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FEBS Lett. 2015 Sep 14;589(19 Pt A):2590-602. doi: 10.1016/j.febslet.2015.04.026. Epub 2015 Apr 23.

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

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Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Queensland 4067, Australia. Electronic address:
Department of Computer Science, National University of Singapore, Singapore 117417, Singapore.
Human Genome Centre, The Institute of Medical Science, The University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan.


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 organisation 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 their limitations and challenges, in particular at detecting sparse and small or sub-complexes and discerning 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.


Complexes in diseases; Dynamic and fuzzy complexes; PPI network; Protein complex prediction

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