UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge
Authors: Chenao Li
Shuo Yan
Enyan Dai
Venue: NeurIPS 2025 poster ()
Keywords: enzyme
protein
cleavage site prediction
enzyme active site
protein representation learning
Primary area: machine_learning_for_sciences

Abstract: Enzyme-catalyzed protein cleavage is essential for many biological functions. Accurate prediction of cleavage sites can facilitate various applications such as drug development, enzyme design, and a deeper understanding of biological mechanisms. However, most existing models are restricted to an individual enzyme, which neglects shared knowledge of enzymes and fails to generalize to novel enzymes. Thus, we introduce a unified protein cleavage site predictor named UniZyme, which can generalize across diverse enzymes. To enhance the enzyme encoding for the protein cleavage site prediction, UniZyme employs a novel biochemically-informed model architecture along with active-site knowledge of proteolytic enzymes.  Extensive experiments demonstrate that UniZyme achieves high accuracy in predicting cleavage sites across a range of proteolytic enzymes, including unseen enzymes. The code is available in  https://github.com/Ao-LiChen/UniZyme

UniZyme: A Unified Protein Cleavage Site Predictor Enhanced with Enzyme Active-Site Knowledge
Chenao Li, Shuo Yan, Enyan Dai Hong Kong University of Science and Technology (Guangzhou)
Abstract
Enzyme-catalyzed protein cleavage is essential for many biological functions, Ac- curate prediction of cleavage sites can facilitate various applications such drug development, enzyme design, and a deeper understanding of biological mecha- nisms. However, most existing models neglects shared knowledge of enzymes and fails to generalize to novel enzymes. Thus, introduce unified protein cleavage site predictor named UniZyme, which can generalize across diverse enzymes. To enhance the enzyme encoding for the protein cleavage site prediction, UniZyme employs novel biochemically-infee infformmd model architecture along with active-site knowledge of proteolytic enzymes. Exten- sive experiments demonstrate that UniZyme achieves high accuracy in predicting cleavage sites across range of proteolytic enzymes, including unseen enzymes. The code available in https: : //github..tttttt/. com/ Ao Lic-LLiChhenneeeee CnnChennnnnnnnnee
restricted to individual enzyme, which
Introduction
During enzyme-catalyred protein hydrolysis, proteolytic enzymes cleave proteins at specific cleavage sites. This process is illustrated in Fig. [ and it is crucial for variety of physiological processes, including cell proliferation, immune response, and cell death [T Accurate prediction of enzyme- catalyzed cleavage sites in the substrate proteins facilitates the identification of therapeutic targets and guides drug design B]. For instance, abnormal protein hydrolysis is closely associated with cancer, viral infections, and neurodegenerative diseases, and predicting the cleavage sites of abnormal proteins under pathological conditions can reveal biomarkers or intervention targets [4] []. Additionally, in the design of enzyme inhibitors prodrugs, identifying key cleavage peptides, such as those cleaved under the catalysis of HIV enzyme, helps enhance drug specificity and minimize off-target
effects
Mapping cleavage sites experimentally via pep- tide assays theghhhruughpu mass spectrom- etry is arduous and costly [8]. Therefore, recent studies have employed machine learning meth- ods to advance the prediction of protein eleavage sites. For example, CAT3 99 predicts caspase-3 scoring
Substrate B
Enzyme
Cheerage
Active Site
cleavage sites based on position-speste matrices (PSSM), and ProsperousPlus (10) in- tegrates multiple methods to comprehensively evaluate cleavage site predictions. However, these methods generally focus on an individual enzyme system, overlooking shared patterns and failing to generalize proteases without labeled data. This limitation impedes tasks like off-target
Fragments
Figure 1: Enzyme-catalyzer protein hydrolysis.
*Corresponding author: Beyandai@hustt-g..eee edu.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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assessment of therapeutic proteins in human body [2] Therefore, is crucial to develop unified protein cleavage site predictor that can generalize across diverse range of proteolytic enzymes.
To develop a unified protein cleavage site predictor for diverse proteolytic enzymes, the information of enzyme should be extracted and incorporated for the prediction. However, due substantial cost of biological experiments, existing cleavage site databases only cover a small number of proteolytic enzymes (Tab. D. which significantly challenges the learning of enzyme information encoder. Despite the limited coverage of enzymes in existing cleavage site datasets, many proteolytic enzymes are annotated with their active sites, which is the core functional region for catalyzing the protein hydrolysis. Specifically, the unique physicochemical environment of these active sites enables recognition of target substrates and lowers the activation energy required for cleaving specific peptide bonds. Therefore, we propose to leverage redundant knowledge of enzyme active sites to enhance the modeling of enzymes in enzyme-catalyzen protein cleavage sites.
However, it is non-trivial to achieve unified cleavage site predictor enhanced with enzyme active-site knowledge. Two major challenges remain be resolved. First, the cleavage process is influenced by various factors of enzymes such as 3D structures and environments of active sites. Hence, how design the architecture of enzyme encoder effectively capture useful information for enzyme-catalyzed cleavage site prediction? Second, the active-site regions of enzymes determine the specificity and efficiency of enzymatic hydrolysis. How can we leverage this rich information of enzyme active sites to improve cleavage site prediction? In an attempt to address the challenges, we propose novel framework named UniZyme. More specifically, a enzyme encoder is deployed along with the active site-aware pooling to produce ligh-quality enzyme representations. We further augment the enzyme encoder by pretraining on a supplemented enzyme set for active-site prediction. Furthermore, joint training of enzyme active-site prediction and substrate cleavage site prediction is applied in UniZyme. In summary, our main contributions are as follows:
We investigate a novel and crucial problem of building unified protein cleavage site predictor that generalizes across diverse proteolytic enzymes;
We propose novel framework UniZyme that effectively integrates the enzyme active-site knowl- edge to enhance the cleavage site prediction in enzyme- protein interaction;
Extensive experiments demonstrate the effectiveness of our UniZyme in predicting cleavage sites of substrate proteins for both seen and unseen proteolytic enzymes.
2 Problem Formulation
In this section, we first introduce the preliminaries of enzyme-catalyzed protein hydrolysis. Then, we present the problem definition of protein cleavage site prediction with enzyme active-site knowledge.
2.1 Preliminaries of Enzyme-Catalyzed Protein Hydrolysis
Protein hydrolysis is a biochemical process where proteins broken down into smaller fragments such as amino acids and peptides under the catalysis of proteolytic enzymes. As illustrated in Fig. [ during the protein hydrolysis, proteolytic enzymes will firstly recognize specific amino acid sequences or structural motifs within substrate proteins. Then, the enzymes catalyze the cleavage of peptide bonds at the cleavage site, leading to the formation of smaller peptide fragments or individual amino acids. The positions of cleavage sites are governed by various factors including substrate's 's amino acid composition, spatial conformation, and unique properties of the enzyme [3-15].
Cleavage Sites in Enzyye-Catallzze Protein Hydrolysis.
Active Sites of Enzymes. The active sites in enzyme provide an environment that lowers the activation energy required for peptide bond cleavage. As shown in Fig. with the active sites, enzymes can recognize and bind to target substrates, enabling the cleavage of specific peptide bonds within substrate proteins [16]. Hence, active site information can benefit the modeling of enzyme-catalyzed protein hydrolysis.
Current Framework of Cleavage Site Prediction. Recent studies have employed machine learning models to predict cleavage sites [1) However, these methods generally train an independent model for each enzyme, which only predicts the cleavage sites of substrate proteins under the catalysis

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of one specific enzyme. Specifically, let Pa denote the substrate protein, this enzyme cleavage site predictor aims to learn the f P + c where c E {0, 1)1191 denotes the labels of cleavage site with the enzyme p However, the training of enzyme-specific model excludes the valuable interaction knowledge from other enzyme-protein systems. In addition, the enzyme-specific model cannot generalize to unseen enzymes, which limits its application on unseen enzymes and other enzymes with limited annotations. Therefore, it is crucial to develop a unified cleavage site predictor capable of identifying cleavage sites in substrate proteins across various enzymes.
Limited Enzyme Coverage in Cleav- age Site Database. Tab. T presents statistics from the MEROPS, which is the most comprehensive cleavage site database. Due to the high cost of experimental assays, MEROPS [22] only includes 866 commonly used proteolytic enzymes. This results in a striking enzyme-
Table 1: Statistics of Cleavage Sites Data in MEROPS, # Proteolytic Enzyme # Substrate Enzyme-Substrate Substrate Ratio
866
10,146
1:11.7
ratio of 1:11.7. The limited enzyme coverage poses significant challenge for developing a unified cleavage site predictor that generalizes across diverse enzyme-substrate systems. Despite the limited enzyme coverage in cleav- age site databases, the lower cost of annotating enzyme active sites has enabled UniProt [23] to provide 10,749 high-quality proteolytic enzymes with labeled active sites across multiple organisms. The rich information of active sites can be helpful in enzyme modeling to facilitate the cleavage site prediction protein hydrolysis.
2.2 Problem Definition
In protein hydrolysis, both enzyme Pe and substrate Pa are proteins composed of amino acid residues that fold into 3D structures. We denote a protein of length N by p = (X, R), where X E RRxx is the feature matrix of N residues, R E R x3 denotes the 3D positions of residues. We denote c E {0, 13PP) as the cleavage site label for the substrate protein O under the catalysis of enzyme pe. The training data for cleavage site prediction can be represented as D. = {(P). P.: "] [D. In this work, we propose to enhance the cleavage site prediction with the active site information of enzymes. Hence, we will also utilize a set of enzymes labeled with active sites a E {0, 1311ol, which denoted as D. = {PF a.) 2
During the test phase, we will predict the cleavage site for each pair of test enzymes and substrates (P). The active sites of test enzymes will not be available for inference. And the test enzyme Pf can be either seen jie, Pf E De or unseen, i.e, P & De. which correspond to supervised setting and zero-shot setting, respectively. For each test substrate protein P/, its cleavage site with the test enzyme Pf is not included in the training set D..- With the above notations and descriptions, the formal definition of building unified cleavage site predictor can be given by:
Problem 1. Given the dataset De annotated for cleavage site prediction and the supplemented dataset Da containing enzymes active sites, we aim to obtain a unified cleavage site predictor:
f (P* P*) C.S
(I)
which can accurately predict cleavage sites of test proteins Pf under the catalysis of test proteolytic enzymes P Note that test enzymes can be either seen or unseen during the training phase.
3 Methodology
In this section, we give the details of the proposed UniZyme As the Fig. 2 shows, apart from the substrate encoder, UniZyme deploys an enzyme encoder to enable the generalization of cleavage site prediction across various enzymes. In addition, active site information in protein hydrolysis is incorporated into enzyme encoder training enhance cleavage site prediction. Two main challenges remain to be addressed: (i) how to design the enzyme encoder to preserve critical information for cleavage site prediction? (ii) how to leverage the rich information of enzyme active sites to improve the cleavage site prediction? ? To tackle the above challenges, our UniZyme deploys a biochemically- informed enzyme encoder which augments the graph transformer with enzyme energy frustration. Furthermore, UniZyme employs active site-aware pooling to preserve the enzyme's 's information crucial for protein hydrolysis. To facilitate enzyme representation learning, UniZyme first pretrains the enzyme encoder using active site prediction with the supplemented enzyme set. Then, joint
3

