSA-annotated catalytic sites.\n\nWeakness:\n\n1. The backbone generation component largely builds upon FrameFlow. The architectural contributions (EnzyAdapter) are relatively minor: a standard cross-attention module and LoRA-based fine-tuning. This might be better framed as a control-oriented application of existing methods rather than a novel generative technique.\n\n2. The paper evaluates against RFdiffusion, Chroma, and EnzyGen—but omits several newer models such as RFDiffusionAA, Proteus, or Proteina which significantly advance generation quality, efficiency, or multimodal conditioning.\n\n3. Minor mistakes: In Table 2 column 1.1, it seems RFDiffusion is the best.\n\n4. The substrate is only incorporated as a feature embedding; there’s no explicit 3D placement or docking compatibility optimization, which is crucial for real catalysis.", "was_revised": true, "final_justification": "After the rebuttal, the technical contribution of EnzyControl is still limited. The comparison with the state-of-the-art baselines seems unfair: the authors did not condition these baseline models for the enzyme tasks, which leads to poor performance. The authors also did not convince reviewers that EnzyControl's success is not due to overfitting.", "rebuttal": "Thank you for your feedback. Your suggestions have enhanced the reliability of our work and the readability of the paper. Below are responses to your questions one by one.\n\n \n\n> **Q1:** The backbone generation component largely builds upon FrameFlow. The architectural contributions (EnzyAdapter) are relatively minor: a standard cross-attention module and LoRA-based fine-tuning. This might be better framed as a control-oriented application of existing methods rather than a novel generative technique.\n\n**Response:** We appreciate your insights and respectfully emphasize that our contribution lies not solely in architectural innovation, but in the integration of domain-specific knowledge from enzymatic catalysis into a principled modeling framework. Our goal is to advance enzyme backbone generation by enhancing both structural consistency and functional relevance. The novelty of our approach is reflected in the following aspects:\n\n- **EC-based Motif Annotation:** EC numbers capture catalytic function, which is inherently shaped by enzyme–substrate interactions. We cluster enzymes by EC number and extract conserved motifs within each cluster. These motifs serve as functionally relevant priors—particularly near catalytic centers—providing the model with biologically meaningful structural constraints.\n- **Substrate Conditioning via EnzyAdapter:** We propose EnzyAdapter to condition backbone generation on substrate molecules. It introduces a cross-modal projector to align substrate and enzyme embeddings, and employs cross-attention layers to inject substrate-specific guidance throughout the generation process, all without altering the base generation architecture. This enhances the model’s ability to produce scaffolds tailored to specific substrates.\n\nAdditionally, we have developed a suite of functionally grounded evaluation metrics (e.g., kcat, EC match rate, ESP score), and constructed EnzyBind, a curated benchmark of high-quality enzyme–substrate complexes based exclusively on experimentally validated structures.\n\nWe will revise the manuscript to clarify these contributions, and we kindly ask the reviewer to reconsider the novelty of our work in light of its domain-driven design and its specific focus on the enzyme backbone generation task.\n\n\n\n> **Q2:** The paper evaluates against RFdiffusion, Chroma, and EnzyGen—but omits several newer models such as RFDiffusionAA, Proteus, or Proteina which significantly advance generation quality, efficiency, or multimodal conditioning.\n\n**Response:** Thank you for the valuable suggestion. In response, we have added RFDiffusion-AA [1], Proteus [2], and Proteina [3] as additional baselines and evaluated them using the same protocol outlined in our original manuscript to ensure fairness and consistency. The updated results are presented in the table below:\n\n| Model        | >0.5scTM | Designability | EC Match Rate | kcat   | Binding Affinity | ESP Score | AAR    | RMSD    |\n| ------------ | -------- | ------------- | ------------- | ------ | ---------------- | --------- | ------ | ------- |\n| RFDiffusonAA | 0.7042   | 0.5416        | 0.1134        | 2.5808 | -6.5233          | 0.6732    | 0.1257 | 19.5187 |\n| Proteus      | 0.6944   | 0.5697        | 0.3926        | 2.1407 | -6.4613          | 0.6816    | 0.1718 | 10.6912 |\n| Proteina     | 0.7213   | 0.6328        | 0.4583        | 2.4592 | -6.3522          | 0.6709    | 0.1632 | 7.2409  |\n\nWe appreciate your feedback, which has helped strengthen the empirical rigor and completeness of our evaluation.\n\n\n\n> **Q3:** Minor mistakes: In Table 2 column 1.1, it seems RFDiffusion is the best.\n\n**Response:** Thank you for pointing out this mistake. We have corrected the error in Table 2 and carefully reviewed the manuscript to ensure that similar inconsistencies are resolved. The revised version reflects the correct results.\n\n\n\n> **Q4:** The substrate is only incorporated as a feature embedding; there’s no explicit 3D placement or docking compatibility optimization, which is crucial for real catalysis.\n\n**Response:** We thank you for this insightful comment, which indeed inspired us to think more deeply about the role of substrate placement and docking compatibility in catalytic modeling.\n\nWhile the substrate is incorporated into our framework as a **feature embedding**, it also serves as a **conditioning signal** for guiding enzyme backbone generation. Through the **EnzyAdapter** module, the model enables cross-modal interaction between the substrate embedding and the enzyme backbone, allowing substrate-specific refinement of the generated 3D structure.\n\nIn addition, we have already performed **structure-based docking** to evaluate compatibility between the generated enzymes and their target substrates (see Fig. 10 in the paper). Based on the docking results, we compute binding affinity scores and observe that our model achieves a **3% improvement** over the best-performing baseline, indicating improved structural compatibility for catalysis.\n\nMotivated by the reviewer’s suggestion, we further explored two **docking-aware optimization strategies** to better incorporate substrate placement into the generation process:\n\n- **Sampling-based selection:** For each input, we generate multiple candidate backbones, perform docking with the substrate, and select the structure with the highest predicted binding affinity. This provides a simple yet effective way to optimize for docking compatibility post-generation.\n\n- **Motif-branching beam search:** Inspired by beam search, we randomly extend the annotated motif with short N- and C-terminal fragments to create diverse partial scaffolds. For each, we generate complete backbones, dock them with the substrate, and choose the motif variant with the best docking score as the seed for further generation.\n\nTo illustrate the effectiveness of the first strategy, we implemented a prototype comparison, and the resulting structures showed improved docking compatibility.\n\nBefore optimation (binding affinity = -6.92): MKVFSPALDNPEYYAGILSPEQVKELVALGFTVYILGREHPKSKFTMAELEAAGAVIVKSLEELKGKHDLVLLSVPPGLDDKTRLPIDTIKKGAIVIGRMKAKTNPEILKALAERGLTVFDMELISPENCDPAMNVVDALGEHVGKVAVRLAKELSSKPFARKETADGVIPAKKVLVLGWGTAGAAAAREAIALGAEVYVWDIDPEARAVAEAIGATFIAADAEALAEELEKADVIITTDAKRDGKGVVVLSEEDVKKLKPDSVIVDTTVEDGGACPLAKAGEVVEFNGVKIVGKKNLDSLAPAESTAAYSQCMLNFIKPLVGKGDGELKIDMSRPCVKDTLVVYNGKIKSKLE\n\nAfter optimation (binding affinity = -8.38): MKIFSYALKNPDVYAGILSPEQVKELVALGFEVYISGFEHPKSSFTMEELKAAGATIVDTLEELKGKHDIVLTSVPPGLDNTTALPVDTIKPGAILIGRLNAERNPEIIKALAARNLTAFDLERISKDKCPAETNVVDALGKEIGKVAVELAKELSSKPFAAEETADGLIPAKKVLVLGMGTTGASAAREAIKLGAEVYMYDINPEAKKIAEEIGATFIEEGEEALAAVLKEADVIICTDAMKDGKGLVVLSAEDVKTLKPDSVIVDTTVERGGACPLAKPGEVVEFEGVKIVGKKNLDSLNPAASQKAFSKCMLNFIKPLVNKGDGELKLNMSDPCVKDTLVCYKGKIVSPME\n\nWe thank you again for the suggestion, and we will include a description of these docking-guided extensions in the revised manuscript.\n\n\n**Reference:**\n\n[1] Krishna R, Wang J, Ahern W, et al. Generalized biomolecular modeling and design with RoseTTAFold All-Atom[J]. Science, 2024, 384(6693): eadl2528.\n\n[2] Wang C, Qu Y, Peng Z, et al. Proteus: exploring protein structure generation for enhanced designability and efficiency[J]. bioRxiv, 2024: 2024.02. 10.579791.\n\n[3] Geffner T, Didi K, Zhang Z, et al. Proteina: Scaling flow-based protein structure generative models[J]. arXiv preprint arXiv:2503.00710, 2025."}]

