Allostery is one of the most powerful and common ways of regulation of protein activity. However, for most allosteric proteins identified to date the mechanistic details of allosteric modulation are not yet well understood. Uncovering common mechanistic patterns underlying allostery would allow not only a better academic understanding of the phenomena, but it would also streamline the design of novel therapeutic solutions. This relatively unexplored therapeutic potential and the putative advantages of allosteric drugs over classical active-site inhibitors fuel the attention allosteric-drug research is receiving at present. A first step to harness the regulatory potential and versatility of allosteric sites, in the context of drug-discovery and design, would be to detect or predict their presence and location. We will describe a simple computational approach to predict the existence and position of allosteric sites in a given protein structure on the basis of the effect a ligand exerts on the overall flexibility of the protein upon binding.
By querying the literature and a recently available database of allosteric sites, we gathered 213 allosteric proteins with structural information that we further filtered into a non-redundant set of 91 proteins1. We performed normal-mode analysis and observed significant changes in protein flexibility upon allosteric-ligand binding in 70% of the cases. These results agree with the current view that allosteric mechanisms are in many cases governed by changes in protein dynamics caused by ligand binding. Furthermore, we implemented an approach that achieves a positive predictive value of 65% in identifying allosteric sites within the set of predicted cavities of a protein by combining the analysis of flexibility with an analysis of local structural conservation2.
Based on these findings, we have developed a simple and fast web server for the identification of cavities with a potential regulatory function in proteins (http://bioinf.uab.cat/pars)3, and have exploited this tool for genome-wide detection of putative targets of antimicrobials with new modes of action in gram-negative bacteria (http://bioinf.uab.cat/antibactr)4.