Appendix A Dataset Construction A.1 Subcellular Location Categorization and Terminology Mapping To facilitate model classification, we first categorize the detailed subcellular localizations of proteins. Existing datasets often use coarse-grained classifications ( e.g., DeepLoc categorizes subcellular 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 specific to finer subcellular compartments. Inspired by the subcellular location categories in HPA and DeepLoc, we propose a finer-grained classification scheme consisting of 20 subcellular categories. Notably, ”Nucleus” and ”Cytoplasm” categories serve as umbrella terms for several finer locations to ensure compatibility with DeepLoc during evaluation. When aligning protein localization annotations from the UniProt and HPA databases to our refined categorization, we 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 et al., 2022 ) and create a unified mapping, as shown in Table 7 , which allows for consistent categorization across the two databases. Domain experts were extensively engaged to ensure and validate the accuracy of the classification standards and data alignment procedures. We invited cell biologists from several prestigious universities and research institutes to review and revise the dataset, which ensures that CAPSUL is 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 plausibility of localization assignments. Through the above processes, we have established a fine-grained subcellular localization classification standard and successfully unified annotations from multiple databases under a unified labeling framework. Table 7: Categorization of CAPSUL and terminology mapping between HPA and Uniprot. 20 fine-grained categories HPA UniProt Nucleus Nuclear Membrane Nuclear membrane Nucleus membrane, Nucleus envelope, Nucleus inner membrane, Nucleus outer membrane Nucleoli Nucleoli, Nucleoli fibrillar center, Nucleoli rim Nucleolus Nucleoplasm Kinetochore, Mitotic chromosome, Nuclear bodies, Nuclear speckles, Nucleoplasm Nucleus matrix, Nucleus lamina, Chromosome, Nucleus speckle Cytoplasm Cytosol Aggresome, Cytoplasmic bodies, Cytosol, Rods Rings Cytosol Cytoskeleton Actin filaments, Cleavage furrow, Focal adhesion sites, Cytokinetic bridge, Microtubule ends, Microtubules, Midbody, Midbody ring, Mitotic spindle, Intermediate filaments Cytoskeleton Centrosome Centriolar satellite, Centrosome Centrosome Mitochondria Mitochondria Mitochondrion, Mitochondrion envelop, Mitochondrion inner membrane, Mitochondrion outer membrane, Mitochondrion membrane, Mitochondrion matrix, Mitochondrion intermembrane space Endoplasmic Reticulum Endoplasmic reticulum Endoplasmic reticulum, Endoplasmic reticulum membrane, Endoplasmic reticulum lumen, Microsome, Rough endoplasmic reticulum, Smooth endoplasmic reticulum, Sarcoplasmic reticulum Golgi Apparatus Golgi apparatus Golgi apparatus, Golgi apparatus membrane, Golgi apparatus lumen Cell Membrane Cell Junctions, Plasma membrane Cell membrane, Apical cell membrane, Apicolateral cell membrane, Basal cell membrane, Basolateral cell membrane, Lateral cell membrane, Cell projection Endosome Endosomes Endosome Lipid Droplet Lipid droplets Lipid droplet Lysosome/Vacuole Lysosomes Lysosome, Vacuole, Vacuole lumen, Vacuole membrane, Lysosome lumen, Lysosome membrane Peroxisome Peroxisomes Peroxisome, Peroxisome matrix, Peroxisome membrane Vesicle Vesicles Vesicle Primary Cilium Basal body, Primary cilium, Primary cilium tip, Primary cilium transition zone Cilium Secreted Proteins Secreted Proteins Secreted Sperm Acrosome, Annulus, Calyx, Connecting piece, End piece, Equatorial segment, Flagellar centriole, Mid piece, Perinuclear theca, Principal piece Acrosome, Calyx, Perinuclear theca 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 proteins. 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 in three subsets is shown in Table 8 . Although the data is randomly assigned to different subsets, we have verified the distribution characteristics among classes to maintain a similar proportional relationship, ensuring balance and representativeness across the subsets. Table 8: Label counts for training, validation, and test set of CAPSUL. Subcellular Locations Counts Training Set Validation Set Test Set Sum Nucleus 5,312 1,128 1,150 7,590 Nuclear Membrane 313 63 76 452 Nucleoli 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 713 140 147 1,000 Mitochondria 1,247 259 262 1,768 Endoplasmic Reticulum 1,146 275 289 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 63 16 15 94 Lysosome/Vacuole 313 65 75 453 Peroxisome 71 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
