FK506-Binding Protein 2 Participates in Proinsulin Folding.

Apart from chaperoning, disulfide bond formation, and downstream processing, the molecular sequence of proinsulin folding is not completely understood. Proinsulin requires proline isomerization for correct folding. Since FK506-binding protein 2 (FKBP2) is an ER-resident proline isomerase, we hypothesized that FKBP2 contributes to proinsulin folding. We found that FKBP2 co-immunoprecipitated with proinsulin and its chaperone GRP94 and that inhibition of FKBP2 expression increased proinsulin turnover with reduced intracellular proinsulin and insulin levels. This phenotype was accompanied by an increased proinsulin secretion and the formation of proinsulin high-molecular-weight complexes, a sign of proinsulin misfolding. FKBP2 knockout in pancreatic ß-cells increased apoptosis without detectable up-regulation of ER stress response genes. Interestingly, FKBP2 mRNA was overexpressed in ß-cells from pancreatic islets of T2D patients. Based on molecular modeling and an in vitro enzymatic assay, we suggest that proline at position 28 of the proinsulin B-chain (P28) is the substrate of FKBP2's isomerization activity. We propose that this isomerization step catalyzed by FKBP2 is an essential sequence required for correct proinsulin folding.

Structural modeling of HCV NS3/4A serine protease drug-resistance mutations using end-point continuum solvation and side-chain flexibility calculations.

Computational methods of modeling protein-ligand interactions have gained widespread application in modern drug discovery. In continuum solvation-based methods of binding affinity estimation, limited description of solvent environment and protein flexibility is traded for a time scale that fits medicinal chemistry test cycles. The results of this speed-accuracy trade-off have been promising in terms of modeling structure-activity relationships of ligand series against protein targets. The potential of these approaches in recapitulating structural and energetic effects of resistance mutations, which involve large changes in binding affinity, remains relatively unexplored. We used continuum solvation binding affinity predictions and graph theory-based flexibility calculations to model thirteen drug resistance mutations in HCV NS3/4A serine protease, against three small-molecule inhibitors, with a 2-fold objective: quantitative assessment of binding energy predictions against experimental data and elucidation of structural/energetic determinants of resistance. The results show statistically significant correlation between predicted and experimental binding affinities, with R(2) and predictive index of up to 0.83 and 0.91, respectively. The level of accuracy was consistent with what has been reported for the inverse problem of binding affinity estimation of congeneric ligands against the same target. The quality of predictions was poor for mutations involving induced-fit effects, primarily, because of the lack of entropy terms. Flexibility analysis explained this discrepancy by indicating characteristic changes in side-chain mobility of a key binding site residue. The combined results from two approaches provide novel insights regarding the molecular mechanism of resistance. NS3/4A inhibitors, with large P2 substituents, derive high affinity with optimal van der Waals interactions in the S2 subsite, in order to overcome unfavorable desolvation and entropic cost of induced-fit effects. High-level resistance mutations tend to increase the desolvation and/or entropic barrier to ligand binding. The lead optimization strategies should, therefore, address the balance of these opposing energetic contributions in both the wild-type and mutant target.