3 research outputs found
Methods for protein complex prediction and their contributions towards understanding the organization, function and dynamics of complexes
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.Comment: 1 Tabl
Predicting and characterising protein-protein complexes
Macromolecular interactions play a key role in all life processes. The construction
and annotation of protein interaction networks is pivotal for the
understanding of these processes, and how their perturbation leads to disease.
However the extent of the human interactome and the limitations of
the experimental techniques which can be brought to bear upon it necessitate
theoretical approaches. Presented here are computational investigations
into the interactions between biological macromolecules, focusing on the
structural prediction of interactions, docking, and their kinetic and thermodynamic
characterisation via empirical functions. Firstly, the use of normal
modes in docking is investigated. Vibrational analysis of proteins are shown
to indicate the motions which proteins are intrinsically disposed to undertake,
and the use of this information to model flexible deformations upon
protein-protein binding is evaluated. Subsequently SwarmDock, a docking
algorithm which models flexibility as a linear combination of normal modes,
is presented and benchmarked on a wide variety of test cases. This algorithm
utilises state of the art energy functions and metaheuristics to navigate the
free energy landscape. Information derived from Langevin dynamics simulations
of encounter complex formation in the crowded cytosolic environment
can be incorporated into SwarmDock and enhances its performance.
Finally, a benchmark of binding free energies derived from the literature is
presented. For this benchmark, a large number of molecular descriptors are
derived. Machine learning methods are then applied to these in order to
derive empirical binding free energy, association rate and dissociation rate
functions which take account of the conformational changes which occur
upon complexation