Appendix B Dataset Reliability B.1 Overview of Data Sources In Section 3 , we provide a 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 incorporated experimentally resolved structures of proteins as templates during its prediction process ( Jumper et al., 2021 ) . AlphaFold explicitly describes how its pipeline automatically searches 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 are used as template inputs alongside MSA-based evolutionary information, enabling AlphaFold to leverage high‑quality experimental structural data in its predictions. 2) AlphaFold-predicted structures have been demonstrated to achieve exceptionally high accuracy , competitive with experimental 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 Å r.m.s.d. 95 (C α root-mean-square deviation at 95% residue coverage), which is often within the margin of error of experimental structures ( Jumper et al., 2021 ) . 3) 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 CAPSUL. 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 standards, where annotations (including subcellular localization annotations) are derived from authoritative experimental studies and peer-reviewed literature, complemented by computational analyses and homology-based inferences. Each localization entry is 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 categories reference in CAPSUL. HPA provides a unique and experimentally grounded resource for human protein subcellular localization. Its Subcellular Atlas is built upon systematic immunofluorescence imaging combined with antibody-based profiling in multiple well-characterized human cell lines. This approach allows direct visualization of protein distribution within distinct subcellular compartments, thereby offering cell-type-specific and high-resolution localization evidence. These measures substantially reduce the likelihood of false annotations and provide users with a clear indication of annotation confidence. B.2 An Alternative Attempt for Protein Structure Inputs Table 9: Partial evaluation on protein structure input from AlphaFold2 and Boltz-2. Subcellular Locations CDConv t (1,223 protein structures from AlphaFold2) CDConv t (1,223 protein structures from Boltz-2) Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.798 0.709 0.751 0.788 0.565 0.658 Nuclear Membrane - - - - - - Nucleoli 0.500 0.064 0.113 0.267 0.025 0.047 Nucleoplasm 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 Cytosol 0.645 0.429 0.515 0.541 0.622 0.579 Cytoskeleton 0.548 0.085 0.147 0.467 0.035 0.065 Centrosome - - - - - - Mitochondria 0.694 0.200 0.311 0.358 0.152 0.213 Endoplasmic 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 Droplet - - - - - - Lysosome/Vacuole - - - - - - Peroxisome - - - - - - Vesicle 0.800 0.019 0.037 0.667 0.010 0.019 Primary Cilium - - - - - - Secreted Proteins 0.724 0.375 0.494 0.655 0.339 0.447 Sperm - - - - - - Micro Avg 0.745 0.407 0.527 0.668 0.371 0.477 Macro Avg 0.408 0.184 0.227 0.341 0.165 0.195 t The original MLP is replaced by Transformer layers. “–” indicates that no prediction is made for that location. In our study, we used protein structural data predicted by AlphaFold2, the most accurate and widely adopted source of protein structural information, to provide structure inputs for our structure-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 of 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 et al., 2025 ) . From this work, we extracted the subset overlapping with our CAPSUL benchmark, yielding 1,223 proteins. For these 1,223 proteins, we 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-based structural inputs outperform Boltz-based inputs . This observation further supports the rationale behind our use of AlphaFold2-predicted structures in constructing CAPSUL. As the most accurate and widely adopted source of protein structural information, AlphaFold2 provides high-quality structural inputs that lead to strong downstream performance in subcellular localization. Appendix C Experiment Details C.1 Implementation Details The experiments were performed utilizing NVIDIA RTX 3090, A40 and A100 GPUs. We employ an early stopping strategy to mitigate overfitting with a tolerance of 5 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 our structure-based models, each node represents an amino acid, and edges encode the relationships between amino acids. Specifically, the edges are constructed as follows: Edge Criteria. There are two types of adjacency to form the edges in the graphs. Sequential adjacency 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 Åsphere centered at a 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 innovative 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 processing the input feature vectors: A GCN updates node representations via neighborhood