Most processes in living organisms occur through an intricate network of protein-protein interactions, in which any malfunctioning
can lead to pathological situations. Therefore, current research in biomedicine is starting to focus on protein interaction networks.
A detailed structural knowledge of these interactions at molecular level will be necessary for drug discovery targeting protein-protein interactions.
The challenge from a structural biology point of view is determining the structure of the specific complex formed upon interaction
of two or several proteins, and/or locating the surface residues involved in the interaction and identify which of them are the most
important ones for binding (hot-spots). In this line, an increasing number of computer tools are available to complement experimental efforts.
Docking algorithms can achieve successful predictive rates in many complexes, as shown in the community assessment experiment
CAPRI, and have already been applied to a variety of cases of biomedical interest. On the other side, many methods for interface and hotspot
prediction have been reported, based on a variety of evolutionary, geometrical and physico-chemical parameters. Computer predictions
are reaching a significant level of maturity, and can be very useful to guide experiments and suggest mutations, or to provide a
mechanistic framework to the experimental results on a given interaction. We will review here existing computer approaches for proteinprotein
docking, interface prediction and hot-spot identification, with focus to drug discovery targeting protein-protein interactions.
Keywords: Protein-protein interactions, docking, hot-spots, interface prediction, drug discovery, biomedicine, surface residues, mutations, hot-spot identification, CAPRI.
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