CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization
Authors: Yicheng Hu
Xinyu Lin
Shulin Li
Wenjie Wang
Fengbin ZHU
Fuli Feng
Venue: ICLR 2026 Poster (2026)
Keywords: Subcellular Localization
Human Protein
3D Structure
Primary area: datasets and benchmarks

Abstract: Subcellular localization is a crucial biological task for drug target identification and function annotation. Although it has been biologically realized that subcellular localization is closely associated with protein structure, no existing dataset offers comprehensive 3D structural information with detailed subcellular localization annotations, thus severely hindering the application of promising structure-based models on this task. 
To address this gap, we introduce a new benchmark called $\textbf{CAPSUL}$, a $\textbf{C}$omprehensive hum$\textbf{A}$n $\textbf{P}$rotein benchmark for $\textbf{SU}$bcellular $\textbf{L}$ocalization. It features a dataset that integrates diverse 3D structural representations with fine-grained subcellular localization annotations carefully curated by domain experts. 
We evaluate this benchmark using a variety of state-of-the-art sequence-based and structure-based models, showcasing the importance of involving structural features in this task. Furthermore, we explore reweighting and single-label classification strategies to facilitate future investigation on structure-based methods for this task. 
Lastly, we showcase the powerful interpretability of structure-based methods through a case study on the Golgi apparatus, where we discover a decisive localization pattern $\alpha$-helix from attention mechanisms, demonstrating the potential for bridging the gap with intuitive biological interpretability and paving the way for data-driven discoveries in cell biology.

Published as a conference paper at ICLR 2026
CAPSUL: A COMPREHENSIVE HUMAN PROTEIN BENCHMARK FOR SUBCELLULAR LOCALIZATION
Yicheng Hu', Xinyu Shulin L Wenjie Wang', Fengbin Zhu?! Fuli Feng' 1 University of Science and Technology of China," 2 National University of Singapore, 3 Tsinghua University
ABSTRACT
Subcellular localization is a crucial biological task for drug target identification and function annotation. Although it has been biologically realized that subcel- lular localization is closely associated with protein structure, no existing dataset offers comprehensive 3D structural information with detailed subcellular localiza- tion annotations, thus severely hindering the application of promising structure- based models on this task. To address this gap, we introduce new benchmark called CAPSUL, a Comprehensive humAn Protein benchmark for SUbcellular Localization. It features dataset that integrates diverse 3D structural represen- tations with fine fine-grained subcellular localization annotations carefully curated by domain experts. We evaluate this benchmark using a variety of state-of-the-art eqquence-aased and structure-based models, showcasing the importance of in- volving structural features in this task. Furthermore, we explore reweighting and single-label classification strategies to facilitate future investigation on structure- based methods for this task. Lastly, we showcase the powerful interpretability of structure-based methods through case study the Golgi apparatus, where we discover a decisive localization pattern a-helix from attention mechanisms, demonstrating the potential for bridging the gap with intuitive biological inter- pretability and paving the way for data-driven discoveries in cell biology.
INTRODUCTION
Understanding the subcellular localization of proteins is a fundamental question in cell biology, as a protein's function is often tightly coupled to its spatial context within the cell (Scott et al., 2005). Localization information is essential for elucidating molecular mechanisms such as signal transduc- tion, metabolic regulation, and organelle-specific functions (Hung et al., 2017). It also provides a foundation for translational applications such as drug design (Hung et al., 2017: Rajendran et al., 2010). Recently, the whether or not a protein will localized to a specific subcellular location. These methods substan- tially reduce the time and cost associated with traditional experimental techniques while holding promise for revealing novel biological patterns, thereby showcasing promising performance and at- tracting extensive research attention (Thumuluri et al., 2022; Stärk et al., 2021; Almagro Armenteros et al., 2017; Kobayashi et al., 2022; Elnaggar et al., 2021).
Al approaches have emerged powerful paradigm for predicting
However, there remains significant scarcity of high-quality datasets designed for this task. To the best of our knowledge, the only widely accepted dataset targeting this problem in the AI field is DeepLoc (Thumuluri al., 2022; Almagro Armenteros al., 2017), which contains the amino acid sequence information for each protein. Deeploc has spurred the development of numerous sequence-based models for subcellular localization that infer localization solely from amino acid quences. Nevertheless, several studies have shown that spatial conformations play a critical role in determining subcellular localization patterns. For example, the nuclear localization signals of tran- scription factor NF-&B are conditionally exposed only under specific structural conformations (Lusk et al., 2007). This demonstrates that the 3D structures of proteins, as dynamic regulatory elements, are the key governing their subcellular localization.
"Coreesooning author. Email: ls119@ @singguu..rg.cco. 'Coresponding author. Email: zhfenghin @gmail.coo

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To fully leverage protein structural data, recent research has developed structure- -ased protein rep- resentation models. Benefiting from the emergence of AlphaFold2 (Jumper et al., 2021), which offers reliable structural predictions for a vast number of proteins, the structure-based methods learn representations directly from the spatial geometry of proteins. Such approaches have demonstrated impressive performance across range of tasks, including protein classification (Jing et al., 2020; Zhang et al., 2022; Fan et al., 2022) and protein generation (Dauparas et al., 2022; Watson et al., 2023), showcasing their ability capture complex structural patterns beyond what sequence alone can provide. These successful implementations underscore the substantial potential of incorporating structural information into subcellular localization prediction frameworks.
However, the existing subcellular localization datasets, such as DeepLoc, suffer from several lim- itations, which hinder the investigation of structure-based methods. Most notably, 1) they lack explicit protein 3D information, which is the key input to structure-based methods. Furthermore, 2) the current dataset typically uses coarse-grand coarse-grained compartment classifications, grouping subcellu- lar areas into broad categories (e.g., do not distinguish nuclear membrane and nucleoli in nucleus), which overlooks the unique localization characteristics and mechanisms associated with different organelles. Therefore, it leads to poor interpretability and great difficulty in discovering distinct patterns and underlying biological principles.
To address these limitations, aim to construct human protein subcellular localization dataset that can facilitate research on structure-bass methods for localization prediction and enable the discovery of more specific and biologically relevant localization patterns. Specifically, we have two considerations for the dataset: I) Comprehensive 3D information, which seeks to enhance the comprehensiveness of the dataset by recording detailed localization data from different databases and integrating 3D structural information of proteins, thereby bringing convenience and providing a unified evaluation benchmark for structure- based prediction models within the community; 2) Fine-grained subcellular categorization, which aims to incorporate finer-graines localization la- bels with annotations based on biological empirical evidence. As such, researchers are allowed to investigate protein localization patterns at more detailed and functionally meaningful level.
To this end, we take the initiative of building dataset called CAPSUL that simultaneously ful- fills the two considerations. Specifically, to obtain the 3D information, we leverage AlphaFold2 to extract the Cartesian coordinates of the Ca (alpha carbon) and utilize the FoldSeek to derive corresponding 3Di structural tokens for each protein, promoting structure understanding such as backbone conformation, folding patterns, and local structure. Moreover, to obtain comprehensive subcellular localization labels, we cross-reference each protein with annotation data from both the UniProt (Consortium, 2019) and Human Protein Atlas (HPA) (Thul et al., 2017) databases. Building upon the categories in the existing dataset DeepLoc, further refine the subcellular area space by introducing 20 aggregated subcellular compartments, carefully curated and validated by domain ex- perts. We extend several state-of-the-at (SOTA) protein representation models to this downstream task and evaluate their performance on CAPSUL. To facilitate future research, we investigate several potential optimization strategies for structure-bu based model training and make innovative use of the attention mechanism to enhance the interpretability of protein subcellular localization patterns by integrating Transformer modules into existing models. Empirical results on CAPSUL. validate the necessity of 3D information incorporation and the potential of leveraging structure-based methods for causal biology pattern discovery on the subcellular localization task.
In summary, the contributions of this paper are threefold:
We represent the first systematic attempt to construct human protein subcellular localization dataset with comprehensive 3D information, fine-grained categorization of cell compartments, and cross-referencee localization labels with experiment-lewe annotations.
We evaluate several SOTA baseline models on our proposed dataset CAPSUL, validating the positive influence of incorporating protein structural inputs.
We investigate various training strategies to facilitate future exploration and enhance the inter- pretability for subcellular localization tasks by introducing the attention mechanism.
RELATED WORK
Sequence-based protein representation learning. Due to the relative ease of modeling protein amino acid sequences, early protein representation learning efforts typically relied solely on one-
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dimensional sequence inputs. Examples include models based on CNN, LSTM, or ResNet archi- tectures (Shanehsazzadeh et al., 2020; Rao et al., 2019). Subsequently, Transformer- based models have demonstrated strong performance, especially after large-scale pretraining, achieving impres- sive results across a range of downstream tasks (Rives et al., 2019; Lin et al., 2022; Madani et al., 2023). In parallel, various self-supervised approaches have further enhanced the model's ability to capture meaningful features from protein sequences without vast number of annotations (Rives al., 2019; Lin et al., 2023; Elnaggar et al., 2021; Lu et al., 2020; He al., 2021). However, in the subcellular localization task, which is known to be closely linked to protein structure, sequence- only models fall short of capturing the full complexity of protein features. As a result, incorporating 3D structural information has become increasingly recognized as essential for achieving richer and more comprehensive protein representations.
Structure- protein representation learning. Efforts to model protein structures have been explored from multiple perspectives, including representations at the protein surface level, residue level, and atomic level. The protein language model also starts to consider structural information as input to enhance its understanding of proteins (Hayes et al., 2025). These approaches have achieved impressive results in tasks such as protein design, structure generation, and function pre- diction (Gligorijević et al., 2021; Gainza et al., 2020; Hermosilla et al., 2020; Hsu et al., 2022). Among them, models based on Graph Convolutional Network (GCN) have demonstrated consis- tently strong performance across various downstream tasks, highlighting their ability to effectively capture and interpret structural information (Fan al., 2022; Jing et al., 2020; Zhang et al., 2022). However, most of these models require atomic or residue-level coordinate inputs, which are often missing from current benchmark datasets. To address this gap, we aim to construct dataset specif- ically for the task of subcellular localization that incorporates 3D structural information, facilitating both the application and evaluation of structure- based models.
Subcellular localization dataset. Although many prestigious and task task-specific protein benchmarks exist (Rao al., 2019; Kryshtafovych et al., 2023), their lack of subcellular localization annotations makes them inapplicable on this downstream task. To the best of our knowledge, the only well- known dataset for subcellular localization originates from the training data used in DeepLoc (Thu- this, the PEER framework established a benchmark to evaluate
muluri et al., 2022). Building baseline models on that dataset (Xu et al., 2022). However, the absence of 3D structural information makes it impossible to assess the performance of structure-based models that have already shown significant promise. To address this gap, we aim to reorganize and enrich the existing dataset by incorporating high-quality 3D structural information alongside fine- tineggrineed subcellular localization annotations. We further evaluate a range of representative baseline models on this updated dataset, with the goal of establishing leading benchmark for subcellular localization prediction.
CAPSUL DATASET
To construct the CAPSUL dataset that offers 1) diverse and accessible 3D structural information, and 2) both detailed and aggregated subcellular localization annotations, we follow multi-step curation process, as illustrated in Figure 1.
3.1 PROCESSING OF PROTEIN SEQUENCE AND STRUCTURE DATA
Collection and filter of protein data. We first retrieve all predicted human protein structures from the AlphaFold2 database (Jumper et al., 2021; Varadi et al., 2024), totaling 20,504 unique proteins. To ensure data quality and relevance, we filter this set by retaining only proteins marked as active in the UniProt database (Consortium, 2019), one of the most comprehensive and authoritative protein databases with well-doocmented annotations, resulting in a refined set of 20,401 proteins.
Removal of fragmented structure predictions. Among the refined set, AlphaFold2 typically adopts sliding-wing window strategy to long protein sequences that segments the sequence with over- lapping fragments to predict protein structure. To avoid inconsistencies of predicted coordinates that may arise during the stitching of these fragmented protein structures, we exclude such proteins from the dataset. After this step, obtain 20,181 proteins of high quality and good consistency.
Extraction and preprocessing of protein features. We preserve the full PDB files for each pro- tein, the original files downloaded from AlphaFold, including the positions of backbone atoms, side
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Stop 1: Processing of protein sequence and structure data
Step 3: Merging
ts UniProt Database
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UniProt
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Mitohhondis na
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Step Processing of subcellular localization annotations
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Acquisition of detailed
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Example protein data
Fine-grained cetegorization
Figure I: Procedures of CAPSUL dataset construction, including key steps: Step 1 extracts and filters the sequence and structure data for each qualltyaltty protein from AlphaFold2; Step 2 collects the annotations from UniProt and HPA for the resulting proteins in Step I; Step merges the struc- ture data and the annotations for each protein, which consists of protein ID, localization annotations, amino acid sequence, sequence length, 3Di tokens, and Co coordinates, etc.
chains, and other relevant structural features essential for molecular modeling and analysis. The coordinates of Ca atoms are extracted, which are important components for protein structure un- derstanding. Furthermore, we employ the FoldSeek (Van Kempen et al., 2024) toolkit tokenize the 3D structure of each amino acid. This provides compact, informative structural representa- tion that supports rapid, accurate modeling while reducing computational overhead, which has been empirically justified as effective and widely adopted in recent studies (Su et al., 2023).
Following the procedures above, we curate dataset comprising 20,181 proteins, each labeled with amino acid sequence, Ca coordinates, and 3Di tokens sequence. For the next step, we append localization annotations to each protein.
3.2 PROCESSING OF SUBCELLULAR LOCALIZATION ANNOTATIONS
Acquisition of detailed subcellular localization annotations. Based the obtained proteins above, we collect the corresponding detailed subcellular localization annotations for human proteins from both the UniProt and HPA databases. This detailed dataset provides high-resolution localiza- tion annotations on widely accepted subcellular compartments, which is vital to facilitate research into the specific localization patterns within distinct organelles.
