DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

The prediction of protein subcellular localization is of great relevance for proteomics research. We find that the attention output correlates well with the position of sorting signals.

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• 1 Here, we propose an update to the popular tool DeepLoc with multi-localization prediction and improvements in both performance and interpretability.
• 2 For training and validation, we curate eukaryotic and human multi-location protein datasets with stringent homology partitioning and enriched with sorting signal information compiled from the literature.
• 3 We achieve advanced performance in DeepLoc 2.0 by using a pre-trained protein language model.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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