aggregation, i.e., mi(0)=xi , mi(l+1)=σ(∑j∈N(i)W(l)mj(l)+b(l)) . mi(l) is the representation of node i at layer l (the first layer is initialized with node embeddings xi various in different baselines), N(i) denotes the neighbors of node i , W(l) and b(l) are trainable weights and bias, and σ is a non-linear activation function ( e.g. , ReLU). After L layers of graph convolution, we obtain the final node representations {mi(L)}i=1n . To enhance interpretability and capture global interactions among residues, we replace the traditional average pooling with a Transformer encoder T(⋅) to obtain the residue representation, i.e., h=(h1,…,hn)=T({mi(L)}i=1n) , where h∈Rn×d . Similarly, Graph Transformer and Graph Mamba substitute the convolution-based encoder with their respective architectures, while adhering to the same overall procedure to obtain the global protein representation. Given a protein structure represented as a graph, we introduce a diffusion -based refinement process in which node coordinates or geometric features are gradually perturbed with Gaussian noise and then denoised through a learned reverse process. The diffusion module serves as an auxiliary representation-learning stage designed to enhance geometric feature extraction prior to the downstream subcellular localization prediction. This allows the network to 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 positive pairs ( i.e., pairing protein samples that localize to the same compartment), and positive–negative pairs ( i.e., pairing one protein that localizes to the compartment with another that does not). On top of the original loss function, we incorporate a contrastive loss to encourage higher embedding similarity for positive pairs while enforcing lower similarity for positive–negative pairs. We provide a formal mathematical description of how the contrastive loss is incorporated. Let hˉ denote the protein-level representations from model ( i.e., average embedding obtained before MLP classifier). For each of the 20 independent classification tasks, we compute the cosine similarity between representations of positive–positive pairs and positive–negative pairs, and construct the contrastive loss accordingly. For class c∈{1,…,20} , let • Pc={i∣yi,c=1} denote the set of positive samples, • Nc={i∣yi,c=0} denote the negative samples, • hˉi denotes the protein-level embedding of the sample i . The positive–positive similarity matrix is S^(+,+)_ij = cos( z _i, z _j), i,j ∈ P _c. We encourage positive samples to be close to each other by minimizing L ^(+,+)_c = 1 - 1 — P c — 2 ∑_i,j ∈ P _c S^(+,+)_ij. The positive–negative similarity matrix is S^(+,-)_ij = cos( z _i, z _j), i ∈ P _c, j ∈ N _c. We encourage positive and negative samples to be dissimilar by minimizing L ^(+,-)_c = 1 — P c —— N c — ∑_i ∈ P _c ∑_j ∈ N _c S^(+,-)_ij. Thus, the contrastive loss for class c is L ^ contrast _c = L ^(+,+)_c + L ^(+,-)_c. Finally, the overall contrastive loss averaged over all classes is L ^ contrast = 1 C ∑_c=1^C L ^ contrast _c. ESM-C+CDConv Fusion Model. In the early fusion model, the structural representations produced 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 is then passed through the pretrained ESM-C Transformer for interaction, followed by mean pooling 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 1×10−4 . 2) ESM-C (600M) , the MLP hidden layers are set to (512,256) (to (512) when finetuning), and learning rate to 5×10−4 . 3) FoldSeek , the embedding dimensions are set to 256, transformer layers to 2, transformer heads to 4, and learning rate to 1×10−4 . 4) CDConv , the kernel channels are set to 24, feature channels to (256,512), geometric adjacency to 4Åand 8Å(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 5×10−4 . 5) GearNet-Edge , the max sequence length is set to 3,000, spatial adjacency set to [5Å,10Å], KNN adjacency set to [5,10], sequential adjacency set to 2, convolution hidden dimensions to (512,512,512), transformer layers to 2, transformer heads to 2, and learning rate to 1×10−5 . 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 set to 5×10−5 . 7) Graph Mamba , the Mamba layers are set to 5, geometric adjacency to 10Å, sequential adjacency to 5, node dimensions set to 256, and learning rate set to 1×10−4 . 8) Graph Diffusion , the node dimensions are set to 64, geometric adjacency to 10Å, sequential adjacency to 5, timesteps set to 200, variance of Gaussian noise set to 1×10−4 in the beginning and 0.02 in the end, weight for diffusion loss to 0.1 compared with classification loss, and learning rate to 5×10−4 . 9) CDConv with Contrastive Loss , the weight for contrastive loss is set to 0.1 compared with classification loss, and other settings are the same as the originial CDConv. 