Abstract
This study sought to identify a single molecule capable of managing all three manifestations of metabolic syndrome–hyperglycaemia, dyslipidaemia and hypertension. Two Protein Data Bank (PDB) depositions were selected and used to establish the baseline affinity that any designed molecule in this study should ideally exceed in order to be considered for further optimisation. These were PDB depositions 3VN2 and 2P54 describing the bound co-ordinates of the Peroxisome Proliferator Activated Receptor (PPAR)γ partial agonist and Angiotensin II Receptor (Ang(II)R) blocker telmisartan and of the experimental PPARα fibrate agonist GW590735 bound to their respective cognate receptors. These small molecules were extracted from their cognate receptors, docked into their non-cognate counterparts, conformational analysis performed, and the optimal conformers were selected as template scaffolds in two parallel processes. The first was a fragment based de novo approach. Here, molecular moieties from the optimal telmisartan and GW590735 scaffolds modelled in their non-cognate targets and considered critical to binding were identified and modelled, in order to produce seed structures capable of sustaining molecular growth at user-directed sites designated as H.spc atoms subsequent to their being docked within the non-cognate Ligand Binding Pockets (LBPs). The second approach was a Virtual Screening (VS) exercise. Here, the optimal telmisartan and GW590735 conformers were submitted as query molecules to VS databases both individually and in the form of a consensus pharmacophore. This VS exercise identified structurally diverse molecules which were electronically and spatially similar to the queries and which were capable of modulating the target receptors. The molecular cohorts identified through both VS and the de novo approaches were filtered for Lipinski Rule compliance. The molecules that survived filtering were then re-docked into the non-cognate PPARα and/or γ_LBPs, conformational analysis re-performed and the affinity of the optimal conformer measured for its cognate receptor quantified. Comparison was made to the baseline and non-cognate receptor affinities previously established, and the molecules exhibiting dual affinities exceeding baseline values were selected for further optimisation. The use of the “tried and tested” Ang(II)R blocker and fibrate scaffolds as templates predisposes to the identification of novel structures devoid of unacceptable toxicity.
Highlights
A total of 70 apo- and holo- Peroxisome Proliferator Activated Receptor (PPAR) related crystallographic depositions were identified from the pdb at the time of study (November 2012). 67 of these were identified as suitable for this study; a resolution of up to 3Å was considered suitable for a pdb crystallographic deposition to be included in the study
All four Ramachandran plots generated in PROCHECK [13], for the pdb crystallographic depositions that were chosen as baseline for this study had most residues, that is more than 90%, in the most favoured regions of the plot which further reinforced their suitability as template crystallographic depositions
The fact that this volume remained largely constant across the board as shown in Table 1 implies the spatial similarity from a volume perspective of the two PPAR subtypes considered and of the magnitude of the two high affinity ligands used as templates for this study
Summary
They belong to the nuclear hormone receptor superfamily, and comprise three subtypes: PPARα, PPARγ, and PPARβ/δ. It has been demonstrated that PPARα is involved in in glucose homeostasis and the development of insulin resistance [1]. Activation of the PPARβ/δ receptor results in modification of the body’s energy fuel preference from glucose to fat [2]. The PPARγ subtype is a strong inducer of adipogenesis and is involved in glucose metabolism [1]. This means that the PPAR family of nuclear receptors as a whole is a regulator of energy homeostasis and metabolism. PPAR agonism has potential in a wide spectrum of pathologies including adipocyte differentiation, cancer, diabetes, dyslipidaemia, inflammation, lung diseases, neurodegenerative disorders, reproductive disorders, obesity and pain
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