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Le
Representation
Cleavage Sites KESV-KKK
Cleavage Site Prediction
Energetic Frustration
Softmax
Substrate
Substrate Encoder
Substrate Representation H
Distance
Matmall
Active Sites GHGDQSC
a
Active Prediction
Enzyrne Representation h"
Matrix
Q
K
Enzyme Eriooder
Active Site-Aware Pooling
Enzyme
Structure
Enzyme Feature X
Enzyme pe
Overall frarnework of Unizyme Figure 2: Architecture of siochemically-infomee enzyme encoder and framework of UniZyme.
Bicchermiaally-infomed enzyme encoder
loss of active site prediction and cleavage site prediction employed to optimize the UniZyme for accurate cleavage site prediction. Next, introduce each component in detail.
3.1 Hiochemically-Infoomee Enzyme Encoder
Enzymes' 3D structures, especially the local environments around their active sites, are crucial for catalyzing protein hydrolysis. Although direct active-site annotations are often unavailable for test data, recent studies indicate that local energetic frustration can identify functionally important regions 24]. Building on these insights, we propose integrates both the spatial positions of residues and their energetic frustration scores (25)
saechmicalll-infornee enzyme encoder that
Encoding Energetic Frustration. Previous studies indicate that local energetic frustration, referring regions in protein not optimized for minimal energy, is commonly observed around enzyme active sites and can significantly influence catalysis (24). To quantify this phenomenon, frustration score F(i j) is computed for each residue pair (i.j) within an enzyme p following 24
F (i.,)) E(i, (i,jd
(2)
Grand (i,j)
where E(i,) j) is the actual interaction energy between residues (i.j) in the enzyme Pt, Mrana (i, and raand (i,j) represent the mean and standard deviation of interaction energies that derived from randomized configurations (see Appendix [ for details). A higher F(i.j) implies stronger local energetic frustration, suggesting that the residue pair is more likely to belong functionally important region. Therefore, we incorporate this frustration score to provide useful biochemical information to the enzyme encoder.
Integrating Energy and 3D Position Transformer. Following prior works 26 we encode the 3D positions by computing pair-wise distance between residues: D(i,j) ||.; r r2,l2, where E R is the Ca-atom coordinate of residue i. Both energetic frustration score and distance matrix capture pairwise relationships akin to the spatial encoding in graph transformers. Therefore, we locate those pair-wise signals to provide complementary information for the self-attention score computation. Concretely, for an enzyme P R), we process both Fii,j) and distance matrix Dii.)) with a Gaussian Basis Kernel function followed by a MLP:
ennergy MLP((ennrryy (F(i o MLP(0asst (D(i,))))
where denergy and daist denote the learnable Gaussian Basis Kernel function that can map energy frustration and distance score d-dimensional vector (See Appendix C for more details). MLP is further deployed to transform these vectors to the space of attention scores. We then add the resulting ædist and & genergy bias terms the self-attention mechanism. Denote A as the (i.j) -elemeet of the Query-Kor product matrix in k-th attention layer, we have:
(s.f)
, = (h W (h) "kk)
generg + dist
d
H = softmax A H Wv
where H E xxd denotes the updated representation matrix. And Wo, W.. and Wy are projection matrices for the query, key, and value transformations.

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3.2 Enhancing Enzyme Representation Learning with Active Site Knowledge
To incorporate crucial active-site knowledge into enzyme representation learning, we use three strategies: (i) an auxiliary active-site prediction task to strengthen the enzyme encoder, (ii) large-scale pretraining for active-site prediction to capture general catalytic patterns, and (iii) an active site-aware pooling mechanism that emphasizes catalysis-related residues. Next, we give more details.
Active Site Prediction. Active sites play a key role in catalyzing the protein cleavage. Hence, active-site information can provide essential understandings of enzyme functions. As a result, we deploy the active site prediction the auxiliary task to benefit the enzyme encoder training by:
á = sigmoid wa);
where à; [0, 1] denotes the probability of the i-th residue being the active site, h, I is the representation of i-th residues in the enzyme, and Wa Rdd denotes the learnable parameters for active site prediction.
Pretraining with the Supplemented Enzyme Set D. The number of enzymes annotated in cleavage site database is limited, which poses a significant challenge for the effective training of enzyme encoders [27]. Despite the limited enzyme coverage in cleavage site database, abundant enzymes annotated with active sites. Therefore, we select enzymes highly homologous to the target proteolytic enzymes in biological function to expand the pretraining dataset. Formally, the objective function of pretraining the enzyme encoder on the supplemented enzyme set DD can be written as:
min La Oe
1
Σ (P",a)ED.
(BCE(å,
(6)
Dal
where 0g denotes the parameters of enzyme encoder. å = [ā i..:.. denotes the probability vector of active site on the enzyme p (вCE denotes the element-wise binary cross entropy loss. By pretraining on a large corpus of enzyme sequences, we allow the model capture broader structural and functional patterns common across enzymes.
Active Site-Aware Pooling. To obtain enzyme representation from sequence of residue represen- tations, a pooling operation such as mean pooling is required. However, residues that are active sites are critical for enzymatic activity. Intuitively, these active sites should contribute more in the aggregated enzyme representation (28). Therefore, we design an active site-aware pooling mechanism, whose pooling weights are based on the predicted active site probabilities. Let å, R be the predicted probability that i-th residue is an active site in an N-residue enzyme. The active site-aware pooling can be written as:
h soft softmax(( H,, wy [(a),
where E IR Xx is residue representation matrix from by the enzyme encoder. f(-) a learnable function that will map each à; to the pooling weight wy, see in appendix C With the active site-aware pooling, we would be able to encourages the model to focus on catalytically relevant segments the enzyme.
3.3 Cleavage Site Prediction
Substrate Protein Encoding. As annotations tying substrate residues to intrinsic energetic states are unavailable, we omit the energetic frustration of the substrate protein. And we only input residue feature matrix X and distance matrix Dd the substrate O are integrated in the transformer: H = Transformer x D*), where H R [P'|xd is the substrate representation. Further details are provided in Appendix C
Cleavage Site Prediction. During protein hydrolysis, enzymes generally recognize local residue sequences about 15-30 residues in length. To reflect this biological behavior, we predict whether subsequence of length / in substrate P will be cleaved by enzyme pe, Formally, this process be written by:
MIPPCCON h²))
where High a contiguous slice taken directly from the substrate representation matrix H and h is the enzyme representation obtained by Eq. 7).. The length of the subsequence is set as 31 (15
5

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residues on each side.). The optimization function of cleavage site prediction can be written as:
Lc(D.) Dal (PEPP,Ce.'EDD.
1
Σ
(BCE(C (%
where @ = & ..... Ip-1 denotes the probability vector of cleavage site within the substrate p
given the enzyme ps, |BCE is the element-wise binary cross entropy loss.
3.4 Final Objective Function
For each enzyme p E De in the cleavage site database, their active sites are also included in the Da. Consequently, combine the cleavage site prediction loss and the active site prediction to jointly train the whole framework by:
min (D.) (D.)
(10)
where & denotes all parameters in UniZyme including the enzyme encoder, substrate encoder, active prediction module and cleavage site prediction module. D C Da provides the active-site annotations for the enzymes in De.
4 Experiments
In this section, we conduct experiments to answer the following research questions:
RQI:
How does the UniZyme perform in supervised cleavage site prediction?
RQ2:
How well does UniZyme generalize to cleavage site prediction for zero-shot enzymes?
RQ3: How do the design of biochemically-infomen enzyme encoder and utilization of active-site knowledge contribute to the performance of UniZyme?
4.1 Experimental Setup
Dataset. The cleavage site dataset D. is sourced from the MEROPS database, which provides annotations for roughly 10k substrate proteins across 876 enzymes. With a standard dataset expansion procedure commonly used in cleavage site prediction IO 18 20 21, which propagates substrate- site annotations across enzymes within the same family, we obtain 220k valid enzyme-substrate pairs. The supplemented dataset D, constructed by combining enzyme active-site annotations in MEROPS [22) and UniProt (23]. Specifically, MEROPS provides active sites for the enzymes already included in D UniProt provides hydrolase enzymes with the EC number of 3.4.* * * that highly homologous to the proteolytic enzymes in De, resulting to 11,530 enzymes with active sites.
Evaluation. To demonstrate the generalization ability of UniZyme, evaluate its performance tein cleavage site prediction for both enzymes (supervised) and un- Substrate-Fazymn Pairs 197.613
Table 2: Splits of MEROPS for evaluation.
pro-
Training
Supervised Test
Zero-Shot Test
Enzyme Families 677
69
23 5,345
20,360
seen enzymes (zero-shot). The
dataset split of the MEROPS for supervised and zero-shot setting are in Tab. 2 The dataset construc-
tion details can be found in Appendix(A]
Supervised Setting: In this scenario, target enzymes are paired with novel substrates that were not present the training data. We restrict our evaluation to enzyme families with at least five unique substrates, yielding 69 enzyme families and a total of 21k enzyme substrate pairs. For each family, we randomly split the data 70/10/20 into training, validation, and test sets.
Zero-shot Setting: In the zero-shot setting, target enzymes are entirely held out during both training and pretraining. We consider only families with at least five distinct enzymes, reserving 20% of each family's enzymes test set. In total, this yields 23 enzyme families (5.3k enzyme- mzzme-suustrrate pairs), with all test enzymes sharing under 60% sequence identity with any enzyme seen during training pretraining.
Baseline Methods. To evaluate our model, we compare with the three categories of baselines. (i) Specialized models: CAT3 [9] and ScreenCap3 [18] are two models specifically designed for

