Identification of a Small Molecule That Selectively Inhibits Mouse PC2 over Mouse PC1/3: A Computational and Experimental Study

<div><p>The calcium-dependent serine endoproteases prohormone convertase 1/3 (PC1/3) and prohormone convertase 2 (PC2) play important roles in the homeostatic regulation of blood glucose levels, hence implicated in diabetes mellitus. Specifically, the absence of PC2 has been associated with chronic hypoglycemia. Since there is a reasonably good conservation of the catalytic domain between species translation of inhibitory effects is likely. In fact, similar results have been found using both mouse and human recombinant enzymes. Here, we employed computational structure-based approaches to screen 14,400 compounds from the Maybridge small molecule library towards mouse PC2. Our most remarkable finding was the identification of a potent and selective PC2 inhibitor. Kinetic data showed the compound to be an allosteric inhibitor. The compound identified is one of the few reported selective, small-molecule inhibitors of PC2. In addition, this new PC2 inhibitor is structurally different and of smaller size than those reported previously. This is advantageous for future studies where structural analogues can be built upon.</p> </div

Distribution of molecular weights for 115 compounds docked utilizing both FRED and GlideXP.

<p>These are the top 115 scoring compounds obtained from docking the Maybridge database to the mouse PC2 models. The compounds are color-coded based on molecular weight. Yellow is the median and represents a molecular weight of 283.31 Da.</p

Maybridge Compound Screening of PC1/3 and PC2.

<p>Negative numbers represent stimulation.</p><p>% error is shown in parenthesis.</p><p>Bold represent compounds with relevant inhibition or stimulation effect towards either PC.</p

The active site and potential allosteric sites of PC2, as determined using two structural models of the enzyme.

<p>The first and second numbers in parentheses denote the ranking of each site in model 6 and the homology model, respectively. (See the section <i>Generating structural models of PC2 for ligand docking</i> for details).</p

Enzymatic assay of RJC00847 against PC2.

<p><b>A</b>). The effect of increasing the concentration of the inhibitor on the detection of fluorescent product, 7-amino-4-methylcoumarin (AMC). <b>B</b>). Concentration-response curve, from which an IC₅₀ value of 1.1±0.06 µM was determined.</p

Selected peptides and small molecules active towards PC2.

<p>Selected peptides and small molecules active towards PC2.</p

Distribution of ligands within subsites in the binding pocket of PC2.

<p>(A) Ligands accommodated in distinct subsites; (B) A ligand that spread into the P2 and P4 subsites; (C) Ligands that overlapped with the P1 and P4 sites, which may be used as frameworks to link fragments in the P1, P2 and P4 subsites shown in (A).</p

Percent sequence identity and gaps between the mouse PC2 and template sequences.

a<p>The pdbids for each template are given.</p>b<p>The residue numbering is for the intact pro-protein for the domains in the given templates.</p

Compounds that activated PC2 (HTS05737 and JFD02062) and PC1/3 (BTB03195).

<p>RJC00847 selectively inhibited PC2.</p

Rapid Scanning Structure–Activity Relationships in Combinatorial Data Sets: Identification of Activity Switches

We present a general approach to describe the structure–activity relationships (SAR) of combinatorial data sets with activity for two biological endpoints with emphasis on the rapid identification of substitutions that have a large impact on activity and selectivity. The approach uses dual-activity difference (DAD) maps that represent a visual and quantitative analysis of all pairwise comparisons of one, two, or more substitutions around a molecular template. Scanning the SAR of data sets using DAD maps allows the visual and quantitative identification of activity switches defined as specific substitutions that have an opposite effect on the activity of the compounds against two targets. The approach also rapidly identifies single- and double-target R-cliffs, i.e., compounds where a single or double substitution around the central scaffold dramatically modifies the activity for one or two targets, respectively. The approach introduced in this report can be applied to any analogue series with two biological activity endpoints. To illustrate the approach, we discuss the SAR of 106 pyrrolidine bis-diketopiperazines tested against two formylpeptide receptors obtained from positional scanning deconvolution methods of mixture-based libraries