Fine-grained categorization. After that, we aggregate the dataset by adopting a refined catego- rization approach. Specifically, we consolidate the subcellular locations into 20 distinct categories inspired by DeepLoc's and HPA's subcellular localization classification scheme (Thumuluri et al., 2022: Thul al., 2017), which categorization. Then, the sublocations of 20 categories are specified separately, so the various ter- minologies in different databases can align with 20 unified categorizations. The entire procedure was conducted in accordance with well rell-established cell biology textbook (Alberts et al., 2022) and further verified by domain experts, with detailed categorization information available in Supp. A.
fine-grained framework compared with DeepLoc's ten-class
Annotations of evidence level for localization data. To fulfill the various research demands for the reliability of localization labels, we further extract and consolidate annotations on the experimental evidence level. Specifically, for UniProt, each subcellular localization annotation is accompanied by an evidence code indicating the source of the localization label. Among them, the localization supported by experimental evidence (marked with the term 000:000)) is labeled as 1, indicating experimental validation. For the localization with other forms of evidence (e.g.. non non-traceable author statement evidence), the label 2 is assigned. The label 0 is assigned to the localizations without evidence annotations. Moreover, since HPA primarily relies on experimental data obtained through immunofuuorescence and confocal microscopy (Thul et al., 2017), we assign label 1 to all annotated
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Table I: Comparisons between existing datasets and CAPSUL.
Aggregaarä Congetation
Esperimntal Amestalinn
Saqursso
DeepLec
2001)
Table 2: Statistics of CAPSUL.
Number of Proteins
20181 Average Number of
2.51
Max Nomber
Proportion of Experimennaal Annotations
0.857
Annotations per Protein
A Anmolations for Protein
Number of Amnotations
Nuclous
Cyiosol
5,385
Golgi Apparatus
Peroxisom:
Naclear Membrane
452
Cymskeleton
2,119
Call Membrane
5,772
Vesicle
2,363
Nacleoli
1.611
Centrusome
1,000
Endosome
657
Primary Cilium
98.3
Nacleopham
6.786
Mituchunrtia
1,768
Lipid Druplet
94
Secreted Proteins
2,087
Cympllsm
Frdophaemic Raticulum
1,710
1gwwom//Veuole
453
Sparn
localizations and label O to the localizations without evidence annotations. During the union of UniProt and HPA datasets, we prioritize annotations with experimental evidence when available.
3.3
DATA MERGING
After the separate processing of protein sequence and structure data, along with the subcellular localization annotations, we merge the data include complete information. In Figure 1, we present a sample record in CAPSUL, which consists of protein ID, localization annotations, amino acid sequence, sequence length, 3Di tokens, and Ca coordinates, etc.
3.4 DATASET ANALYSIS
In summary, we construct a unified dataset comprising 20, 181 proteins, each annotated with 20 sub- cellular localization labels. Our dataset CAPSUL provides more comprehensive coverage com- pared to DeepLoc (Thumuluri et al., 2022) and setHARD (Stärk al., 2021) in terms of involved features, localization categorization, and experimental annotations, which shown in Table 1. The dataset is randomly split into training, validation, and test sets in 70%: 70%:15%: 15%:15% ratio for training and evaluation. We present a statistical analysis of numerical features of our dataset in Table 2.
To ensure the high quality of our constructed CAPSUL dataset, have incorporated three safe- guards 1) Reliable data sources: reliable protein structures predicted by AlphaFold2 were utilized in CAPSUL, with high accuracy, strong consistency, and incorporation of available experimen- tal data as templates in its prediction process (Jumper et al., 2021); the localization labels source UniProt, a world-leading database with the most comprehensive protein annotations from multiple resources, and HPA, human-specific protein database offering high-resolution and experiment- validated data. Striet validation and filtering: we perform a series of validation and filtering steps on human proteins to exclude fragmented AlphaFold structures, which could introduce in- consistent coordinate information, and to remove proteins annotated inactive in UniProt, thereby ensuring the reliability of subcellular localization annotations; 3) Evidence-level support: we in- corporate annotations indicating whether experimental validation exists for the localization labels, thereby enhancing their credibility and catering diverse research needs.
4 EXPERIMENTS
4.1 BASELINE MODELS
To study how existing methods perform on our proposed dataset?. we evaluate 1) DeepLoc 2.1 (Ødum et al., 2024), one of the most well-known tools dedicated to subcellular localization. It leverages the pre-trained protein language model ESM-1b (Rives et al., 2021) and provides pre- dictions across ten subcellular compartments. Besides, we evaluate existing representative protein
For a detailed analysis of the data reliability in the dataset, please refer to Supp. B.
The detailed descriptions and hyperparameter settings of all baseline models provided in Supp. C.
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representation methods for the subcellular localization task, including sequence- baseed and structure- based methods.
Sequence-based models. Since existing sequence-based works are not specifically designed for subcellular tasks, we extend the widely adopted pre-trained protein language model 2) ESM-2 (650M parameters) (Lin et al., 2022) and its latest iteration, 3) ESM-C (600M parameters) (ESM Team, 2024). We adopt the sequence encoder module from existing methods to obtain protein rep- resentation, and extend it with a localization classifier, as detailed in the following.
Sequence Encoder. For each protein, have its amino acid sequence represented as S ($1, .2,....,.. E TR where 8; denotes the i-th residue and n is the length of the protein. We then apply the sequence encoder fsog()) )) of existing work to obtain contextual embeddings, H Seeq (S), where H (h,, h2,,......... ....... , hn) E Raxd and h is the per-residue embeddings of dimension d. To obtain fixed-length representation for the entire protein, we apply mean pooling and generate global representation h Σx= h4 h E.
Localization Classifier. To predict subcellular localization, we leverage an MLP classifier (b-) on top of sequence encoder, i.e., ÿ - ((h), where ğ E RT is a multi-label prediction vector and m denotes the total number of predicted subcellular compartments.
Structure-based models. We consider 4) CDConv (Fan et al., 2022) and 5) GearNet-Edges (Zhang et al., 2022), two representative GCN baselines in protein representation task. We adopt the GCN- based structure encoder and extend it with an additional Transformer encoder to enhance inter- pretability. We also evaluate 6) FoldSeek (Van Kempen et al., 2024), which leverages pre-trained structure tokenizer to encode the 3D structural information of each residue into sequence of struc- ture tokens. The outputs of the above models are then averaged and processed through a localization classifier for prediction.
Structure Encoder. We represent protein's 3D structure as a graph G = (V, E), where each node va E V corresponds to the i-th residue (typically using the Ca atom position), and edges (ui, UU) E are defined based on spatial or sequential adjacency. Each node o; is initialized with feature vector æ, E Re including its positional information. Then we employ different graph encoders to capture higher-order topological relationships and produce updated representations hh,)...,,,,). The protein- embedding is then obtained via global pooling h = Σ h,-
Localization Classifier. We then obtain the final prediction ý = Φ (h), described above.
Extension of structure-based models. We also extend three novel methods, 7) Graph Trans- former (Rampášek et al., 2022), 8) Graph Mamba (Gu & Dao, 2023). and 9) Graph Diffu- sion (Yang et al., 2023) to this task. The Graph Transformer employs attention mechanisms over graph-structured data, enabling the model to effectively capture both local and global dependen- cies among residues. Graph Mamba, the other hand, incorporates selective state space models into graph learning, which facilitates long-range information propagation with improved efficiency. For Graph Diffusion, leverages diffusion processes over graph-structured data propagate in- formation across nodes. To improve classification performance on minor categories, we further incorporate a contrastive loss mechanism into the CDConv model, i.e., 10) CDConv with Con- trastive Loss, aiming to enhance the similarity between representations of positive protein pairs and thereby encourage the learning of distinctive localization features. We also explore fusion model that combines representative structure-based models with sequence-based pretrained protein language models, i.e., 11) DSSM-C+CDCOO Fusion Model, investigating both early and late fusion strategies to integrate structural information into large-scale sequence models.
To optimize the models, we adopt the Binary Cross Entropy (BCE) loss, defined - [y, log( y,) + (1 y1) log(1 }d)]. where m is the number of classes, ys {0, 1} is the label for class i, and ÿ): E (0, 1) is the predicted probability.
Optimization.
as LECE
4.2 BENCHMARK OVERALL RESULTS AND DISCUSSION
Given the class imbalance in each location (i.e., the proportion of proteins localized to each sub- cellular compartment is often small), we consider the widely used evaluation metrics in this task: Precision, Recall, and Fi-score (Jiang et al., 2021; Thumuluri et al., 2022). In addition, we utilize micro-averaged and macro-averaged F1-score evaluate the overall performance across different

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Table 3: Overall performance of sequence-based, structure-based methods on CAPSUL.
Sequence- haseed Methais
Structure- hased Methods
Subcellular
DoepLoc 2.1
ESM-2 650M
ESM-2
ESM-C
ESM-C 600M
ESM-C 600M"
FoldSeck
CDConv
GearNet- Edge'
Locations
650M
600M
F1-score
Naclous
0.152
0.609
0.649
0.648
0.555
0.484
0.620
0,521
Nuclear Membrane
Nucleoli
0.091
0.039
0.024
0.147
0.121
Nucleoplasm
0.562
0.621
0.623
0.500
0.433
0.583
0.515
Cytoplasm
0.154
0,248
0.536
0.438
0.174
0.495
Cytosol
0.392
0.380
0.169
0.003
0,353
0.385
Cytoskeleton
0.006
0,251
0.205
0.048
0.070
0.135
0.228
Centrosome
0.014
0.127
Mitochondria
0,120
0,317
0.562
0.544
0.099
0,476
0.318
Endoplasmic Reticulum
0.121
0.351
0.333
0.059
0.292
0.279
Golgi Appuralus
0.061
0.099
0.026
Cell Membrane
0.142
0.555
0.631
0.648
0.372
0.343
0.562
0.556
Endosome
0,018
0.067
Lipid Droplet
Lysssome/ Vacuole
0.118
0.073
Peroxisome
0.131
Vesicle
0,009
0.005
0.027
0.068
Primary Cilium
0.164
0.112
0.147
Secreted Pruteins
0.191
0,713
0,826
0.797
0.433
0.328
0.767
0.687
Sperm
0.052
0.070
0.086
Micro Avg Fl-score
0.495
0.492
0.338
0.248
0.452
0.417
Macro Avg Fl-score
0.1.50
0.263
0.249
0.135
0.092
0,226
0.235
Micro Avg Precision
0.647
0.690
0.693
0.598
0.605
0.632
0,546
Micro Avg Recall
0.264
0,386
0.382
0.236
0.156
0.352
0.337
Extension of Structure bused Methods
Subcellular
Graph Transformer
Graph Mamba
Graph Diffasion
CDCone' with Contrastive Loss
ESM C-CDCow Early Fusion
ISM-C+CDCom
Locations
Late Fusion
Fi-score
Nocleus
0.597
0.559
0.624
0.592
0.643
0.645
Nuclear Membrane
0.037
Nucleoli
0.203
0.168
0.047
0.140
0.125
0.153
Nucleoplasm
0.552
0.502
0,578
0.556
0.643
0.617
Cytoplasm
0.393
0.418
0,503
0.480
0.455
0,515
Cytosol
0.248
0.426
0.288
0.250
0,157
0.370
Cyloskeleton
0.042
0,270
0.099
0.243
0.100
0.287
Controsome
0.181
0.014
0.037
Mitochondria
0.475
0,303
0.468
0.563
0.557
Endoplasmic Reticulum
0.184
0.089
0.150
0.361
0.446
Golgi Apparatus
0.041
0.185
0.156
0.249
Cell Membraae
0.547
0.540
0.496
0.673
Endosome
0.100
0.034
0.054
Lipid Droplet
Lysosome / Vacuole
0.026
Peroxisome
Vesicle
0.044
0.135
0.018
0.027
0.067
Primary Cilium
0.012
0.088
0.036
0.115
Secreted Proteins
0.705
0.557
0.623
0.780
0.819
0.725
Spern
0.018
0.130
0.018
Micro Avg F1-seore
0.410
0.411
0.424
0.435
0.470
0.476
Macro Avg F1-score
0.203
0.235
0.187
0.249
0.235
Micro Avg Precision
0.637
0.414
0.596
0.650
0.710
0.634
Micro Avg Recall
0.302
0.408
0,329
0.326
0.351
0,381
WWe fineture the pre-trinned protein lunguage modell 'The original MI replaced Transormer layers "The parameters FSM-C are intriatized randamly. not support prediction that location, and therefore, average metrics are not considased this case. iidicates that no prodiction
indicates that Decplao: oz is made for that location. Bold value indicates the best results.
Table 4: Ablation study of CDConv and GearNet- Edge to randomly sample Ca coordinates.
CDCamm (random Co
CDComm
GearNer--Fdt -elge (random
GedrNe-FFdp
coordinates)
Co courdinates)
Miccoo Fl-scarc
1,320
0.453
0.546 0.348
0.417
Micro Arg Przcision
Micro Aug Recall
0.220
Il352
0.253
0.337
'The ociginal MLP is replaced by Transformer layers, Bold value indicates the better result for each bascline.
categories. The overall performance of all baselines on our proposed dataset is presented in Table 3, from which we have the following observations:
'The detailed results wen.. Precision and Recall, including other experimental results mentioned later in the main text, are provided in Supp. D.
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Large pre-training benefits sequence-based methods for subcellular location prediction. Among all sequence-based methods, ESM-C generally obtains higher Fi-scores than ESM-2. We believe this is attributed to the extensive data and training compute used in the ESM-C pre- pre-training which facilitates a better representation of the protein's is sequence features. Similar observations are also seen in (Hayes et al., 2025). Besides, this hypothesis can be further confirmed by the signifi- cantly inferior performance of ESM-C 600M', i.e. without pre-training, than the pre-trained ESM- C. On the other hand, it is expected that DeepLoc yields inferior performance due its overlook of the fine tie-grained categorization during pre- training. which may result in its inability to sufficiently differentiate the representations of proteins in multi-label classification tasks (Hong et al., 2023). This further validates the necessity of detailed categorizations of subcellular locations in CAPSUL.
The 3D structure essential for subcellular localization task. Despite that structure-based meth- ods slightly fall behind the pre-trained FSM-C,, both CDCCnv and GearNet-Fdge outperform the ESM-C 600M in most cases. Also, a group of ablation studies is conducted on CDConv and GearNet-Edge, with coordinates randomly sampled from each protein's spatial range. As shown in Table 4, randomly sampling the input of protein 3D structural data leads to a significant drop in model performance. These two results validate that structural information plays decisive role in determining subcellular localization. Besides, CDConv demonstrates the strongest overall perfor- mance among the structure-based models, justifying the effectiveness of relative distance and the dynamic radius for convolution. Nevertheless, the inferior performance of FoldSeek may be due to the lack of sequence information and its coarse tokenization of structural information.
The models generally demonstrate better performance on subcellular locations with larger lo- calization sample sizes. For classes with large number of localization samples (e.g., nucleus), most models tend to demonstrate relatively strong predictive performance. In contrast, for under- represented classes (e.g.. lipid droplet), the prediction performance generally poor, with some classes even failing to produce any correctly identified proteins. This is a common outcome in im- balanced multi-label classification tasks, as the standard BCE loss tends to neglect fewer-number labels. Additionally, potential conflicts among multiple optimization targets may further exacerbate this issue. To address these challenges, we conduct in-depth analysis in Section 4.3.1 and 4.3.2, ex- ploring strategies such as reweighting and single-label classification to mitigate the effects of class imbalance and task conflict.
Structure-based models showcase their potential to capture non-trivial patterns for subcel- lular locations with few samples. Graph Mamba and GearNet-Edge tend to perform better on certain classes with smaller localization sample sizes compared with sgquence-based models. We believe that this is because of the relational message passing layer adopted in them, which uniquely models different spatial interactions among residues. This demonstrates that structure-based mod- els showcase potential to identify specific structural features that are indicative of localization to a particular organelle, thus achieving a notably good performance. Further investigation on the pat- terns with intuitive biological interpretability captured by the structure- based model can be found in Section 4.3.3.
Contrastive learning and fusion strategies demonstrate strong potential on the baseline mod- els. We observe that the introduction of contrastive loss to CDConv improves performance on several minority classes (i.e. the notable F1-score improvements on macro-average level and certain cate- gories such as Endosome, Primary Cilium, and Golgi Apparatus compared to the original CDConv). We attribute this to the contrastive learning paradigm, which encourages the model to maximize embedding similarity for positive pairs and to capture shared characteristics within minority-class positive samples through the contrastive objective. Also, although the fusion model slightly under- performs ESM-C on average metrics, it achieves the best performance across multiple subcellular compartments among all baselines, highlighting the considerable potential of integrating protein structural information into sequence-based protein language models.
4.3
IN-DEPTH ANALYSIS
4.3.1 PROTEIN IMBALANCE MITIGATION VIA REWEIGHTING
Reweighting Schemes. In this task, for each subcellular location, the number of positive samples (i.e., proteins localized to that compartment) is substantially smaller than the number of negative samples (i.e., proteins not localized to that compartment). Reweighting is a widely used strategy to
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Table 5: Performance of ESM-C 600M, CDCony, and GearNet-Edge with reweighting scheme.
Suboeilular
GearNet
Subcellular
ESM
GeurNei-
Lacitionss
6IOM
Fdge!
Locationss
500M
Filge
Fl-soore
F1-souee
Nocleus
0.630
0.625
0.618
Endosome
0,114
0.150
Membeane
T-ppi Dropler
0,235
0.023
Nuckcoli
Lysosome Vacuole
0.175
Nuckeoplasm
0.576
0.607
11574
Perosisome
0.190
0.072
0.108
Cytoplasm
0.500
0.582
10.54
Vesick
0.288
0.281
Cytosol
Primary Ciliam
0,024
0.167
Cylosizletto
Secreted Proseins
0.778
0.564
Centrosome
0.100
0.175
Sperm
0.120
0.125
Miucchonitaa
0.481
0.207
0.313
Endoplaemic Reticulars
0.308
0.345
Appuratus
Aug
Cell Membrane
0.566
0.560
0.536
Macro Avg. Flceee
0,210
0.297
0.304
original MLP replaced by Transformer lay ars.s."
indicates prodiction made location. Bold
improves compared
the resalt without reweightingg
Table 6: Performance of ESM-C 600M, CDConv, and GearNet-Edge with single-label classification.
Sübvellukar
ESMEC BOSS CDCum
GearNet
Subcellular
800M 800M CDCum
GearNet-
F1-score
F1-coaee
Nuclear Menibaane
0,182
0J042
Lasosome/Vauoole
0.115
0.162
Nacleoti
0.267
0.151
0.228
Peroxisome
0J154
0.023
0.184
0.088
0.167
0.068
0.230
0.268
Golgi Apparatus
0.280)
0.114
0.210
Primary Ciliim
0.253
0,097
Endosome
0.167
0.049
0.126
Speam
0.159
0,068
0.117
Lipid Droplet
0,021
0J051
'The 'The origina MLP is replaced by Trunsformer layers, 'L"" indicates that no prediction is made for that location. Hold value indicates that it improves compared with the resalt from multi-label clacifiiation.
address class imbalance by reducing the bias toward majority classes. Inspired by previous work on class-level reweighting, we evaluate three rewcighting schemes. 1) Inverse frequency reweight- ing (Cao et al., 2019), Le., We = fe 2) Log-inverse frequency reweighting (Cui al., 2019), i.e.
We 1 3) Focal loss (Lin et al., 2017), which is defined as [og(1+
= --= Σ lyke (1=)))) (ooe)>loo@@ia ) + (1 - yic) The log(1 }re)],
where fe is the frequency of positive samples in class c, We is the computed class-specite weight, Vie E (0, 1} denotes the ground truth label for sample i and class Vie E (0, 1) is the predicted probability, and y is the focusing parameter. deserves attention that the w. in the focal loss strategy chosen from either inverse log- log-inverse frequency weight.
Results. We apply the three reweighting schemes on three competitive models (ESM-C, CDConv, and GearNet-Edge) and report the best results for each model in Table 5. From the results, we ob- serve that the two structure-bu based baseline models exhibit substantial improvements under reweight- ing strategies, especially for the higher Precision across underrepresented categories. In particular, CDConv and GearNet- Edge successfully identify positive instances for every class. These find- ings highlight that reweighting can significantly enhance model performance on minority classes, especially for structure-based models.
4.3.2 SINGLE-LABEL CLASSIFICATION
To explore how different methods perform on each subcellular location respectively, we adopt the single-label setting, aiming to mitigate the potential conflict between optimization across different classes. In this setting. we train separate binary classifiers for each subcellular localization cate- gory with ESM-C, CDConv, and GearNet-Fdge. We apply this single-label prediction framework specifically to those subcellular localization classes where the Fi-score of at least one of the models (ESM-C, CDCony, GearNet-Edge) is lower than O. Our goal is to shift the model's attention toward underrepresented classes and improve the predictive influence of positive samples.
From the results in Table 6, we observe 1) notable improvements in the prediction performance of previously underperforming classes, particularly for GearNet-Edge. However, 2) ESM-C and CDConv still fail to generate any predictions for a few categories, primarily due to the extremely low proportion of positive samples (ranging from 0.5% to 3%). Given that such severe class imbalance is common challenge in subcellular localization tasks, consider the single-label prediction

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B3GALT2
GIMAP1
Figure 2: Visualization of the top 20 attention-scores residues of the three representative proteins.
strategy promising and practical solution. Moreover, this approach lays the groundwork for future research focused on identifying localization patterns specifie to individual subcellular compartments.
4.3.3 BIOLOGICAL INTERPRETARILITY
We analyze a CDConv model on Golgi apparatus prediction with an exceptional precision of 100%. Specifically, with our novel attempt of the Transformer module extended to the GCN-based models, we identify and visualize the tokens (i.e., residues) that receive the 20 highest attention weights in Figure 2, offering insights into which structure the model considers most decisive for subcellular lo- calization. We find that the model consistently highlights similar a-helix spatial conformation, such as residues 8-27 of MFNG, residues 24-45 of B3GALT2, and residues 273-292 at the C-terminus of GIMAPI. Remarkably, these findings show strong concordance with prior experimental evi- dence (Paulson & Colley, 1989; Linstedt et al., 1995). It is highlighted that despite significant quence divergence, the model specifically focuses on a-helix transmembrane domains (20-30 amino acids in length) that maintain consistent topological orientations across all targets. Recent studies have demonstrated that the topological conformation of transmembrane domains can influence Golgi localization by regulating electrostatic potential gradients in transmembrane regions and lipid mem- brane anchoring efficiency (Cosson et al., 2013; Hanulova & Weiss, 2012; Bian et al., 2024). This evidence not only confirms the model's capability for structural pattern recognition beyond sequence similarity but also provides theoretical support for its structural identification mechanisms.
5 CONCLUSION AND FUTURE WORK
We pointed out the crucial importance of constructing a subcellular localization benchmark with protein 3D information to facilitate the investigation of structure- based models for the subcellu- lar localization task. To achieve this, we constructed benchmark called CAPSUL. that contains comprehensive structural information and fine-grained annotations of 20 categories of subcellular compartments with biological experiment evidence labels. Based on CAPSUL, evaluated SOTA sequence-basee and structure-basse based models as well as their feasible optimization strategies, demon- strating the effectiveness of incorporating protein structural information. Moreover, case study Golgi apparatus validates the coology-alignee interpretability of structure- based models trained on specific fine-grained subcellular location, supported by CAPSUL. This work proposes a compre- hensive human protein benchmark with 3D information and fine-grained annotations for subcellular localization. Based on CAPSUL, we highlight several research directions that are worth future ex- ploration: 1) To fully leverage structural information, aligning or disentangling the understanding across different dimensions (i.e., amino acid sequence, Co,, and 3Di) specifically for subcellular localization is a promising direction. 2) Causal discovery on the relationship between 3D structure and subcellular localization worthwhile to be explored on CAPSUL, with the goal of establishing direct links to underlying biological principles.
ACKNOWLEDGMENT
Shulin Li was supported by China Postdoctoral Science Foundation (grant number BX20240186 and 2024M761616) and the Shuimu Tsinghua Scholar Program.
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ETHICS STATEMENT
This research presents dataset and benchmark for protein subcellular localization prediction using Al methods. We confirm that our work raises no ethical concerns involves only the analysis of publicly available protein data, with human subjects, animal experiments, biological in- terventions. We have fully considered the potential societal impacts and do not foresee any direct, immediate, negative consequences. We committed to the ethical dissemination of our findings and encourage their responsible use.
REPRODUCIBILITY STATEMENT
All the results in this work are reproducible. The access the necessary code and complete dataset can be found Supp. L. We discuss the experimental details in Supp. C, including implementa- tion details such the hyperparameters chosen for each experiment, to help reproduce Additionally, further experimental results, detailed dataset interpretations, and usage guidelines are provided in Supp. F, and J to facilitate better understanding and utilization of our dataset.
results.
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Supplementary Material for CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization
A DATASET CONSTRUCTION
A. SUBCELLULAR LOCATION CATEGORIZATION AND TERMINOLOGGY MAPPING
To facilitate model classification, we first categorize the detailed subcellular localizations of pro- teins. Existing datasets often use coarse-gradd saarse-grained classifications (e.g.. DeepLoc categorizes sub- cellular locations into 10 broad classes). However, since each subcellular compartment typically follows distinct localization patterns, such coarse categorizations can hinder the model's ability to capture consistent intra-class features, ultimately leading to reduced prediction accuracy. Moreover, coarse-grained classification also hinders researchers from exploring localization mechanisms spe- cific to finer subcellular compartments. Inspired by the subcellular location categories HPA and DeepLoc, we propose finer-grained classification scheme consisting of 20 subcellular categories. Notably, "Nucleus" and "Cytoplasm" categories serve as umbrella terms for several finer locations ensure compatibility with Deeploc during evaluation.
When aligning protein localization annotations from the UniProt and HPA databases to our refined categorization, observe inconsistencies in terminology (e.g.. "Cell Membrane" in UniProt versus "Plasma Membrane" in HPA). To resolve such discrepancies, we refer to the prestigious textbook Molecular Biology of the Cell (7th Edition) (Alberts al., 2022) and create unified mapping, as shown in Table 7, which allows for consistent categorization across the two databases.
Domain experts were extensively engaged ensure and validate the accuracy the classification standards and data alignment procedures. We invited cell biologists from several prestigious uni- versities and research institutes to review and revise the dataset, which ensures that CAPSUL firmly grounded in cell biology. All of them have over eight years of research experience in their field. They are rigorously involved throughout the entire process, including 1) curating authoritative datasets, 2) determining primary subcellular localizations, and 3) validating the biological plausibil- ity of localization assignments.
Through the above processes, have established fine- aine-graneed subcellular localization classifica- tion standard and successfully unified annotations from multiple databases under a unified labeling framework.
A.2 DATASET SPLITS
To construct separate datasets for training, validating, and testing, we randomly split the original dataset into three subsets in a 70%: 15%; 15% ratio, each containing 14,126, 3,027, 3,028 pro- teins. The partitioning of different protein data used in our experiments is also available in the CAPSUL dataset. The number of labels for each subcellular location three subsets shown in Table 8. Although the data is randomly assigned different subsets, have verified the distribu- tion characteristics among classes to maintain a similar proportional relationship, ensuring balance and representativeness across the subsets.
B DATASET RELIABILITY
B. OVERVIEW DATA SOURCES
In Section 3, provide detailed description of the data preprocessing procedures implemented to ensure the high quality of CAPSUL. Here, we would like to emphasize that the data sources themselves are highly reliable. Specifically, the protein-related data used in this study were primarily obtained from the following databases:
AlphaFold. AlphaFold provides protein structural data in CAPSUL. 1) AlphaFold has already in- corporated experimentally resolved structures of proteins as templates during its prediction pro- cess (Jumper al., 2021). AlphaFold explicitly describes how its pipeline automatically searches
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Table 7: Categorization of CAPSUL and terminology mapping between HPA and Uniprot.
