One of the challenges of the post-genomic era is to provide accurate function annotations for orphan and unannotated protein sequences. With the recent availability of huge protein-protein interactions for many model species, it becomes an opportunity to computational methods to elucidate protein function based on many strategies. In this respect, most automated computational approaches integrate diverse kinds of functional interactions to deduce protein functions by transferring annotations across different species by relying on similar sequence, structure 2D/3D, amino acid motifs or phylogenetic profiles. In this work, we introduce a new approach called TANA (Transferring Annotation via Network Alignment) for inferring protein function which is based on our approach MAPPIN for GNA (Global Network Alignment). The main originality of the introduced approach stands on discovering functional modules within the PPI network by transferring annotation via network alignment. Doing so, we are able to discover the functions of proteins that could not to be easily described by sequence homology. We assess the performance of our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and highlight a sharp significant improvement over other competitive methods, in particular for predicting molecular functions.