Combining structural and coevolution information to unveil allosteric sites

Understanding allosteric regulation in biomolecules is of great interest to pharmaceutical research and computational methods emerged during the last decades to characterize allosteric coupling. However, the prediction of allosteric sites in a protein structure remains a challenging task. Here, we integrate local binding site information, coevolutionary information, and information on dynamic allostery into a structure-based three-parameter model to identify potentially hidden allosteric sites in ensembles of protein structures with orthosteric ligands. When tested on five allosteric proteins (LFA-1, p38-alpha, GR, MAT2A, and BCKDK), the model successfully ranked all known allosteric pockets in the top three positions. Finally, we identified a novel druggable site in MAT2A confirmed by X-ray crystallography and SPR and a hitherto unknown druggable allosteric site in BCKDK validated by biochemical and X-ray crystallography analyses. Our model can be applied in drug discovery to identify allosteric pockets.

Automated summary

This study developed a structure-based three-parameter model that integrates local binding site information, coevolution information, and information on dynamic allostery to identify potentially hidden allosteric sites in ensembles of protein structures. The model successfully ranked all known allosteric pockets in the top three positions when tested on five allosteric proteins. Additionally, two novel druggable sites were identified and confirmed in MAT2A and BCKDK protein structures. This model can be used in drug discovery to identify allosteric pockets.