MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction

## Abstract

Abstract Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biological processes. Accurately predicting PTM sites and their specific types is therefore essential for elucidating protein function and understanding disease mechanisms. Existing computational approaches predominantly focus on protein sequences to predict PTM sites, driven by the recognition of sequence-dependent motifs. However, these approaches often overlook protein structural contexts. In this work, we first compile a large-scale sequence-structure PTM dataset, which serves as the foundation for fair comparison. We introduce the MeToken model, which tokenizes the micro-environment of each amino acid, integrating both sequence and structural information into unified discrete tokens. This model not only captures the typical sequence motifs associated with PTMs but also leverages the spatial arrangements dictated by protein tertiary structures, thus providing a holistic view of the factors influencing PTM sites. Designed to address the long-tail distribution of PTM types, MeToken employs uniform sub-codebooks that ensure even the rarest PTMs are adequately represented and distinguished. We validate the effectiveness and generalizability of MeToken across multiple datasets, demonstrating its superior performance in accurately identifying PTM types. The results underscore the importance of incorporating structural data and highlight MeToken’s potential in facilitating accurate and comprehensive PTM predictions, which could significantly impact proteomics research. The code and datasets are available at github.com/A4Bio/MeToken .

## 1 Introduction

Post-translational modifications (PTMs) are chemical modifications that occur to proteins following biosynthesis (Consortium, ; Hanahan & Coussens, ), playing a pivotal role in regulating biological processes including signal transduction (Meng et al., ; Walsh et al., ), protein degradation (Jensen, ; Craveur et al., ; Humphrey et al., ), and cellular localization (Li et al., ; Deribe et al., ). PTMs can alter the properties and functions of a protein by modifying its chemical structure, often serving as switches that toggle protein activity. The diverse nature of PTMs, which include phosphorylation, glycosylation, acetylation, ubiquitination, and methylation, reflects their varied roles across different cellular processes and conditions (Walsh & Jefferis, ). Given their complexity and the critical functionality they impart, accurately predicting PTM sites and their specific types is essential for elucidating protein functions, advancing therapeutic development, and unraveling the molecular bases of diseases (Cruz et al., ; Tsikas, ).

Figure: Figure 1: The comparison of sequence-based and MeToken schemes. While sequence-based methods focus on sequence motifs around modification sites, MeToken first encodes the micro-environment at both sequence and structure levels and predicts the PTM types with token embeddings.
Refer to caption: x1.png

Despite their significance, predicting PTM types remains a formidable challenge due to the inherent complexity of protein structures and the subtle, context-dependent nature of modification sites. Researchers have struggled with several key difficulties: (i) Predominant computational approaches focus on sequence-dependent motifs while neglecting tertiary structural information. As depicted in Figure 1(a), the N-glycosylation modification exhibits a well-defined ’Asn-X-T/S’ sequence motif, a finding thoroughly illustrated by (Bause & Hettkamp, ). However, the situation is notably more complex for phosphorylation, where the sequence motifs are less consistent and more variable. The reliance on sequence data alone limits the ability to predict PTM types. (ii) Lack of large-scale sequence structure PTM dataset. Though the sequence-structure co-modeling manner has been well explored in protein function prediction (Wang et al., ; Hermosilla et al., ; Zhang et al., ; Fan et al., ; Hu et al., ), the PTM prediction significantly lacks in terms of a comprehensive, annotated sequence-structure dataset at the residue level. (iii) The severe long-tail distribution of PTM types. The distribution of PTM types is highly skewed, with a few types being overwhelmingly more common than others. The lack of representation not only complicates model training with imbalanced data but also hampers the model’s generalization ability.

In this paper, we first compile a large-scale sequence-structure PTM dataset featuring over 1.2 million residue-level annotated sites across multiple PTM types. Furthermore, we introduce the MeToken (Micro-environment Token) model, which integrates both sequence and structural information from the sequence-structure pair. Recognizing that PTMs are influenced not only by the immediate sequence environment but also by the intricate spatial context, MeToken employs a micro-environment-based approach to capture comprehensive contextual information for each amino acid, thereby providing a holistic view that is critical for predicting potential PTMs.

To decipher the typical micro-environment patterns associated with PTMs, we develop a codebook that distills complex and high-dimensional data into simpler, representative tokens with distinct and meaningful representations. The codebook is constructed to learn a set of discrete tokens that correlate specifically to PTMs with minimal redundancy, ensuring that each token captures unique and interpretable information about the micro-environment. Furthermore, we propose a uniform sub-codebook strategy that effectively addresses the long-tail distribution of PTM types, which projects the PTM types into a uniformly distributed token space, ensuring that even the rarest PTMs are adequately represented and distinctly discriminated. By leveraging our tokenization strategy, MeToken not only enhances interpretability but also significantly improves its predictive performance by fine-tuning the model specifically for the PTM prediction task. We conduct extensive experiments to validate the effectiveness and generalizability of MeToken across multiple datasets, demonstrating its superior performance in accurately identifying PTM types.

The contributions of our work are summarized as:

- •
Recognizing the critical importance of structural context in PTM prediction, we have compiled a large-scale sequence-structure PTM dataset, which serves as the foundational basis for the PTM predictions based on sequence-structure pairs.
- •
In analogy to sequence motifs used in sequence-based PTM prediction, we introduce the concept of the micro-environment token. The contextual information at both the sequence and structural levels can be uniquely represented within a set of discrete tokens.
- •
To address the severe long-tail problem prevalent in PTM data, we have devised a codebook construction strategy that incorporates uniform sub-codebooks, ensuring that PTMs are adequately represented and distinctly discernible. Extensive experiments demonstrate the effectiveness and generalizability of MeToken in accurately predicting PTM types.

## 2 Related Work

#### Post-Translation Modification Prediction

Early work on PTM prediction primarily involved manually extracting sequence motifs from around potential modification sites and employing various learning-based methods for prediction (Xue et al., ; Lee et al., ; Gao et al., ; Jia et al., ; ; Luo et al., ; Kirchoff & Gomez, ; Meng et al., ). For example, PPSP (Xue et al., ) utilized Bayesian decision theory, focusing on a motif of nine amino acids. LysAcet (Li et al., ) used support vector machines (SVM) and sequence coupling patterns. Other methods like PWAA introduced position weight amino acid composition encoding (Qiu et al., ), while LightGBM-CroSite combined binary encoding and position weight matrices (Liu et al., ). DeepNitro (Xie et al., ) incorporated positional amino acid distributions and position-specific scoring matrices. Deep-Kcr integrated sequence-based features and employed convolutional neural networks (CNN) (Lv et al., ). DeepSuccinylSite employed one-hot and word embeddings to enhance predictions (Thapa et al., ), and Adapt-Kcr combined adaptive embedding with convolutional and long short-term memory networks (LSTM), along with an attention mechanism (Li et al., ). While these methods have shown effectiveness, they are limited by their reliance on sequence motifs, which cannot fully capture the intricate complexity and contextual dependencies of PTM sites. Despite efforts to integrate sequence and structural data in PTM prediction, the attempt has been largely constrained by the scarcity of comprehensive datasets (Li et al., ; Yan et al., ). The largest sequence-structure database, PRISMOID (Li et al., ), includes 3,919 protein structure entries with 17,145 non-redundant modification sites. In this work, we compile a large-scale sequence-structure PTM dataset with over 1.2 million annotated sites across over 180,000 proteins.

#### Sequence-structure Co-modeling

Sequence-structure co-modeling has become a common manner in predicting protein property and function. LM-GVP (Wang et al., ) combined protein language model and graph neural networks (GNN) to predict protein properties from both their amino acid sequences and 3D structures. DeepFRI (Gligorijević et al., ) employed a similar strategy and used homology models. GearNet (Zhang et al., ) is a seminal work that pretraining protein representations based on 3D structures using a graph encoder and multiview contrastive learning. ProNet (Wang et al., ) employed a hierarchical graph network to learn detailed and granular protein representations. CDConv (Fan et al., ) utilized independent weights for regular sequential displacements and direct encoding for irregular geometric displacements. While these methods utilized local sequence-structure patterns to learn global protein-level properties, PTM prediction differs fundamentally as it directly employs the local sequence-structure micro-environments to predict modifications at the residue level.

#### Micro-environment Tokenization

Vector quantization (VQ) is a pivotal technique for efficient data compression and representation. The basic premise involves compressing latent representations by mapping them to the nearest vector within a codebook. Vanilla VQ (Van Den Oord et al., ) straightforwardly assigns each vector in the latent space to the closest codebook vector. Product Quantization (Jégou et al., ; Chen et al., ; El-Nouby et al., ) segments the high-dimensional space into a Cartesian product of lower-dimensional subspaces. Residual quantization (Juang & Gray, ; Martinez et al., ; Lee et al., ; Zeghidour et al., ) iteratively quantizes vectors and their residuals, representing the vector as a stack of codes. Lookup-free VQ (Mentzer et al., ; Yu et al., ) quantizes each dimension to a fixed set of values. Although advancements in vector quantization have shown promise in molecular modeling (Xia et al., ; Gao et al., ; Wu et al., ), the specific application of micro-environment tokenization in PTM prediction remains underexplored, particularly given the challenges posed by the long-tail distribution of PTMs.

## 3 Preliminaries

A protein $\mathcal{P}$ is composed of $N$ amino acids, which are organized as a string of primary sequence, denoted as $\mathcal{S}=\{s_{i}\}_{i=1}^{N}$. Each element $s_{i}$ within this sequence is referred to as a residue, representing one of the 20 standard amino acid types. In the real physicochemical environment of a cell, these sequences fold into stable three-dimensional structures. This three-dimensional conformation is defined by the coordinates of backbone atoms, represented as $\mathcal{X}=\{\bm{x}_{i,\omega}\}_{i=1}^{N}$, where $\bm{x}_{i,\omega}\in\mathbb{R}^{3}$ and $\omega$ encompasses the main backbone atoms, specifically $\{\textrm{C}\alpha,\textrm{N, C, O}\}$. The sequence-structure pair of a protein is represented as $\mathcal{P}=(\mathcal{S},\mathcal{X})$.