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C14.003 enzyme family. (ii) Deep models for an individual enzyme: ProsperousPlus [10]. Deep- Cleave (20 and DeepDigest (29 are state-of-the-an deep learning methods focusing an individual enzyme system. To compare with UniZyme across multiple enzyme families, multiple predictors are trained for each baseline method, where each predictor trained with data of an individual enzyme. Thus, these baselines are limited to supervised setting where an enzyme is provided with substantial experimental data. (ii) Models revising UniZyme with baseline enzyme encoders: Re- search cleavage site prediction for zero-shot enzymes remains limited. The closest works are enzyme-substrate reaction predictors, namely ClipZyme (30) and ReactZyme [31] which encode both enzymes and substrates for reaction prediction. However, these models operate the substrate level and cannot directly predict specific cleavage sites. Therefore, to demonstrate the superiority of the proposed enzyme encoder and enhancement strategies in Unizyme, we substitute the Unizyme's enzyme encoder with enzyme encoders from ClipZyme and ReactZyme. This yields two baselines for comparison in both supervised and zero-shot settings. For ClipZyme, we utilize their EGNN enzyme encoder pretrained for enzyme-substrate reaction prediction. See Appendix baselines.
B for more details of
Implementation Details. We utilized the esm2-112-35M-URSE model to generate 480-dimensional residue features for both enzymes and substrates. The hyperparameter À is selected based on the validation set under supervised setting. Hyperparameter analysis are in 4.5 Each experiment is conducted with 5 runs with different random seeds. To ensure fair evaluation, hyperparameters of trainable baselines were selected by validation set. More details are in Appendix
4.2 Supervised Cleavage Site Prediction
To answer RQ1, we compare our UniZyme with various existing methods in supervised cleavage site prediction, ensuring that all models are trained and evaluated on the same data. As mentioned in Sec. 1.1 base- lines designed for individual enzymes re- quire training separate predictor for each enzyme family. ClipZyme and ReactZyme are baselines revising our UniZyme with their enzyme encoders, avoiding repeated training. The overall comparisons all 69 enzymes in supervised setting are given Tab. 3 and Fig. Results 8 represen-
Table 3: Performance comparisons overall 69 en- zyme families under supervised setting.
Average PR-AUC (%)
Rate Rank (%)
Average Rank
Model
UniZyme
79.3
75.0
1.26
React/yyme ClipZyme
70.0
7.5
2.51
74.7
17.5
1.91
ProsperousPlus
7.2
4.65
DeepCleave
6.5
0.0
4.61
DeepDigest
4.2
0.0
5.52
tative enzymes are given in Tab. 4 Specifically, we can observe that: (i) Methods focusing on a single enzyme generally show poor performance; whereas those trained on multiple enzymes such as Our UniZyme and ReactZyme achieve significantly better results. This highlights the advantage of developing a unified cleavage site predictor across diverse proteolytic enzymes. (ii) Compared with ClipZyme which adopts an enzyme encoder pretrained with the enzyme-substrate reaction task, the proposed UniZyme achieves much better performance. It implies the effectiveness of active-site information in enhancing the enzyme encoder. (iii) Our UniZyme also consistently outperforms the ReactZyme by large margin. This is because of the deployment of gocchemialll-innommme enzyme encoder and the active-site knowledge.
Table 4: PR-AUC (%) on 8 out of 69 enzyme families under supervised setting. Note that ScreenCap3 and CAT3 specialized models for the C14.003 enzyme family.
Model
CO1.034
C14.003
C14.005
M13.001
M24.026
S01.011
S01.224
008.070
UniZyme 78.1
45.9
52.2.10.9
60.1.1.1
87.2.10.8
82.1
62.4.11.1
8..
ReaciZyme
64.41007
43.8-0.88
47.610.9
48.0-1.7
70.3114
69.611.2
27.010.9
66.0.10.8
ClipZyme
71.1:10.7
35.3
43.2:11.0
37.1
73.1
73.8
41.7:10.6
73.8:10.9
DeepDigest
3.2400.5
0.5+1.1
24+1.1
16.7+1.3
1.4:0.4
21.1=0.9
1.2+1.0
2.2+112
DeepCleave
4.8.11.1
1.0.1.11
4.2.0.9
19.011.0
7.1112
26.911.4
5.0.0.55
18.310.6
ProsperousPlus
1.6
26.6+22.1
15.9+0.5
21.1+0.8
2.4+0.88
1.03++100
4.3++0.9
1.
CAT3
18.5+6.2
ScreenCap3
29.2.116.0
7

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100
UniZyme
ReaciZyme
Clipžyme
Prosperous
DeepCleave
DeepDigest
PR-AUC (%)
60
MSk on1
S01,182
M52 nns
MSk ut28
007
0776
500
501.300
Mio
o01
201
501
151
501.
CO
823
201.251
807.278
C1400
Min
1912
M21.200
Mit.
1977
ML5.
501
u22
$01.280
MItcon
C14
$01.177
Cl4
201
$01.224
sof 073
Figure 3: Per-family PR-AUC (%) across 69 supervised enzymes (Part 1: Enzymes 1-35). Results for the remaining enzymes are provided in Appendix M (Fig.
4.3 Cleavage Site Prediction for Zero-Shot Enzymes
To answer RQ2, we evaluate the performance of UniZyme on the zero-shot benchmarks, where enzymes are unseen during the training/pretraining phase. We adopt Needleman -unnschhh algorithm to ensure all zero-shot enzymes have under 60% sequence similarity with any enzyme used for training and pretraining. Since enzyme-specine models cannot handle novel enzymes, we only compare UniZyme to ReactZyme and ClipZyme, both of which are modified to predict cleavage sites novel enzymes. The summarized results are given in Tab. 5 6 and Fig. 5 From the results, we can observe that our UniZyme consistently outperform the baseline methods. In particular, UniZyme exceeds ReactZyme and ClipZyme by more than 7% in PR-AUC across most enzyme families. This improvement stems from the utilization of active-site knowledge in the enzyme modeling and the biochemical infformed encoder, promoting the generalization ability of UniZyme to unseen enzymes.
To further demonstrate the generalization ability of our model on unseen enzymes, we applied it identify potential HIV-1 enzyme substrates and predict their cleavage sites as shown in Fig. 4 We present the model's prediction on an unseen HIV-1 1 enzyme acting (P62157) with four experimentally validated HIV-1 cleavage sites. As shown in Fig. 4 UniZyme can accurately predict the four annotated cleavage sites, achieving 100% accuracy. Additionally, UniZyme successfully predicts the cleavage sites test case (Uniprot: P00698). This demonstrates the ability to analyze cleavage sites for any potential protein of HIV-1 enzymes. This result provides valuable insights for therapeutic intervention and the development inhibitors targeting HIV-1 enzymes.
147-residue substrate
Table 5: PR-AUC (%) on 8 out of 23 zero-shot enzyme families.
Model
A01.009
C01.060
C02.002
M10.001
M10.004
M12.217
S01,010
S01.217
UniZyme
37.5.10.6
84.311.3
66.4.1..6
81.1110
82.8:1888
93.21:1.3
61.0.10.8
65.0.1:.2
ReaciZyme
18.0:10.3
62.7.10.8
4.2
76.0:11.2
71.0:22.8
7..0:E1.1
23.0
42.9:10.8
ClipZyme
25.2++0.6
59.2+-0.9
48.641.2
76.8+-0.6
56.5+:3.5
84.4+=099
18.940.9
41.3++0.6
Table 6: Performance comparisons on overall 23
enzyme families under zero-shot setting.
Average PR-AUC (%)
Rate of Rank (%)
Average Rank
Model
Substrate P62157
(b) Substrate PO0698 No Ground Truth)
UniZyme
71.1
78.3
1.30
(Accuracy 1.0)
ReactZyme
64.7
21.7
2.04
Figure 4: Predicted HIV-1 enzyme cleavage sites highlighted in red (threshold 0.5).
ClipZyme
61.7
0.0
2.65
8

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UniZyme ReactZyme ClipZyme
80
PR-AUC (%)
20
M0.000
191.140
M30,000
c14.000
2122317
c02.001
$01,230
$01.131
X8280011
M1000r
014,0s
Min.aut
co1.
M13.001
c14.004
131
s01.217
s01 101.010
G13 m04
M10.0ut
501,
M10
caz
AD1
Figure 5: PR-AUC (%) on the sero-shot benchmark.
4.4 Ablation Studies
To answer RQ3, we conduct a series of ablation studies to understand the contributions of sicchmmcaally-infomee enzyme encoder and the active-site knowledge. To demonstrate the ef- fectiveness of the biochemically-nffoee structure, resulting in variant named UniZyme's SE. To show the benefits brought by pretraining on general enzymes with active site prediction, we trained a variant, UniZyme P. which excludes the enzyme pretraining phase. To further demonstrate the enhancement of active-site knowledge the model, we remove the active site prediction in the wetraining/training phase. Additionally, the active site-aware pooling replaced with average pooling, resulting in a variant named UniZyme', AA Fig, 6 shows the PR-AUC scores across different enzyme families in both zero-shot and supervised settings. More details can be found in the Tab. 17 From these results, observe:
enzyme encoder, we remove energy frustration and 3D
UniZyme consistently achieves better results than UniZyme SE. This indicates that the incorpo- ration of structural-energy features in the biochemically-intee informedd enzyme encoder enable stronger generalization and performance in cleavage site prediction.
than UniZyme on both supervised and zero-shot setting. This verifies that pretraining on supplemented enzyme set with active site prediction can produce more transferable enzyme encoder for cleavage site prediction.
UniZyme' significantly performs
UniZyme outperforms UniZyme', A by a large margin. This demonstrates that the active-site knowledge can enhance the enzyme-catalyzed cleavage site prediction.
4.5 Hyperparameter Analysis
In this subsection, we investigate how the hyperparameter À affects the UniZyme. controls to contribution of active site prediction loss to the training of UniZyme. To explore the hyperparameter analysis, we vary À as {100, 10. 1, 0.1, 0.01 in the training phase of UniZyme. Due the expensive computational cost in training on the full dataset De, we conduct the hyperparameter analysis with 3% of training data in various enzyme families. Performance on these enzymes are given Fig. 7 We can find that while X = 100 produces competitive ROC-AUC results, it leads to suboptimal PR-AUC. Small values like 0.1 and 0.01 cause noticeable drop ROC AUC (e.g. C14.005). Among tested values, À 10 demonstrates the most consistent performance, achieving strong PR-AUC (e.g.. M10.004) while maintaining competitive ROC AUC across datasets such as C14.003 and A01.009 .009. Thus, selected x 10 as the optimal choice for final training.

