CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization

• URL: http://arxiv.org/abs/2603.18571v1
• Date: Thu, 19 Mar 2026 07:33:04 GMT
• Title: CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization
• Abstract summary: We introduce a new benchmark called $mathbfCAPSUL$, a dataset that integrates diverse 3D structural representations with fine-grained subcellular localization annotations.<n>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.<n> Lastly, we showcase the powerful interpretability of structure-based methods through a case study on the Golgi apparatus.
• Score: 50.559485139565076
• License: http://creativecommons.org/licenses/by/4.0/
• 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 $\mathbf{CAPSUL}$, a $\mathbf{C}$omprehensive hum$\mathbf{A}$n $\mathbf{P}$rotein benchmark for $\mathbf{SU}$bcellular $\mathbf{L}$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 $α$-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.

Related papers

• K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles [65.21921642886367]
  K-Models is a novel framework that integrates ordinal constraints and estimates key underlying elements of the random process generating the observed functional profiles.<n>The proposed method is evaluated through simulations and real-world applications.
  arXiv  Detail & Related papers  (2026-05-14T13:35:44Z)
• MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentation [15.740955468843035]
  We propose a semi-supervised segmentation framework designed to robustly identify and preserve relevant topological features.<n>This consistency mechanism helps distinguish biologically meaningful structures from transient and noisy artifacts.<n>We introduce a novel matching strategy that integrates spatial overlap with global structural alignment.
  arXiv  Detail & Related papers  (2025-10-02T00:08:28Z)
• subCellSAM: Zero-Shot (Sub-)Cellular Segmentation for Hit Validation in Drug Discovery [1.1866227238721938]
  We present a novel method that applies a segmentation foundation model in a zero-shot setting, guided by an in-context learning strategy.<n>Our approach employs a three-step process for nuclei, cell, and subcellular segmentation, introducing a self-prompting mechanism.<n>We validate our method on both standard cell segmentation benchmarks and industry-relevant hit validation assays.
  arXiv  Detail & Related papers  (2025-08-19T10:07:27Z)
• DISPROTBENCH: A Disorder-Aware, Task-Rich Benchmark for Evaluating Protein Structure Prediction in Realistic Biological Contexts [76.59606029593085]
  DisProtBench is a benchmark for evaluating protein structure prediction models (PSPMs) under structural disorder and complex biological conditions.<n>DisProtBench spans three key axes: data complexity, task diversity, and Interpretability.<n>Results reveal significant variability in model robustness under disorder, with low-confidence regions linked to functional prediction failures.
  arXiv  Detail & Related papers  (2025-06-18T23:58:22Z)
• Persistent Homology for Structural Characterization in Disordered Systems [6.538609026810757]
  We propose a unified framework based on persistent homology (PH) to characterize both local and global structures in disordered systems.<n>It can simultaneously generate local and global descriptors using the same algorithm and data structure.<n>It has shown to be highly effective and interpretable in predicting particle rearrangements and classifying global phases.
  arXiv  Detail & Related papers  (2024-11-21T18:24:06Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• Exploring The Potential Of GANs In Biological Sequence Analysis [0.966840768820136]
  We propose a novel approach to handle the data imbalance issue based on Generative Adversarial Networks (GANs) GANs are utilized to generate synthetic data that closely resembles the real one. We perform 3 distinct classification tasks by using 3 different sequence datasets.
  arXiv  Detail & Related papers  (2023-03-04T13:46:45Z)
• Unsupervised hierarchical clustering using the learning dynamics of RBMs [0.0]
  We present a new and general method for building relational data trees by exploiting the learning dynamics of the Restricted Boltzmann Machine (RBM) Our method is based on the mean-field approach, derived from the Plefka expansion, and developed in context of disordered systems. We tested our method in an artificially hierarchical dataset and on three different real-world datasets (images of digits, mutations in the human genome, and a family of proteins)
  arXiv  Detail & Related papers  (2023-02-03T16:53:32Z)
• Transfer Learning for Protein Structure Classification at Low Resolution [124.5573289131546]
  We show that it is possible to make accurate ($geq$80%) predictions of protein class and architecture from structures determined at low ($leq$3A) resolution. We provide proof of concept for high-speed, low-cost protein structure classification at low resolution, and a basis for extension to prediction of function.
  arXiv  Detail & Related papers  (2020-08-11T15:01:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.