Modeling the sequence and structure of a protein $\mathcal{P}$ as a residue-level protein graph $\mathcal{G}=(\mathcal{V},\mathcal{E})$ is a widely recognized approach (Tan et al., ; Luo et al., ; Kong et al., ; Jin et al., ; Kong et al., ). In this representation, $\mathcal{V}$ represents the node embeddings, which are derived from the amino acid sequence $s_{i}$ and the spatial arrangement $\{\bm{x}_{i,\omega}|\omega\in\{\textrm{C}\alpha,\textrm{N, C, O}\}\}$ of the backbone atoms within each residue. The edges, denoted as $\mathcal{E}$, encapsulate the interactions between residues, represented as edge embeddings that reflect the various types of connections. The node and edge embedding construction is detailed in the Appendix B. Building on the concept of the micro-environment, as outlined in previous works (Huang et al., ; Lu et al., ; Wu et al., ), the micro-environment of a residue $i$ within $\mathcal{G}$ is characterized by its node set $V_{\textrm{ME}}^{i}\subseteq\mathcal{V}$, can be formally defined as:

$$ $V_{\textrm{ME}}^{i}=\left\{v_{j}\ \Big{|}\ |i-j|\!\leq\!d_{s},\|\textrm{C} \alpha_{i}-\textrm{C}\alpha_{j}\|\!\leq\!d_{r},v_{j}\in\mathcal{N}^{(K)}_{i}, \forall j\right\},$ (1) $$

where $d_{s}$ and $d_{r}$ represent the cut-off distances within the sequence and in 3D space, respectively. $C\alpha_{i}$ and $C\alpha_{j}$ denote the 3D coordinates of the carbon-alpha atoms in residues $i$ and $j$, and $\mathcal{N}^{(K)}_{i}$ indicates the $K$-hop neighborhood of residue $i$ in the 3D structure.

Thus, the types of edges in the micro-environment focus on three specific interactions: (i) sequential edge, connecting two residues in the sequence within a cutoff distance $d_{s}$; (ii) radius edge, connecting two residues if the spatial Euclidean distance between them is less than a threshold $d_{r}$; and (iii) $K$-nearest edge, connecting two residues that are among the spatial $K$-nearest neighbors to each other. Consequently, the complete micro-environment of residue $i$ is represented as $\mathcal{G}_{\textrm{ME}}^{i}=(V^{i},E_{\textrm{ME}}^{i})$, where $V_{i}\in\mathcal{V}$ includes the node embedding of the residue $i$ and $E_{\textrm{ME}}^{i}$ includes the edge embeddings connecting residue $i$ with its neighboring nodes from three kinds of edges in the set $V_{\textrm{ME}}^{i}$.

Figure: Figure 2: Illustration of the micro-environment of residue $i$. This figure depicts the local neighborhood of residue $i$, highlighted in red, which is interconnected through various types of edges: sequential edges (blue), $R$-radius edges (pink), and $K$-nearest edges (green). The entire micro-environment, including residue $i$ and its interconnected neighbors, is then tokenized into a unified representation token for PTM prediction.
Refer to caption: x2.png

In this study, our primary objective is to effectively encode the micro-environment of each residue $i$ within the protein graph $\mathcal{G}$, by seamlessly integrating crucial sequence and structural information into a single, unified discrete token. As depicted in Figure 2, the micro-environment of residue $i$ is adeptly tokenized into a unified representation token. The core of our tokenization process involves developing a mapping function $\mathcal{F}:\mathcal{G}_{\textrm{ME}}^{i}\rightarrow\bm{e}^{j}$, where $\bm{e}^{j}\in\mathbb{R}^{d}$ denotes a $d$-dimensional token embedding. This embedding is not merely a numerical representation but a rich encapsulation of the complex patterns of sequence and spatial features that characterize the unique micro-environment surrounding residue $i$. Here, $j$ signifies the token index within the codebook $\mathcal{C}$, ranging over $[1,|\mathcal{C}|]\cap\mathbb{Z}$.

## 4 MeToken

In this section, we detail the methodologies underlying our proposed MeToken model, which aims to tackle the challenges posed by the diverse nature and varying frequency of PTM types observed across different proteins. The MeToken model is specifically designed to manage the long-tail distribution of PTM types and capture subtle, yet crucial, biochemical signals in protein micro-environments.

### 4.1 Uniform Sub-codebook Construction

Recognizing that the distribution of PTM types exhibits a severe long-tail pattern, where many PTM types are infrequently observed, it is imperative to ensure that rare PTMs are adequately represented in the learning process. To achieve this, we allocate an identical size of sub-codebook to each PTM type, irrespective of their frequency in the dataset. This uniformity ensures that no PTM type is inherently disadvantaged by a lack of representative power within the model’s architecture.

Figure: Figure 3: The long-tail distributed PTM types are projected into a uniformly distributed token space. Red arrows depict the consolidation of token embeddings within each sub-codebook, enhancing intra-class similarity. In contrast, gray arrows represent the dispersion of token embeddings across different sub-codebooks, promoting inter-class distinctiveness.
Refer to caption: x3.png

As illustrated in Figure 3, each sub-codebook is designed to capture distinct patterns that are characteristic of specific PTM types. However, merely maintaining a uniform size for each sub-codebook does not inherently prevent the potential risk of token embeddings collapsing into non-discriminative clusters. To address this, we implement a uniform loss mechanism specifically designed to refine the distribution of token embeddings, which is formally defined as follows:

$$ $\mathcal{L}_{u}=-\frac{1}{|\mathcal{C}|}\sum_{i=1}^{|\mathcal{C}|}\log\frac{ \sum_{j=1}C_{ij}\mathds{1}_{S_{i}=S_{j}}}{\sum_{j=1}C_{ij}},$ (2) $$

where $S_{i}$ denotes the PTM type of token $i$, $C_{ij}=\exp(\frac{\bm{e}^{T}_{i}\bm{e}_{j}}{\|\bm{e}_{i}\|\|\bm{e}_{j}\|}/\tau
_{u})$ represents the cosine similarity between tokens $i$ and $j$ scaled by the temperature $\tau_{u}$, and $\mathds{1}_{S_{i}=S_{j}}$ is an indicator that is 1 if token $i$ and $j$ share the same PTM type, and 0 otherwise. This formulation maximizes intra-class similarities, thereby enhancing the homogeneity of embeddings within each PTM class. Simultaneously, it minimizes inter-class similarities, promoting heterogeneity across different PTM types.

### 4.2 Temperature-scaled Vector Quantization

Vanilla vector quantization (Van Den Oord et al., ) maps each input vector to the nearest vector in a predefined set, known as a codebook. This mapping results in a "hard assignment" that can be represented as:

$$ $z_{i}=\arg\min_{j}\|\bm{h}_{i}-\bm{e}_{j}\|^{2},$ (3) $$

where $\bm{h}_{i}\in\mathbb{R}^{d}$ represents the input vector and $\bm{e}_{j}$ denotes the nearest codebook vector. The index $z_{i}$ identifies the specific codebook vector that minimizes the Euclidean distance to $\bm{h}_{i}$. A fundamental limitation of this method is the non-differentiable nature of the mapping process due to the hard assignment, which relies on the nearest neighbor search. This search uses the encoder output to find the closest code, thereby rendering the quantization step non-differentiable:

$$ $\mathcal{L}_{vq}=\mathcal{L}_{recon}+\|\mathrm{sg}[\bm{h}_{i}]-\bm{e}_{z_{i}} \|_{2}^{2}+\beta\|\bm{h}_{i}-\mathrm{sg}[\bm{e}_{z_{i}}]\|_{2}^{2},$ (4) $$

where $\mathcal{L}_{recon}$ is the reconstruction loss. $\mathrm{sg}[\cdot]$ indicates the stop-gradient operation, and $\beta$ is the trade-off hyperparameter. However, the rigid and non-differentiable characteristics of hard quantization may not effectively capture the subtle and complex variations typical in PTM datasets, particularly in the context of our model which employs uniform sub-codebook construction. These challenges can lead to gradient sparsity and impede the learning process, necessitating a more flexible approach.

In our model, we implement a temperature-scaled vector quantization mechanism that introduces a temperature parameter, $\tau_{v}$, to modulate the quantization process. The quantization process can be mathematically defined as follows:

$$ $\hat{\bm{h}}_{i}=\sum_{j=1}^{|\mathcal{C}|}a_{ij}\bm{e}_{j},\quad a_{ij}=\frac {\exp(\bm{h}_{i}^{T}\bm{e}_{j}/\tau_{v})}{\sum_{k=1}^{|\mathcal{C}|}\exp(\bm{h }_{i}^{T}\bm{e}_{k}/\tau_{v})},$ (5) $$

where $a_{ij}$ represents the attention weight assigned to the $j$-th codebook vector for the $i$-th input vector. These weights are computed using a softmax function, scaled by the temperature parameter $\tau_{v}$, which modulates the sharpness of the resulting attention distribution. A higher $\tau_{v}$ leads to a more diffuse distribution across the codebook vectors, promoting a broader exploration of the vector space and mitigating the risk of premature convergence to suboptimal quantizations. Initially set at 1, $\tau_{v}$ is gradually reduced towards zero during training. As $\tau_{v}$ diminishes, $a_{ij}$ transitions towards a one-hot distribution, rendering the quantization process increasingly deterministic. The quantization selects the codebook vector corresponding to the highest attention weight, identified by $z_{i}=\arg\max_{j}a_{ij}$. Tthe codebook training loss incorporates both the reconstruction loss and a uniform loss:

$$ $\mathcal{L}_{codebook}=\mathcal{L}_{recon}+\alpha\mathcal{L}_{u}$ (6) $$

where $\alpha$ is set as 0.1 empirically, balancing the reconstruction loss and the uniform loss.

Figure: Figure 4: Vanilla VQ looks up the codebook and hard-assigns the nearest code, while temperature-scaled VQ employs a softer, probabilistic assignment approach where codebook vectors are assigned based on weights. These weights are modulated by $\tau_{v}$, which adjusts the sharpness throughout the training process, transitioning from exploratory to more deterministic assignments as $\tau_{v}$ decreases.
Refer to caption: x4.png

### 4.3 PTM Prediction with Learned Codebook

The learned codebook, derived from the training set, contains vectors that capture the unique attributes of protein micro-environments. As depicted in Figure 5, during the prediction phase, we extract the codebook vector $\bm{e}_{z_{i}}$ corresponding to the quantized index $z_{i}$ associated with each residue’s micro-environment. This retrieval initiates the prediction process. To predict the specific PTM type for each residue, we employ a prediction network built from three layers of PiGNN (Gao et al., ), a graph neural network tailored for protein graph data. This network directly processes the quantized codebook vectors $\bm{e}_{z_{i}}$ transforming these richly encoded representations into predictions about PTM types. The predictor network is trained using the cross-entropy loss, which is defined as:

$$ $\mathcal{L}_{pred}=-\sum_{i}\sum_{c}y_{ic}\log p(y_{ic}|\bm{h}_{i}),$ (7) $$

where $y_{ic}$ indicates whether the $i$-th site has the PTM type $c$, $\bm{h}_{i}$ is the micro-environment embedding.