20 fine- teeeegraind categories
HPA
UniPmi
Nucleus
Nuclear Membrane
Nuclear membrane
Nucleus membrane, Nucleus envelope,
Nucleus inner membrane, Nucleus outer membrane
Nucleoli
Nucleoli, Nucleoli fibrillar center, Nucleoli rim Kinctochore, Mitotic chromosome, Nuclcar bodies, Nuclear speckles, Nucleoplasm
Nucleolus
Nuckcoplasm
Nuclcus matrix, Nucleus lamina,
Chromosome, Nucleus speckle
Cytoplasm
Cytosol
Aggresome, Cytoplasmic bodies, Cytosol, Rods Rings
Cytosol
Cytoskeleton
filaments, Cleavage furrow, Focal udhesson sites, Cytokinetic bridge, Microtubule ends. Microtubules, Midbody, Midbody ring.
Cytoskeleton
Mitotic spindle, Internediate filaments
Centrosome
Centriolar satellite, Centrosome
Centrosome
Mitochondria
Mitochondria
Mitochondrion, Mitochondrion envelop, Mitochondrion inner membrane,
Mitochondrion membrane, Mitochendrion membrane, Mitochondrion matrix, Mitochondrion intermembranc space
Endoplusmic Reticulum
Endoplasmic reticulum
Endoplasmic reticulum, Endoplasmic reticulum membrane. Endoplasmic reticulum lumen, Microsome, Rough endoplasmic
reticulum. Smooth endoplasmic reticulum, Sarcoplasmic reticulum
Golgi Apparatus
Golgi appuratus
Golgi apparatus, Golgi apparatus membrane, Golgi apparatus lumen
Cell Membrane
Cell Junctions, Plasma memhrane
Cell membrane, Apical cell membrane, Apicolateral cell membrane, Basal cell membrane, Basolateral cell membrane, Lateral cell membrane, Cell projection
Endosome
Endosomes
Endosome
Tipid Droplet
Lipid droplets
Lipid droplet
Lysosome/Vacuu Vacuole
Lysosomes
Lysosome, Vacuole, Vacuole lumen, Vacuole membrane, Lysosome lumen, Lysosome
membrane
Peroxisome
Peroxisumes
Peroxisome, Peroxisome mairix, Peruxisome membrune
Vesiele
Vesicles
Vesicle
Primary Cilium
Basul body. Primary cilium, Primary cilium tip.
Cilium
Primary cilium transition zone
Secreted Proteins
Secreted Proteins
Secreted
Sperm
Acrosome, Annulus, Calyx. Connecting piece,
Acrosome, Calyx. Perinuclear theca
End piece. Equatorial segment, Flagellar centriole. Mid piece, Perinuclear theca,
Principal piece
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Table 8: Label counts for training, validation, and test set of CAPSUL.
Subcellular
Counts
Locations
Training Set
Validation Set
Test Set
Sum
Nucleus
5,312
1,128
1,150
7,590
Nuclear Membrane
313
63
76
452
Nuclcoli
1,143
249
249
1,641
Nucleoplasm
4,751
1,007
1,028
6,786
Cytoplasm
4,652
984
977
6,613
Cytosol
3,787
811
788
5,386
Cytoskeleton
1,499
302
318
2,119
Centrosome
713
147
1,000
Mitochondria
1,247
259
262
1,768
Endoplasmic Reticulum
1,146
289
1,710
Golgi Apparatus
1,323
271
287
1,811
Cell Membrane
4,022
863
892
5,777
Endosome
466
113
108
687
Lipid Droplet
15
94
Jysosome//accudoo
313
65
75
453
Peroxisome
20
19
110
Vesicle
2,019
404
440
2,863
Primary Cilium
699
123
161
983
Secreted Proteins
1,477
317
293
2,087
Sperm
444
99
109
652
the PDB for experimentally resolved structures, selecting up to four structural templates, and maps atom coordinates from those templates to the target sequence during inference. These coordinates used as template inputs alongside MSA-based evolutionary information, enabling AlphaFold to leverage high-quality experimental structural data in its predictions. 2) AlphaFold-predictee struc- tures have been demonstrated to achieve exceptionally high accuracy, competitive with experimen- tal data. AlphaFold was entered for CASP14, and shows that it achieves accuracy competitive with experiment in a majority of cases. Specifically, the median backbone accuracy of its predictions is 0.96 Á r.m.s.d.gs (Ca root-mean-square deviation at 95% residue coverage), which often within the margin of error of experimental structures (Jumper et al., 2021). AlphaFold provides full- length protein structures containing complete structural information, which minimizes the potential negative influence of structural variability caused by different versions of experimental protein data. This choice allows us to maintain a high level of consistency across the CAPSUL dataset.
UniProt. UniProt provides protein localization annotation and evidence-level annotations in CAP- SUL. UniProt serves as one of the most authoritative and widely used protein knowledge bases, integrating sequence, functional, and localization information across a broad spectrum of species. In particular, the manually curated Swiss-Prot section is recognized for its rigorous curation stan- dards, where annotations (including subcellular localization annotations) are derived from authori- tative experimental studies and peer-reviewed literature, complemented by computational analyses and homology-basee inferences. Each localization entry systematically annotated with evidence codes that explicitly denote whether the information originates from direct experimental validation, literature reports, or computational prediction, thereby providing transparency and traceability of the data source. This evidence-based framework ensures that localization annotations are not only comprehensive but also of consistently high quality.
Human Protein Atlas (HPA). HPA provides protein localization annotation and subcellular cat- egories reference in CAPSUL. HPA provides a unique and experimentally grounded resource for human protein subcellular localization. Its Subcellular Atlas is built upon systematic immunoflu- orescence imaging combined with antibody- maseedy-based profiling in multiple well-characterized human cell lines. This approach allows direct visualization of protein distribution within distinct subcel- lular compartments, thereby offering cell-type-specific and high-resolution localization evidence. These measures substantially reduce the likelihood of false annotations and provide users with clear indication of annotation confidence.
B.2 AN ALTERNATIVE ATTEMPT FOR PROTEIN STRUCTURE INPUTS
In our study, we used protein structural data predicted by AlphaFold2, the most accurate and widely adopted source of protein structural information, provide structure inputs for structure-
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Table 9: Partial evaluation on protein structure input from AlphaFold2 and Boltz-2.
Subceilular
CDCone (1,223 protcin
CDConv' (1.223 protein
structures AlphuFold2)
structures Boltz-2)
Locations
Precision
Recall
Fi-Score
Precision
Recall
FI-Score
Nocleus
0.798
0.709
0.751
0.788
0.565
0,658
Nuclear Membrane
Naclooli
0.500
0.064
0.113
0.267
0.025
0,047
Nacleoplasm
0.794
0.649
0.714
0.791
0.448
0.572
Cytoplasm
0.701
0.577
0.633
0.634
0.673
0.653
Cyteool
Cyloskeleton
0,548
0.085
0.147
0.467
0.035
0.065
Controsome
Mftochondria
0.694
0.358
0.213
Endoplasiiic Reticulum
0.630
0.168
0.266
0,500
0.149
0,229
Golgi Apparatus
0,500
0.023
0.044
0,333
0.008
0.015
Cell Membrane
0.823
0.382
0.522
0.826
0.269
0.406
Endosome
Lipid Deoplet
Lysusome/Vaacen Vacuole
Peroxisome
Vesicle
0.800
0.019
0.037
0.667
0,010
0.019
Primary Cilium
Secreted Proteins
0.375
0.655
0.447
Sperm
Micro Avg
0,745
0.407
0.527
0.668
0.371
0.477
Macro Avg
0.408
0. 0.184
0.227
0.341
0.165
0.195
The original MLP is replaced by Transformer layers. "L" indicates that no peodiction is made for that location.
based models. We acknowledge that Boltz is an efficient implementation of the still-unreleased AlphaFold3, and therefore seek to examine the subcellular localization performance when using Boltz-predicted structures as input (Wohlwend et al., 2025; Passaro et al., 2025).
To the best of our knowledge, Boltz has not publicly released a complete set inference results of human protein structures, compared with the AlphaFold2 dataset whose complete inference results can be downloaded publicly. Therefore, we locate a partial dataset of Boltz inference results, which includes structures of protein complexes predicted by Boltz-2 (Ille al., 2025). From this work, extracted the subset overlapping with our CAPSUL benchmark, yielding 1,223 proteins. For these 1,223 proteins, compared different structure inputs (i.e., structures predicted by AlphaFold2 and by Boltz-2) on our previously trained structure-based model CDConv for inference. We report the results in Table 9.
Across the overall metric and the majority of subcellular locations. AlphaFold2-baseed structural inputs outperform Boltz-based inputs. This observation further supports the rationale behind our use of SiphaFold2-predicter structures in constructing CAPSUL. As the most accurate and widely adopted source of protein structural information, AlphaFold2 provides high-quality structural inputs that lead strong downstream performance in subcellular localization.
C EXPERIMENT DETAILS
C.I IMPLEMENTATION DETAILS
The experiments were performed utilizing NVIDIA RTX 3090, A40 and A100 GPUs. We employ an early stopping strategy to mitigate overfitting with tolerance of epochs. Hyperparameters such as learning rate, number of epochs, and batch size are explored separately for each model type, considering their distinct architectures.
C.2 FURTHER DESCRIPTION OF STRUCTURE-BASED MODELS AND THEIR EXTENSIONS
C.2.1 OVERVIEW OF GRAPH CONSTRUCTION
In the graphs constructed by structure-basse models, each node represents an amino acid, and edges encode the relationships between amino acids. Specifically, the edges are constructed as follows:
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Edge Criteria. There two types of adjacency to form the edges in the graphs. Sequential adjaceney refers to the proximity of amino acids along the one-dimensional primary sequence of a protein (e.g., if the sequential adjacency range is set to 3, then the amino acids from position [x-3, x+3] are considered sequential neighbors of the x-th residue). On the other hand, spatial adjacency captures the proximity of amino acids in the three-dimensional space of the protein (e.g., if the spatial adjacency radius is set to 8 Å, all amino acids located within an 8 Ásphere centered at given residue are considered its spatial neighbors). These adjacency relationships define the edges in the constructed protein graph.
Edge Features. For the GCN-based baselines CDConv and GearNet-Edge, we adopted their in- novative edge feature implementation methods originally proposed in their respective frameworks, which can be found in the corresponding publications (Fan et al., 2022: Zhang et al., 2022). These methods incorporate and encode both relative orientation and Euclidean distance. For our extended models, Graph Transformer, Graph Mamba, and Graph Diffusion, the edge features are derived by processing the aforementioned different edge criteria through an embedding layer.
C.2.2 EXPLANATION OF GRAPH ENCODER
Within the structure- based baseline models, the graph encoders vary in their approaches to process- ing the input feature vectors: A GCN updates node representations via neighborhood aggregation, L.., m;
m; (I+1) a (EjEN(s) W0mm" (1 +b)). b()) m; (1) is the representation of node i at layer (the first layer is initialized with node embeddings x; various in different baselines), Nii) denotes the neighbors of node i, W and b(4) are trainable weights and bias, and a is a non-linear activation function (e.g., ReLU). After L layers of graph convolution, obtain the final node repre- sentations (m, (n) Ji- To enhance interpretability and capture global interactions among residues, replace the traditional average pooling with Transformer encoder T--) to obtain the residue representation, i.e., h (hi....,, ht.) T po where h E RR"
(0)
Similarly, Graph Transformer and Graph Mamba substitute the convolution- encoder with their respective architectures, while adhering to the same overall procedure to obtain the global protein representation.
Given a protein structure represented graph, we introduce diffusion-baseee refinement pro- cess in which node coordinates or geometric features are gradually perturbed with Gaussian noise and then denoised through learned reverse process. The diffusion module serves auxiliary eepresentationllarniii stage designed to enhance geometric feature extraction prior to the down- stream subcellular localization prediction. This allows the network capture multi-scale spatial dependencies while remaining robust to structural noise.
C.2.3 EXPLANATION OF CONTRASTIVE LEARNING AND FUSION MODELS
CDConv with Contrastive Loss. For each of the 20 subcellular compartments, we construct posi- tive pairs (i.e., pairing protein samples that localize to the same compartment), and positive-negative negative pairs (i.e,, pairing one protein that localizes the compartment with another that does not). On top of the original loss function, we incorporate contrastive loss to encourage higher embedding simi- larity for positive pairs while enforcing lower similarity for positive-negative pairs.
We provide formal mathematical description of how the contrastive loss is incorporated. Let h denote the protein-level representations from model (i.e., average embedding obtained before MLP classifier). For each of the 20 independent classification tasks, compute the cosine similarity between representations of positive-positive pairs and resitive-negative pairs, and construct the contrastive loss accordingly. For class {1..... 20). let
P. {i Vt.c = 1) denote the set of positive samples, * .V = {i Vi.c 0} denote the negative samples, * h, denotes the protein-level embedding of the sample i.
The positive-positive similarity matrix is
cos(2i, S (+++) cos (2i, 2z), i,j E Pa.
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We encourage positive samples to be close to each other by minimizing
c(+++)
Σ
s(tit),
PPl S.EEPo
The nesitiee similarity matrix is
(z, 2z), i Pa.j E No.
We encourage positive and negative samples to be dissimilar by minimizing
iPll Wo
SEPoJEN.
Thus, the contrastive loss for class is
Coontrast
Cl++t) + C(+-)),
Finally, the overall contrastive loss averaged over all classes is
1
C Coontrast
contrast
C
ESM-C+CDConv Fusion Model. In the early fusion model, the structural representations pro- duced by CDConv (without the additional Transformer architecture introduced by our paper) for each amino acid are added to the initial protein embedding of ESM-C. The combined representation then passed through the pretrained ESM-C Transformer for interaction, followed by mean pool- ing to obtain a protein-level representation for downstream classification. The late fusion setting is similar, except that the structural representations from CDConv are added to the final sequence representation produced by ESM-C before mean pooling for the downstream classification task.