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0.8
0.55
0.8
0.5
PR ATIC
0.6
ROC-AU AUG
0.99
PRAUC
0.6
0.45
0.44
0.35
0.2
0.98
C14005
C14003
N00004
0.2
C14035
C14033
MI0004
0.3
C03.003
014,005
100
0.1
0.02
100
0.1
0.01
(a) Supervised
(h) Zero-shot
(a) ROC-AUC
(b) PR-AUC
Figure 6: Ablation studies of UniZyme.
Figure 7: Hyperparameter sensitivity analysis.
Related Works
Protein Representation Learning. Protein representation learning aims to effectively capture and represent the structural and functional features of proteins for downstream tasks. Inspired by large language models, recent years have seen the emergence of sequence- -bssd pre-trained models such as ESM 32 and ProtTrans [33]. In terms of methods that utilize structural information, geometric graph neural networks (8) and transformers with structural constraints [26
39 40
have become
widely used architectures. These approaches show that structural pretraining can significantly benefit
downstream performance.
Beyond individual model designs, systematic benchmarks have begun to evaluate how different pretraining paradigms transfer real-world applications. particular, Protap [ establishes comprehensive benchmark that jointly compares backbone architectures, pretraining objectives, and models across both general and specialized protein tasks. It covers five represen- tative applications innludiig aazyme-catalyzed cleavage site prediction and PROTAC-mediatee degradation and provides unified data splits, evaluation protocols, and analysis of how structural inductive biases interact with pretraining scale. Within this framework, UniZyme is integrated as the representative seryyme-substrale model, demonstrating how incorporating biochemical priors such as energy frustration and active-site cues can complement large-scale foundation encoders.
Cleavage Site Prediction. Early prediction of enzyme-catalyzed cleavage sites relied substrate sequence motifs, such as CAT3
[ and Screen- Cap3 [18]. Subsequent works including Procleave [21] and ProsperousPlus [10 began integrate substrate structural features better capture enzymatic
preferences. Deep learning approaches such as DeepCleave [20]. DeepDigest [29], and DeepNeu- ropePred (17 further advanced the field by leveraging CNNs and protein language models. However, these are enzyme- -ppecifcc models, applicable only to individual targets and often ignoring active-site information. Building on these developments and contextualized by the Protap benchmark, we
propose a unified cleavage-site predictor enhanced with explicit active-site knowledge.
6 Conclusion and Future work
In this paper, we study a novel problem developing a unified protein cleavage site predictor for diverse proteolytic enzymes. Specifically, we design sechemically-informee enzyme encoder and incorporate redundant enzyme active-site information. Our experimental results demonstrate that UniZyme outperforms basclines by large margin across various enzyme-substrate families, particularly excelling in zero-shot scenarios. Ablation studies further demonstrate the effectiveness of each proposed module in UniZyme. There two directions that need further investigation. First, while this study focuses on proteolytic enzymes, we will extend to other categories enzymes and substrates, and investigate whether enzyme-catalyzed reactions follow scaling law. Second, if more hydrolysis process data becomes available, incorporating dynamic structural information may improve prediction accuracy.
7 Acknowledgment
This material is based upon work supported by, or in part by, the National Natural Science Foundation of China (NSFC) under grant number 62506316. The findings this paper do not necessarily reflect the view of the funding agencies.
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NeurIPS Paper Checklist
Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The abstract and introduction clearly enumerate the three main contributions of UniZyme and these match the experimental results demonstrating its superior performance and generalization.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.
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is fine include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
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Answer: [Yes]
Justification: We conduct a failure case analysis in Appendix K to analyze the limitation of our work. implies the future directions to improve the proposed UniZyme.
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For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
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Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics https: http:://////is/eeeeeeeeeees httpe//ppp...pc.oiiicc/ccccctata
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Question: Does the paper discuss both potential positive socictal impacts and negative societal impacts of the work performed?
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Question:
Answer: [NA]
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Algorithm Training algorithm of UniZyme
Require: Supplemented enzyme set D.. Cleavage site prediction dataset D., hyperparameters À Ensure: A unified cleavage site predictor fo
1: Initialize features of enzyme and substrate protein by ESM-2
2: Pretrain enzyme encoder on Da with L. by Eq.
3: for epoch = to N do
4: for each batch (Pe, P*) in Dr do
5: Compute the distance matrix D and energetic frustration matrix F from enzyme structure by (2.
6:
Encode the enzyme and substrate protein by (q.44)
7:
Obtain the enzyme representation with active site-aware pooling by Eq. Predict active sites of enzymes by Eq.(5)
8:
9:
Predict eleavage sites of substrate proteins by Eq.(8)
10: Update 8 via V(C. + XCa)
11: end for
12: if validation loss increases for 3 epochs then
13: break
14: end if
15:
end for
A Details of Data Curation and Benchmark Construction
A.1 Data Curation and Preprocessing
For the cleavage dataset, we downloaded enzyme-substrate pairs from the MEROPS 22 database, collected substrate sequences from the UniProt database, and retrieved enzyme sequences recorded in MEROPS. Additionally, we compared the enzyme sequences between MEROPS and UniProt, excluding those with discrepancies, as such inconsistencies often result from asynchronous updates. To maintain controllable sequence lengths, we filtered out all enzyme and substrate sequences exceeding 1,500 residues.
Regarding the supplemented enzyme set with active sets, we first searched in the UniProt [23 database for enzymes with EC numbers starting with 3.4. *, * and filtered for reviewed data. Then, selected entries with annotated active sites as our pretraining dataset. In addition, proteolytic enzymes MEROPS are all annotated with active sites, and are combined the supplemented enzyme set.
Protein structures were collected from the PDB 42 and AlphaFoldDB [43]. For proteins without available structures in these databases, generated their structures using OmegaFold (44.
Additionally, all datasets are used in accordance with their respective licenses: MEROPS is distributed under the GNU Lesser General Public License (L.GPI., UniProt under Creative Commons Attribution 4.0 (CC BY 4.0), the Protein Data Bank under CCO, AlphaFold DB under BY 4.0, and OmegaFold under the MIT License.
A.2 Data Expansion
The MEROPS database classifies enzymes into categories based on their substrate cleavage sites. Enzymes belonging to the same MEROPS category typically share highly similar cleavage-site characteristics [2] Drawing previous work, assume that minor sequence differences among enzymes of the same category can be disregarded. Consequently, the hydrolysis information from a substrate substrate-enzyme enzyme pair is extended to all enzymes in that category.
Therefore, we expanded our dataset by matching each substrate not only with the originally mapped enzyme but also with other enzymes in the same MEROPS category. Through this procedure, we obtained approximately 220K valid suzyme-substrate pairings, involving 677 unique enzymes. Detailed distributions of enzyme and substrate pairs are provided in Tab. 7
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Table 7: Dataset statistics of training datasets.
Utilization
Datasets
# Substrate-Eazyym Pairs
Enzymes
Substrates
Active site dataset
UniProt
NA
11,530
NA
Cleavage site dataset
MEROPS
197,613
677
7,475
A.3 Construction of Supervised and Zero-shot Benchmarks
Supervised Setting. We selected MEROPS enzyme families containing at least five distinct substrates, yielding 69 families and approximately 21K enzyme-substrate pairs. All pairs in each family were randomly split 70/10/20 into training, validation and test sets. To ensure the test substrates were sufficiently distinct from those in training, we collected all substrates per family and computed pairwise sequence similarity using the Booellean-Wunseee algorithm (BLOSUM62, gap opening penalty=10, gap extension penalty=0.5). Substrates exhibiting less than 50% similarity to any other were deemed independent, and 20% of independent substrates were sampled form the final test set. This procedure evaluates model generalization on more divergent substrates within each MEROPS family.
Zero-shot Setting. Following the same selection criteria, we identified MEROPS families with at least five enzymes, yielding 23 families and roughly 5.3K enzyme- pairs. In each family, 20% of the enzymes were randomly set aside as a zero-shot test set. To guarantee that these test enzymes were truly unseen, we computed pairwise sequence identities against all training and pretraining enzymes using the Needleman- Wunnsch algorithm (BLOSUM62, gap opening penalty 10, gap extension penalty 0.5) and retained only those enzymes whose identity fell below 60% threshold. This procedure prevents any overlap between zero-shot test enzymes and the training/pretraining pool, rigorously evaluating the model's ability to generalize novel enzymes.
B Details of Baselines
Below, we provide additional details on how we adapt, retrain, or utilize each baseline for comparison. Unless otherwise specified, all default hyperparameters are used in the original implementations of these methods. For any required data, we convert our data format accordingly.
ProsperousPlus (TO) and DigpSt [29] all provide publicly available code, enabling us to retrain their models within our supervised setting. We use the same training and test sets as those used for our method, specifically for the supervised benchmark. We adopt the default training code from each repository while ensuring that all other settings remain consistent.
ScreenCap3 [1]] and CAT3 (9] specialized for the C14.003 enzyme, do not provide publicly available datasets source code for retraining. Instead, they each offer prediction platform: web server for ScreenCap3 and standalone software for CAT3. We use these platforms to generate predictions on our test Since their training data are not publicly accessible, we can only report their performance as is, with the caveat that neither model can be applied to other enzymes.
We also compare with two recent enzyme- substrate interaction models, ClipZyme 30 and Re- actZyme B1), which were originally proposed for reaction rather than cleavage prediction. Re- actZyme encodes enzymes with an ESM-2 plus MLP pipeline, but since its trained weights unavailable, we retrain it from scratch on our dataset. ClipZyme employs an E(n) Equivariant Graph Neural Network (EGNN) incorporate structural information into its enzyme encoder. Both models use phoraig-pooling to aggregate the extracted enzyme features and are trained without activation-sine loss. To highlight the effect of leveraging active-site knowledge, we keep the original pretrained EGNN for ClipZyme as is and integrate it into our cleavage-site prediction framework, adding only a linear projection layer to interface with the cleavage-site prediction module.
nubstaeeubttrate
C Implementation Details
Framework and Hardware. We implemented our models in PyTorch and trained using the Adam optimizer with a learning rate of 1 x 1 and batch size of 48. All experiments were conducted
23

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on eight NVIDIA A6000 GPUG) We adopted an early stopping strategy with a patience of 3 epochs, monitoring the validation loss prevent overfitting.
Substrate Representations. Similar to the enzyme pipeline, but without energetic frustration, each residue is embedded by ESM-2 padded 1500 length. We compute pairwise Ca-distances D (i,i,) |r, rylla, then applying reciprocal transform. Each distance entry is processed by Gaussian basis kernel and MI.P, yielding bias term Tdiist added to the attention score:
(h. (h) R Wxx) WK)
Pdist
(11)
thus incorporating structural information. The substrate representation H [X is obtained via
H = Transformer(X" ,D),,
(12)
with the same architecture as the enzyme encoder but omitting energy-related parameters.
Pooling Weight Function. In Active Site-Aware Pooling module, ʃ()) is learnable mapping that transforms each predicted active-site probability into its final pooling weight, in direct analogy to how Gaussian kernels map energy and distance into attention biases.
Concretely, we first pass the scalar probability à, through a Gaussian basis expansion:
Pact (ās) = [mect, (ā.), ... Paet, x< (āx)) E RK
(13)
which produces a richer, multi-dimensiooal embedding. We then apply an MLP to collapse this embedding to single weight:
[(as) MLP(Oact(a))
(14)
Energy Frustration Calculation. We computed residue-pair frustration using the Frustratometer
tool 45 with AWSEM (Associative Water-mediated Structure and Energy Model) potentials [46), disabling electrostatic interactions (Kelectrostatics = 0) and enforcing a minimum sequence separation of 12 residues between residue pairs. Specifically, for each pair of residues (i.j) in enzyme pe, the actual interaction energy E(i,j) was extracted from the AWSEM potential. To capture local energetic fluctuations, generated ensemble of randomized configurations (where the sequence or side-chain identities are shuffled while preserving the protein backbone), thereby obtaining a distribution of interaction energies for each pair.
Let Urand and Grand (i, be the mean and standard deviation these interaction energies over the randomized ensemble. The frustration score F(i,j) is then computed as:
F(i,)) E(i.j) - (i.)
(15)
(r
A higher F(i.j) indicates that the local region around residues (i,j) is more frustrated (i.e., further from minimal AWSEM-derived energy). Such regions often correspond to sites functional importance in enzymes.
To estimate how E(i,j) deviates from an energetically minimal arrangement, we generated an ensemble of randomized "decoy" configurations for the same residue pair. These decoys preserve global geometry (e.g. backbone coordinates) but shuffle aspects such as side-chain packing or local environment, depending on the chosen protocol within the Frustratometer. Each decoy thus provides a distinct pairwise interaction energy. By sampling multiple decoys, we obtain an approximate distribution of energies E& j), from which we compute:
K
1
Prand Praud(i.)) =
Ex (i,
(16)
K
1
Grand (1.
Ex(i. (1)
(17)
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---