Figure: Figure 5: The PTM prediction process with the learned codebook.
Refer to caption: x5.png

## 5 Experiment

#### Datasets

Experiments are conducted on three datasets: our large-scale dataset, PTMint, and qPTM. We constructed a large-scale dataset by integrating dbPTM (Li et al., ), the most extensive sequence-based PTM dataset available, with structural data obtained from the Protein Data Bank (PDB) (Berman et al., ) and the AlphaFold database (Varadi et al., ; ). We utilized MMseqs2 (Steinegger & Söding, ) to cluster the data based on sequence similarity with a threshold of 40% and grouped the data into clusters, which were then allocated to the training, validation, or test set. Our large-scale PTM dataset with over 1.2 million annotated sites across over 180,000 proteins is the largest PTM dataset with both sequence and structure data. To assess the generalizability, we used the pre-trained models on the large-scale dataset to directly test on the PTMint (Hong et al., ) and qPTM (Yu et al., ) datasets. The details of the dataset construction and the statistics of open-sourced datasets are outlined in Appendix C.

#### Baselines

We compare the performance against a selection of established PTM prediction models. Specifically, we include sequence-based baselines such as EMBER(Kirchoff & Gomez, ), DeepPhos(Luo et al., ), and MusiteDeep (Wang et al., ). MIND (Yan et al., ) is as a sequence-structure baseline. In addition to these PTM-specific models, we incorporate baselines from broader protein modeling domains. For structure-based baselines, we adapt StructGNN (Ingraham et al., ), GraphTrans (Ingraham et al., ), GVP (Jing et al., ), and PiFold (Gao et al., ), which are primarily designed for structure-based protein sequence design. Similarly, ESM-2(Lin et al., ) is fine-tuned to serve as an additional sequence-based PTM predictor. Moreover, GearNet (Zhang et al., ) and CDConv (Fan et al., ), originally designed for protein function prediction, are also adapted to act as sequence-structure co-modeling baselines.

#### Metrics

Regarding the PTM prediction problem as a multi-class classification problem, we employed a comprehensive suite of metrics, such as accuracy, precision, recall, F1-score, MCC, AUROC, and AUPRC within our multi-class context. We detail these metrics in the Appendix D.

### 5.1 MeToken Outperforms Baseline Models on The Large-scale Dataset

To evaluate the performance of MeToken, we conducted extensive tests on our large-scale dataset that incorporates both sequence and structural data. Table 1 presents the performance comparison of MeToken against a range of baseline models. It is noteworthy that both sequence-based and structure-based baselines exhibit comparable performances, each utilizing only a single modality.

Among these, the finetuned ESM-2 emerged as the most robust, achieving the best performance across all baselines. Despite its strong performance, MeToken demonstrates superior capabilities, achieving a remarkable 6.46% improvement in F1-score over ESM-2. This significant performance underscores the efficacy of MeToken in leveraging micro-environment information from sequence-structure data.

**Table 1: Performance comparison on the large-scale dataset. The mean/std of three runs is reported.**
| Input | Method | F1-score | Accuracy | Precision | Recall | MCC | AUROC | AUPRC |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Seq | EMBER | 10.94${}_{\pm\textrm{0.77}}$ | 72.89${}_{\pm\textrm{0.12}}$ | 15.34${}_{\pm\textrm{0.94}}$ | 9.55${}_{\pm\textrm{0.67}}$ | 19.75${}_{\pm\textrm{1.07}}$ | 64.47${}_{\pm\textrm{2.12}}$ | 9.89${}_{\pm\textrm{0.71}}$ |
| DeepPhos | 3.58${}_{\pm\textrm{0.00}}$ | 74.33${}_{\pm\textrm{0.01}}$ | 5.75${}_{\pm\textrm{0.18}}$ | 4.18${}_{\pm\textrm{0.01}}$ | 2.96${}_{\pm\textrm{0.09}}$ | 59.34${}_{\pm\textrm{1.42}}$ | 5.17${}_{\pm\textrm{0.16}}$ |  |
| MusiteDeep | 3.55${}_{\pm\textrm{0.00}}$ | 74.18${}_{\pm\textrm{0.06}}$ | 3.09${}_{\pm\textrm{0.00}}$ | 4.17${}_{\pm\textrm{0.00}}$ | 0.00${}_{\pm\textrm{0.00}}$ | 48.85${}_{\pm\textrm{1.04}}$ | 4.01${}_{\pm\textrm{0.01}}$ |  |
| ESM-2 | 43.94${}_{\pm\textrm{0.07}}$ | 89.75${}_{\pm\textrm{0.01}}$ | 55.67${}_{\pm\textrm{1.94}}$ | 42.15${}_{\pm\textrm{0.04}}$ | 75.83${}_{\pm\textrm{0.01}}$ | 91.52${}_{\pm\textrm{0.03}}$ | 47.31${}_{\pm\textrm{0.14}}$ |  |
| Str | StructGNN | 12.86${}_{\pm\textrm{0.06}}$ | 75.62${}_{\pm\textrm{0.08}}$ | 21.16${}_{\pm\textrm{0.61}}$ | 11.06${}_{\pm\textrm{0.18}}$ | 29.73${}_{\pm\textrm{0.26}}$ | 78.94${}_{\pm\textrm{0.46}}$ | 13.51${}_{\pm\textrm{0.18}}$ |
| GraphTrans | 5.03${}_{\pm\textrm{0.49}}$ | 74.14${}_{\pm\textrm{0.11}}$ | 12.23${}_{\pm\textrm{0.72}}$ | 5.12${}_{\pm\textrm{0.40}}$ | 9.87${}_{\pm\textrm{1.82}}$ | 74.75${}_{\pm\textrm{0.08}}$ | 7.99${}_{\pm\textrm{0.27}}$ |  |
| GVP | 17.51${}_{\pm\textrm{1.12}}$ | 77.62${}_{\pm\textrm{0.16}}$ | 32.19${}_{\pm\textrm{4.87}}$ | 14.43${}_{\pm\textrm{0.92}}$ | 37.95${}_{\pm\textrm{0.89}}$ | 82.47${}_{\pm\textrm{0.52}}$ | 18.83${}_{\pm\textrm{1.29}}$ |  |
| PiFold | 8.87${}_{\pm\textrm{4.69}}$ | 75.11${}_{\pm\textrm{1.37}}$ | 18.42${}_{\pm\textrm{5.43}}$ | 8.03${}_{\pm\textrm{3.71}}$ | 22.59${}_{\pm\textrm{10.15}}$ | 76.81${}_{\pm\textrm{4.37}}$ | 11.02${}_{\pm\textrm{4.43}}$ |  |
| Seq<br>+<br>Str | GearNet | 16.63${}_{\pm\textrm{1.64}}$ | 85.75${}_{\pm\textrm{1.13}}$ | 17.09${}_{\pm\textrm{1.37}}$ | 21.01${}_{\pm\textrm{1.45}}$ | 67.64${}_{\pm\textrm{2.44}}$ | 89.37${}_{\pm\textrm{0.29}}$ | 21.79${}_{\pm\textrm{0.25}}$ |
| CDConv | 18.11${}_{\pm\textrm{0.62}}$ | 87.29${}_{\pm\textrm{0.09}}$ | 19.18${}_{\pm\textrm{1.97}}$ | 22.58${}_{\pm\textrm{1.52}}$ | 70.88${}_{\pm\textrm{0.16}}$ | 89.42${}_{\pm\textrm{0.21}}$ | 21.94${}_{\pm\textrm{0.41}}$ |  |
| MIND | 26.70${}_{\pm\textrm{0.72}}$ | 86.30${}_{\pm\textrm{0.24}}$ | 33.35${}_{\pm\textrm{4.07}}$ | 25.21${}_{\pm\textrm{0.84}}$ | 66.63${}_{\pm\textrm{0.48}}$ | 77.37${}_{\pm\textrm{1.74}}$ | 22.29${}_{\pm\textrm{0.36}}$ |  |
| MeToken | 50.40${}_{\pm\textrm{0.06}}$ | 89.91${}_{\pm\textrm{0.05}}$ | 58.08${}_{\pm\textrm{0.38}}$ | 47.93${}_{\pm\textrm{0.14}}$ | 76.03${}_{\pm\textrm{0.06}}$ | 92.20${}_{\pm\textrm{0.03}}$ | 51.26${}_{\pm\textrm{0.14}}$ |  |

### 5.2 MeToken Generalize Well on PTMint and qPTM

To further evaluate the effectiveness and generalizability of MeToken, we conducted rigorous tests on two additional datasets, PTMint and qPTM, after training the models on our large-scale dataset. We refined the qPTM dataset by excluding data with sequence similarity greater than 40% compared to our training set to prevent data leakage, while the PTMint dataset remained unfiltered due to its relatively small size. The summarized results in Tables 2 and 3 highlight MeToken’s consistent superiority over all baselines across various metrics on both datasets.

The PTMint dataset, although smaller in scale, contains a well-curated collection of samples spread across several protein types, making it an ideal testbed for evaluating PTM predictions. The results from Table 2 demonstrate that MeToken significantly outperforms sequence-only and structure-only models, showcasing its capability to integrate micro-environments from complex data modalities.