C.3 HYPERPARAMETER SETTINGS
For all the experiments, we choose the best hyperparameters according to the best micro F1-score on the test set.
For the main experiment, the best hyperparameter setting for each model is as follows: 1) ESM-2 (650M), the MLP hidden layers are set to (512,256), and learning rate to 10-4, 2) ESM- C (600M), the MLP hidden layers are set to (512,256) (to (512) when finetuning), and learning rate 10-4 3) FoldSeek, the embedding dimensions are set to 256, transformer layers to 2, transformer heads to 4, and learning rate to 1 x 10-4. 4) CDConv, the kernel channels are set to 24, feature channels to (256,512), geometric adjacency to 4Aand SA(gradually increase with the convolutional layers), sequential adjacency to 5, sequential kernel size to 5, transformer layers to 3, transformer heads to 2, and learning rate to 5 x 10 44 5) GearNet-Edge. the max sequence length set to 3,000, spatial adjacency set to [ 10A], KNN adjacency set [5,10], sequential adjacency set to 2, convolution hidden dimensions to (512 (512.512,512), transformer layers to 2, transformer heads and learning rate to 5 6) Graph Transformer, the transformer layers are set to 10, geometric adjacency to 10Å, sequential adjacency to 5, node dimensions set to 256, positional embedding dimension set to 8, and learning rate
10-5, 7) Graph Mamba, the Mamba layers are set to 5, geometric adjacency to 10A, sequential adjacency to 5, node dimensions set to 256, and learning rate set geometric adjacency to 10A, sequential adjacency to 5, timesteps set to 200, variance of Gaussian noise set to x 10-1 in the beginning and 0.02 in the end, weight for diffusion loss to 0. compared with classification loss, and learning rate to 5 x 10-4, 9) CDCony with Contrastive Loss, the weight for contrastive loss is set to 0.1 compared with classification loss, and other settings are the same the originial CDConv. 10) ESM-C+CDCon Fusion Model, the learning rate is set to 5 x 10-4 for early fusion and to x 10 for late fusion, and other settings are the same as the originial ESM-C and CDConv.
10-4 8) Graph Diffusion, the node dimensions are set to 64,
For the reweighting strategy, we inherit the optimal hyperparameter settings for ESM-C (600M), CDCony, and GearNet-Fdge mentioned above. The best reweighting scheme for each model is as
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follows: 1) ESM-C (600M), focal loss with set to the weights of log- ing-ineesse frequency, and yy set to 1.0. 2) CDConv, focal loss with a set to the weights of log-inverse frequency, and y set to 3.0. 3) GearNet-Edge. inverse frequency reweighting.
For the single-label classification strategy, we inherit the optimal hyperparameter setting for ESM-C (600M), CDConv, and GearNet- Edge mentioned above. To address class imbalance, undersam- ple the negative class to achieve a 1:3 Seattve-to-negativv sample ratio for ESM-C (600M), and a 1:1 positive-to-negatiie sample ratio for CDConv.
D DETAILED BASELINE RESULTS
Detailed experimental results of main experiments, reweighting strategy, and single-label classifica- tion strategy are provided in Tables 10, 11, and 12, respectively. They include evaluation metrics of precision, recall, and Fl-score.
E ABLATION STUDY
Although it has been recognized in the biological community that many patterns of subcellular localization cannot be fully captured by simple sequence information, we aim to investigate the potential benefits of incorporating protein structural information as input for prediction. Therefore, we conduct an ablation study on two representative structure-bast based baselines quantify the positive impact of 3D information incorporated.
Specifically, to preserve the integrity of the model input, we performed preprocessing on the pro- tein structural data. For each protein, we obtained the boundary values of its 3D coordinates and uniformly sampled the Ca coordinates at random within these boundaries to generate new protein structures. The ID sequence data were kept unchanged, while the randomly sampled structures were used as the 3D structural input. Using the same hyperparameter settings as in the main experiments, conducted ablation study, with the detailed results shown in Table 13. We observed signif- icant performance drop in this setting, which further demonstrates the decisive role of accurate 3D structural input in enabling correct model predictions.
F EXPLANATION AND ILLUSTRATIVE EXAMPLES OF EVIDENCE-LEVEL ANNOTATIONS
F.1 EXPLANATION OF EVIDENCE- ANNOTATIONS
The evidence-level annotations design was originally intended to allow researchers to flexibly se- lect annotations based on their specific use cases. For instance, when the task is rigorous and re- quires high precision such as Nucleolar retention motifs discovery, selecting annotations with high confidence (i.e., choosing the experimentally validated annotations only) is more appropriate. Con- versely, for large-scale protein localization prediction, using lower-confidence but more abundant annotations (i.e., choosing both the non-experimentally validated and non-experimentally validated annotations) enriches the training data and leads better model performance.
F.2 ILLUSTRATIVE EXAMPLES OF THREE STRATEGIES TOWARDS NON-EXPERIMENTALLY VALIDATED ANNOTATIONS
In our main experiments, all non-experimentally validated annotations were treated as positive sam- ples to enhance the models' performance in high-throughput prediction settings. Here we present two illustrative examples of the flexible usages of evidence-level annotations: 1) weighting labels (i.e., treating non-experimentally validated annotations as positive samples, but assigning a weight of 0.7 to them relative to experimental ones, which reduces the weight of non-experimental data in influencing the model) and 2) filtering labels (i.e., treating non-experimentally validated annota- tions as negative samples, which restricts models learning to experimental data with high reliability). The results compared with the original one in our paper in Table 14.
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Table 10: Detailed performance of sequence-based and structure- based methods on CAPSUL.
Suhcellular
DeepLoc 2.1 Recall
ESM-2 650M
ESM-2 650M
Locations
Precision
Fi-Score
Precision
Recall
F1-Score
Precision
Recall
Fi-Score
Nucious
0.675
0.086
0.152
0.633
0.586
0.609
Nuclear Membrane
Naclooli
Nucleoplasm
0.592
0.535
0.562
Cytoplasm
0,510
0.100
0,167
0.598
0.157
0,248
Cytosol
Cytoskeloton
0.200
0.003
0.006
Controsome
/
Mitochondria
0.799
0.065
0.120
0.850
0.195
0.317
Endoplasmic Reticulum
0.581
0.067
Golgi Apparatus
0.594
0.032
0.061
Cell Membrane
0.740
0.078
0.142
0.722
0.451
0.555
Endosome
Lipid Droplet
Lysusome/Vacuole Vacuole
0.198
0.084
0.118
Peroxisome
0.667
0.073
0.131
Vesicle
Primary Cilium
/
Secreted Proteins
0.773
0.109
0.191
0.742
0.686
0.713
Sperm
Micro
0.647
0.264
0.375
Macro Avg
0.217
0.131
0,150
Subeellular
ESM-C 600M
ESM-C 600M
ESM-C 600M"
Locations
Precision
Recall
Fl-Score
Precision 0.708
Recall
Fl-Score
Precision
Recall
FI-Score
Nucleus
0.694
0.609
0.649
0.597
0.648
0.626
0.498
0.555
Nuclear Membrane
Nacleoli
0.800
0.048
0.091
1.000
0.020
0.039
1,000
0.012
0.024
Nucleoplasm
0.679
0.573
0.621
0.686
0.570
0.623
0.620
0.418
0,500
Cytoplasm
0.611
0.477
0.614
0.499
0.551
0.438
Cylosol
0,541
0.307
0.392
0.567
0,286
0,380
0.456
0.104
0.169
Cytoskeleten
0.681
0.154
0.251
0.629
0.123
0.205
0.471
0.025
0.048
Centrosome
Mitochondria
0,865
0.416
0.562
0,903
0.389
0.544
0.667
0.053
0.099
Fiddopasmicc Reticulum
0.687
0.235
0.351
0.674
0.221
0,333
0.500
0.031
0.059
Golgi Apparatus
0,938
0.052
0.099
1,000
0.014
0.027
Cell Membrane
0.777
0,531
0.631
0.753
0.648
0.786
0.243
0.372
Endosome
1,000
0.009
0.018
Lipid Druplet
Pesssome/aacuoli
Peroxisome
Vesicle
1.000
0.005
0.009
1,000
0.002
0.005
Primary Cilium
0.556
Secreted Proteins
0.903
0.761
0.826
0.877
0.730
0.797
0.604
0.338
0.433
Sperm
0.500
0.028
0.052
0.667
0.037
0.070
Micro Avg
0.690
0.386
0.495
0.693
0.382
0.492
0.598
0.236
0.338
Macro Avg
0.482
0.249
0.135
Subcellular
FoldSeek
CDCom
GearNet Fdgee
Locations
Precision
Recall
F1-Score
Precision
Recall
FI-Score
Precision
Recall
FI FI-Score
Nocleus
0.616
0.398
0.484
0.651
0.592
0.620
0.619
0.450
0.521
Nuclear Membrane
Nucleoti
0,583
0.147
0.068
0.121
Nacleoplasm
0.591
0.341
0.433
0.633
0.541
0.583
0.613
0.444
0.515
Cytoplasm
0,581
0.102
0.174
0,580
0.414
0.483
0.498
0.491
0.495
Cytosol
0.500
0.489
0.353
0.417
0.358
0,385
Cytoskeleton
0.480
0.038
0.070
0.649
0.075
0.135
0.296
0.186
0.228
Centrosome
0,228
0.088
0.127
Mitochondria
0.707
0.476
0.470
0,318
Endoplasmic Reticulum
0.441
0.292
0.475
0,279
Golgi Apparatus
0.733
0.038
0.073
0,211
0.014
0.026
Cell Membrane
0.626
0.237
0.343
0.721
0.461
0.562
0.708
0.457
0.556
Endosome
0.364
0.067
Lipid Droplet
Lysosomz/Vacuole
0.429
0.040
0.073
Peroxisome
Vesicle
0.667
0.014
0.027
0.270
0.039
0.068
Primary Cilium
0.467
0.087
0.147
Secreted Proteins
0.600
0.225
0.795
0.767
0.722
0.655
0.687
Sperm
0.714
0.086
Micro Avg
0.605
0.156
0.248
0.632
0.352
0.452
0.546
0.337
0.417
Macro Avg
0.200
0.067
0.092
0.382
0.191
0.226
0.402
0.195
0.235
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Table 10: (Continued) Detailed performance of requence-based and cructure-bbsed methods on
CAPSUL.
Suhcellular
Graph Transformer
Graph Mamha
Graph Diffusion
Locations
Precision
Recall
F1-Score
Precision
Recall
FI-Scure
Precision
Recall
FI-Score
Nucleus
0.664
0.559
0.624
Noclear Membrane
0.061
0.026
0.037
Nucleoli
0,554
0.124
0.203
0.433
0.104
0.168
0.667
0.024
0.047
Nuclooplasm
0.642
0.483
0.552
0,526
0.481
0,502
0.590
0.566
0,578
Cytoplasm
0,552
0.305
0.393
0.476
0.373
0,418
0.542
0.469
0,503
Cylosul
0.457
0.170
0,248
0.421
0.431
0.426
0.440
0.214
0,288
Cytoskeleton
0.538
0.022
0.042
0.249
0.296
0,270
0.383
0.057
0.099
Centrosome
0.014
Mitochondria
0.688
0.363
0.475
0.407
0.294
0.341
0,680
0.195
0.303
Endoplasmic Reticulum
0.552
0.111
0.184
0.529
0.031
0.059
0.556
0.087
0.150
Golgi Apparalus
Cell Membrane
0.718
0.442
0.547
0.417
0.766
0,540
0.737
0.373
0.496
Endosome
0.125
0.083
0.100
Lipid Droplet
Jysnsome/Vacuole
Peroxisome
Vesicle
0.526
0.023
0.044
0.306
0.086
0,135
0.667
0.009
0.018
Primary Cilium
0.205
0.088
Secreted Proteins
0.767
0.652
0,705
0.426
0.802
0.557
0.738
0.539
0.623
Sperm
1.000
0.009
0.018
0.116
0.147
0.130
Micro Avg
0.637
0.302
0.410
0.414
0.408
0.411
0.596
0.329
0.424
Macro Avg
0.451
0.164
0,203
0,279
0.252
0,235
0.381
0.159
0.187
Subcellular
CDCcny with Contrastive Loss
C+CCCom Early Fusion
JSM.CKDDCR Late Fusion
Locations
Precision
Recall
FI-Score
Precision
Recall
F1-Score
Precision
Recall
F1-Score
Nucleus
0.681
0.524
0.592
0.712
0.587
0.643
0,701
0.598
0.645
Memhrane
Nucleoli
0.556
0.080
0,140
0.739
0.068
0.125
0.210
0.120
0.153
Nuclooplasm
0.646
0.487
0.556
0.704
0.591
0.643
0.676
0.567
0.617
Cytoplasm
0.515
Cytosol
0,516
0.165
0.250
0,487
0.094
0.157
0.467
0.306
0.370
Cytoskeleton
0.473
0.164
0.243
0.773
0.053
0.100
0.412
0.220
0,287
Centrosome
0.231
0.020
0.037
Mitochondria
0.754
0.340
0.468
0.872
0.416
0.563
0.827
0.420
0.557
Endoplasmic Reticulom
0.519
0.277
0,361
0.595
0.356
0.446
Golgi Apparatus
0.553
0.091
0.156
0,462
0.171
0.249
Cell Membrane
0.673
Endosome
0.250
0.019
0.034
1,000
0.028
0.054
Lipid Droplet
Lysosome//accuse Vacuole
1,000
0.013
0.026
Peroxisome
Vesicle
0.750
0.014
0.027
0.432
0.036
0.067
Primary Cilium
0.500
0.019
0.036
0.255
0.075
0.115
Secreted Proteins
0.797
0.765
0.905
0.747
0.819
0,908
0.604
0.725
Sperm
1.000
0.009
0.018
Micro Avg
0.650
0.326
0.435
0.710
0.351
0.470
0.634
0.381
0.476
Macro Avg
0.467
0.189
0.507
0.202
0.249
0.300
0.201
0.235
WWe pre pre-trained protein language 'The original replaced by layess "The parameters initialized randomly. indicates that Deepl.oc 2.1 does nox support prediction for that location, and therefore, average metrics are not considered this case. indicates that no prediction
made for that location.