10) ESM-C+CDConv Fusion Model , the learning rate is set to 5×10−4 for early fusion and to 1×10−4 for late fusion, and other settings are the same as the originial ESM-C and CDConv. For the reweighting strategy, we inherit the optimal hyperparameter settings for ESM-C (600M), CDConv, and GearNet-Edge mentioned above. The best reweighting scheme for each model is as follows: 1) ESM-C (600M) , focal loss with α set to the weights of log-inverse frequency, and γ set to 1.0. 2) CDConv , focal loss with α set to the weights of log-inverse frequency, and γ set to 3.0. 3) GearNet-Edge , inverse frequency reweighting. For the single-label classification strategy, we inherit the optimal hyperparameter settings for ESM-C (600M), CDConv, and GearNet-Edge mentioned above. To address class imbalance, we undersample the negative class to achieve a 1:3 positive-to-negative sample ratio for ESM-C (600M), and a 1:1 positive-to-negative sample ratio for CDConv. Appendix D Detailed Baseline Results Detailed experimental results of main experiments, reweighting strategy, and single-label classification strategy are provided in Tables 10 , 11 , and 12 , respectively. They include evaluation metrics of precision, recall, and F1-score. Table 10: Detailed performance of sequence-based and structure-based methods on CAPSUL. Subcellular Locations DeepLoc 2.1 ESM-2 650M ESM-2 650M f Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.675 0.086 0.152 - - - 0.633 0.586 0.609 Nuclear Membrane / / / - - - - - - Nucleoli / / / - - - - - - Nucleoplasm / / / - - - 0.592 0.535 0.562 Cytoplasm 0.510 0.100 0.167 - - - 0.598 0.157 0.248 Cytosol / / / - - - - - - Cytoskeleton / / / - - - 0.200 0.003 0.006 Centrosome / / / - - - - - - Mitochondria 0.799 0.065 0.120 - - - 0.850 0.195 0.317 Endoplasmic Reticulum 0.581 0.067 0.121 - - - - - - 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 / / / - - - - - - Lysosome/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 Avg / / / - - - 0.647 0.264 0.375 Macro Avg / / / - - - 0.217 0.131 0.150 Subcellular Locations ESM-C 600M ESM-C 600M f ESM-C 600M 0 Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.694 0.609 0.649 0.708 0.597 0.648 0.626 0.498 0.555 Nuclear Membrane - - - - - - - - - Nucleoli 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 0.536 0.614 0.499 0.551 0.507 0.385 0.438 Cytosol 0.541 0.307 0.392 0.567 0.286 0.380 0.456 0.104 0.169 Cytoskeleton 0.681 0.154 0.251 0.629 0.123 0.205 0.471 0.025 0.048 Centrosome 1.000 0.007 0.014 - - - - - - Mitochondria 0.865 0.416 0.562 0.903 0.389 0.544 0.667 0.053 0.099 Endoplasmic 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 0.568 0.648 0.786 0.243 0.372 Endosome 1.000 0.009 0.018 - - - - - - Lipid Droplet - - - - - - - - - Lysosome/Vacuole - - - - - - - - - Peroxisome - - - - - - - - - Vesicle 1.000 0.005 0.009 - - - 1.000 0.002 0.005 Primary Cilium 0.682 0.093 0.164 0.556 0.062 0.112 - - - 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 0.618 0.215 0.263 0.482 0.206 0.249 0.362 0.106 0.135 Subcellular Locations FoldSeek CDConv t GearNet-Edge t Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.616 0.398 0.484 0.651 0.592 0.620 0.619 0.450 0.521 Nuclear Membrane - - - - - - - - - Nucleoli - - - 0.583 0.084 0.147 0.531 0.068 0.121 Nucleoplasm 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 0.001 0.003 0.489 0.277 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 0.359 0.476 0.470 0.240 0.318 Endoplasmic Reticulum - - - 0.441 0.218 0.292 0.475 0.197 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 0.037 0.067 Lipid Droplet - - - - - - - - - Lysosome/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 0.328 0.795 0.741 0.767 0.722 0.655 0.687 Sperm - - - - - - 0.714 0.046 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 Table 10: (Continued) Detailed performance of sequence-based and structure-based methods on CAPSUL. Subcellular Locations Graph Transformer Graph Mamba Graph Diffusion Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.664 0.543 0.597 0.562 0.556 0.559 0.617 0.631 0.624 Nuclear 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 Nucleoplasm 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 Cytosol 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 - - - 0.128 0.313 0.181 1.000 0.007 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 Apparatus 0.857 0.021 0.041 0.182 0.188 0.185 - - - 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 - - - - - - - - - Lysosome/Vacuole - - - - - - - - - Peroxisome - - - - - - - - - Vesicle 0.526 0.023 0.044 0.306 0.086 0.135 0.667 0.009 0.018 Primary Cilium 0.500 0.006 0.012 0.205 0.056 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 Locations CDConv