Rational design of modulators of the unfolded protein response.

摘要

Proteins fold into their native conformation and undergo a series of post-translational modifications in the endoplasmic reticulum (ER). Disruption of any of these processes results in ER stress. Cells respond to ER stress by activation of the unfolded protein response (UPR), a coordinated program to increase cell survival under ER stress through reducing general translation rate and promoting protein folding. In most of solid tumors, induction of the UPR is a critical survival mechanism. The requirement for the UPR in tumor progression thus raises the possibility of targeting this pathway as a novel therapeutic intervention in cancer. PERK, one of the UPR transducers, is an eIF2alpha kinase. Compromising PERK function inhibits tumor growth via lower phosphorylation of eIF2alpha, suggesting that inhibiting the kinase activity of PERK towards eIF2alpha may inhibit tumor maintenance and progression. To date, however, no specific small molecule inhibitor of PERK has been identified. The goal of this study was to use a novel computational chemistry based approach to identify the pair wise receptor-ligand atomic contacts responsible for selective PERK inhibition. Compounds inhibiting PERK-mediated phosphorylation in an in vitro kinase inhibition assay were identified using molecular modeling, virtual library screening (VLS) and chemoinformatics technologies. The most potent PERK selective inhibitors utilize three specific kinase active site contacts that, when disrupted in chemically similar compounds, abrogates the inhibition. Thus, three structural interactions are important in establishing inhibition of PERK by small molecules: (a) a strong van der Waals contact with PERK residue Met7, (b) interactions with the N-terminal portion of the activation loop and (c) groups providing electrostatic complementarity to Asp144 side chain. Interestingly, the activation loop contact is required in a compound for PERK selectivity to emerge. These structural-activity relationships may aid in the structure-based PERK inhibitor design.