We present a method that improves subcellular localization prediction for proteins based on their sequence by leveraging structure prediction and Graph Neural Networks. We demonstrate that Language Models, trained on protein sequences, and Graph Neural Nets, trained on protein's 3D structures, are both efficient approaches. They both learn meaningful, yet different representations of proteins; hence, ensembling them outperforms the reigning state of the art method.