Protein function prediction is gradually emerging as an essential field in biological as well as computational studies. Though the later has clinched a significant footprint but still it has been observed that application of computational information gathered from multiple sources has a greater influence than the one derived from a single source. Considering this fact a methodology, PFP-GO, is proposed where heterogeneous sources like Protein Sequence, Protein Domain and Protein-Protein Interaction Network have been processed separately for ranking each individual functional GO term. Based on this ranking, GO terms are propagated to the target proteins. While Protein sequence enriches the sequence based information, Protein Domain and Protein-Protein Interaction Network embed structural/functional and topological based information respectively during the phase of GO ranking. Performance analysis of PFP-GO is also reckoned based on Precision, Recall and F-Score. The same when compared to the other existing state-of-art found to perform reasonably better. PFP-GO has achieved an overall Precision, Recall and F-Score of 0.67, 0.58 and 0.62 respectively.