Predicting protein-protein interactions using first principle methods and statistical scoring

Proteins are a combination of different PDB structures. To understand the interactions of the proteins, we have proposed a methodology that integrates the first principle parameters for protein interaction along with the number of PDB structures defining these proteins. Annotating possibly interacting proteins pairs with their Pfam and GO domains increases the strength of each interaction and can identify the important link between the two proteins. We propose a novel technique to predict protein interactions by integrating a protein's physico-chemical properties and the number of PDB structures that uses sliding window algorithm to compute the optimal interacting score. The proposed method identified ~94% true prediction from a known set of interacting protein dataset and a 100% prediction for non-interacting dataset. The prediction model that was developed was applied to an unknown protein dataset and we identified a novel interacting protein pairs with high relevance.