PROTEINs are key players in cellular pathways and interactions between them play main role in wide range of process in cells such as cellular motion, signal transduction and transport. Furthermore, information and knowledge about PROTEIN-PROTEIN interactions and constructing of PROTEIN interaction graph has very important role in understanding of biological process. Since PROTEIN-PROTEIN interaction information can define FUNCTION of a PROTEIN through position of that PROTEIN in a PROTEIN web, further such information have key role in supporting of biological researches. Akin to the complete sequencing of genomes, complete descriptions of interactomes is a fundamental step towards a deeper understanding of biological process. Experimental detection of PROTEIN-PROTEIN interactions include CO-IP, TAP-MS, Y2H but these methods limited by time consuming, high cost and have more false positives. Computational analysis of PPI networks is increasingly becoming a mandatory tool to understand the FUNCTIONs of unexplored PROTEINs. Thus in silico methods which include sequence-based approaches, structure-based approaches, chromosome proximity, phylogenetic tree, phylogenetic profile and gene fusion approaches were developed. Also a variety of web server have been developed to PREDICTION the interactions that have been detected by experimental approach such as: STRING, PRISM, Mirror tree, InterpreTS and struct2net. Recent developments have also led to the construction of networks having all the PROTEIN-PROTEIN interactions using computational methods for signaling pathways and PROTEIN complex identification in specific diseases. In this review, we discuss about in silico methods in PREDICTION of PPI that is important field in PROTEIN research.