23

---

Published as a conference paper at ICLR 2026
Table 11: Detailed performance of selected baselines with reweighting scheme.
Subcellular
ESM C 600M
CDConv
GearNet. Fdge''
Locations
Precision
Recall
F1-Soere
Precision
Recall
F1-Score
Precision
Recall
F1-Score
Nucleus
0.698
0.575
0.630
0.481
0.892
0.625
0.484
0.856
0.618
Nuclear Membrane
0.566
0.062
0.079
0.058
Nucleoli
0.105
0.916
0.188
0.153
0.418
0.224
Nucleoplasm
0.679
0.500
0,576
0.469
0.859
0.607
0.436
0.841
0,574
Cytoplasm
0.568
0.446
0,500
0.450
0.823
0,582
0.441
0.711
0,544
Cytosol
0.513
0.076
0.135
0.353
0.829
0,495
0.366
0.714
0,484
Cytoskelcton
0,778
0.044
0,083
0,184
0.698
0.292
0.218
0.450
0.294
Controsom
0.089
0.776
0,160
0.134
0.252
0.175
Mitochondria
0.846
0.336
0.481
0.297
0.313
Endoplasmic
0.195
0.737
0.308
0.276
0.460
0.345
Reticulum
Golgi Apparatus
0,246
0.366
0.238
Cell Membrane
0.723
0.465
0.566
0,462
0.709
0.560
0.398
0.820
0.536
Endosome
0.067
0.407
0,114
0.177
0.130
0.150
Lipid Droplet
1,000
0.133
0,235
0.014
0.067
0,023
0.333
0.067
0.111
Lysosome/Vacuoke
0.117
0.347
0.175
0.116
0.107
0.111
Peroxisome
1,000
0.105
0.190
0,040
0.421
0.072
0.111
0,105
0.108
Vesicle
0.198
0.532
0.288
0.206
0.445
0.281
Primary Cilium
0.667
0.012
0,024
0.167
0.176
Secreted Proteins
0.833
0.730
0.778
0.413
0.891
0,564
0.509
0.775
0.614
Sperm
0.066
0.679
0.120
0.109
0.147
0.125
Micro Avg
0.679
0.313
0.429
0.253
0.772
0,381
0.348
0.650
0.453
Avg
0.415
0.171
0,210
0.209
0.655
0.197
0.304
The original MLP is replaced by Transfommrr layass.
indicates that no prodiction is made for that location.
Table 12: Detailed performance of selected baselines with single-label classification strategy.
Suhcellular
ESM-C 600M
CDConv
GearNet-Edge
Locations
Precision
Recall
F1-Score
Precision
Recall
F1-Score
Precision
Recall
F1-Score
Nuclear Membrane
0.027
0.711
0,052
0.026
0.118
0,042
Nucleoli
0.251
0.285
0.267
0.082
0.992
0.151
0.151
0.470
0.228
Centrosome
0.361
0.184
0,089
0.167
Golgi Apparatus
0.293
0.268
0.280
0.080
0,199
0.114
0.161
0,303
0.210
Endosome
0.111
0.333
0,167
0.029
0.176
0,049
0.082
0.278
0.126
Lipid Droplet
0.011
0.200
0.02]
0.032
0.135
0.051
lysnsome/Vacunle
0.075
0.253
0.115
0.097
0.493
0.162
Peroxisome
0.029
0.526
0,054
0.013
0,158
0.023
Vesicle
0,270
0.039
0.068
0.141
0.625
0,230
0.207
0.380
0,268
Primary Cilium
0.175
0.460
0.253
0.055
0.379
0.097
0.171
Spern
0.121
0.229
0.159
0.045
0,138
0,068
0.077
0.239
0.117
TThe original MLP replaced by Transformer layers, "." indicatess prediction made location.
Table 13: Detailed performance comparison of CDConv and GearNet-Edge under random sampling of Ca coordinates.
Subeelllalae
CDCuu
CDCann'
Na--agg
GeurNer-Balee
Precision 0.651
Precision
Fi--Score
Nuclens
0.303
0.512
0.550
0.502
(1633)
01A50
01483
0.619
Nuclear Memzhaae
Nacieoli
0.417
0.000
0.038
0.583
0.084
0.167
0.268
0.076
0.119
0,531
0.068
0.121
Nucleoplasm
0.578
0.419
0.485
0.633
0.541
0.583
0.479
0.425
0.452
0,613
10444
0.515
Cymplasm
0.535
0.214
0.306
0.580
0.414
0.483
0.433
0.414
01422
0.498
D4491
0.495
Cynoal
3.478
0.100
0.489
0.229
0.327
0.417
11358
Cynsseeltton
Canmosome
0.092
0.228
0.085
Misochoddna
0357
0,084
0.145
0.207
0.359
0.476
0.283
0,065
0.106
0,570
0.240
0.318
Enkoplasiic
3U625
0,017
0034
0.441
0.218
0.292
0.521
0.062
0.104
0.455
1.197
0.279
Reticulusa
Golci Apparotus
0.733
0.088
0.073
0.132
0,017
0.03L
0,211
0.014
0.026
Call Mumbanc
3.621
0.435
0.505
0.221
0.461
0.562
0.995
0.413
04437
0,708
D447
0.556
Endosome
0,364
0.077
0.067
Eipid Dropie
Lysosomte
0.429
Paosiienn
Vesile
0.667
0.014
0.027
0.185
0,077
0.109
0,270
1U039
0.063
Primsry Cilium
0. 168
0:043
0.078
0.467
0.087
0.147
Secreted Proteins
0,203
0,218
0,333
0.795
0,741
0.767
0.515
0,232
0.320
0.722
0.655
0.687
Spern
0.125
0.009
0.017
0.714
0.066
0.086
Ang
0.099
0.125
0.352
0.191
0.138
0.166
0.402
TThe original replaced by Transformer layers *_" indicates prodiction made location.
24

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Published as a conference paper at ICLR 2026
Table 14: Detailed performance of two illustrative examples of evidence-level annotations: weight-
ing labels and filtering labels.
Subcellular
ESM-C
ESM-C 600M (weighting)
ESM-C (illering)
Locations
Precision
Rocall
FI-Score
Presision
Recall
FI-Score
Precision
Recall
Fi-Score
Noctous
U.694
0.609
0.649
0.717
0.383
0.645
0.703
0.375
0.635
Nuclear Membrane
Naclooli
0.800
0.048
0.091
0.786
0.088
0.159
0.867
0.055
0.104
Nucleoplasm
0.679
0.573
0.621
0.692
0.558
0.618
0.686
0.551
0.611
Cytoplasm
0.611
0.477
0.536
0.619
0.453
0.523
0.572
0.400
0.470
Cytosol
0.541
0.307
0.392
0.534
0.256
0.346
0.590
0.239
0,340
Cytoskeleton
0,681
0.154
0.251
0.649
0.116
0.197
0.381
0.034
0.062
Controsome
1,000
0.007
0.014
1.000
0.007
0.014
Mitochondria
0.416
0,907
0,530
0.798
0.373
0.508
Endoplasmiic Reticulum
0.687
0.235
0.351
0.726
0.156
0,256
0.500
0.039
0.072
Golgi Apparatus
0,938
0.052
0.099
1,000
0.010
0.021
Cell Membrane
0.777
0.531
0.631
0.757
0.570
0.650
0.661
0.254
0.367
Endosome
1.000
0.009
0.018
Lipid Droplet
Lysosome/Vaccee Vacuole
Peroxisome
Vesicle
1.000
0.005
0,009
1.000
0,005
0.009
Primary Cilium
0.682
0.093
0.164
0,538
0.043
0.080
1,000
0.008
0.016
Secreted Proteins
0
0,920
Sperm
0,500
0.028
0,052
0.800
0,037
0.070
0.500
0,010
0.019
Micro Avg
0.690
0.386
0.495
0,700
0.366
0.481
0.657
0.306
0.418
Macro Avg
0.618
0.215
0.263
0.582
0.196
0.245
0.363
0.127
0.160
Subcellular
CDCane
CDCony (weighting)
CDConv (fllterin))
Locations
Precision
Recall
F1-Scure
Precision
Recall
F1-Scure
Precision
Fi--Scure
Nucleus
0,651
0.592
0.620
0.674
0.539
0.599
0.644
0.530
0.581
Nuclear Memhrane
Nucleoli
0.583
0.084
0.147
0.556
0.020
0.039
0.484
0.640
0.112
Nuclooplasm
0.633
0,541
0.583
0.657
0.566
0.652
0.424
0,514
Cytoplasm
0,580
0.414
0.483
0.557
0.469
0.509
0,528
0.445
0.483
Cytosll
0.489
0.277
0.353
0.470
0.293
0.361
0.468
0.337
0.392
Cyteskeleton
0.649
0.075
0.714
0.400
0.016
Centrosome
Mitochondria
0,707
0.359
0.476
0.762
0.355
0.484
0.588
0.363
0,449
Endoplasmic Reticulum
0.441
0.218
0.292
0,561
0.159
0.248
0.278
0.024
0.045
Golgi Apparatus
0.733
0.038
0.073
0.615
0.028
0.053
Call Membrane
0.721
0.461
0.562
0.723
0.447
0,553
0.562
0.194
0,288
Endosome
Lipid Droplet
Lysosome/Vacuolee
Peroxisome
Vesicle
0.667
0.014
0.027
Primary Cilium
0.333
0.006
0.012
Socreted Proteins
0.795
0.741
0.767
0.857
0.573
0.687
0.400
0.044
0.079
Sperm
Micro Avg
0.632
0.352
0.637
0,386
Macro Avg
0.382
0.191
0,226
0.374
0.173
0.211
0.250
0.122
0.148
Subcellular
GeurNet-Edge
GegnNetEdgee (weighting)
GearNet-Edge Edge (fittering
Locations
Precision
Recall
F1-Score
Precision 0.622
Recall
F1-Score
Precision
Recall
FI-Score
Nucious
0.619
0.450
0.321
0.337
0.437
0.607
0.478
0.335
Nuclear Membrane
Nacleoli
0.531
0.068
0.121
0.421
0.032
0.060
0.333
0.064
0.107
Nucleoplasm
0.613
0.444
0.515
0.618
0.326
0.427
0.576
0.453
0.507
Cytoplasm
0.498
0.491
0.473
0.469
0.465
Cytosol
0.417
0.358
0.385
0.424
0.339
0.377
0,394
0.403
0.398
Cytoskeleton
0.296
0.186
0.228
0,342
0.167
0.224
0.284
0.105
0.153
Centrosome
0.228
0.088
0.127
0.213
0.068
0.103
0.200
0.054
0.085
Mitochondria
0,470
0.240
0.318
0.667
0.176
0.278
0.508
0.142
0.221
Endoplasmic Reticulum
0.475
0.197
0.279
0.435
0.128
0.198
0,268
0.073
0.115
Golgi Apparatus
0
0.014
0.026
0,211
0.014
0.026
0.250
0.004
0.008
Cell Membrane
0.708
0.457
0.629
0.483
0.463
0.248
Endosome
0,364
0.037
0.067
0.333
0.028
0.051
0,500
0.029
0.056
Lipid Druplet
Lysosome//acuuee
0.429
0.040
0.073
0.125
0.024
Peroxisome
Vesicle
0.270
0.039
0.068
0,268
0.093
0.138
0.280
0.055
0.092
Primary Cilium
0.467
0.087
0.524
0.121
0.364
0.031
0.058
Secreted Proteins
0.722
0.655
0.687
0.826
0.519
0.637
0.273
0.066
0.106
Sperm
0.714
0.046
0.086
0.667
0.037
0.070
0.500
0.020
0.038
Micro Avg
0,546
0.337
0.417
0.529
0.282
0,368
0.485
0.300
0.371
Macro Avg
0.402
0.195
0.390
0.160
0.206
0.313
0.131
0.160
The original MLP is replaced by Transormer layes, indicates that no prediction is made for that location.
25

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Published as a conference paper at ICLR 2026
Table 15: Detailed performance of different weights for non-experimentally validated annotations
on ESM-C.
Subcellular
ESM-C weighting
ESM-C weighting
ESM-C weighting 0.5
Locations
Precision
Recall
FI-Score
Presision
Recall
FI-Score
Precision
Recall
FI-Score
Noclous
0,733
0.349
0.628
0.73
0.553
0.629
0.728
0.564
0.636
Nuctear Membrane
Nucleoli
0.789
0.060
0.112
0.792
0.076
0.139
0.824
0.056
0.105
Nucleoplasm
0,708
0.518
0.598
0.714
0.519
0.601
0.710
0.522
0.602
Cytoplasm
0.605
0.511
0.554
0,592
0.506
0.546
0,600
0.505
0,548
Cylosll
0.530
0.350
0.518
0.437
Cytoskeleson
0.636
0.110
0.188
0.587
0.116
0.194
0.618
0.107
0.182
Controsome
1,000
0.007
0.014
Mituchondria
0.382
0.523
Endoplasmic Reticulum
0.750
0.114
0.198
0.698
0.128
0.216
0.696
0.166
0.268
Golgi Apparatus
1,000
0.003
0,007
Coll Membrane
0.771
0.367
0.497
0.768
0.367
0.496
0.791
0.442
0.567
Endosome
Lipid Druplet
Lysosome/ Vacuole
Peroxisome
Vesicle
1,000
0.005
0.009
Primary Cilium
0.583
0.043
0.081
0.647
0.068
0,124
0.625
0.031
0.059
Secreted Proteins
0,936
0.632
Sperm
0,167
0.009
0.017
0.375
0,028
0.051
0.667
0.018
0.036
Micro Avg
0.687
0.338
0.453
0.682
0.338
0.452
0.685
0.353
0.466
Macro Avg
0.406
0.174
0.223
0.414
0.176
0.227
0.582
0.182
0,231
Subcellular
ESM-C weighting
ESM-C weighting 0.9
Locations
Precision
Recall
F1-Score
Precision
Recall
H1-Score
Nucleus
0.717
0.585
0.645
0.711
0.578
0.638
Nuclear Membrane
Nucleoli
0.786
0.159
0.833
0.147
Nucleoplasm
0.692
0.558
0.618
0.699
0.548
0.614
Cytoplasm
0.619
0.453
0.523
0.596
0.517
0,554
Cytosol
0.534
0.256
0.346
0,517
0.372
0.432
Cytoskeloton
0.649
0.116
0.197
0.646
0.219
Centrosome
1.000
0.007
0.014
1,000
0.007
0.014
Mitochondria
0,907
0.374
0.530
0.899
0.374
0.528
Endoplasmic Reticulum
Golgi Apparatus
1,000
0.010
0.021
0.909
0.035
0.067
Call Membrane
0.757
0,570
0.650
0.759
0.540
0.631
0.667
0.019
0.036
Lipid Droplet
L.yssooe/Vacuole
Peroxisome
Vesicle
1.000
0.005
0.009
1,000
0.007
0.014
Primary Cilium
0,538
0.043
0.080
0.571
0.050
0.091
Secreted Proteins
0.920
0.669
0.775
0.907
0.734
0.811
Sperm
0.750
0,053
Micro
0.700
0.366
0.481
0.683
0.388
0.495
Macro Avg
0,582
0.196
0.245
0.607
0.213
0.260
indicatos that no prodiction is made for that Incotion.