with Contrastive Loss ESM-C+CDConv Early Fusion ESM-C+CDConv Late Fusion Precision Recall F1-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 Nuclear Membrane - - - - - - - - - Nucleoli 0.556 0.080 0.140 0.739 0.068 0.125 0.210 0.120 0.153 Nucleoplasm 0.646 0.487 0.556 0.704 0.591 0.643 0.676 0.567 0.617 Cytoplasm 0.590 0.404 0.480 0.682 0.341 0.455 0.572 0.469 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 Reticulum 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 0.746 0.422 0.539 0.774 0.530 0.629 0.732 0.622 0.673 Endosome 0.250 0.019 0.034 1.000 0.028 0.054 - - - Lipid Droplet - - - - - - - - - Lysosome/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 0.780 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 0.234 0.507 0.202 0.249 0.300 0.201 0.235 f We finetune the pre-trained protein language model. t The original MLP is replaced by Transformer layers. 0 The parameters of ESM-C is initialized randomly. “/” indicates that DeepLoc 2.1 does not support prediction for that location, and therefore, average metrics are not considered in this case. “–” indicates that no prediction is made for that location. Table 11: Detailed performance of selected baselines with reweighting scheme. Subcellular Locations ESM-C 600M CDConv t GearNet-Edge t Precision Recall F1-Score 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 - - - 0.033 0.566 0.062 0.046 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 0.133 0.353 0.829 0.495 0.366 0.714 0.484 Cytoskeleton 0.778 0.044 0.083 0.184 0.698 0.292 0.218 0.450 0.294 Centrosome - - - 0.089 0.776 0.160 0.134 0.252 0.175 Mitochondria 0.846 0.336 0.481 0.191 0.672 0.297 0.247 0.427 0.313 Endoplasmic Reticulum - - - 0.195 0.737 0.308 0.276 0.460 0.345 Golgi Apparatus - - - 0.152 0.648 0.246 0.177 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/Vacuole - - - 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 0.096 0.640 0.167 0.123 0.311 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 Macro Avg 0.415 0.171 0.210 0.209 0.655 0.197 0.253 0.424 0.304 t The original MLP is replaced by Transformer layers. “–” indicates that no prediction is made for that location. Table 12: Detailed performance of selected baselines with single-label classification strategy. Subcellular Locations ESM-C 600M CDConv t GearNet-Edge t 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 0.124 0.361 0.184 0.051 0.333 0.089 0.099 0.531 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 0.021 - - - 0.032 0.133 0.051 Lysosome/Vacuole 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 0.104 0.472 0.171 Sperm 0.121 0.229 0.159 0.045 0.138 0.068 0.077 0.239 0.117 t The original MLP is replaced by Transformer layers. “–” indicates that no prediction is made for that location.
Appendix E Ablation Study Table 13: Detailed performance comparison of CDConv and GearNet-Edge under random sampling of C α coordinates. Subcellular Locations CDConv t (ablation) CDConv t GearNet-Edge t (ablation) GearNet-Edge t Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.595 0.512 0.550 0.651 0.592 0.620 0.515 0.459 0.485 0.619 0.450 0.521 Nuclear Membrane - - - - - - - - - - - - Nucleoli 0.417 0.020 0.038 0.583 0.084 0.147 0.268 0.076 0.119 0.531 0.068 0.121 Nucleoplasm 0.578 0.419 0.486 0.633 0.541 0.583 0.479 0.428 0.452 0.613 0.444 0.515 Cytoplasm 0.535 0.214 0.306 0.580 0.414 0.483 0.432 0.414 0.422 0.498 0.491 0.495 Cytosol 0.478 0.069 0.120 0.489 0.277 0.353 0.394 0.279 0.327 0.417 0.358 0.385 Cytoskeleton - - - 0.649 0.075 0.135 0.252 0.119 0.162 0.296 0.186 0.228 Centrosome - - - - - - 0.184 0.061 0.092 0.228 0.088 0.127 Mitochondria 0.537 0.084 0.145 0.707 0.359 0.476 0.283 0.065 0.106 0.470 0.240 0.318 Endoplasmic Reticulum 0.625 0.017 0.034 0.441 0.218 0.292 0.321 0.062 0.104 0.475 0.197 0.279 Golgi Apparatus - - - 0.733 0.038 0.073 0.132 0.017 0.031 0.211 0.014 0.026 Cell Membrane 0.621 0.425 0.505 0.721 0.461 0.562 0.595 0.413 0.487 0.708 0.457 0.556 Endosome - - - - - - - - - 0.364 0.037 0.067 Lipid Droplet - - - - - - - - - - - - Lysosome/Vacuole - - - - - - - - - 0.429 0.040 0.073 Peroxisome - - - - - - - - - - - - Vesicle - - - 0.667 0.014 0.027 0.185 0.077 0.109 0.270 0.039 0.068 Primary Cilium - - - - - - 0.368 0.043 0.078 0.467 0.087 0.147 Secreted Proteins 0.703 0.218 0.333 0.795 0.741 0.767 0.515 0.232 0.320 0.722 0.655 0.687 Sperm - - - - - - 0.125 0.009 0.017 0.714 0.046 0.086 Micro Avg 0.586 0.229 0.329 0.632 0.352 0.452 0.450 0.283 0.348 0.546 0.337 0.417 Macro Avg 0.254 0.099 0.126 0.382 0.191 0.226 0.252 0.138 0.166 0.402 0.195 0.235 t The original MLP is replaced by Transformer layers. “–” indicates that no prediction is made for that location. 