where K is the number of randomized decoys (typically on the order of a few hundred in the Frustratometer).
Gaussian Basis Kernel Function. Following Transformer-M 26 we employ a set of learnable Gaussian basis kernels to transform scalar input (e.g., the distance Di j) the frustration score F(i,j)) into fixed-dimensional embedding. Concretely, suppose we have K Gaussian kernels parameterized by {n² o 1== defined
T==1- For an input scalar æ, the Gaussian basis kernel function ((x) is
<(x) [oxp (-2((=))))) exp((2(== (-2((=(-)))
exp (xp(-2("**">)
(18)
Each kernel center μ² and width o² is learnable, allowing the model to adaptively capture different regions of the input space. We apply this basis expansion both D and F(i.j), producing K K-dimennional vector for each pair (i.j). An MIP then projects this kernel output into the space of attention biases. We set the number of Gaussian basis functions to K 10, each parameterized by learnable centers 2z and widths o². Notably, we maintain separate sets of Gaussian parameters for the energy and structure channels, ensuring that the model can adaptively learn distinet representations for each.
Training Algorithm. Each sample's ESM-2 embeddings (padded to length 1500), along with distance and energy frustration matrices, are fed into our model to predict both active-site and cleavage-site site residues. We use weighted binary cross-entropy loss and optimize with Adam for up to 15 epochs, applying early stopping (patience 3) based on validation loss.
D Comparison of Active-Site Prediction Module
To comprehensively evaluate the active-site prediction module UniZyme, we compared it with two representative structure-based models that utilize enzyme structural and sequence information: GraphEC [47] and NodeCoder [88] Both models were reimplemented using their official repos- itories and retrained the same large-scale active-site prediction dataset employed by UniZyme. The dataset statistics are summarized in Table [ The performance different models reported in Table 9 UniZyme achieves consistently superior results compared the baselines.
Table 8: Data statistics for the active-site prediction task.
Dataset
Number of Enzymes
Number of Active Sites
Training
9220
24891 1
Test
2349
6459
Table 9: Comparison of active-site prediction performance.
Model
AUROC (%)
AUPR (%)
Precision (%)
Recall (%)
F1 ( (%)
UniZyme
89.5
35.1
65.3
45.6
53.7
GraphEC
80.3
28.0
52.1
38.7
44.4
NodeCoder
67.4
17.8
32.6
22.3
26.5
Random Guess
50.0
3.2
3.2
50.0
6.0
E Structural Source Sensitivity Analysis
To evaluate the robustness UniZyme with respect to the source of structural data, we partitioned the test set into four quadrants based on whether the enzyme and substrate structures were obtained from experimental (natural) or predicted sources. Table reports the PR-AUC results under both supervised and zero-shot settings.
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Table 10: PR-AUC (%) of UniZyme under different structural combinations.
Structure Source
Zero-shot PR-AUC (%)
Supervised PR-AUC (%)
Both Natural Structures (3%)
72.2
80.3
Natural Enzyme + Generated Substrate (7%) Natural Substrate Generated Enzyme (8%)
71.9
77.9
70.5
81.9
Both Generated Structures (82%)
69.4
78.3
Statistical Significance Testing
To confirm that the performance improvements of UniZyme over baseline models are statistically significant, we conducted two-sample t-tests on PR-AUC scores across enzyme families under both supervised and zero-shot settings. Table 11 summarizes the results.
Table 11: Two-sample t-test results comparing UniZyme with baseline models.
Setting
Comparison
t-value
p-value
Supervised
UniZyme vs ReactZyme
7.18
7.8e-10
Supervised
UniZyme ClipZyme
5.09
3.0e-06
Zero-shot
UniZyme vs ReactZyme
2.61
1.6e-02
Zero-shot
UniZyme ClipZyme
4.27
3.1e-04
G Cross-Task Transferability: EC Number Classification
To assess whether the enzyme encoder of UniZyme captures generalizable biochemical signals, we evaluated it the EC number classification task for proteases (EC 3.4. The same dataset split used the cleavage-site prediction task was reused here. Table 12 presents the results terms of AUROC.
Table 12: Performance of enzyme encoders on EC number classification.
Model
AUROC (%)
UniZyme
94.1
ClipZyme
90.2
ReactZyme
82.3
H Interpretability and Mechanistic Consistency
We conducted interpretability analyses to understand how UniZyme utilizes active-site information for cleavage-site prediction. Higher predicted active-site confidence consistently corresponds to better PR-AUC, indicating that UniZyme effectively leverages active-site cues (Table T3). Gradient- based attribution analysis further shows that active-site residues contribute more strongly to cleavage prediction than background residues (Table [4), confirming that the model's attention is aligned with catalytic regions. Moreover, perturbation experiments demonstrate that masking top predicted active-site residues causes a substantial PR-AUC drop, whereas perturbing random residues has minimal effect (Table T5). verifying that UniZyme's predictions are causally linked to the identified catalytic sites.
Mechanistic Basis of Enzyme Cleavage. During protein hydrolysis, enzyme active sites provide specific geometric and electrochemical environment that enables cleavage only at substrate residues exhibiting optimal complementarity. Thus, active-site geometry and chemistry directly influence cleavage-site specificity. Structural biology and mutational evidence strongly support this relationship: single residue change in the SI pocket of canonical serine proteases (e.g., trypsin chymotrypsin)
26

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can dramatically alter specificity by reshaping charge preference and side-chain accommodation A9 [5]. Loop variations near the active-site pocket can also reshape neighboring subsites and modulate substrate scope [5I]. Furthermore, structural studies of Alzheimer's 7-secretase show that the architecture of the binding cleft and distal exosites critically determine substrate recognition, where mutations at the interface shift cleavage patterns [52]. Energetic and mutational scanning analyses of viral proteases (e.g., HCV NS3/4A) also reveal that substrates optimally filling the active-site groove undergo efficient catalysis, while suboptimal packing results in weak or absent cleavage [53]
These mechanistic observations provide biological grounding for UniZyme's interpretability analyses: the model's 's high sensitivity to predicted active-site residues mirrors the physicochemical principles underlying real enzymatic catalysis, reinforcing that UniZyme captures not only statistical correlations but also mechanistic causality.
Table 13: PR-AUC (%) across bins of predicted active-site confidence.
Active-Site Probability Range Zero-shot PR-AUC (%)
Supervised PR-AUC (%)
[0.8, 1.0]
78.6
85.4
[0.6, 0.8)
73.8
80.3
[0.4, 0.6)
63.1
73,9
[0.2, 0.4)
57.7
61.6
[0, 0.2)
52.1
56.2
Table 14: Average attribution magnitude for active-site and background residues.
Embed. Sens.
Upstream Attr. (Sup.)
Embed. Sens. (Zero-shot)
Upstream Attr.
Residue Type
(Sup.)
(Zero-shot)
Active-site residues
0.68
0.74
0.55
0.60
Background residues
0.23
0.10
0.19
0.08
Table 15: Effect of perturbing pooling weights PR-AUC (%).
Perturbation Target
Sup. Orig.
Sup. Post
A (Sup.)
Zero Orig.
Zero Post
A
(Zero)
Predicted active-site residues (Top-5)
66.2
13.1
71.1
57.4
13.7
Random non active-site residues
79.3
78.8
1.5
71.1
69.4
I Computational Effectiveness
Tab. [6 reports wall-clock times measured on NVIDIA A6000 GPUs. Training was conducted on 8xA6000 GPUs; inference was profiled on a single A6000 GPU. As shown, UniZyme's end-to-end training cost is comparable to baselines, and its average per-pair inference latency remains within practical bounds (around 1s overhead).
Table 16: Training and inference times.
Model
Total Training Time (h) Inference per Pair (s)
Pretraining of UniZyme
12.5
UniZyme
30.9
5.2
Clip7yme
31.4
3.5
ReactZyme
30.3
4.4
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J Ablation Studies
The Tab. 17 reports the average PR-AUC across 69 supervised families and 23 zero-shot families. Full UniZyme achieves the best results in both settings, and performance progressively declines when the SE, A, or P modules are ablated.
Table 17: Ablation Studies on 69 supervised and 23 zero-shot enzyme families.
Model
Supervised
Zero-shot
UniZyme
79.3.11.2
71.1
UniZymelSE
7..3+119
65.3#1.4
UniZymelA
1..5+1.5
60.6
UniZymelP
73.0:2.2
62.1#2.7
K Failure Case Analysis (Limitation)
In our supervised experiments, the M10.003 family consistently lagged behind C14.003 and C14.005, despite having comparable dataset sizes. To understand this, we computed the Shannon entropy of the amino acid distribution positions P1-P6 around the cleavage site (P3-P4) Tab, [8 Lower
entropy indicates more conserved residues, which simplifies pattern recognition.
Table 18: Shannon entropy at positions around the cleavage site (P3 P4).
Clan
P1
P2
P3 (cleavage site)
P4 (cleavage site)
P5
P6
C14.005
4.10
4.09
3.20
3.99
4.11
4.11
C14.003
4.11
4.01
3.40
3.96
4.05
4.11
M10.003
3.97
4.00
3.95
3.66
4.03
3.90
Both C14.003 and C14.005 exhibit markedly lower entropy at the cleavage-site residues (P3, P4), indicating conserved amino acids that aid substrate recognition. By contrast, M10.003 shows uniformly higher entropy, reflecting greater sequence diversity and fewer distinctive cleavage motifs. Moreover, metalloprotease, M10.003's activity depends complex metal-ion coordination in its active-site, further complicating its cleavage specificity.
These observations suggest that M10.003's higher substrate diversity and more intricate catalytic mechanism underlie its lower prediction accuracy. In future work, we plan integrate metal-ion binding data and more detailed active-site pocket features to better capture the unique determinants of metalloprotease specificity.
L Broader Impacts
Accurately predicting protease-substrace cleavage sites across wide enzymatic landscape is pivotal for therapeutic molecule design, industrial biocatalysis, and systematic studies of discase-associated proteolysis. By performing large-scale scale virtual mapping of these interactions, UniZyme enlarges the set of candidate targets that experimentalists can pursue, thereby accelerating lead prioritization in etrly-stage drug discovery, guiding the engineering of enzymes with enhanced specificity and stability, and enabling rapid evaluation of emerging viral proteases to bolster pandemic preparedness. Like any computational framework that substantially improves the efficiency of protease screening and functional prediction, UniZyme could be misapplied instance, to create proteases that undermine existing biologics aid in the synthesis of harmful compounds.
M Additional Experiments and Visualizations
As shown in Fig. g evaluate the model's capability predicting enzyme active sites.
we conducted zero-shot testing on all enzymes not included in the training data
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1.0
1.0
Random (AUC 0.080)
UniZyme (AUC = 0.288)
True Positive Rate
0.8
0.8
Precision
0.6
0.6
0.4
0.4
0.2
0.2
Random (AUC 0.500)
UniZyme (AUC 0.867)
0.0
0.0
0.2
0.6 False Positive Rate
1.0
0.0
0.2
0.4
0.6
0.8
1.0
Recall
(a) ROC Curve
(b) PR Curve
Figure 8: Model performance of active-site prediction
100
UniZyme
ReaciZyme
ClipZyme
ProsperousPlus
DeepCleave
DirpDigest
PR-AUC (%)
A24.001
S0L.16
S02.099
508.07
M34.002
508 t77
014.00s
50R
078
506 071
Mle.00
s08.070
M10.00)
M10.144
501
801.010
M10000
201
:30
$01.139
002.001
co2.060
M1200M
CoL.E36
on
M10.012
S01. 135
501.216
c14.005
MBL.00
M12.033
501.
501 133
M12.012
sol .00
Figure 9: Per-family PR-AUC (%) across 69 supervised enzymes(Part Enzymes 36-69 coree-
sponding Fig. 3
29


