Drug discovery has focused on the paradigm “one drug, one target” for a long time. However,
small molecules can act at multiple macromolecular targets, which serves as the basis for drug repurposing.
In an effort to expand the target space, and given advances in X-ray crystallography, protein-protein
interactions have become an emerging focus area of drug discovery enterprises. Proteins interact with
other biomolecules and it is this intricate network of interactions that determines the behavior of the system
and its biological processes. In this review, we briefly discuss networks in disease, followed by
computational methods for protein-protein complex prediction. Computational methodologies and techniques
employed towards objectives such as protein-protein docking, protein-protein interactions, and
interface predictions are described extensively. Docking aims at producing a complex between proteins,
while interface predictions identify a subset of residues on one protein that could interact with a partner,
and protein-protein interaction sites address whether two proteins interact. In addition, approaches to
predict hot spots and binding sites are presented along with a representative example of our internal project
on the chemokine CXC receptor 3 B-isoform and predictive modeling with IP10 and PF4.
Keywords: Protein-protein interactions, protein-protein interface, disease networks, hot spots, molecular recognition, proteinprotein
docking, machine learning methods, binding site identification.
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