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

TL;DR

This review summarizes computational methods developed between 2003 and 2015 for predicting protein complexes from PPI networks, highlighting their performance, challenges, and applications in understanding cell organization and disease.

Contribution

It provides a comprehensive evaluation of existing methods, discusses integration of diverse data types, and explores applications in disease research, advancing the understanding of complex formation.

Findings

Methods vary in effectiveness on yeast PPI datasets.

Challenges include detecting small, sparse, and overlapping complexes.

Integrating expression and structural data enhances understanding of complex dynamics.

Abstract

Complexes of physically interacting proteins constitute fundamental functional units responsible for driving biological processes within cells. A faithful reconstruction of the entire set of complexes is therefore essential to understand the functional organization of cells. In this review, we discuss the key contributions of computational methods developed till date (approximately between 2003 and 2015) for identifying complexes from the network of interacting proteins (PPI network). We evaluate in depth the performance of these methods on PPI datasets from yeast, and highlight challenges faced by these methods, in particular detection of sparse and small or sub- complexes and discerning of overlapping complexes. We describe methods for integrating diverse information including expression profiles and 3D structures of proteins with PPI networks to understand the dynamics of complex formation, for instance, of time-based assembly of complex subunits and formation 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 and to discover novel therapeutic targets. We hope this review aptly commemorates a decade of research on computational prediction of complexes and constitutes a valuable reference for further advancements in this exciting area.