Author rebuttal:

Meta-review: This paper proposes EnzyControl, a framework for enzyme backbone generation that integrates functional motif annotations from MSAs with substrate-specific conditioning through a lightweight EnzyAdapter module. The authors also contribute EnzyBind, a curated benchmark of over 11k experimentally validated enzyme–substrate complexes, and demonstrate improvements over baselines in structure quality, functional preservation, and catalytic metrics. 

Strengths include the novelty of explicitly incorporating substrate information into backbone generation, the usefulness of the EnzyBind dataset, strong empirical evidence across multiple metrics, and generally clear exposition. 

Weaknesses include reliance on predicted functional metrics without wet-lab validation, restriction to single-chain and single-substrate cases, modest architectural novelty relative to existing methods, and some debate about whether enzyme–substrate interactions are modeled explicitly enough. The most important reasons for acceptance are the strong technical execution, the integration of enzymology-specific priors into generative modeling, and the introduction of a high-quality benchmark that will likely support further research. 

During the rebuttal period, the authors addressed concerns by adding new baselines (Proteina, Proteus, RFdiffusion-AA), providing experiments on motif misannotation, clarifying motif integration and evaluation pipelines, extending discussion of multi-substrate extensions, and offering docking-aware optimizations.  On balance, I recommend acceptance, as the paper makes a meaningful step forward in enzyme design and provides resources and methods that will be valuable to the community despite some limitations.
Decision: Accept (poster)