Drafting the topological network of protein‐protein interactions by in‐depth data mining and inference

Abstract

The biological knowledge had often been represented by network, especially the protein‐protein interaction (PPI) network since the advent of systems biology. While current experimental methods for mapping PPI networks remained costly and often inefficient, the development of new computational approaches for building the networks was very imperative. Here, an in silico method for efficiently mapping protein interaction networks was presented in dependence on the integration of in‐depth data mining for proteomic information (including sequences, motifs, structures and interactions) with the smart predictive tricks. The sketch of PPI network was generated from an initial target protein (as a start node) by progressively determining the peripheral interacting partners that acted as new start nodes respectively in the following cycle. Analyses of protein functions and discovery of active pathways or subnetworks with definitely physiological implications were also addressed in detail according to an optimum search algorism. The integrative computational approach could draft the topological networks of PPI in a high‐throughput and low‐cost fashion before actual experimental procedures.This work was supported by a grant from National Natural Science Foundation of China (NSFC, no. 20803098)