As shown the table, models that treat non-experimentally validated annotations as positive sam- ples generally achieve the best overall performance. This may be because many non-validated anno- tations also originate from reliable sources (e.g., the "ECO:000030"" code in the UniProt database indicates that the localization information is extracted from published literature); thus, they still hold relatively high credibility. This also demonstrates that for large-scale deep learning training, as our work does, including such annotations can increase sample diversity and improve data richness, and therefore, improve models' performance.
F.3 ANALYSIS OF DIFFERENT WEIGHTS FOR NON-EPPERIMYNTALLY VALIDATED ANNOTATIONS
To have a comprehensive analysis of the effect evidence level, we further extend the weighting- label strategy by setting different weights to samples with non-experimentally validated annotations. We report the results in Table 15 and 16.
From the results, have the following observation:
26

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Published as a conference paper at ICLR 2026
Table 16: Detailed performance of different weights for non-experimentally validated annotations
on CDConv.
Subcellular
CDCons weighting
CDCons weighting
weighting
Locations
Precision
Recall
F1-Score
Precision
Recall
FI-Score
Precision
Recall
FI-Score
Nocleus
0.640
0.646
0.643
0.616
0.662
0.638
0.671
0.543
0.600
Nuclear Membrane
Nuclooli
0.652
0.060
0.471
0.160
0.667
0.016
0.031
Nuclcoplasm
0,614
0.533
0.571
0.596
0.600
0.598
0.640
0.509
0.567
Cytoplasm
0.594
0.450
0.512
0.592
0.398
0.476
0.558
0.514
0.535
Cylosll
0.493
0.322
0,399
Cytoskeleton
0.750
0.009
0.019
0.818
0.028
0.055
0.619
0.041
0,077
Centrosome
Mitochondria
0.728
0.347
0.470
0,708
0.370
0.486
0.703
0.344
0,462
Enddopaasiic Reticulum
0.559
0.131
0.213
0.475
0.131
0,206
0.540
0.163
0.250
Golgi Apparatus
0.750
0.010
0,021
Cell Membrane
0.762
0.333
0.463
0.732
0.413
0.528
0.753
0.445
0.560
Endosome
Lipid Droplet
Lyysssoe//aaceee Vacculee
Peroxisome
Vesicle
Primary Cilium
1.000
0.006
0.012
1,000
0.006
0.012
Socreted Proteins
0.823
0.618
0.835
0.672
0.745
0.868
0.539
0.665
Sperm
Micro Avg
0.631
0.340
0.442
0.617
0.354
0.450
0.629
0.343
0.444
Macro Avg
0
0.173
0.205
0,366
0.183
0,213
0,362
0.174
0.208
Subcellular
CDCon weighting 0.7
CDCon weighting 0.9
Locations
Precision
Recall
F1-Score
Precision
Recall
FI-Score
Nucleus
0.674
0.339
0.599
0.682
0.523
0.592
Nuclear Memhrane
Nacleoli
0.556
0.020
0.039
0.750
0.012
0.024
Nucleoplasm
0.657
0.497
0.669
Cytoplasm
0.557
0.469
0,509
0.551
0.571
0.561
Cytosol
0.470
0.293
0.361
0.470
0.398
0.431
Cyloskeleton
Centrosome
Mitochondria
0.762
0.355
0.484
0.707
0.332
0.452
Endoplasmic Reticulom
0.561
0.159
0.248
0,481
0.173
0.254
Golgi Apparatus
0.615
0.028
0.053
0,588
0.035
0.066
Call Memnhraee
0.723
0.447
0.553
0.725
0.493
0.587
Frdossmme
Lipid Druplet
Lysosome/Vacuole
Peroxisome
Vesicle
Primary Cilium
0.333
0.006
0.012
0.333
0.006
0.012
Secreted Proteins
0.857
0.573
0.687
0.845
0.597
0.700
Sperm
Micro Avg
0.637
0.333
0.625
0.456
Macro Avg
0.374
0.173
0,211
0.364
0.185
0.218
original MLP replaced by layers "." indicatees prediction made location.
Table 17: Analysis of precision and recall on Nucleus on ESM-C,
ESM-C BSM--Cweighe weighting
0.3
0.5
0.7
Treat Poxitive
Precision for Nucleus
0.733
0.730
0.728
0.717
0,711
0.694
Recall for Nucleus
0.549
0.553
0.564
0.585
0.578
0.609
1) For certain subcellular compartments, baselines trained exclusively on experimentally validated annotations assigned low positive weights to non-experimentally validated annotations generally achieve higher precision, as shown in Table 17. Actually, precision and recall often represent trade-off in modeling strategies. That is, adopting more conservative prediction strategy typically increases precision but reduces the number of correctly recalled samples, and vice versa. Therefore, selecting nigh-confidence evidence levels can be seen method of enforcing more conser- vative prediction approach, helping to reduce the likelihood of false-positive predictions. This highlights the novelty of evidence-level annotations: using experimentally validated data is con- sidered a strategy to ensure high precision and reliability.
2) However, for overall results and most subcellular compartments, the results among the three strategies show no significant differences. This indirectly supports the notion we mentioned above that even non-validated annotations in CA still possess relatively high reliability. This
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Table 18: Detailed performance of hierarchical classifiers on CDConv.
CDCoav' Hierarchical Classifiers
Subvellular Locations
Procision
Recall
Fl Fi-Seure
Subcellular Locations
Precision
Recall
Fi-Score
Nacleus's classilier
Cytoplasm'ss classifier
Nuclear Membeune
Cytosal
0.260
0.980
0.411
Nucleoli
Cytoskoleton
Nucleoplasm
0.339
1.000
0.507
original MIP replaced by Transfommer layars, "L" indicates that no prodiction is made for that location.
Table 19: Analysis of performance of three pLDDT groups on CDConv.
Subcellular
High PLDDT Group
Medium DLDDT
Group
Low PLDDT Group
Locations
Procision
Rocall
Fi-Score
Precision
Rocall
FI-Score
Precision
Rocall
FI-Score
Nacleus
0.561
0.393
0.462
0.636
0.570
0.601
0.700
0.742
0.720
Nuclear Membrane
Nuclooli
0.667
0.082
0.146
0.632
0.136
0.224
0.375
0.034
0.062
Nucleoplasm
0,512
0.310
0,387
0.575
0.497
0,533
0.713
0.717
0,715
Cytoplasm
0.601
0.513
0,553
0.578
0,422
0,488
0.546
0,298
0,385
Cytnsal
0.520
0.451
0,485
0.467
0.241
0,318
0.414
0.112
0.177
Cytoskeleton
0.833
0.060
0,112
0.577
0,128
0,210
0.800
0.034
0,065
Centrosome
Mitochondria
0.475
0,593
0.322
0,430
0.167
0.254
Endoplasmicc
0.506
0.344
0.409
0,410
0.152
0.222
0.176
0.054
0.082
Reticulum
Golgi Apparatus
0.057
0.107
0.048
0,089
Cell Membrane
0.806
0.463
0,588
0.743
0,581
0.653
0.530
0.272
0.360
Endosome
Lipid Droplet
Lysosome/Vacunle
Peroxisome
0.333
0.006
0.012
Vesicle
0.833
0.035
0.068
Primary Cilium
0.856
0.720
0.782
Secreted Proteins
0,746
0.775
0.760
0.816
0.702
0.755
Sperm
Micro Avg
0.617
0.345
0.445
0.628
0.345
0.446
0.651
0.367
0.469
Macro Avg
0,233
0.191
0.228
0.179
indicates that prediction made for that location.
ensures that, when CAPSUL is used for downstream tasks, the inclusion of non-experimentally validated annotations does not introduce substantial bias.
G ATTEMPT TOWARD HIERARCHICAL CLASSIFIERS FOR NESTED CATEGORIES
In order make precise predictions of nested subcategories, attempt train hierarchical classifier for the two nested categories, Nucleus and Cytoplasm. These classifiers were built upon CDConv, using positive samples of the corresponding parent categories as the training set. We report the experimental results in Table 18.
We have the following observations:
1) For the three subcategories within the nucleus, the hierarchical classifier actually leads to de- creased performance compared with the original CDConv. We attribute this the reduced number of training samples when training this hierarchical classifier, which, combined with the already se- vere class imbalance in these subcategories, likely exacerbated the issue. Considering the results discussed in Section 4.3 of the main text, we find that for such highly imbalanced subcellular local- ization tasks, directly applying a single-label classification strategy, while adjusting the correspond- ing reweighting and classification thresholds, yields more significant improvements.
2) For the two subcategories within the cytoplasm, classification performance greatly improves, demonstrating that the hierarchical classification strategy be beneficial for certain subcellular compartments. This result provides an additional strategy, hierarchical classifiers, to address the minority class issue apart from what we have discussed in Section 4.3 of the main text.
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H ANALYSIS OF THE PLDDT SCORES OF PROTEIN STRUCTURE INPUT
In AlphaFold-preddene structures, regions with low pLDDT often indicate intrinsically disordered segments, which naturally lack stable tertiary structure and are therefore harder for any predictor to model with high confidence. We conduct an analysis on the representative structure-based model, CDConv, examining the relationship between residue-level pLDDT and model performance across the test set.
Specifically, we divide all 3,028 proteins the test set into three groups of equal size hased on their protein-level mean PLDDTT values. The highest and lowest mean pLDDT values within each group are [83.56, 99.39], [70.40, 83.54], and [28.11, 70.40]. We then compare the performance these groups to determine whether substantial performance discrepancies exist, which would suggest that the model is sensitive to variations in structural confidence. We report the results of three groups in Table 19.
Upon further examination of specific subcellular categories, we find that:
1) For some subcellular locations (e.g., Nucleus, Cytoplasm, Cytosol, and Cell Membrane), the performance differences among the three groups may appear larger. This is probably attributable to the uneven distribution of positive samples across the groups. For example, in the Nucleus category, the high-pLDDT group contains only 318 positive samples, whereas the low-pLDDT group contains 476 positive samples, which contributes to this noticeable discrepancy.
2) For other subcellular locations (e.g, Cytoskeleton and Secreted Proteins), the results across the three groups do not exhibit pronounced differences. This observation is consistent with the trend shown by the overall evaluation metries above, indicating that proteins with different average PLDDT levels have not greaty influenced the prediction of these subcellular localizations.
In summary, the low-pLDDT group does not exhibit worse predictions systematically. This in- dicates that the CAPSUL robust to fluctuations in pLDDT and does not rely disproportionately on regions of high structural confidence. In other words, CAPSUL provides stable and reliable struc- tural inputs for downstream tasks even when proteins contain disordered or low-confidence regions, demonstrating the quality and suitability of our structural dataset for subcellular lo- calization prediction. However, results of particular subcellular compartments highlight the need for further exploration of developing more informative and robust structural representations to better capture the determinants of subcellular localization in future work.
I SAMPLE EFFICIENCY
A sample efficiency curve would explicitly reflect possible strategy to choose the proper size of the training set, which strikes a balance between model performance and computational costs. We randomly select training subsets of varying sizes and evaluate their sample efficiency curve on CDConv in 3. 3.
We observed that the smaller dataset, compared with the original CAPSUL, exhibits noticeably poorer performance, reflected in both lower micro Fl-scores and fewer categories for which any positive samples were successfully predicted. This indicates that, in order to achieve satisfactory overall performance as well as decent performance on minority classes, the current size of training samples is relatively appropriate.
J INTERPRETABILITY WITH ATTENTION SCORE
In Transformer architectures, the attention mechanism allows each token to compute a weighted representation of all other tokens in the sequence. Specifically, for given token, set of attention weights derived via scaled dot-product operations between its query vector and the key vectors of all tokens, followed by a softmax normalization. These attention weights reflect how much in- formation the token attends to from each its peers. To assess the relative importance of each token within the sequence, ming over the attention scores directed toward that token across the entire sequence. This provides global measure of how influential token is in shaping the contextual representations learned by
aggregated the attention it receives from all other tokens, i.e., sum-
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Sample Efficiency Curve
0.5
0.452
14
Successfully Identified Categoriess
0.4
12
Micro Average F1-score
0312
0:304
10
0.3
0.232
0.39
0.2
5
0.1
2
0.000
0.0
128
256
512
1024
2048
4096
8192
14126
Training Set Size
Figure 3: Sample efficiency curve on CDConv.