**Table 2: Performance comparison on the PTMint dataset. The mean/std of three runs is reported.**
| Input | Method | F1-score | Accuracy | Precision | Recall | MCC | AUROC | AUPRC |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Seq | EMBER | 21.27${}_{\pm\textrm{3.81}}$ | 81.53${}_{\pm\textrm{1.05}}$ | 21.98${}_{\pm\textrm{4.91}}$ | 21.40${}_{\pm\textrm{2.34}}$ | 8.16${}_{\pm\textrm{1.11}}$ | 66.60${}_{\pm\textrm{2.24}}$ | 30.23${}_{\pm\textrm{1.21}}$ |
| DeepPhos | 19.32${}_{\pm\textrm{1.20}}$ | 85.97${}_{\pm\textrm{1.50}}$ | 18.55${}_{\pm\textrm{0.85}}$ | 20.68${}_{\pm\textrm{2.34}}$ | -2.00${}_{\pm\textrm{2.99}}$ | 53.64${}_{\pm\textrm{2.06}}$ | 22.66${}_{\pm\textrm{0.79}}$ |  |
| MusiteDeep | 19.01${}_{\pm\textrm{0.01}}$ | 90.54${}_{\pm\textrm{0.11}}$ | 18.11${}_{\pm\textrm{0.02}}$ | 20.00${}_{\pm\textrm{0.00}}$ | 0.00${}_{\pm\textrm{0.00}}$ | 49.70${}_{\pm\textrm{2.66}}$ | 22.11${}_{\pm\textrm{1.01}}$ |  |
| ESM-2 | 36.46${}_{\pm\textrm{2.23}}$ | 93.07${}_{\pm\textrm{0.59}}$ | 41.78${}_{\pm\textrm{7.87}}$ | 36.30${}_{\pm\textrm{1.05}}$ | 61.50${}_{\pm\textrm{3.41}}$ | 97.79${}_{\pm\textrm{0.09}}$ | 60.82${}_{\pm\textrm{2.02}}$ |  |
| Str | StructGNN | 20.55${}_{\pm\textrm{1.66}}$ | 87.29${}_{\pm\textrm{0.78}}$ | 21.69${}_{\pm\textrm{1.45}}$ | 20.23${}_{\pm\textrm{1.71}}$ | 13.52${}_{\pm\textrm{4.98}}$ | 77.74${}_{\pm\textrm{0.04}}$ | 33.71${}_{\pm\textrm{2.60}}$ |
| GraphTrans | 20.55${}_{\pm\textrm{3.43}}$ | 87.56${}_{\pm\textrm{1.45}}$ | 19.53${}_{\pm\textrm{3.15}}$ | 22.18${}_{\pm\textrm{4.34}}$ | 5.91${}_{\pm\textrm{5.47}}$ | 63.50${}_{\pm\textrm{2.30}}$ | 26.41${}_{\pm\textrm{1.90}}$ |  |
| GVP | 25.00${}_{\pm\textrm{4.64}}$ | 87.41${}_{\pm\textrm{1.49}}$ | 25.23${}_{\pm\textrm{4.17}}$ | 26.11${}_{\pm\textrm{5.43}}$ | 29.56${}_{\pm\textrm{6.04}}$ | 76.88${}_{\pm\textrm{1.59}}$ | 33.17${}_{\pm\textrm{0.76}}$ |  |
| PiFold | 20.32${}_{\pm\textrm{3.28}}$ | 89.19${}_{\pm\textrm{0.15}}$ | 21.54${}_{\pm\textrm{4.76}}$ | 20.59${}_{\pm\textrm{3.05}}$ | 13.28${}_{\pm\textrm{4.21}}$ | 71.02${}_{\pm\textrm{0.89}}$ | 27.73${}_{\pm\textrm{0.83}}$ |  |
| Seq<br>+<br>Str | GearNet | 32.09${}_{\pm\textrm{0.24}}$ | 94.31${}_{\pm\textrm{0.16}}$ | 28.66${}_{\pm\textrm{0.25}}$ | 40.00${}_{\pm\textrm{0.00}}$ | 68.23${}_{\pm\textrm{0.55}}$ | 97.69${}_{\pm\textrm{0.12}}$ | 56.47${}_{\pm\textrm{1.32}}$ |
| CDConv | 29.30${}_{\pm\textrm{3.77}}$ | 92.75${}_{\pm\textrm{0.65}}$ | 26.62${}_{\pm\textrm{3.59}}$ | 39.50${}_{\pm\textrm{2.68}}$ | 59.98${}_{\pm\textrm{2.17}}$ | 97.83${}_{\pm\textrm{0.29}}$ | 56.97${}_{\pm\textrm{0.96}}$ |  |
| MIND | 35.04${}_{\pm\textrm{1.49}}$ | 93.25${}_{\pm\textrm{0.58}}$ | 34.07${}_{\pm\textrm{4.53}}$ | 38.46${}_{\pm\textrm{1.32}}$ | 64.40${}_{\pm\textrm{2.25}}$ | 81.30${}_{\pm\textrm{3.50}}$ | 51.56${}_{\pm\textrm{1.19}}$ |  |
| MeToken | 42.07${}_{\pm\textrm{1.26}}$ | 94.51${}_{\pm\textrm{0.15}}$ | 38.74${}_{\pm\textrm{0.60}}$ | 49.59${}_{\pm\textrm{1.41}}$ | 70.92${}_{\pm\textrm{1.45}}$ | 98.55${}_{\pm\textrm{0.19}}$ | 73.22${}_{\pm\textrm{2.57}}$ |  |

On the other hand, the qPTM dataset provides a more expansive evaluation landscape due to its large volume of samples and diversity of protein types, making it perfectly suited for assessing the scalability and adaptability of PTM models. As illustrated in Table 3, MeToken not only excels in performance but also consistently achieves superior metrics across all evaluated categories when compared to competing models. The strong performance of MeToken across these varied datasets underscores its capacity to generalize well beyond the confines of its initial training environment. This trait is especially crucial in proteomics, where predictive models must reliably perform across a wide array of unseen data, encompassing diverse biological contexts and experimental conditions.

**Table 3: Performance comparison on the qPTM dataset. The mean/std of three runs is reported.**
| Input | Method | F1-score | Accuracy | Precision | Recall | MCC | AUROC | AUPRC |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Seq | EMBER | 21.07${}_{\pm\textrm{1.60}}$ | 67.86${}_{\pm\textrm{1.53}}$ | 28.37${}_{\pm\textrm{2.43}}$ | 21.91${}_{\pm\textrm{1.61}}$ | 17.41${}_{\pm\textrm{2.95}}$ | 58.89${}_{\pm\textrm{2.24}}$ | 28.88${}_{\pm\textrm{0.57}}$ |
| DeepPhos | 17.85${}_{\pm\textrm{1.13}}$ | 65.36${}_{\pm\textrm{1.39}}$ | 18.08${}_{\pm\textrm{2.49}}$ | 20.33${}_{\pm\textrm{0.79}}$ | 1.42${}_{\pm\textrm{3.91}}$ | 47.52${}_{\pm\textrm{2.68}}$ | 20.98${}_{\pm\textrm{1.12}}$ |  |
| MusiteDeep | 16.04${}_{\pm\textrm{0.09}}$ | 66.98${}_{\pm\textrm{0.63}}$ | 13.39${}_{\pm\textrm{0.12}}$ | 20.00${}_{\pm\textrm{0.00}}$ | 0.00${}_{\pm\textrm{0.00}}$ | 46.42${}_{\pm\textrm{4.46}}$ | 20.81${}_{\pm\textrm{0.95}}$ |  |
| ESM-2 | 57.40${}_{\pm\textrm{1.38}}$ | 86.79${}_{\pm\textrm{1.15}}$ | 61.57${}_{\pm\textrm{2.13}}$ | 56.47${}_{\pm\textrm{0.91}}$ | 74.26${}_{\pm\textrm{2.22}}$ | 94.91${}_{\pm\textrm{1.02}}$ | 60.80${}_{\pm\textrm{2.63}}$ |  |
| Str | StructGNN | 23.90${}_{\pm\textrm{3.27}}$ | 71.45${}_{\pm\textrm{1.44}}$ | 36.40${}_{\pm\textrm{4.25}}$ | 23.87${}_{\pm\textrm{2.89}}$ | 31.60${}_{\pm\textrm{2.58}}$ | 82.25${}_{\pm\textrm{0.35}}$ | 46.12${}_{\pm\textrm{2.54}}$ |
| GraphTrans | 20.85${}_{\pm\textrm{3.49}}$ | 68.95${}_{\pm\textrm{1.59}}$ | 29.21${}_{\pm\textrm{8.70}}$ | 22.07${}_{\pm\textrm{2.76}}$ | 21.20${}_{\pm\textrm{3.88}}$ | 75.77${}_{\pm\textrm{4.02}}$ | 36.54${}_{\pm\textrm{0.91}}$ |  |
| GVP | 28.31${}_{\pm\textrm{2.32}}$ | 73.24${}_{\pm\textrm{0.62}}$ | 40.66${}_{\pm\textrm{7.84}}$ | 26.76${}_{\pm\textrm{2.02}}$ | 38.95${}_{\pm\textrm{2.08}}$ | 83.56${}_{\pm\textrm{1.69}}$ | 50.94${}_{\pm\textrm{2.95}}$ |  |
| PiFold | 22.29${}_{\pm\textrm{3.10}}$ | 69.37${}_{\pm\textrm{1.27}}$ | 31.53${}_{\pm\textrm{12.39}}$ | 23.73${}_{\pm\textrm{2.00}}$ | 22.99${}_{\pm\textrm{7.39}}$ | 70.69${}_{\pm\textrm{1.53}}$ | 35.35${}_{\pm\textrm{1.15}}$ |  |
| Seq<br>+<br>Str | GearNet | 31.05${}_{\pm\textrm{0.26}}$ | 78.95${}_{\pm\textrm{0.75}}$ | 27.68${}_{\pm\textrm{0.25}}$ | 39.90${}_{\pm\textrm{0.00}}$ | 61.16${}_{\pm\textrm{0.82}}$ | 92.31${}_{\pm\textrm{0.90}}$ | 54.49${}_{\pm\textrm{0.78}}$ |
| CDConv | 45.20${}_{\pm\textrm{3.20}}$ | 81.12${}_{\pm\textrm{1.35}}$ | 47.68${}_{\pm\textrm{0.25}}$ | 51.23${}_{\pm\textrm{3.31}}$ | 66.25${}_{\pm\textrm{2.24}}$ | 92.99${}_{\pm\textrm{0.27}}$ | 56.65${}_{\pm\textrm{0.97}}$ |  |
| MIND | 43.88${}_{\pm\textrm{11.54}}$ | 84.46${}_{\pm\textrm{0.54}}$ | 50.10${}_{\pm\textrm{4.11}}$ | 43.01${}_{\pm\textrm{10.99}}$ | 67.65${}_{\pm\textrm{1.68}}$ | 84.13${}_{\pm\textrm{2.31}}$ | 51.34${}_{\pm\textrm{1.19}}$ |  |
| MeToken | 61.95${}_{\pm\textrm{1.07}}$ | 88.39${}_{\pm\textrm{1.04}}$ | 64.97${}_{\pm\textrm{1.14}}$ | 60.40${}_{\pm\textrm{1.17}}$ | 77.56${}_{\pm\textrm{1.44}}$ | 95.88${}_{\pm\textrm{0.52}}$ | 64.05${}_{\pm\textrm{1.19}}$ |  |

