CAPSUL: A Comprehensive Human Protein Benchmark for Subcellular Localization
arXiv:2603.18571v1 Announce Type: new 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
arXiv:2603.18571v1 Announce Type: new 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 $\alpha$-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.
Executive Summary
This article introduces CAPSUL, a comprehensive human protein benchmark for subcellular localization, addressing the gap in existing datasets lacking detailed subcellular localization annotations with 3D structural information. The CAPSUL dataset integrates diverse 3D structural representations with fine-grained subcellular localization annotations curated by domain experts. The authors evaluate this benchmark using state-of-the-art sequence-based and structure-based models, demonstrating the importance of incorporating structural features in subcellular localization tasks. They also explore reweighting and single-label classification strategies to facilitate future investigation on structure-based methods for this task. The study showcases the powerful interpretability of structure-based methods through a case study on the Golgi apparatus, highlighting the potential for bridging the gap between data-driven discoveries and intuitive biological interpretability in cell biology.
Key Points
- ▸ Introduction of CAPSUL, a comprehensive human protein benchmark for subcellular localization
- ▸ Evaluation of CAPSUL using state-of-the-art sequence-based and structure-based models
- ▸ Exploration of reweighting and single-label classification strategies for structure-based methods
Merits
Comprehensive Dataset
CAPSUL addresses the gap in existing datasets by integrating 3D structural representations with fine-grained subcellular localization annotations.
Demerits
Limited Domain Expertise
The study relies heavily on curated annotations by domain experts, which may introduce bias and limit the generalizability of the results.
Expert Commentary
The CAPSUL dataset is a significant contribution to the field of subcellular localization, providing a comprehensive benchmark for structure-based models. The authors' evaluation of CAPSUL using state-of-the-art models demonstrates the importance of incorporating structural features in subcellular localization tasks. However, the study's reliance on curated annotations by domain experts raises concerns about bias and generalizability. Future research should aim to develop more robust and generalizable methods for subcellular localization prediction. Additionally, the study's findings can inform the development of policies and guidelines for the curation and use of subcellular localization data in cell biology research.
Recommendations
- ✓ Future researchers should aim to develop more robust and generalizable methods for subcellular localization prediction that can be applied to diverse datasets.
- ✓ Policies and guidelines should be developed to ensure the curation and use of subcellular localization data in cell biology research are done in a robust and generalizable manner.
