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

🤖 Plain-English Summary

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.

🔑 Key Findings

• Here, we propose an update to the popular tool DeepLoc with multi-localization prediction and improvements in both performance and interpretability.
• 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.
• We achieve advanced performance in DeepLoc 2.0 by using a pre-trained protein language model.

🔗 Related Resources