Abstract

Allostery is a common mechanism of controlling many biological processes such as enzyme catalysis, signal transduction, and metabolic regulation. The use of allostery to regulate protein activity is an important and promising strategy in drug discovery and biological network regulation. In order to modulate protein activity by allostery, predictive methods need to be developed to discover allosteric binding sites. In the present study, we developed a new approach to identify allosteric sites in proteins based on the coarse-grained two-state Go̅ model. Starting from the concept that allostery is a conformation population shift process, we first constructed an ensemble of two functional states of a protein and tuned the energy landscape to bias one state. We then added perturbations to a binding site and monitored the population distribution of the new ensemble. If population redistribution occurred, then the binding perturbed site was predicted as a potential allosteric site. Our approach successfully identified all the known allosteric sites in a set of test proteins. Several new allosteric sites in the test proteins were also predicted. By use of one of the new allosteric sites predicted from Escherichia coli phosphoglycerate dehydrogenase (PGDH), novel allosteric regulating molecules were screened by molecular docking and enzymatic assay. Three novel allosteric inhibitors were discovered and their binding modes were confirmed by mutation experiments and competitive assay. The IC50 of the strongest inhibitor discovered was 21 μM, which is comparable to that of the native allosteric inhibitor l-serine. The novel allosteric site discovered in PGDH is l-serine-independent, and inhibitors targeting this site can be used as novel regulators of the E. coli serine synthesis pathway. Our approach for allosteric site prediction is generally applicable and the predicted sites can be used in discovering novel allosteric regulating molecules.

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