Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes

摘要

Complexes of physically interacting proteins constitute fundamentalfunctional units responsible for driving biological processes within cells. Afaithful reconstruction of the entire set of complexes is therefore essentialto understand the functional organization of cells. In this review, we discussthe key contributions of computational methods developed till date(approximately between 2003 and 2015) for identifying complexes from thenetwork of interacting proteins (PPI network). We evaluate in depth theperformance of these methods on PPI datasets from yeast, and highlightchallenges faced by these methods, in particular detection of sparse and smallor sub- complexes and discerning of overlapping complexes. We describe methodsfor integrating diverse information including expression profiles and 3Dstructures of proteins with PPI networks to understand the dynamics of complexformation, for instance, of time-based assembly of complex subunits andformation of fuzzy complexes from intrinsically disordered proteins. Finally,we discuss methods for identifying dysfunctional complexes in human diseases,an application that is proving invaluable to understand disease mechanisms andto discover novel therapeutic targets. We hope this review aptly commemorates adecade of research on computational prediction of complexes and constitutes avaluable reference for further advancements in this exciting area.