Developing SCL2205 : A Protein Sequence-based Spatial Modelling Dataset for the Protein Language Model Frontier

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ID: 329119
2026
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Abstract
Abstract Motivation Deep learning (DL) has substantially advanced protein subcellular localisation (SCL) prediction, yet its potential remains constrained by suboptimal input preparation and limited high-quality reference data. Furthermore, existing state-of-the-art (SoTA) predictors suffer from performance metric inflation due to unmitigated training-to-testing data leakage during homology augmentation. We address these challenges by introducing SCL2205, a leak-minimised benchmark dataset and pipeline curated specifically to support trustworthy, scalable, and reproducible DL-based SCL modelling. Results SCL2205 was constructed from the universal protein knowledgebase (UniProtKB) using rigorous preprocessing, manual label mapping, and stringent partitioning. When evaluated on independent test sets, SCL2205 yielded up to a 10.8 percentage point improvement in macro area under the precision–recall curve (PR-AUC) over SoTA baselines (mean Δ 95% CI=0.07−0.12 ), with maximum benefits observed when paired with modern protein language models (PLMs). Crucially, we quantify for the first time a systemic 5.2%±0.32 data leakage rate in conventional homology augmentation workflows—even when restricting sequence similarity searches to just 10% of the training set. Availability and Implementation The dataset is openly available on Dryad under a CC0 1.0 Universal licence (https://doi.org/10.5061/dryad.2ngf1vj1t). The dataset interface is available as an installable Python package, p-scldata (v2026.2.0), under the MIT licence on the Python Package Index (PyPI). Full code and data repositories are hosted on GitHub (https://github.com/ousodaniel/scldata) and archived on Zenodo (https://doi.org/10.5281/zenodo.21796423). Supplementary Information Supplementary File S1 contains code snippets, per-class PR-AUC breakdowns, class prevalence details, statistical comparison tests, and supplementary figures. Supplementary File S2 contains the exact mapping used in curation.
Reference Key
openalex_W7213560870 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Daniel O. Ouso, Gianluca Pollastri
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag666
URL
https://doi.org/10.1093/bioinformatics/btag666
Keywords Keywords not found

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