### 5.3 Ablation Study

To highlight the impact of components, we compare MeToken against several baseline configurations. The results, as detailed in Table 4, offer a depiction of how each element contributes to the overall effectiveness: (i) Micro-env (Me): This baseline employs an end-to-end model that considers micro-environments without tokenization. Serving as a fundamental benchmark, it attains an F1-score of 40.04%. (ii) Me + Uni-Codebook: By integrating micro-environment tokenization with a uniform sub-codebook that acknowledges the long-tail distribution of PTM types, this configuration yields a substantial enhancement, elevating the F1-score to 48.04%. (iii) Me + TS-VQ: This setup explores the use of an unconstrained codebook paired with temperature-scaled vector quantization (TS-VQ). Despite TS-VQ’s theoretical benefits—softer assignments and broader exploration—this configuration disappointingly results in an F1-score of only 37.46%.

These findings underscore the crucial necessity for a well-structured codebook; in the absence of such a framework, the inherent advantages of TS-VQ cannot be fully realized, particularly when navigating the complexities introduced by the long-tail distribution of PTM types. Ultimately, our comprehensive implementation of MeToken, which synergistically combines micro-environment tokenization with a uniform sub-codebook and TS-VQ, achieves the highest performance, attaining an impressive F1-score of 50.40%. This result illustrates the effectiveness of our integrated approach in maximizing the model’s potential.

**Table 4: The ablation of MeToken on the large-scale dataset.**
| Method | Accuracy | Precision | Recall | F1-score | MCC | AUROC | AUPRC |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Micro-env (Me) | 88.82 | 49.09 | 37.81 | 40.04 | 73.48 | 91.98 | 42.95 |
| Me + Uni-Codebook | 88.74 | 51.95 | 47.70 | 48.04 | 73.37 | 92.32 | 48.62 |
| Me + TS-VQ | 88.22 | 41.29 | 38.62 | 37.46 | 72.54 | 91.25 | 40.51 |
| MeToken | 89.91 | 58.08 | 47.93 | 50.40 | 76.03 | 92.20 | 51.26 |

### 5.4 Visualization and biological understanding of our model

We visualize the code embeddings learned by MeToken using t-SNE (Van der Maaten & Hinton, ) in Figure 6. Due to space constraints, we used abbreviated labels, with their full names available in Appendix G. It reveals three key observations that offer biologically relevant insights:

① Relationship between different types of Glycosylation: C-glycosylation is positioned notably far from N- and O-glycosylation. Unlike N- and O-glycosylation, where the sugar attaches to nitrogen (N) or oxygen (O) atoms, C-glycosylation involves the formation of a covalent bond between the sugar and a carbon atom (Ihara et al., ; Lovelace et al., ). This spatial separation in the embedding space aligns with the fundamental differences in their underlying biochemical mechanisms.

② Competitive modification between phosphorylation and acetylation sites: The scattered distribution of phosphorylation and acetylation sites in the plot reflects the competitive nature of these PTMs (Shukri et al., ; Csizmok & Forman-Kay, ). Both modifications often occur within the same microenvironment and compete for access to nearby residues, resulting in the observed pattern where these two PTMs are dispersed, sometimes adjacent or interleaved.

③ Distribution of phosphorylation and ubiquitination sites
These sites are distributed in a dispersed manner, lacking a central cluster but instead spread across several distinct regions. This fragmented pattern aligns with observations from previous studies (Shi, )(Johnson & Lewis, )(Johnson, ), which emphasize the intricate interplay between these two PTMs. They often function in a coordinated fashion, regulating diverse aspects of protein behavior.

Figure: Figure 6: The visualization of the learned codebook.
Refer to caption: x6.png

## 6 Conclusion and Limitation

In this study, we focus on PTM prediction, a fundamental challenge in proteomics research. By constructing a large-scale sequence-structure PTM dataset, we established a robust benchmark. Our MeToken harnesses the power of micro-environment tokenization, utilizing uniform codebook loss and temperature-scaled vector quantization to model the interactions within protein micro-environments for predicting long-tail PTM types. Our results demonstrate that MeToken significantly outperforms state-of-the-art approaches, underscoring the importance of incorporating structural data for a comprehensive understanding of PTM sites. The findings offer a pathway to more precise PTM prediction and enhance our understanding of protein functionality.

Despite its strengths, our study has several limitations. While our dataset is a blend of PDB data and AlphaFold database, it relies predominantly on AlphaFold predictions. This dependence may introduce biases inherent to the AlphaFold model. Furthermore, our model currently does not consider interactions between the protein and other molecules, such as enzymes, ligands, or interacting proteins, which play crucial roles in the natural environment of PTMs. Addressing these limitations will be a primary focus of our future research endeavors.

## Acknowledgements

This work was supported by National Science and Technology Major Project (No. 2022ZD0115101), National Natural Science Foundation of China Project (No. U21A20427), Project (No. WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of Westlake University and Integrated Bioengineering of Westlake University and Project (No. WU2023C019) from the Westlake University Industries of the Future Research Funding.

## Appendix A Codebook Suppresses the Impact of Long-Tail Distribution

The “long-tail distribution” issue refers to the phenomenon where a small number of PTM types are highly prevalent, while many others are rare. From an engineering perspective, this issue necessitates a solution that ensures all PTM types, including the rare ones, are adequately represented in the model’s learning process. This led us to the development of the sub-codebook strategy in our MeToken model. The engineering solutions can be summarized as follows:

#### Uniform representation

By allocating an identical size sub-codebook to each PTM type, we ensure that rare PTMs have as much representation as others. This uniform distribution of codebooks prevents the model from being dominated by frequent PTMs, promoting a balanced learning process.

#### Strong discrimination

Each sub-codebook is designed to capture distinct patterns specific to its corresponding PTM type. This focused representation allows the model to learn more precise and discriminative features for each PTM type. By ensuring that each PTM type has its own dedicated sub-codebook, the model can better differentiate between various types, improving its ability to identify and predict even the rarest PTMs accurately.

In summary, the sub-codebook strategy addresses the long-tail distribution issue by providing a balanced and discriminative representation for all PTM types, thereby enhancing the model’s overall performance and reliability in PTM prediction.

## Appendix B Node and Edge Embedding

Following (Gao et al., ), we implement the initial node and edge embeddings for the protein graph with manually constructed structural features. As shown in Table 5, this comprehensive encoding captures the intricate local structural information inherent in each residue of a protein, facilitating a detailed representation crucial for our PTM prediction tasks.

**Table 5: The initial node and edge embeddings.**
| Level | Feature | Illustration |
| --- | --- | --- |
| Intra-residue Node | Angle | $\big{\{}\sin,\cos\big{\}}\times\big{\{}\alpha_{i},\beta_{i},\gamma_{i},\phi_{i<br>},\psi_{i},\omega_{i}\big{\}}$ |
| Distance | $\left\{\mathrm{RBF}(\|\omega_{i}-\mathrm{C}\alpha_{i}\|)\;\Big{|}\;\omega\in\{<br>\textrm{C, N, O}\}\right\}$ |  |
| Direction | $\left\{\mathbf{Q}_{i}^{T}\;\frac{\omega_{i}-\mathrm{C}\alpha_{i}}{\|\omega_{i}<br>-\mathrm{C}\alpha_{i}\|}\;\Big{|}\;\omega\in\{\textrm{C, N, O}\}\right\}$ |  |
| Inter-residue Edge | Orientation | $\mathbf{q}(\mathbf{Q}_{i}^{T}\mathbf{Q}_{j})$ |
| Distance | $\left\{\mathrm{RBF}(\|\omega_{i}-\omega_{j}\|)\;\Big{|}\;j\in\mathcal{N}(i),<br>\omega\in\{\textrm{C}_{\alpha},\textrm{C, N, O}\}\right\}$ |  |
| Direction | $\Big{\{}\mathbf{Q}_{i}^{T}\;\frac{\omega_{i}-\omega_{j}}{\|\omega_{i}-\omega_{<br>j}\|}\;\Big{|}\;j\in\mathcal{N}(i),\omega\in\{\textrm{C}_{\alpha},\textrm{{C,<br>N, O}}\}\Big{\}}$ |  |

Specifically, the node embeddings incorporate a variety of geometric and structural characteristics that provide a comprehensive view of each residue’s local environment: (i) angle features, such as $\alpha_{i}$ (angle for $\textrm{N}_{i}\textrm{-C}\alpha_{i}\textrm{-C}_{i}$), $\beta_{i}$ (angle for $\textrm{C}_{i-1}\textrm{-N}_{i}\textrm{-C}\alpha_{i}$), $\gamma_{i}$ (angle for $\textrm{C}\alpha_{i}\textrm{-C}_{i}\textrm{-N}_{i+1}$), $\phi_{i}$ (dihedral angle for $\textrm{N}_{i}\textrm{-C}\alpha_{i}$), $\psi_{i}$ (dihedral angle for $\textrm{C}\textrm{-C}\alpha_{i}$), and $\omega_{i}$ (dihedral angle for $\textrm{C}_{i}\textrm{-N}_{i}$). (ii) distance features, where we employ radial basis functions (RBFs) to transform distances between backbone atoms and the central $\textrm{C}\alpha_{i}$ atom into features, capturing the relative positioning of significant structural elements within each residue. (iii) direction features, which are defined by the local coordinate system $\mathbf{Q}_{i}$ and the normalized vectors pointing from the $\textrm{C}\alpha_{i}$ atom to the C, N, O atoms.