Reviews:
[{"review_id": "5GzT5xrLvH", "reviewer": "Reviewer_8nAw", "summary": "This submission proposes a Unified Protein Cleavage Site Predictor that is trained with pan-genome proteolytic enzymes. The authors have made the following contributions. First, in the problem formulation, they extend the enzyme-specific setting to a general prediction formulation, introducing experimentally unavailable enzymes. Secondly, they introduce a novel encoder to incorporate energy and distance information. The experiments show a general performance gain across the enzyme species.", "questions": "To expand the dataset, the authors make an assumption that \" Drawing on previous work, we assume that minor sequence differences among enzymes of the same category can be disregarded. Consequently, the hydrolysis information from a substrate–enzyme pair is extended to all enzymes in that category.\" This is a strong claim. Do you have theoretical or experimental support?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1.\tThe extension of problem formulation leads to the expansion of training enzymes (either experimentally available or not). Therefore, the generalization to unseen proteins is improved.\n2.\tFollowing a general multi-modality scheme, the distance and energy features are incorporated as attention bias, generating a novel enzyme encoder that makes the prediction better.\n\nWeakness:\n1.\tGenerally this work is an algorithmic application of advance representation learning to an enzymatic prediction task. The general benefits of the encoder or representation design is not discussed w.r.t general protein function modeling or language modeling.", "was_revised": true, "final_justification": "The rebuttal with clarification and additional results has addressed my concerns. I am happy to keep the orginal positive score.", "rebuttal": "### **Response to Weakness 1: “The general benefits of the encoder or representation design is not discussed”**\n\nTo assess the general benefits of our UniZyme enzyme encoder, we conducted the EC number classification task on protease (EC 3.4.\\*.\\*), reusing the cleavage dataset split described in our work. Specifically, we compared UniZyme with existing enzyme encoders of ReactZyme and ClipZyme in EC functional prediction. From the table below, we observe that UniZyme achieves the highest AUROC. These results suggest that our encoder captures generalizable functional signals beyond cleavage prediction. We plan to extend this analysis to GO (Gene Ontology) function prediction and incorporate more baseline comparisons in future work.\n\nResults of predicting EC numbers with different enzyme encoders  \n| Enzyme Encoders | AUROC (%) |\n|:-----------------:|:-----------:|\n| UniZyme         | 94.1      |\n| ClipZyme        | 90.2      |\n| ReactZyme       | 82.3      |\n\n### **Response to Question 1: Theoretical and experimental support of dataset expansion**\n\nWe expanded the dataset at the enzyme family level based on empirical evidence, biological reasoning, and prior practice:  \n\n**Active Sites Are Conserved Within Families.**  Proteases from the same family often share similar catalytic mechanisms and substrate preferences, even if their overall sequences differ. For example, trypsin and chymotrypsin (S01 family), as well as MMP-2 and MMP-9 (M10 family), have nearly identical active sites and exhibit overlapping cleavage patterns [1]. This consistency supports the use of shared annotations within a family.\n\n**Experimental Protocols Commonly Use Family Representatives.**  In biochemical and proteomic studies, it’s standard to select one enzyme as a stand-in for the entire family. Caspase-3, for instance, is frequently used to represent all C14 family members [2], while MMP-9 often stands for the M10 family. These choices reflect a practical assumption that members of the same family behave similarly in experiments [3].\n\n**Previous Models Follow the Same Approach.**  Earlier predictive models such as DeepDigest[4], DeepCleave[5], and ProsperousPlus[6] were trained using family-level data without relying on individual enzyme sequences. This modeling strategy reflects a broadly accepted assumption that enzymes from the same family tend to share cleavage patterns.\n\n**MEROPS Classification Supports Annotation Sharing.**  The MEROPS [7] database organizes proteases into families based on both sequence similarity and functional traits. Members of a family typically have conserved catalytic residues, similar active-site geometry, and shared substrate specificity. These features make it reasonable to extend known cleavage annotations across enzymes in the same family.\n\n### **Referrence**\n\n**[1] Cieplak P, Strongin AY. Matrix metalloproteinases - From the cleavage data to the prediction tools and beyond. Biochim Biophys Acta Mol Cell Res. 2017 Nov;1864(11 Pt A):1952-1963.**\n\n**[2] Wejda M, Impens F, Takahashi N, Van Damme P, Gevaert K, Vandenabeele P. Degradomics reveals that cleavage specificity profiles of caspase-2 and effector caspases are alike. J Biol Chem. 2012 Oct 5;287(41):33983-95.**\n\n**[3] Alessandro Bonadio, Solomon Oguche, Tali Lavy, Oded Kleifeld, Julia Shifman bioRxiv 2023.04.11.536383**\n\n**[4] Jinghan Yang, Zhiqiang Gao, Xiuhan Ren, Jie Sheng, Ping Xu, Cheng Chang, and Yan Fu. Deepdigest: prediction of protein proteolytic digestion with deep learning. Analytical Chemistry, 93(15):6094–6103, 2021**\n\n**[5] Fuyi Li, Jinxiang Chen, André Leier, Tatiana Marquez-Lago, Quanzhong Liu, Yanze Wang, Jerico Revote, A Ian Smith, Tatsuya Akutsu, Geoffrey I Webb, Lukasz Kurgan, and Jiangning Song. Deepcleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites. Bioinformatics, 36(4):1057–1065, 09 2019.**\n\n**[6] Fuyi Li, Xudong Guo, Cong Wang, Tatsuya Akutsu, Geoffrey Webb, Lachlan Coin, Lukasz Kurgan, and Jiangning Song. Prosperousplus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction. Briefings in Bioinformatics, 24, 09 2023.**\n\n**[7] Neil D Rawlings, Alan J Barrett, and Alex Bateman. Merops: the database of proteolytic enzymes, their substrates and inhibitors. Nucleic Acids Research, 40(Database issue):D343–D350, 2012.**"}, {"review_id": "HRMUuBTngE", "reviewer": "Reviewer_574s", "summary": "This paper proposed a new Protein Cleavage Site Predictor model named UniZyme, which integrates the enzyme active-site knowledge to enhance the cleavage site prediction in enzyme-protein interaction. The model addresses the challenges in enzyme encoding and active site information utilization through active site-aware pooling, pretraining on active site prediction, and joint training methods. Experiments demonstrate that UniZyme achieves high accuracy in predicting substrate cleavage sites for both known and novel proteases.", "questions": "1. The article mentions a 60% sequence similarity threshold. Is this a universal standard? What kind of impact would different ratios have on the final results? Can this be verified?\n\n2. During the comparative experiments, which methods maintained encoder consistency, and which ones did not? In cases where there was no consistency, was the influence of the encoder more critical?\n\n3. All the experiments appear to have been conducted on a pre-organized dataset. Is this a dataset that the authors organized themselves, or is it an existing public dataset? If it is a dataset organized by the authors, would this have an adverse effect on the baseline comparisons?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. This paper focuses on an interesting and important topic: cleavage site prediction. The application of energetic frustration within the model framework and the energy-based handling of active sites during training and testing phases provide valuable insights and inspiration.\n2. The paper provides comprehensive experiments addressing RQ1, RQ2, and RQ3, effectively demonstrating the model's performance advantages on the MEROPS database.\n\nWeaknesses:\n\n1.The presentation quality of the paper needs improvement; for example, Figure 2 appears somewhat rough and could benefit from better formatting.\n\n2.The model framework primarily represents a combination of existing approaches rather than novel methodological contributions, incorporating:\n\n1）Local frustration around enzyme active sites.\n\n2）Towards self-explainable graph neural network\n\n3）One transformer can understand both 2d & 3d molecular data", "was_revised": true, "final_justification": "The author's rebuttal content has basically addressed my concerns, so I have decided to raise the score to 4.", "rebuttal": "Thanks for agreeing with our contributions. We address your questions in the following: \n\n### **Response to Weakness 1** \nThank you for your suggestions in presentation. We will revise according to your suggestions to improve the presentation quality in the camera-ready version. \n\n### **Response to methodological contributions (Weakness 2)**  \nWe’d like to highlight how UniZyme goes well beyond simply combining existing elements: \n\nFirst of all, our work **addresses a novel problem by developing a unified protein cleavage site predictor for diverse proteolytic enzymes**. Unlike prior approaches that train separate models per enzyme family, our UniZyme leverages a biochemically informed enzyme encoder that integrates active-site knowledge. The following technology is specifically designed for the challenge of enzyme catalyzed cleavage site prediction: \n\n**Biochemically-Informed Enzyme Encoder:** We clarify that previous findings indicating that energetic frustration scores can highlight functional regions within enzymes were purely observational and had not been incorporated into enzyme modeling. Inspired by this, we propose a novel enzyme encoder in UniZyme, which integrates energetic frustration scores into the enzyme encoding process. This innovation significantly enhances the modeling of enzyme functions in substrate cleavage tasks. \n\n**Enzyme Encoder Augmented by Active Site Prediction:** Active sites play a critical role in protein cleavage, thus providing essential insights into enzyme functions. To leverage this information, we introduce an auxiliary active-site prediction task to strengthen the enzyme encoder. To our knowledge, UniZyme is among the earliest methods to integrate an active-site prediction task to capture functionally relevant enzyme representations. \n\n**Active Site-Aware Pooling:** We design an active site-aware pooling mechanism, whose pooling weights are based on the predicted active site probabilities. This encourages the model to focus on catalytically relevant segments of the enzyme \n\nExtensive ablation studies in Sec. 4.4 further empirically demonstrate the effectiveness of our novel designs.  \n\n\n### **Response to the similarity threshold in splitting training and test set (Question 1)** \n\nA wealth of studies on enzyme function has shown that when sequence similarity falls below 60%, enzymes exhibit markedly greater functional divergence [1]. Moreover, previous deep-learning efforts in this area have likewise adopted a 60% similarity cutoff [2]. \n\n\nTo further verify the impact of sequence similarity, we grouped the test set by sequence identity to the training set. From the table below, we observe that even at very low similarity levels, UniZyme’s performance only slightly decreases, and it still consistently outperforms baseline methods. This result demonstrates the strong generalization capability of UniZyme under challenging conditions. \n \n\n| Similarity Range (%) | Zero-shot PR-AUC (%) |  |  | Supervised PR-AUC (%) |  |  | \n|:---:|:---:|:---:|:---:|:---:|:---:|:---:| \n|  | UniZyme | React Zyme | Clip Zyme | UniZyme | React Zyme | Clip Zyme | \n| [0-20) | 69.4 | 60.1 | 57.8 | 77.4 | 67.2 | 71.2 | \n| [20-40) | 72.2 | 64.3 | 60.4 | 80.5 | 70.5 | 75.4 | \n| [40-60) for Zero-shot & [40-50) for supervised | 73.5 | 65.8 | 64.2 | 81.3 | 72.5 | 76.3 | \n\n \n\n### **Response to the encoder consistency (Question 2)**\n\nDeepDigest, DeepCleave, ProsperousPlus, CAT3, and ScreenCap3 are cleavage-site predictors trained on individual enzyme family. These models do not encode enzymes explicitly and thus lack a generalizable enzyme encoder. For ReactZyme and ClipZyme, they are originally designed for enzyme-substrate reaction prediction. Hence, we reused only their enzyme encoder modules in our framework for a fair comparison:  \n\n**ReactZyme uses ESM-2 + MLP as the encoder.**\n\n**ClipZyme uses a pretrained EGNN to encode enzyme structure.**\n\nNeither ReactZyme nor ClipZyme utilizes active-site information in their enzyme encoder training. In contrast, UniZyme incorporates a biochemically-informed encoder that is augmented with active-site knowledge. \nThe comparisons among these models presented below demonstrate the effectiveness of using active-site information in UniZyme. \n\n| Model | Supervised PR-AUC (%) | Zero-shot PR-AUC (%) | \n|:----:|:----:|:----:| \n| UniZyme | 79.3 | 72.8 | \n| ReactZyme | 74.7 | 67.2 | \n| ClipZyme | 70.0 | 64.9 | \n\n \n\n### **Response to the dataset curation (Question 3)** \n\nWe curated a high-quality dataset specifically designed for the input requirements of our unified model, following a standardized and transparent pipeline described in Appendix A. Specifically, we: \n\n1. Downloading enzyme-substrate pairs from the MEROPS database. \n\n2. Collecting substrate sequences from UniProt and verified enzyme sequences for consistency between MEROPS and UniProt, discarding mismatches. \n\n3. Filtering sequences exceeding 1,500 residues to ensure manageable input lengths. \n\n4. Expanding the dataset by propagating cleavage annotations to all enzymes within the same MEROPS family, following a standard strategy previously used in cleavage site prediction literature (e.g., ProsperousPlus). \n\n5. Collecting structural data from PDB and AlphaFoldDB; for missing entries, we generated 3D structures using OmegaFold. \n\nThis resulted in a cleavage site dataset containing ~220,000 enzyme–substrate pairs across 866 enzymes. Then we applied rigorous train/validation/test splits for evaluation: \n\n**Supervised setting:** Selected enzyme families with ≥5 substrates. Within each family, we ensured test substrates shared <50% sequence identity with training data (Needleman–Wunsch alignment). The split ratio was 70/10/20. The final supervised test set include 69 enzyme families with 20,360 enzyme-substrate pairs.  \n\n**Zero-shot setting:** Selected enzyme families with ≥5 enzymes. We held out 20% of enzymes per family that shared <60% identity with any training or pretraining enzyme, to simulate novel enzyme scenarios. The final zero-shot  test set includes 23 enzyme families with 5,345 enzyme-substrate pairs. \n\nAll baseline models were retrained on this dataset using the same train/validation/test splits to ensure fair and consistent comparisons. The dataset and code are publicly available via the GitHub link provided in our paper. \n\nGiven the large and diverse test sets covering 69 families in supervised settings and 23 in zero-shot settings, our benchmarks comprehensively evaluate model generalization across diverse enzymes and substrates. \n\n### **Reference**\n**[1] Röttig M, Rausch C, Kohlbacher O. Combining Structure and Sequence Information Allows Automated Prediction of Substrate Specificities within Enzyme Families. PLOS Comput Biol. 2010;6(1):1–8.**\n\n**[2] Hua C, Zhong B, Luan S, Hong L, Wolf G, Precup D, Zheng S. ReactZyme: a benchmark for enzyme–reaction prediction. In: Proceedings of the 38th International Conference on Neural Information Processing Systems (NIPS ’24); 2025:Article 832. Vancouver, BC, Canada: Curran Associates Inc.**"}, {"review_id": "s8mSfx0cin", "reviewer": "Reviewer_y6mZ", "summary": "This work introduces UniZyme for predicting protein cleavage sites. Unlike most existing methods that are specific to a single enzyme, UniZyme is a unified predictor designed to generalize across a diverse range of proteolytic enzymes, including those not seen during training (a zero-shot setting). Experiments show that UniZyme significantly outperforms existing methods in both supervised and zero-shot scenarios.", "questions": "- In the \"Experimental Setup\" section (lines 242-243), the text refers to Table 7 for details on the \"supervised and zero-shot setting\" splits, but this information appears to be in Table 2. Could you please clarify or correct this reference?\n- The model uses ESM-2 for initial residue features. Given the availability of various other advanced protein language models (PLMs), could you please provide a more detailed rationale for this specific choice over other alternatives?\n- The proposed biochemical enzyme encoder is an interesting contribution. Have the authors explored its applicability or potential performance on other related protein function prediction tasks?\n- Protein structures often contain flexible regions that are crucial for function. How does the current model, which relies on static structures, account for this structural flexibility, and what impact might this have on prediction accuracy?", "limitations": "- The authors have acknowledged several limitations in the manuscript.\n\n- While the paper reports means and standard deviations, it does not include statistical hypothesis testing to formally establish the significance of the performance differences between UniZyme and the baseline models.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The proposed biochemically-informed enzyme encoder is interesting.\n- The paper clearly articulates the research problem and provides precise mathematical formalisms for the task\n- The ablation studies demonstrate the contribution of each model component.\n\nWeaknesses:\n- The paper lacks a comparative analysis of its active site prediction module against other active site prediction methods.\n- The model's dependency on 3D structures is a significant limitation, and the authors do not sufficiently discuss the performance implications or strategies for cases where only low-quality or predicted structures are available.", "was_revised": true, "final_justification": "The reviewers addressed my concerns. I think the model architecture is a little complex.", "rebuttal": "Thanks for your comprehensive feedback and suggestions. We address your questions in the following: \n\n \n\n### **Response to the comparative analysis of active site prediction module (Weaknesses 1)** \n\nWe clarify that the primary objective of UniZyme is to leverage active-site prediction as an auxiliary task to enhance enzyme modeling, rather than optimizing active-site prediction performance alone. Our ablation studies in Section 4.4 clearly demonstrate the effectiveness of this auxiliary task for improving enzyme-catalyzed substrate cleavage-site prediction. \n\n \n\nWe will include a comparative analysis of our active‑site prediction module against other established active‑site prediction methods in the camera‑ready version. \n\n ### **Response to the limitation of 3D structures (Weaknesses 2)** \n\nWe clarify that our method is primarily built upon predicted structures:  \n\n**Training phase:** around 90% of enzyme and substrate structures in our training set were predicted using OmegaFold or from AlphaFoldDB. \n\n**Test phase:** as shown in the table below, over 80% of enzyme-substrate pairs in the test set rely entirely on predicted structures. \n\nTo assess structural sensitivity, we partitioned the test set into four categories according to the structural source of the enzyme and substrate. The results below indicate only minor performance differences between predicted and experimental structures, demonstrating that our model maintains strong predictive capability even with predicted or lower-quality structural data. \n\n \n| Structure                                      | Zero-shot Avg. PR-AUC (%) | Supervised Avg. PR-AUC (%) | \n|:------------------------------------------------:|:---------------------------:|:----------------------------:| \n| Both Natural Structure 3%                      | 72.2                      | 80.3                       | \n| Natural Enzyme & Generated Substrate 7%        | 71.9                      | 77.9                       | \n| Natural Substrate & Generated Enzyme 8%        | 70.5                      | 81.9                       | \n| Both Generated Structure 82%                   | 69.4                      | 78.3                       | \n\n \n\n### **Response to the Reference of Table 2 and Table 7 (Question 1)** \n\nThank you for catching this point. We will correct it in the camera‑ready version. \n\n\n### **Response to the reason of using ESM-2 (Question 2)** \n\nWe chose the ESM‑2 esm2_t12_35M_UR50D variant primarily for its lightweight architecture and fast inference speed, which made it highly suitable for large-scale training under limited computational resources.  \n\nAt the time of model development, ESM-3 was only released at sizes ≥1.6B parameters, and the smaller ESM-C variants (300M and 600M) were not yet available. To validate this design choice, here we present ESM‑2 with ESM‑C 300M and 600M and observed only marginal performance improvements, while the inference cost increased significantly. This confirms that the use of a lightweight model like ESM‑2 35M strikes a practical and effective balance between performance and efficiency. \n \n\n| Backbone Model         | Zero-shot PR-AUC (%) | Supervised PR-AUC (%) | Training Time (hrs)                  | \n|:------------------------:|:----------------------:|:-----------------------:|:--------------------------------------:| \n| ESM-2 35M 480DIM    | 71.1                 | 79.3                  | 12.5 (pre-train) + 30.3 (finetune)   | \n| ESM-C 300M 960DIM   | 71.7                 | 80.2                  | 16.6 (pre-train) + 48.6 (finetune)   | \n| ESM-C 600M 1152DIM  | 72.3                 | 80.4                  | 20.4 (pre-train) + 56.2 (finetune)   | \n\n\n### **Response to the related protein function prediction tasks (Question 3)** \n\nTo assess the general benefits of our UniZyme enzyme encoder, we conducted the EC number classification task on protease (EC 3.4.*.*), reusing the cleavage dataset split described in our work. Specifically, we compared UniZyme with existing enzyme encoders of ReactZyme and ClipZyme in EC functional prediction. From the table below, we observe that UniZyme achieves the highest AUROC. These results suggest that our encoder captures generalizable functional signals beyond cleavage prediction. We plan to extend this analysis to GO (Gene Ontology) function prediction and incorporate more baseline comparisons in future work.  \n\nResults of predicting EC numbers with different enzyme encoders \n \n| Enzyme Encoder | AUROC (%) |  \n|:----------------:|:-----------:|  \n| UniZyme | 94.1 |  \n| ClipZyme | 90.2 |  \n| ReactZyme | 82.3 | \n \n\n### **Response to the impact of flexible regions (Question 4)** \n\nWe thank the reviewer for the suggestion. While proteins are flexible, prior studies show that catalytic residues at enzyme active sites are significantly more rigid than other regions, with lower B-factors on average. For example, a study of 69 apo-enzyme structures found that active site residues lie in regions of reduced atomic displacement compared to non-catalytic residues [1] This structural rigidity ensures that a static representation captures the essential catalytic geometry. \n\n\n### **Response to statistical hypothesis testing (Limitation 1)** \n\nTo further support the reported performance gaps, we conducted statistical hypothesis testing using two-sample t-tests. The table below presents the t-values and p-values comparing UniZyme against ClipZyme and ReactZyme across both Supervised and Zero-shot settings on the 69-family and 23-family test sets.  \n\n| Setting | Comparison | t-value | p-value |  \n|:------------:|:------------------------:|:---------:|:-----------:|  \n| Supervised | UniZyme vs ReactZyme | 7.18 | 7.80e-10 |  \n| Supervised | UniZyme vs ClipZyme | 5.09 | 3.01e-06 |  \n| Zero-shot | UniZyme vs ReactZyme | 2.61 | 1.60e-02 |  \n| Zero-shot | UniZyme vs ClipZyme | 4.27 | 3.08e-04 | \n\nOverall, UniZyme shows statistically significant improvements over both ReactZyme and ClipZyme in both Supervised and Zero‑shot evaluations, with the strongest effects observed in the Supervised setting. \n\n ### **Reference**\n**[1] Yuan Z, Zhao J, Wang ZX. Flexibility analysis of enzyme active sites by crystallographic temperature factors. Protein Eng. 2003 Feb;16(2):109-14.**"}, {"review_id": "TeJpCdDXhe", "reviewer": "Reviewer_8og7", "summary": "This paper proposes a unified protein cleavage site prediction model UniZyme, which aims to solve the problem that existing methods only target a single enzyme and lack generalization capabilities. The authors designed a framework that includes a biochemically aware enzyme encoder and an active site-aware pooling mechanism by integrating the active site knowledge and biochemical information of the enzyme, and enhanced the model performance through pre-training and joint training strategies. Experiments show that UniZyme significantly outperforms existing baselines in both supervised learning and zero-shot scenarios, especially in predicting the cleavage sites of unseen enzymes.", "questions": "1. Please provide some interpretable analysis of the relationship between the enzyme's active site and its cleavage site\n2. Could you show whether length of enzymes has significant impact?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Breaking the traditional single enzyme prediction model, a unified cross-enzyme prediction model based on active site knowledge is proposed for the first time, solving the pain point that existing methods cannot be generalized to new enzymes.\n2. The key catalytic regions are captured by energy frustration score and active site-aware pooling, and the structural information is processed by Gaussian kernel function, which reflects the deep modeling of enzyme catalytic mechanism.\n3. Comprehensive benchmarking is carried out to verify the performance in supervised (69 enzyme families) and zero-shot (23 enzyme families) scenarios. The PR-AUC indicators are significantly better than ClipZyme, ReactZyme and other baselines, especially in the zero-shot scenario, with an advantage of more than 7%.\n4. Ablation experiments confirm the necessity of energy frustration (UniZyme\\SE), pre-training (UniZyme\\P) and active site pooling (UniZyme\\A), and the mechanism analysis is rigorous.\n5. Conduct failure case analysis, and analyze the limitations from the perspective of amino acid sequence entropy and catalytic mechanism for the poor prediction effect of the M10.003 family, reflecting the rigor of the research.\n6. The paper has a clear logical structure, which progresses from the problem background, method design to experimental verification. The method section describes in detail the mathematical derivation and implementation details of modules such as enzyme encoders and active site pooling.\n\nWeakness:\n1. The framework diagram of the method is not clear enough, and the process is vague. It is recommended to put the active site prediction a between Enzyme Encoder and Active Site-Aware Pooling, which is more logical.\n2. Limitations of enzyme encoder: The article mentions that \"energetic frustration score calculation is based on AWSEM potential energy\", but does not explain how to deal with the potential energy differences of different enzyme families (such as the energy characteristics of serine proteases and metalloprotease). It is recommended to supplement the adaptability analysis of specific enzyme families, or provide details of potential energy parameter settings in the appendix.\n3. The results in the article are mainly presented in tabular form. It is recommended to display the distribution of the data for a more intuitive comparison.\n4. The existing description does not explain the specific form of the pooling weight function f(.) (such as linear/nonlinear transformation). It is recommended to clarify whether the function is an activation function (such as ReLU) or a weighted mapping.\n5. The article can provide the basis for the relationship between the active site and the cleavage site of the enzyme. It is recommended that the article can supplement the interpretable analysis of the model.", "was_revised": true, "final_justification": "I would like to thank the authors for their detailed reply, addressing all my questions and providing extensive additional experiments to support the arguments. I would like to ask the authors to include all new experiments and explanations in the final version of their paper.", "rebuttal": "Thanks for your valuable feedback. We address your questions in the following: \n\n\n### **Response to clarity of framework diagram (Weakness 1)** \nThank you very much for your valuable and practical suggestion. We will revise this diagram in the camera‑ready version. \n\n \n### **Response to limitation of enzyme encoder (Weakness 2)** \nWe use a single, consistent AWSEM parameter set for all enzyme families, as detailed in Appendix C and our GitHub repository. Specifically, we disable electrostatics (kelectrostatics = 0), enforce a 12‑residue sequence separation, generate 500 decoys by preserving backbone coordinates and randomizing side‑chains, and include the standard bond, angle, dihedral, contact, hydrogen‑bond, water‑mediated, and burial terms. We chose this uniform configuration because Freiberger et al. demonstrated that the same AWSEM coarse‑grained potential successfully identifies frustration patterns around active sites across diverse protease families [1]. \n\n\n\n### **Response to Data presentation (Weakness 3)** \n\nThank you for the suggestion. In the camera‑ready version, we will update the figures and include more informative visualizations. \n\n \n\n### **Response to pooling weight function f(.) (Weakness 4)**  \nHere, f(.) is a learnable mapping that transforms each predicted active‑site probability into its final pooling weight, in direct analogy to how we use Gaussian kernels to map energy and distance into attention biases.  \n\nConcretely, we first pass the scalar probability $\\hat a_i$ through a Gaussian basis expansion: \n\n$$ \\phi_{\\mathrm{act}}(\\hat a_i) = \\bigl[\\phi_{\\mathrm{act},1}(\\hat a_i), \\dots, \\phi_{\\mathrm{act},K}(\\hat a_i)\\bigr] \\in \\mathbb{R}^K, $$ \n\nwhich produces a richer, multi‑dimensional embedding. We then apply an MLP to collapse this embedding to a single weight: \n\n$$ w_i = f(\\hat a_i) = \\mathrm{MLP}\\bigl(\\phi_{\\mathrm{act}}(\\hat a_i)\\bigr). $$ \n\n \n\n### **Response to interpretable analysis of the relationship between the enzyme's active site and its cleavage site (Weakness 5 and Question 1)** \n\nTo address this, we provide an interpretability analysis that explores the relationship between predicted active-site probabilities and the quality of cleavage site prediction. Specifically, we aim to test whether higher-confidence active site signals are associated with more accurate cleavage predictions, thus demonstrating that the model indeed uses active-site information in a meaningful and interpretable way. \n\nHere we present the PR-AUC of cleavage site prediction across different ranges of predicted active site probabilities, aiming to illustrate the interpretability of how active site signals influence substrate cleavage site prediction. We categorize all test samples into five groups based on the average predicted active-site probability (of actual active sites) $\\hat{a}_i$ of the enzyme. From the table below, we can find that cleavage prediction improves with higher predicted active site probabilities in both zero-shot and supervised settings, indicating that the model effectively leverages active site signals. \n\n| Avg. Active Site Prob. Range | Zero-shot Avg. PR-AUC (%) | Supervised Avg. PR-AUC (%) |  \n|:----------------------------:|:----------------------:|:-----------------------:|  \n| [0.8, 1.0] | 78.6 | 85.4 |  \n| [0.6, 0.8) | 73.8 | 80.3 |  \n| [0.4, 0.6) | 63.1 | 73.9 |  \n| [0.2, 0.4) | 57.7 | 61.6 | \n| [0, 0.2) | 52.1 | 56.2 | \n\n \n\n### **Response to impact of the length of enzymes (Question 2)** \n\nTo assess whether enzyme length influences cleavage prediction performance, we grouped test enzymes by sequence length and reported the PR-AUC under both zero-shot and supervised settings.  \n\n \nAs shown in the table below, UniZyme maintains strong and stable performance across a wide range of lengths. While shorter enzymes ([0, 300)) achieve slightly higher accuracy, the model continues to perform robustly even for longer sequences, demonstrating its generalization ability across enzyme sizes. \n \n\n| **Enzyme Length Range (aa)** | **Zero-shot PR-AUC (%)** | | | **Supervised PR-AUC (%)** | | |  \n|:---------------------------:|:--------------------------------------:|:------------------------------:|:------------------------------:|:-------------------------------------:|:------------------------------:|:------------------------------:|  \n| | **UniZyme** | **ReactZyme** | **ClipZyme** | **UniZyme** | **ReactZyme** | **ClipZyme** |  \n| [0, 300) | 73.0 | 65.4 | 63.5 | 82.9 | 74.6 | 77.2 |  \n| [300, 600) | 68.1 | 60.2 | 62.5 | 75.7 | 68.3 | 70.4 |  \n| [600, 900) | 70.6 | 61.7 | 58.2 | 82.5 | 72.1 | 75.6 |  \n| [900, 1200) | 71.8 | 62.9 | 63.8 | 81.7 | 70.2 | 72.3 |  \n| [1200, 1500] | 68.8 | 59.6 | 56.1 | 74.3 | 66.7 | 73.0 | \n\n### **Reference**\n**[1] M.I. Freiberger, A.B. Guzovsky, P.G. Wolynes, R.G. Parra, & D.U. Ferreiro, Local frustration around enzyme active sites, Proc. Natl. Acad. Sci. U.S.A. 116 (10) 4037-4043**"}]

Author rebuttal:

Meta-review: This submission presents UniZyme, a model for predicting protein cleavage sites. Both the AC and reviewers agreed that this work is interesting and promising. Some reviewers initially raised concerns about the lack of comparative analysis with other existing methods, as well as issues with clarity of presentation. These points were addressed well in the rebuttal, and all reviewers ultimately supported acceptance. I therefore recommend acceptance of this paper.
Decision: Accept (poster)