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-based baselines to quantify the positive impact of 3D information incorporated. Specifically, to preserve the integrity of the model input, we performed preprocessing on the protein structural data. For each protein, we obtained the boundary values of its 3D coordinates and uniformly sampled the C α coordinates at random within these boundaries to generate new protein structures. The 1D 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, we conducted an ablation study, with the detailed results shown in Table 13 . We observed a significant performance drop in this setting, which further demonstrates the decisive role of accurate 3D structural input in enabling correct model predictions. Appendix F Explanation and Illustrative Examples of Evidence-level Annotations Table 14: Detailed performance of two illustrative examples of evidence-level annotations: weighting labels and filtering labels. Subcellular Locations ESM-C 600M ESM-C 600M (weighting) ESM-C 600M (filtering) Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.694 0.609 0.649 0.717 0.585 0.645 0.703 0.575 0.633 Nuclear Membrane - - - - - - - - - Nucleoli 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 Centrosome 1.000 0.007 0.014 1.000 0.007 0.014 - - - Mitochondria 0.865 0.416 0.562 0.907 0.374 0.530 0.798 0.373 0.508 Endoplasmic 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/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 0.903 0.761 0.826 0.920 0.669 0.775 - - - 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 Locations CDConv t CDConv t (weighting) CDConv t (filtering) Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.651 0.592 0.620 0.674 0.539 0.599 0.644 0.530 0.581 Nuclear Membrane - - - - - - - - - Nucleoli 0.583 0.084 0.147 0.556 0.020 0.039 0.484 0.640 0.112 Nucleoplasm 0.633 0.541 0.583 0.657 0.497 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 Cytosol 0.489 0.277 0.353 0.470 0.293 0.361 0.468 0.337 0.392 Cytoskeleton 0.649 0.075 0.135 0.714 0.063 0.116 0.400 0.008 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 - - - Cell Membrane 0.721 0.461 0.562 0.723 0.447 0.553 0.562 0.194 0.288 Endosome - - - - - - - - - Lipid Droplet - - - - - - - - - Lysosome/Vacuole - - - - - - - - - Peroxisome - - - - - - - - - Vesicle 0.667 0.014 0.027 - - - - - - Primary Cilium - - - 0.333 0.006 0.012 - - - Secreted 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 0.452 0.637 0.333 0.438 0.577 0.291 0.386 Macro Avg 0.382 0.191 0.226 0.374 0.173 0.211 0.250 0.122 0.148 Subcellular Locations GearNet-Edge t GearNet-Edge t (weighting) GearNet-Edge t (filtering) Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.619 0.450 0.521 0.622 0.337 0.437 0.607 0.478 0.535 Nuclear Membrane - - - - - - - - - Nucleoli 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 0.495 0.473 0.466 0.469 0.452 0.479 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 0.211 0.014 0.026 0.211 0.014 0.026 0.250 0.004 0.008 Cell Membrane 0.708 0.457 0.556 0.629 0.392 0.483 0.463 0.170 0.248 Endosome 0.364 0.037 0.067 0.333 0.028 0.051 0.500 0.029 0.056 Lipid Droplet - - - - - - - - - Lysosome/Vacuole 0.429 0.040 0.073 0.125 0.013 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 0.147 0.524 0.068 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 0.235 0.390 0.160 0.206 0.313 0.131 0.160 t The original MLP is replaced by Transformer layers. “–” indicates that no prediction is made for that location. F.1 Explanation of Evidence-level Annotations The evidence-level annotations design was originally intended to allow researchers to flexibly select annotations based on their specific use cases. For instance, when the task is rigorous and requires high precision such as Nucleolar retention motifs discovery, selecting annotations with high confidence ( i.e., choosing the experimentally validated annotations only) is more appropriate. Conversely, 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 to 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 samples 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 annotations as negative samples, which restricts models learning to experimental data with high reliability). The results are compared with the original one in our paper in Table 14 . As shown in the table, models that treat non-experimentally validated annotations as positive samples generally achieve the best overall performance. This may be because many non-validated annotations also originate from reliable sources ( e.g., the ”ECO:0000303” 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-experimentally Validated Annotations Table 15: Detailed performance of different weights for non-experimentally validated annotations on ESM-C. Subcellular Locations ESM-C weighting 