Edge embeddings focus on characterizing the interactions between adjacent residues, crucial for understanding how proteins fold and function: (i) orientation features, where $\mathbf{q}(\cdot)$ is the quaternions of relative rotation between the local coordinate systems of two residues. (ii) distance features, which capture the distances between the backbone atoms of two residues, transformed into radial basis functions. (iii) direction features, which are defined by projecting the normalized vectors between backbone atoms of neighboring residues onto the local coordinate systems $\mathbf{Q}_{i}$. In our model, the micro-environments of each residue are defined through a comprehensive approach that considers three distinct types of neighbor atom sets. Specifically, the neighborhoods denoted as $\mathcal{N}(i)$ are categorized based on three criteria: sequential order, K-nearest neighbors, and R-radius proximity.

## Appendix C Dataset Construction

Figure: Figure 7: The data processing pipeline for constructing our large-scale PTM dataset.
Refer to caption: x7.png

As illustrated in Figure 7, our dataset construction process was designed to compile a comprehensive and robust resource for investigating post-translational modifications (PTMs) across a wide array of protein types. We began by selecting dbPTM as our foundational sequence-based dataset, which is highly regarded for its extensive catalog of PTM types, encompassing a rich variety of biochemical modifications. In order to enrich this sequence information with structural data, we sourced corresponding structural information from two primary repositories: the Protein Data Bank (PDB) and the AlphaFold database. Our protocol prioritizes experimental data from PDB; however, in instances where such data is unavailable, we resort to high-confidence predictions from AlphaFold, ensuring both accuracy and completeness in our structural dataset representation. Following Dauparas et al. (), we introduced Gaussian noise with a mean of zero and a standard deviation of 0.0005 to the atomic coordinates. The addition of this noise simulates small structural fluctuations and uncertainties that may occur in real-world experimental conditions, thereby improving the generalization ability of models trained on our data.

To ensure the integrity of our training and evaluation process and to prevent data leakage, we employed MMseqs2 (Steinegger & Söding, ), a highly efficient software suite for handling large biological sequence data through clustering. We clustered the raw data based on sequence similarity, setting a stringent threshold to ensure that proteins in the validation and test sets exhibit no more than 40% similarity to those in the training set. This split provides a reliable measure of performance.

Figure: Figure 8: The distribution of PTM classes after class reduction.
Refer to caption: x8.png

Addressing the challenge of sample imbalance—a common issue in PTM datasets where modified sites are significantly outnumbered by unmodified sites, and some types of modifications are substantially rarer than others. As shown in Figure 8, we consolidated PTM types with fewer than 100 samples into a single ’rare sites’ category. We reduced the 73 classes, which included a ’No modification’ class to 26 classes. This new classification framework comprises 24 distinct PTM classes, one aggregated class for rare modifications, and one class representing the absence of modification.

In Table 6, we present a comparative analysis between our dataset and other open-sourced PTM datasets. Our dataset contains 1,263,935 PTM sites across 187,812 proteins, offering a detailed exploration of 72 types of modifications. This not only encapsulates a broader diversity of PTM types than typically available but also includes a significantly larger number of protein entries, establishing it as an unparalleled resource in the field. Notably, unlike other datasets such as dbPTM and qPTM, our dataset integrates both sequence and structural data for each protein entry. This integration is crucial for developing more accurate and predictive models of PTM sites, as the structural context often influences where and how modifications occur. Our dataset not only bridges the gap in the availability of comprehensive sequence-structure PTM data but also sets a new standard for the scale and scope of such resources. It enables advanced modeling techniques that can exploit detailed structural contexts alongside sequence information, paving the way for breakthroughs in understanding the complex mechanisms of post-translational modifications.

The PTMint dataset is a relatively modest collection of 2,477 samples spanning 1,169 proteins with six PTM types. The qPTM dataset offers a broader scope, encompassing 660,030 samples across 40,728 proteins, also classified into the six PTM types. These PTM types are consistent with a subset from our large-scale dataset. Originally, the qPTM dataset lacked comprehensive structural information, which prompted us to enhance it by integrating structural data retrieved from the AlphaFold database.

**Table 6: The comparison between open-sourced PTM datasets with our large-scale dataset.**
| Database | Number of sites | Number of proteins | Modification types | Sequence | Structure |
| --- | --- | --- | --- | --- | --- |
| PTMint | 2,477 | 1,169 | 6 | ✓ | ✓ |
| PTM Structure | 11,677 | 3,828 | 21 | ✓ | ✓ |
| PRISMOID | 17,145 | 3,919 | 37 | ✓ | ✓ |
| qPTM | 660,030 | 40,728 | 6 | ✓ | ✗ |
| dbPTM | 2,235,000 | 223,678 | 72 | ✓ | ✗ |
| Ours | 1,263,935 | 187,812 | 72 | ✓ | ✓ |

## Appendix D Metrics

To comprehensively evaluate the performance of the PTM prediction task, which we regard as a multi-class classification problem, we adopted a robust suite of metrics. These metrics include accuracy, precision, recall, F1-score, the Matthews Correlation Coefficient (MCC), the Area Under the Receiver Operating Characteristic curve (AUROC), and the Area Under the Precision-Recall Curve (AUPRC). Each of these metrics offers distinct insights into the model’s performance, from overall accuracy to the balance between sensitivity and specificity.

#### Accuracy

provides a straightforward measure of the overall correctness of the predictions across all classes. It is calculated as the ratio of correctly predicted observations to the total observations:

$$ $\mathrm{Accuracy}=\frac{\sum_{i=1}^{N}\mathds{1}(y_{i}=\hat{y}_{i})}{N},$ (8) $$

where $y_{i}$ is the true PTM type, $\hat{y}_{i}$ is the predicted PTM type, and $N$ is the total number of instances.

#### Precision

(macro) measures the accuracy of positive predictions. It is especially critical in datasets where false positives carry a significant cost:

$$ $\mathrm{Precision}=\frac{1}{K}\sum_{k=1}^{K}\frac{\mathrm{TP}_{k}}{\mathrm{TP} _{k}+\mathrm{FP}_{k}},$ (9) $$

where $\mathrm{TP}_{k}$ and $\mathrm{FP}_{k}$ are the true positives and false positives for the $k$-class, respectively, and $K$ is the total number of classes.

#### Recall

(macro) assesses the model’s ability to correctly identify all relevant instances per class:

$$ $\mathrm{Recall}=\frac{1}{K}\sum_{k=1}^{K}\frac{\mathrm{TP}_{k}}{\mathrm{TP}_{k }+\mathrm{FN}_{k}},$ (10) $$

where $\mathrm{FP}_{k}$ are the false negatives for the $k$-class.

#### F1-score

(macro) is the harmonic mean of precision and recall for each class:

$$ $\mathrm{F1}\mathrm{-}\mathrm{score}=\frac{1}{K}\sum_{k=1}^{K}\frac{2\times \mathrm{Precision}_{k}\times\mathrm{Recall}_{k}}{\mathrm{Precision}_{k}+ \mathrm{Recall}_{k}}.$ (11) $$

#### Matthews Correlation Coefficient (MCC)

takes into account true and false positives and negatives and is generally regarded as a balanced measure that can be used even if the classes are of very different sizes:

$$ $\mathrm{MCC}=\frac{1}{K}\sum_{k=1}^{K}\frac{\mathrm{TP}_{k}\times\mathrm{TN}_{ k}-\mathrm{FP}_{k}\times\mathrm{FN}_{k}}{\sqrt{(\mathrm{TP}_{k}+\mathrm{FP}_{k })\times(\mathrm{TP}_{k}+\mathrm{FN}_{k})\times(\mathrm{TN}_{k}+\mathrm{FP}_{k })\times(\mathrm{TN}_{k}+\mathrm{FN}_{k})}},$ (12) $$

#### Area Under the Receiver Operating Characteristic curve (AUROC)

evaluates the trade-off between true positive rate and false positive rate, providing an aggregate measure of performance across all classification thresholds. It reflects the model’s ability to discriminate between classes:

$$ $\mathrm{AUROC}=\frac{1}{K}\sum_{k=1}^{K}\mathrm{AUROC}_{k},$ (13) $$

where $\mathrm{AUROC}_{k}$ is the AUROC for the $k$-class, defined by integrating the TPR over the range of possible values of FPR:

$$ $\mathrm{AUROC}_{k}=\int_{0}^{1}\mathrm{TPR}_{k}(\mathrm{FPR}_{k})\,\mathrm{ dFPR}_{k},$ (14) $$

and $\mathrm{TPR}_{k}={\mathrm{TP}_{k}}/({\mathrm{TP}_{k}+\mathrm{FN}_{k}})$ and $\mathrm{FPR}_{k}=\mathrm{FP}_{k}/(\mathrm{TN}_{k}+\mathrm{FP}_{k})$ are the true positive rate and false positive rate for the $k$-class, respectively.

#### Area Under the Precision-Recall Curve (AUPRC)

assesses the trade-off between Precision and Recall for a given class at various threshold levels, which is particularly useful when dealing with imbalanced datasets:

$$ $\mathrm{AUPRC}=\frac{1}{K}\sum_{k=1}^{K}\mathrm{AUPRC}_{k}.$ (15) $$

where $\mathrm{AUPRC}_{k}$ is the AUPRC for the $k$-class, defiend as:

$$ $\mathrm{AUPRC}_{k}=\int_{0}^{1}\mathrm{Precision}_{k}(\mathrm{Recall}_{k})\, \mathrm{dRecall}_{k}.$ (16) $$

## Appendix E implementation Details

We provide detailed implementation information for the models evaluated in our study. These models are categorized into sequence-based, structure-based, and sequence-structure models, each designed to predict PTM sites based on different data modalities.

### E.1 Sequence-based models

#### DeepPhos

As shown in Figure 9, this model (Luo et al., ) is composed of densely connected convolutional neural network (DC-CNN) blocks, designed to capture multiple representations of sequences for the final phosphorylation prediction. Specifically, the DC-CNN block operates as a multi-scale pyramid-like merging structure, adept at extracting features from diverse perspectives. Although it aims to predict phosphorylation sites, we adapt the model to predict other PTM types by modifying the output layer to accommodate the desired PTM classes.

