Hierarchical Multi-label Classification (HMC) is a classification task where classes are organized in a hierarchical taxonomy, and instances can be simultaneously classified in more than one class. This paper investigates the HMC problem of classifying proteins in functions organized according to the Gene Ontology hierarchical taxonomy. This is a complex task, since the Gene Ontology hierarchy is organized as a Directed Acyclic Graph with thousands of classes hierarchically represented. We propose a neural network-based method to incorporate label-dependency during learning. The experimental results show that the proposed method achieves competitive results when compared to the state-of-the-art methods from the literature.