0.1 ESM-C weighting 0.3 ESM-C weighting 0.5 Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.733 0.549 0.628 0.73 0.553 0.629 0.728 0.564 0.636 Nuclear 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 Cytosol 0.530 0.350 0.422 0.518 0.336 0.408 0.511 0.382 0.437 Cytoskeleton 0.636 0.110 0.188 0.587 0.116 0.194 0.618 0.107 0.182 Centrosome - - - - - - 1.000 0.007 0.014 Mitochondria 0.917 0.382 0.539 0.917 0.382 0.539 0.914 0.366 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 Cell Membrane 0.771 0.367 0.497 0.768 0.367 0.496 0.791 0.442 0.567 Endosome - - - - - - - - - Lipid Droplet - - - - - - - - - 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 0.933 0.474 0.629 0.936 0.447 0.605 0.958 0.471 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 Locations ESM-C weighting 0.7 ESM-C weighting 0.9 Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.717 0.585 0.645 0.711 0.578 0.638 Nuclear Membrane - - - - - - Nucleoli 0.786 0.088 0.159 0.833 0.080 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 Cytoskeleton 0.649 0.116 0.197 0.646 0.132 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 0.726 0.156 0.256 0.680 0.235 0.350 Golgi Apparatus 1.000 0.010 0.021 0.909 0.035 0.067 Cell Membrane 0.757 0.570 0.650 0.759 0.540 0.631 Endosome - - - 0.667 0.019 0.036 Lipid Droplet - - - - - - Lysosome/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 0.800 0.037 0.070 0.750 0.028 0.053 Micro Avg 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 “–” indicates that no prediction is made for that location. Table 16: Detailed performance of different weights for non-experimentally validated annotations on CDConv. Subcellular Locations CDConv t weighting 0.1 CDConv t weighting 0.3 CDConv t weighting 0.5 Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.640 0.646 0.643 0.616 0.662 0.638 0.671 0.543 0.600 Nuclear Membrane - - - - - - - - - Nucleoli 0.652 0.060 0.110 0.471 0.096 0.160 0.667 0.016 0.031 Nucleoplasm 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 Cytosol 0.493 0.322 0.390 0.478 0.278 0.352 0.468 0.348 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 Endoplasmic 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 - - - - - - - - - Lysosome/Vacuole - - - - - - - - - Peroxisome - - - - - - - - - Vesicle - - - - - - - - - Primary Cilium 1.000 0.006 0.012 1.000 0.006 0.012 - - - Secreted Proteins 0.823 0.618 0.706 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 0.381 0.173 0.205 0.366 0.183 0.213 0.362 0.174 0.208 Subcellular Locations CDConv t weighting 0.7 CDConv t weighting 0.9 Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.674 0.539 0.599 0.682 0.523 0.592 Nuclear Membrane - - - - - - Nucleoli 0.556 0.020 0.039 0.750 0.012 0.024 Nucleoplasm 0.657 0.497 0.566 0.669 0.479 0.558 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 Cytoskeleton 0.714 0.063 0.116 0.480 0.075 0.130 Centrosome - - - - - - Mitochondria 0.762 0.355 0.484 0.707 0.332 0.452 Endoplasmic Reticulum 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 Cell Membrane 0.723 0.447 0.553 0.725 0.493 0.587 Endosome - - - - - - Lipid Droplet - - - - - - 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 0.438 0.625 0.359 0.456 Macro Avg 0.374 0.173 0.211 0.364 0.185 0.218 t The original MLP is replaced by Transformer layers. “–” indicates that no prediction is made for that location. Table 17: Analysis of precision and recall on Nucleus on ESM-C. ESM-C weighting 0.1 0.3 0.5 0.7 0.9 Treat as Positive 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 To have a comprehensive analysis of the effect of 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, we have the following observation: 1) For certain subcellular compartments, baselines trained exclusively on experimentally validated annotations or assigned low positive weights to non-experimentally validated annotations generally achieve higher precision , as shown in Table 17 . Actually, precision and recall often represent a trade-off in modeling strategies. That is, adopting a more conservative prediction strategy typically increases precision but reduces the number of correctly recalled samples, and vice versa. Therefore, selecting high-confidence evidence levels can be seen as a method of enforcing a more conservative prediction approach, helping to reduce the likelihood of false-positive predictions . This highlights the novelty of evidence-level annotations: using experimentally validated data is considered 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 CAPSUL still possess relatively high reliability . This ensures that, when CAPSUL is used for downstream tasks, the inclusion of non-experimentally validated annotations does not introduce substantial bias.
