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.

Recent advances in DNA sequencing and computational algorithms revealed the SNPs of highest value. Laboratory study of gene and its alleles is difficult and time consuming. In this study, we used several computational algorithms to demonstrate the effects of gene structure and FUNCTION of heat shock PROTEIN 70 (HSP70) in buffalo. HSP70 is a chaperone molecule that expressed in response to stress. On the other hand, buffalo is one of the resistant animals to heat stress. In this study from Affymetrix 90K SNP chip in 112 Khuzestan buffalo and published sequence information is used for Hindi buffalo. In Khuzestan buffalo dataset, snp was not found, but one snp was recognized in Hindi buffalo. Snp analysis (Methionine/Threonine) was done using SIFT, PROVEAN. SIFT with calculate factor 1 and PROVEAN algorithm with calculate factor 0. 33 which showed this mutation or SNP effect is non-destructive and with the normal PROTEIN operation. I-mutant algorithm was used to check mutant hsp70 PROTEIN stability, and predict SNP impacts on the stability of the PROTEIN using regression based on the changes in free energy (DDG=0. 47) showed that "met5thr" snp at 25 º C, PH=7 decrease the PROTEIN stability. Validation of the model by Ramachandran and ProSA Zscore maps indicates a slight deviation in the mutated PROTEIN compared to the normal model. Molecular docking and how to hsp70 connect to its ligand adenosine diphosphate (ADP) indicates in both the natural and mutated models that mutation leads to decrease in PROTEIN stability and change its structure, which also changes the structure of ligand binding site. But generally in this study the mutation does not show significant and destructive effect on PROTEIN structure.