The majority of biological functions are carried out by proteins. Proteins perform their roles only upon arrival to their target location in the cell, hence elucidating protein sub-cellular localization is essential for better understanding their function. The exponential growth in genomic information and the high cost of experimental validation of protein localization call for the development of predictive methods. We present a method that improves sub-cellular localization prediction for proteins based on their sequence by leveraging structure prediction and Graph Neural Networks. We demonstrate how Language Models, trained on protein sequences, and Graph Neural Networks, trained on protein's 3D structures, are both efficient approaches for this task. They both learn meaningful, yet different representations of proteins; hence, ensembling them outperforms the reigning state of the art method. Our architecture improves the localization prediction performance while being lighter and more cost-effective.