Figure: Figure 9: The model architecture of DeepPhos.
Refer to caption: x9.png

#### EMBER

As shown in Figure 10, EMBER(Kirchoff & Gomez, ) employs a modified Siamese neural network tailored for multi-label prediction tasks to generate high-dimensional embeddings of motif vectors. Additionally, it uses one-hot encoded motif sequences and leverages these two distinct representations as dual inputs into the classifier.

Figure: Figure 10: The model architecture of EMBER.
Refer to caption: x10.png

#### MusiteDeep

As shown in Figure 11,
Multi-layer CNN is used as the feature extractor but no pooling layers are used. The last hidden state of multi-layer CNN is copied twice, where one directly inputs into the attention mechanism and the other first trans-positioned and then inputs into another attention mechanism. The output of the two attention mechanisms is combined and input into the fully connected neural network layers. The final layer is a single layer with the softmax output.

Figure: Figure 11: The model architecture of MusiteDeep.
Refer to caption: x11.png

#### ESM

As shown in Figure 12, we utilize the pretrained model from ESM (Lin et al., ) to obtain embeddings of protein sequences. These embeddings serve as a rich representation of the protein data, capturing intricate evolutionary patterns. Following this, an MLP is employed as the classifier.

Figure: Figure 12: The model architecture of ESM.
Refer to caption: x12.png

### E.2 Structure-based models

#### StructGNN

As shown in Figure 13, StructGNN (Ingraham et al., ) is a traditional protein design model that has been adapted in this work to predict residue-level post-translational modification (PTM) sites. The model begins by obtaining structural features through a conventional structure embedding process. These features capture the intricate three-dimensional configurations of protein structures. Following this, the model employs layers of a Message Passing Neural Network, which iteratively refines the structural representations by aggregating information across the network.

Figure: Figure 13: The model architecture of StructGNN.
Refer to caption: x13.png

#### GraphTrans

Figure 14 shows that GraphTrans (Ingraham et al., ) is a Transformer-based model that replaces StructGNN’s MPNN layers with attention mechanisms. This modification allows the model to capture long-range dependencies and intricate interactions within the protein structure more effectively.

Figure: Figure 14: The model architecture of GraphTrans
Refer to caption: x14.png

#### GVP

GVP (Jing et al., ) introduce geometric vector perceptrons, which extend standard dense layers to operate on collections of Euclidean vectors. Graph neural networks equipped with such layers are able to perform both geometric and relational reasoning on efficient representations of macromolecules.

Figure: Figure 15: The model architecture of GVP.
Refer to caption: x15.png

#### PiFold

This model extracts features of protein structures and employs a sophisticated encoder-decoder architecture. The featurizer module leverages virtual atoms to enhance the representation of spatial information, capturing critical characteristics such as distances, angles, and directions. PiGNN that tailored for protein structure representation to encode structural features and MLP as a decoder.

Figure: Figure 16: The model architecture of PiFold.
Refer to caption: x16.png

### E.3 Sequence-Structure models

#### GearNet

As depicted in Figure 17, GearNet (Zhang et al., )—the Geometry-Aware Relational Graph Neural Network—is a simple yet highly effective structure-based protein encoder. GearNet encodes spatial information by incorporating various types of sequential and structural edges into the protein residue graphs. It then performs relational message passing, allowing the model to capture and integrate complex spatial relationships within the protein structure.

Figure: Figure 17: The model architecture of GearNet.
Refer to caption: x17.png

#### CDConv

As illustrated in Figure 18, the Continuous-Discrete Convolution (CDConv) is introduced to simultaneously model the geometry and sequence structures of proteins using distinct approaches for their regular and irregular characteristics. CDConv employs independent learnable weights to handle regular sequential displacements within the protein sequence. For the irregular geometric displacements, it directly encodes these variations to capture the complex spatial relationships inherent in the protein’s three-dimensional structure.

Figure: Figure 18: The model architecture of CDConv.
Refer to caption: x18.png

#### MIND

As illustrated in Figure 19, the MIND model employs a dual-path approach to analyze protein data. One path leverages LSTM networks combined with attention mechanisms to scrutinize the sequence information. The other path constructs the protein structure as a graph and utilizes graph attention mechanisms to analyze the structural data. A MLP then integrates the information from both paths, functioning as a classifier to make final predictions.

Figure: Figure 19: The model architecture of MIND.
Refer to caption: x19.png

## Appendix F Supplementary experiments

### F.1 ESM-3 baseline

In our investigation, we also examined the performance of the ESM-3 model, as shown in Table 7. Surprisingly, our results indicated that fine-tuning ESM-2 outperforms fine-tuning ESM-3 in this context. Previous research (West-Roberts et al., ) has suggested that ESM-3 does not consistently outperform ESM-2, highlighting an intriguing nuance in model performance. One potential explanation for this phenomenon is that ESM-3 incorporates considerations of sequence, structure, and function, which may introduce a level of complexity that does not always lead to enhanced performance for specific tasks like PTM prediction.

**Table 7: Performance comparison between ESM-2 and ESM-3 models.**
| Method | F1 | Acc | Pre | Rec | MCC | AUROC | AUPRC |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ESM2 | 43.94 | 89.75 | 55.67 | 42.15 | 75.83 | 91.52 | 47.31 |
| ESM3 | 40.65 | 87.48 | 45.72 | 38.67 | 70.31 | 89.91 | 41.36 |

### F.2 Other sequence-structure baselines

We also conducted experiments using additional sequence-structure models to further contextualize our findings, as shown in Table 8. The STEPS model (Chen et al., ) structures data as a graph and utilizes sequence language models for embedding sequences, focusing on maximizing the mutual information between sequence and structure through a pseudo bi-level optimization approach. However, unlike MeToken, which is specifically designed for residue-level tasks, STEPS is tailored primarily for protein-level applications, limiting its effectiveness in our targeted analysis. Similarly, MPRL (Nguyen & Hy, ) adopts a multimodal framework that integrates ESM-2 for sequence embedding, alongside VGAE and point cloud techniques for structure embedding, culminating in a fusion module that generates a comprehensive representative embedding. While MPRL demonstrates strong performance in protein-level tasks, it struggles to adequately address the nuances of residue-level tasks, revealing a gap in its applicability. The results indicate that both STEPS and MPRL, while effective in their respective domains, demonstrate limitations when applied to residue-level tasks, further emphasizing the unique capabilities of MeToken in this PTM prediction.

**Table 8: Performance metrics for STEPS and MPRL models.**
| Method | F1 | Acc | Pre | Rec | MCC | AUROC | AUPRC |
| --- | --- | --- | --- | --- | --- | --- | --- |
| STEPS | 15.96 | 66.32 | 13.26 | 20.04 | 2.80 | 41.72 | 19.69 |
| MPRL | 19.04 | 90.39 | 18.08 | 20.11 | 5.02 | 52.68 | 21.55 |

### F.3 The reliability of AlphaFold2’s predicted data

To assess the reliability of our large-scale dataset, we conducted a thorough validation analysis in Table 9. The combination of AlphaFold2 predictions and PDB data produced the highest performance metrics, highlighting the importance of combining predicted and experimental structures. Specifically, the AF2-only dataset demonstrated significantly lower performance, revealing the limitations inherent in relying solely on predictions. In contrast, the PDB-only dataset outperformed AF2, yet it still did not match the performance of the hybrid AF2+PDB dataset. This indicates that while experimental data plays a critical role, the extensive scale of AF2 predictions substantially enhances overall results.

**Table 9: Performance metrics across different datasets.**
| Dataset | F1 | Acc | Pre | Rec | MCC | AUROC | AUPRC |
| --- | --- | --- | --- | --- | --- | --- | --- |
| AF2+PDB | 50.40 | 89.91 | 58.08 | 47.93 | 76.03 | 92.20 | 51.26 |
| AF2 | 11.74 | 87.23 | 10.29 | 14.80 | 54.54 | 41.79 | 12.95 |
| PDB | 41.31 | 73.39 | 40.96 | 47.10 | 52.25 | 91.07 | 40.87 |

These findings suggest that, although AF2 data may introduce certain biases, its large scale contributes positively to performance. Ultimately, the most effective outcomes arise from integrating both predicted and experimental data, achieving an optimal balance between scale and accuracy. This analysis reinforces the conclusion that the integration of AF2 predictions with PDB data not only mitigates potential biases but also maximizes predictive power, thereby enhancing the reliability of our large-scale dataset and its applicability in protein research.

### F.4 Statistics about codebook distribution

To illustrate the distribution of tokens within our codebook, we examine a specific token with index 2588. As depicted in Figure 20, the most prevalent amino acids associated with this token are valine (Val, V) and leucine (Leu, L), both of which are hydrophobic amino acids characterized by similar structural properties. The other three amino acids in the distribution are also hydrophobic, reinforcing this trend. This observation indicates that our model has effectively learned and captured significant physicochemical features of amino acids within the codebook. Such a finding enhances the credibility and interpretability of our model, demonstrating its ability to reflect meaningful biological properties in its decent representations.

Figure: Figure 20: The corresponding amnio acid distribution of the token 2588.

## Appendix G Abbreviations of PTMs

Due to space constraints, we have used abbreviations in some places in the main text for PTMs. Below is the table of abbreviations and their corresponding full names.

**Table 10: Abbreviations and corresponding names of PTMs.**
| Abbreviation | PTM types |
| --- | --- |
| Ac | Acetylation |
| Amide | Amidation |
| C-Glycans | C-linked Glycosylation |
| Formyl | Formylation |
| Gla | Gamma-carboxyglutamic acid |
| GSH | Glutathionylation |
| Hydroxyl | Hydroxylation |
| Lac | Lactylation |
| Mal | Malonylation |
| Met | Methylation |
| MT | Myristoylation |
| Nedd | Neddylation |
| N-Glycans | N-linked Glycosylation |
| O-Glycan | O-linked Glycosylation |
| Oxid | Oxidation |
| Phos | Phosphorylation |
| PCA | Pyrrolidone carboxylic acid |
| SNO | S-nitrosylation |
| PA | S-palmitoylation |
| Suc | Succinylation |
| Sulf | Sulfation |
| Sulfhyd | Sulfoxidation |
| Sumo | Sumoylation |
| Ub | Ubiquitination |

## Peer Reviews

confidence: 4
contribution: 4
initial_rating: 5
presentation: 3
questions: Questions 
How are the no-modification sites determined? Are these all sites in protein sequences where no modifications are reported or are they subsets based on amino acid types?

