A structure based approach for accurate prediction of protein interactions networks

In the recent days, extraordinary revolution in genome sequencing technologies have produced an overwhelming amount of genes that code for proteins, resulting in deluge of proteomics data. Since proteins are involved in almost every biological activity, therefore due to this rapid uncovering of biological "facts", the field of System Biology now stands on the doorstep of considerable theoretical and practical advancements. Precise understanding of proteins, specially their functional associations or interactions are inevitable to explicate how complex biological processes occur at molecular level, as well as to understand how these processes are controlled and modified in different disease states. In this paper, we present a novel protein structure based method to precisely predict the interactions of two putative protein pairs. We also utilize the interspecies relationship of proteins i.e., the sequence homology, which is crucial in cases of limited information from other sources of biological data. We further enhance our model to account for protein binding sites by linking individual residues in structural templates which bind to other residues. Finally, we evaluate our model by combining different sources of information using Naive Bayes classification. The proposed model provides substantial improvements in terms of accuracy, precision, recall when compared with previous approaches. We report an accuracy of 90% when tested for a protein interaction network of yeast proteome.