Proteins perform various functions in living organisms. The task of automatic protein function is defined as finding appropriate association between proteins and functional labels like Gene Ontology(GO) terms. In this paper, we present Prot-A-GAN: an automatic protein function annotation framework using GAN-like adversarial training for knowledge graph embedding. Following the terminologies of GAN: 1) we train a discriminator using domain-adaptive negative sampling to discriminate positive and negative triples, and 2) we train a generator to guide a random walk over the knowledge graph that identify paths between proteins and GO annotations. We evaluate the method by performing protein function annotation using GO terms on human disease proteins from UniProtKB/SwissProt. As a proof-of-concept, the conducted experiments show promising outcome and open up new avenue for further exploration, exclusively for protein function annotation.