Is the quantization technique and the codebook strategy different than the cited work FoldToken? The differences and similarities should be included in the relevant literature part as it is very closely related to the work introduced here.
Of the many structures available for a protein in the PDB, which one is selected ? What criteria are applied on this selection?
Authors suggest that they introduce a sub-codebook strategy to handle the long-tail distribution but at the end of the day, all the rare classes are grouped as a single class. How does that help?
soundness: 4
strengths: - The authors use valid strategies for experimental evaluation. For example, they take into account sequence similarity to prevent leakage in the evaluation. Uses appropriately macro-avearged evaluation metrics. Many decisions are based on valid reasons or experimentation (using ESM-2 vs ESM3) and (PDB vs AlphaFold+PDB vs AlphadFold) 
- Results on two external datasets are provided. 
- The work is interesting in the sense that it uses the structural features of the modified site together with the sequence
- Authors compare against existing PTM models and wider protein language models. They have modified some of the specialized models such as DeepPhos to evaluate for the general PTM classification task.
- Authors provide an ablation study to understand the contribution of the different parts of the method
summary: The work aims at creating a structure and sequence-based representation of microenvironment  of the  residues and use this for post-translational modification (PTM) classification. They compile a large-scale sequence-structure PTM dataset featuring over 1.2 million residue-level annotated sites across multiple PTM types. The method MeToken tries to capture the context of the site using the sequential and structural neighbourhood of the modified residue. The paper learns a codebook that represents the high-dimensional data into simpler, representative tokens. The work is interesting contribution to the field.
weaknesses: Weakness:

A simple baseline of one-hot encoded amino acid sequence representations would be useful to see if certain classes can easily be predicted based on their modification site.

The authors could compare their work to other structure-aware representations. For example, SAProt using the Foldseek structural tokens learns a sturture aware embedding.

The authors demonstrate one of the codebooks as an example but a more extensive analysis on this would be interesting. For example a very basic question would be : "Are the amino acid preferences (K for Sumoylation, S/T/Y for Phosphorylation, etc )correctly captured by the codebooks?"

Information on the hyperparameters used to train MeToken and the baseline models are needed for reproducibility of the results.

A hyperparameter sensitivity analysis would be useful. For example the codebook size, the number of neighbors in the k-nn graph etc. 

Distinguishing some PTMs from others are easier because of the distinct amino acid preferences in their modification sites. Hard evaluation sets up could have been designed to understand the weakness and the strength of the methodology. For example, does the model correctly differentiate a Lysine residue  being ubiquninated or sumoylated?

The authors do not provide results on performance across different PTM classes. Are certain PTMs are much easier because of the available training data?

---

confidence: 3
contribution: 3
initial_rating: 5
presentation: 3
questions: They highlighted the long tail nature of the current datasets in the domain but it lacks some deeper analysis for the distribution e.g. rarity analysis, frequency statistics. It's unclear the extent to which the long tail nature is currently at play for the modeling difficulty in the domain, as what they claimed as a challenge. Additionally, reducing the class imbalance by consolidating PTM types with fewer than 100 samples into a single “rare sites” category might be exacerbate the imbalance issue? The rare classes are not modeled/studied individually then

Also, UMAP is often better at preserving global structure in the final projection. This means that the inter-cluster relations are potentially more meaningful than in t-SNE. So might want to suggest the authors to compare that visualization (see appendix c: https://arxiv.org/pdf/1802.03426)
soundness: 3
strengths: The raised challenges in the current landscape of PTMs prediction are legit. These issues make both modeling and evaluation in the domain difficult, thus the claim for such challenge is of high relevance.

The introduction of MeToken Model, that tokenizes amino acids’ micro-environments by integrating both sequence and structural information into discrete tokens, is relevant (although it seems like most of the boost is from this innovation so it begs the questions why the latter two, see below Weakness comments)

To handle the long-tail distribution of PTM types, MeToken introduces a sub-codebook strategy which arguably serves as a regularizer for the model to learn canonical representations
summary: This paper proposed a pipeline to predict PTMs (mostly in residue level as opposed to other global level) by addressing the issue of lacking structural information in the modeling. Additionally they curated a new dataset for such task as added contribution. Through comparison with baselines and some ablation studies, they show the better performance of the method
weaknesses: The hypothesis that incorporating structural data into PTM prediction can improve accuracy is well-supported by prior research indicating that sequence-only approaches may overlook important spatial context. But from table 4 it seems like this has enough boost compared to other baselines. It's beneficial to investigate deeper (and perhaps more ablation studies on only this arm of contribution) to stress the importance of added benefit of structural information.

Additionally, ESM-2 baseline seems to be very close the proposed method and beats all other baselines so there's still arguably a lot of sequence information that's useful already, so how to showcase their method is not overfitting to structural features might help reviewers to understand the validity of their method.

Again, the latter contributions, uniform sub-codebook strategy and temperature-scaled vector quantization, seems to offer marginal benefit which IMO dilutes the core contribution brought by the Micro-env arm of contribution. In addition, the temperature adjusted approach might necessitate a huge effort for hyperparameter tunning to make it work. But the authors didn't discuss any ramification of it. Further, the uniform sub-codebook strategy might risk embedding redundancy for common PTM types, and this was not discussed either.

---

confidence: 4
contribution: 2
initial_rating: 5
presentation: 3
questions: MeToken's design, with its uniform sub-codebooks and temperature-scaled quantization, may introduce significant computational demands, especially in high-dimensional cases. Have you explored options like pruning or adaptive quantization to mitigate these costs? This could clarify MeToken’s efficiency for large-scale deployment. Another question is that as protein-protein interactions, particularly with enzymes often influence PTM sites, could you consider ways to include these relationships in MeToken’s predictions? Addressing this would potentially expand the model’s real-world applicability and accuracy in cellular contexts.
soundness: 2
strengths: 1. Proposes MeToken that effectively integrates sequence and structural data in PTM prediction. This foresighted approach to using micro-environment tokenization and uniform sub-codebook construction demonstrates promising results in addressing the long-tail distribution of PTM types.
2. Extensive experiments across datasets validate the model's performance, showing significant improvements in metrics such as F1-score and AUPRC over competitive baselines. The experiments reveal that MeToken captures complex biochemical contexts well, potentially setting a new benchmark for PTM prediction.
summary: MeToken introduces a novel approach to PTM prediction by integrating sequence and structural data through micro-environment tokenization and a uniform codebook, enhancing model performance across diverse PTM types. The model shows good results but relies on AlphaFold data and lacks evaluation on biological interactions, suggesting areas for future refinement.
weaknesses: 1. The reliance on AlphaFold-generated data may limit its generalizability in real-world applications where experimental structures are less available or differ significantly.
2. Although the authors address the dimensionality reduction challenge, computational complexity remains an issue, especially given the additional steps in codebook learning and temperature-scaled quantization. Further exploration of optimization methods would enhance applicability for high-throughput or large-scale data analysis.
3. The study focuses primarily on PTM types without including interactions with enzymes or other proteins, which are crucial for PTM formation and regulation in vivo. Extending the approach to consider these interactions could improve the model’s utility in biological applications.

---

confidence: 5
contribution: 2
initial_rating: 6
presentation: 2
questions: How is the imbalance in the samples for no modification class affects the classification? Particularly the merged small PTMs and no modifications.
soundness: 2
strengths: The ablation and comparisons are done and results are reported on various useful metrics like F1, MCC and others.
summary: This paper compiles a large-scale sequence-structure PTM dataset and also propose METoken which uses sub-codebooks for each of the subtypes and temperature scaled vector quantization. On top of the the codebooks and learned tokens for micro-environment of the protein positions it then predicts PTM and its type.
weaknesses: It is not clear how the sequence similarity or redundancy was mitigated in the train-test-validation sets. Please report how minimal redundancy was ensured among test-train and validation sets.

In the rare modification class, how good is the predictor to discriminate the classes is not made clear. In this case authors may consider to report the different metrics for the PTM classes merged.

There are datasets and methods which are specifically learned for particular type of PTM and that has been a trend in the literature. Please report how does those methods perform comapred to METoken.

---

confidence: 5
contribution: 3
initial_rating: 5
presentation: 3
questions: 1. PTMs can be summarised into four types according to the modification types: chemical groups (e.g. Phosphorylation, Acetylation), proteins/peptides (e.g. Ubiquitylation, Sumoylation), complex molecules (e.g. glycosylation), and cleavage (proteolysis). These four types could have different structural preferences. How can the proposed model capture the differences between them? Which types work well using the proposed method?
2. The description "Existing computational approaches predominantly focus on protein sequences to predict PTM sites" in the Abstract is not accurate. There are many graphs and structural-aware methods that have been developed and published.
3. The authors are suggested to provide some biological case studies to interpret the micro-environment tokenization related to the characteristics of PTMs in the biological context.
4. The source codes and datasets should be publicly available upon acceptance.
soundness: 3
strengths: The authors incorporated the protein structural data to enhance the prediction, which is good as structural information could be better presented the functional-related information.
summary: PTM prediction is a very important topic in bioinformatics. This study introduces the MeToken model to consider the micro-environment of amino acids for PTM prediction. The proposed method has shown promising results in performance evaluation tests.
weaknesses: Some descriptions are not very accurate, and the progress of some existing studies is ignored.

Meta-review: This paper proposes MeToken, a model that uses a micro-environment token and uniform sub-codebooks to incorporate sequence and structural info for PTM prediction. The reviewers generally liked the motivation and noted some performance gains over existing methods.

The authors did respond to reviewer concerns (extensively!), adding one-hot baselines, clarifying the codebook strategies, discussing structural data advantages, and acknowledging suggestions to improve the paper.

The reviewers did not fully converge on a strongly positive stance. Some appreciated the novelty and thorough experiments, while others felt the paper’s core contribution was not strongly isolated (structural data vs. codebook), or that more analysis was needed. No unanimous positive agreement emerged.

On my personal reading, I am leaning towards acceptance. Some things to consider then: while structural data incorporation is promising, it is not deeply analyzed why it works better than sequence-only. Another common point: the handling of the long-tail distribution and the “rare classes” aggregation raised questions from at least a couple of reviewers about clarity and whether it genuinely solves the problem.

Decision: Accept (Poster)