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

ABSTRACT: The prediction of protein subcellular localization is of great relevance for proteomics research. 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 state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model. It has the further advantage that it uses sequence input rather than relying on slower protein profiles. We provide two means of better interpretability: an attention output along the sequence and highly accurate prediction of nine different types

SUBMITTER: Thumuluri V 

PROVIDER: S-EPMC9252801 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature