Network inference from AP-MS data: computational challenges and solutions

Abstract

Protein^protein interaction is of primary importance to understand protein functions. In recent years, the high-throughput AP-MS experiments have generated a large amount of bait^prey data, posing great challenges on the computational analysis of such data for inferring true interactions and protein complexes. To date, many research efforts have been devoted to developing novel computational methods to analyze these AP-MS data sets. In this article, we review and classify the key computational methods developed for the inference of protein^protein interactions and the detection of protein complexes from the AP-MS experiments. We hope that our review as well as the challenges highlighted in the article will provide valuable insights into driving future research for further advancing the state-of-the-art technologies in computational prediction, characterization and analysis of protein^ protein interactions and protein complexes from the AP-MS data