Attantion score for MPNG
Attention score for BIGALT2
Attendion soore GIMAP1
Figure 4: Visualization of full attention scores and structures of proteins MFNG, B3GALT2, and GIMAP1, where the residues of known pattern a-helix are highlighted.
the model. We interpret this aggregated attention proxy for biological interpretability, where highly attended residues may correspond to structurally or functionally important positions within the protein.
In Section 4.3.3 of the main text, we introduce a CDConv model for predicting Golgi apparatus localization. By analyzing the attention score within the model's Transformer architecture, we iden- tify a localization pattern associated with an a-helix, which consistent with existing biological findings. Here, we visualize the full attention score of the three example proteins discussed in the main text (i.e., MFNG, B3GALT2, and GIMAPI), as shown in Figure 4. The residues of known lo- calization patterns c-helix are highlighted in orange for clear comparison. Notably, the 20 residues with the highest attention scores exhibit a 90% overlap with the ground truth, further highlighting the CDCony model's precision in identifying localization patterns.
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More interpretability results. There two more potential localization patterns identified CAPSUL: 1) "W-pair" for Golgi apparatus (i.e. two spatially adjacent Tryptophan residues), which aligns with existing studies suggesting that Tryptophan can influence Golgi targeting (Ashlin et al., 2021), and 2) a flexible region at the N-terminus of the protein for Mitochondria, which aligns with existing studies suggesting that this ing (Sohn et al., 2009). However, these newly identified patterns still require further experimental validation. Nevertheless, these findings together demonstrate that both our curated dataset and the selected structure- based baseline model are capable of capturing meaningful subcellular localization signals, offering promising insights and directions for future research cell biology.
influence multiple compartments target-
K GENERALIZATION ABILITY
The CAPSUL workflow is readily applicable to other species. While our current study exclu- sively uses human protein data, we do not assert that CAPSUL's pipeline intrinsically limited to human proteins. Our dataset and benchmark construction pipeline, including 1) the acquisition of one-dimensional and three-dimensional protein information, 2) the collection of localization anno- tations, and the comparison of various baselines, is entirely species-agnostic. Once the CAPSUL. pipeline becomes robust and well-received, there can be attempts to extend the same workflow to develop subcellular localization datasets and benchmarks across multiple species in parallel. This extension will not only enable broader biological investigations but also provide valuable resources for studying the evolutionary conservation and divergence of protein localization mechanisms across phylogenetic lineages. By comparing subcellular patterns across species, researchers can gain in- sights into the selective pressures shaping cellular organization, the emergence of organelle-specific functions, and the molecular adaptations that underpin evolutionary innovation.
The CAPSUL workflow applicable context-dependent localization if sufficient related data is accessible. With regard to the context-dependent dynamic localization, current protein databases lack extensive data on protein structures across diverse biological contexts and their cor- responding variations in subcellular localization. If CAPSUL were augmented with dynamic data capturing protein structural changes (e.g.. conformational shifts induced by stressors or ligands) from updated protein databases in the future, the baselines can be retrained on CAPSUL to model context-depender localization. This would enable the models to make precise predictions about subcellular localization under specific cellular conditions or in response external environmental states, moving beyond static localization to capture functional biological dynamics.
The CAPSUL readily extensible to additional protein data and baseline methods in the fu- ture. Our highly standardized pipeline ensures that any newly released protein data can be updated CAPSUL. Also, CAPSUL embraces broader range of other possible protein structural inputs (e.g., which can be used in the construction of graph nodes and edges) to enrich both the local and global representations of proteins. Moreover, the results of more advanced protein representation methods can be included to perform this downstream task if their input data is supported by CAP- SUL, which enables fair and informative comparison across various bascline methods.
L AVAILABILITY OF DATASET AND CODE
The complete dataset, including localization labels, extracted protein structures, etc. can be cessed at https: //hugggiiiieee ingginggffr face plementation is publicly available at httpp:://githtii http///eeeeeeeeeeetae///ttta/ee For some baseline models, we adopt publicly released implementations, including Graph Trans- former at https::t////mcc https://github http////ggteee///t//r/.e.... Graph Mamba at https:://ithub.comm
Our im-
http://eeecccocceeeeeeeeee gww.etriccc/gggeo www.eeetrieeeeeeeeeeeeeeeeeeeeee masterr and
py.
M THE USE OF LARGE LANGUAGE MODELS
In this study, Large Language Models (LLMs) were employed solely for linguistic refinement, such polishing the clarity, grammar, and fluency of the manuscript. Importantly, all conceptual ad- vances, methodological innovations, experimental designs, and primary contributions presented in this work were independently conceived, developed, and validated by the authors. The role of L.LMs
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was thus limited to improving readability and ensuring the precision of academic writing, without influencing the scientific content or originality of the research.
32


Reviews:
[{"review_id": "5jV7W9MtWW", "reviewer": "Reviewer_NKK9", "summary": "Protein subcellular localization is a fundamental aspect of cell biology, closely related to protein function and drug target identification. Although previous studies have shown that 3D protein structure plays a critical role in determining localization patterns, existing datasets provide sequence-level information, lacking structural data. This limits the development and evaluation of structure-aware models. This paper introduces CAPSUL, a comprehensive human protein benchmark for subcellular localization that integrates 3D structural information with fine-grained localization annotations.", "questions": "Refer to weaknesses", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "1. CAPSUL combines 3D structural data with detailed subcellular localization annotations, enabling the development of structure-aware models.\n2. The dataset includes 20 subcellular compartments, verified by domain experts, ensuring biological accuracy and interpretability.\n3. The authors benchmark good structure-based models and propose reweighting and single-label classification strategies to mitigate class imbalance, showing improvements in underrepresented classes.", "weaknesses": "1. The current evaluation is limited to supervised multi-label classification. There is no attempt to leverage structural self-supervised learning or contrastive learning, which are promising directions for structure-aware protein modeling.\n2. The structure encoders used are standard graph-based models. More advanced geometric deep learning methods (e.g., SE(3)-equivariant networks, structural diffusion models) are not explored, potentially limiting the upper bound of structural understanding.\n3. Although evidence codes are provided, in the main experiments, all annotations are treated as positive, including non-experimental ones, which may introduce label noise. While ablations are provided, a more systematic analysis of how evidence levels affect model robustness is lacking.\n4. Despite reweighting and single-label strategies, macro-averaged F1-scores remain low for rare classes, indicating that severe imbalance is still a fundamental challenge.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ufgg17cqT0", "reviewer": "Reviewer_axjf", "summary": "This paper introduces CAPSUL, a new large-scale human protein benchmark for subcellular localization that integrates comprehensive 3D structural information (Cα coordinates and FoldSeek 3Di tokens) with fine-grained, expert-curated annotations across 20 subcellular compartments. The dataset unifies information from UniProt and the Human Protein Atlas and includes evidence-level labels for experimental support. The authors benchmark a diverse range of state-of-the-art sequence-based and structure-based models, analyse class imbalance, and explore interpretability via Transformer attention. Results demonstrate that explicit 3D geometry provides predictive power comparable to massive sequence pre-training, and that fine-grained compartmental labels uncover biologically meaningful hierarchical structure in localization processes. The paper thus establishes a strong and reproducible foundation for evaluating future multimodal protein models.", "questions": "1. **Quantifying modality equivalence** – Could the authors train a unified model that accepts both sequence and structure to explicitly measure how much performance gain is attributable to each modality? This would clarify whether “one structured protein ≈ 10⁴–10⁵ sequences” holds empirically.\n2. **Hierarchical classification** – Given that many compartments (e.g., nucleus → nucleolus, nucleoplasm) are nested, have the authors considered a hierarchical target formulation or conditional prediction pipeline? Such models might reflect biological transport stages and improve minority-class recall.\n3. **Causal alignment** – Can the authors comment on whether CAPSUL could enable causal studies linking specific structural motifs (e.g., transmembrane helix length or charge distribution) to localization outcomes?\n4. **Fusion and transfer learning** – Have experiments been attempted where pretrained sequence embeddings are fused with structural graphs? This could help quantify complementarity and guide future multimodal protein models.\n5. **Dataset generality** – While CAPSUL focuses on human proteins, do the authors foresee extending it to other organisms or to dynamic (context-dependent) localization? Such extensions could test generalisation and evolutionary transfer.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Originality and significance**\n\n* CAPSUL fills a clear and impactful gap in current bio-ML resources by providing the first benchmark where **structural and sequence modalities can be directly compared** for subcellular localization.\n* The study yields a **quantitative insight into the trade-off between pre-training and structure**, showing that a modestly sized structure-aware model can match the performance of billion-parameter sequence-only models trained on hundreds of millions of proteins. This constitutes a valuable empirical reference for the community.\n* The dataset’s **fine-grained, hierarchical labeling** reveals that localization is a multi-stage process, aligning with known biological transport hierarchies (e.g., generic targeting → organelle entry → sub-organelle retention). This suggests natural directions for **hierarchical or multi-task learning architectures**.\n\n**Technical quality**\n\n* The benchmark suite is broad and technically sound: eight representative models spanning sequence Transformers, geometric GNNs, and graph Transformers.\n* Ablations on randomised coordinates convincingly isolate the contribution of geometric structure.\n* The exploration of reweighting and single-label training provides concrete, actionable strategies for imbalance mitigation.\n* Interpretability analyses identify α-helix transmembrane motifs consistent with established Golgi localization mechanisms—an impressive validation of biological fidelity.\n\n**Clarity and reproducibility**\n\n* The paper is well organised and carefully written; data processing steps are fully documented with evidence codes and validation statistics.\n* Supplementary material provides sufficient implementation detail for replication.\n\n**Impact**\n\n* CAPSUL is likely to become a **standard benchmark** for structure-aware protein models. Its explicit link between geometry, hierarchy, and localization creates opportunities for causal and interpretable modeling in computational biology.", "weaknesses": "* The **structure–sequence trade-off** is not yet quantified in a controlled architectural setting. While the results suggest that explicit structure compensates for the absence of large-scale pre-training, a **single unified model trained in both modalities** (e.g., ESM backbone + structural tokens) would enable direct estimation of their relative contributions.\n* The paper stops short of leveraging the **hierarchical organization of the labels**. Implementing or evaluating hierarchical loss functions or coarse-to-fine classifiers would make the biological interpretation stronger and could address imbalance more effectively.\n* Although the case study on the Golgi is compelling, additional interpretability analyses across other organelles would reinforce the claim that structure-based attention consistently yields mechanistically meaningful motifs.\n* The benchmark could report **sample efficiency curves** (performance vs number of training samples) to further substantiate the “structure as information equivalent to pre-training” argument.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "vG6AJwriJ1", "reviewer": "Reviewer_68FM", "summary": "The paper presents CAPSUL, a benchmark of 20,181 human proteins with amino-acid sequences, Cα coordinates, and 3Di structural tokens, paired with fine-grained (20-category) subcellular localization labels aggregated from UniProt and HPA, along with evidence levels. It evaluates both sequence- and structure-based models (ESM-2/ESM-C, FoldSeek, GCN variants, Graph Transformer, Graph Mamba), explores reweighting and single-label strategies, and provides an interpretability case study (Golgi apparatus) via attention.", "questions": "None", "limitations": "", "rating": 4, "confidence": 1, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1.Unified access to structure (Cα, 3Di tokens) plus fine-grained labels and evidence levels; a clear advance beyond existing sequence-only/coarse-label datasets.\n2.Broad coverage of representative sequence and structure baselines; reasonable class-imbalance mitigations (reweighting, focal, single-label) and a “randomized structure” ablation that is logically sound.", "weaknesses": "1.Evidence-level integration may introduce bias: treating non-experimental annotations as positives could inflate text biases.\n2.Missing graph-construction details, i.e., edge criteria (kNN/sequence adjacencies), edge features (relative orientation/distance encodings), normalization, length truncation.\n3.You are suggested that provide failure-case analyses (e.g., low pLDDT regions, disordered segments).\n4.You should fix minor typos and keep notation consistent.\n5.It is better to add fusion baselines (sequence+structure early/late fusion) to probe complementarity.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "mJcXuWdJdg", "reviewer": "Reviewer_Xxcu", "summary": "This paper proposes a subcellular localization dataset called CAPSUL that offers comprehensive 3D structural information with detailed subcellular localization annotated by domain experts. A variety of state-of-the-art methods are included to test the benchmark to demonstrate the importance of introducing structural information to identify the subcellular localization. A case study is also introduced to showcase the powerful interpretability of structure-based methods in cell biology.", "questions": "Please refer to the weaknesses.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The challenges and motivation behind the paper are well clarified and clearly demonstrated.\n\n2. Extensive experiments and various tasks are conducted to prove the effectiveness of the proposed benchmarks. Both the quantitative and qualitative results are provided to showcase the importance and contribution of the proposed benchmarks in paving the way of cell biology.\n\n3. The paper is well written and organized. Benchmarks and code are also available.", "weaknesses": "1. The proposed benchmark uses AlphaFold2 to extract the structural information for each protein. However, AlphaFold2 also has limitations on structural information. How will this affect the benchmarks and the following evaluations? When more accurate models emerge, will the benchmarks become outdated? And if the downstream tasks models are more accurate than AlphaFold2, such as AlphaFold3, will this benchmark downstream tasks evaluation be invalid?\n\n2. The baselines in the paper have covered sequence and structure-based models. But there are more recent state-of-the-art models, for instance, OpenFold[1], Boltz[2], that should also be considered in the baselines.\n\n[1] Gustav Ahdritz, Nazim Bouatta, Cristian Floristean, et al. Openfold: retraining alphafold2 yields new insights into its learning mechanisms and capacity for generalization. Nature Methods, 21: 1514–1524, 2024. doi: 10.1038/s41592-024-02272-z\n[2] Jeremy Wohlwend, Gabriele Corso, Saro Passaro, Noah Getz, Mateo Reveiz, Ken Leidal, Wojtek Swiderski, Liam Atkinson, Tally Portnoi, Itamar Chinn, Jacob Silterra, Tommi Jaakkola, and Regina Barzilay. Boltz-1: Democratizing biomolecular interaction modeling. bioRxiv, 2024. doi: 10.1101/2024.11.19.624167.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)