Appendix G Attempt Toward Hierarchical Classifiers for Nested Categories Table 18: Detailed performance of hierarchical classifiers on CDConv. CDConv t Hierarchical Classifiers Subcellular Locations Precision Recall F1-Score Subcellular Locations Precision Recall F1-Score Nucleus’s classifier Cytoplasm’s classifier Nuclear Membrane - - - Cytosol 0.260 0.980 0.411 Nucleoli - - - Cytoskeleton 0.331 0.126 0.182 Nucleoplasm 0.339 1.000 0.507 t The original MLP is replaced by Transformer layers. “–” indicates that no prediction is made for that location. In order to make precise predictions of nested subcategories, we attempt to train a 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 decreased performance compared with the original CDConv. We attribute this to the reduced number of training samples when training this hierarchical classifier, which, combined with the already severe 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 localization tasks, directly applying a single-label classification strategy, while adjusting the corresponding 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 can 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.
Appendix H Analysis of the pLDDT Scores of Protein Structure Input Table 19: Analysis of performance of three pLDDT groups on CDConv. Subcellular Locations High pLDDT Group Medium pLDDT Group Low pLDDT Group Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Nucleus 0.561 0.393 0.462 0.636 0.570 0.601 0.700 0.742 0.720 Nuclear Membrane - - - - - - - - - Nucleoli 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 Cytosol 0.520 0.451 0.483 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 0.789 0.475 0.593 0.644 0.322 0.430 0.529 0.167 0.254 Endoplasmic Reticulum 0.506 0.344 0.409 0.410 0.152 0.222 0.176 0.054 0.082 Golgi Apparatus 0.857 0.057 0.107 0.714 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/Vacuole - - - - - - - - - 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 0.443 0.628 0.345 0.446 0.651 0.367 0.469 Macro Avg 0.412 0.198 0.233 0.538 0.191 0.228 0.280 0.157 0.179 “–” indicates that no prediction is made for that location. In AlphaFold-predicted 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 in the test set into three groups of equal size based on their protein-level mean pLDDT 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 of 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 metrics above, indicating that proteins with different average pLDDT levels have not greatly influenced the prediction of these subcellular localizations. In summary, the low-pLDDT group does not exhibit worse predictions systematically . This indicates that the CAPSUL is robust to fluctuations in pLDDT and does not rely disproportionately on regions of high structural confidence. In other words, CAPSUL provides stable and reliable structural inputs for downstream tasks even when proteins contain disordered or low-confidence regions, demonstrating the quality and suitability of our structural dataset for subcellular localization 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.
Appendix K Generalization Ability The CAPSUL workflow is readily applicable to other species. While our current study exclusively uses human protein data, we do not assert that CAPSUL’s pipeline is 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 annotations, and 3) 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 insights into the selective pressures shaping cellular organization, the emergence of organelle-specific functions, and the molecular adaptations that underpin evolutionary innovation. The CAPSUL workflow is applicable to 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 corresponding 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-dependent localization. This would enable the models to make precise predictions about subcellular localization under specific cellular conditions or in response to external environmental states, moving beyond static localization to capture functional biological dynamics. The CAPSUL is readily extensible to additional protein data and baseline methods in the future. Our highly standardized pipeline ensures that any newly released protein data can be updated to CAPSUL. Also, CAPSUL embraces a 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 CAPSUL, which enables fair and informative comparison across various baseline methods.
Appendix L Availability of Dataset and Code The complete dataset, including localization labels, extracted protein structures, etc. can be accessed at https://huggingface.co/datasets/getbetterhyccc/CAPSUL . Our implementation is publicly available at https://github.com/getbetter-hyccc/CAPSUL . For some baseline models, we adopt publicly released implementations, including Graph Transformer at https://github.com/pyg-team/pytorch_geometric/tree/master and Graph Mamba at https://github.com/alxndrTL/mamba.py .
Appendix M The Use of Large Language Models In this study, Large Language Models (LLMs) were employed solely for linguistic refinement, such as polishing the clarity, grammar, and fluency of the manuscript. Importantly, all conceptual advances, methodological innovations, experimental designs, and primary contributions presented in this work were independently conceived, developed, and validated by the authors. The role of LLMs was thus limited to improving readability and ensuring the precision of academic writing, without influencing the scientific content or originality of the research.