Published September 29, 2015 | Version v1
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Rational design of ApoA-I Mimetic-polypharmacophoric of high free binding energy hopping scaffolds generated by integrating nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.

  • 1. Biogenea Pharmaceuticals Ltd

Description

4F is an anti-inflammatory, apolipoprotein A-I (apoA-I)-mimetic peptide that is active in vivo at nanomolar concentrations in the presence of a large molar excess of apoA-I. Physiologic concentrations (∼35 μM) of human apoA-I did not inhibit the production of LDL-induced monocyte chemotactic activity by human aortic endothelial cell cultures, but adding nanomolar concentrations of 4F in the presence of ∼35 μM apoA-I significantly reduced this inflammatory response. A common strategy for virtual screening considers a systematic docking of a large library of organic compounds into the target sites in protein receptors with promising leads selected based on favorable intermolecular interactions. Despite a continuous progress in the modeling of protein-ligand interactions for pharmaceutical design, important challenges still remain, thus the development of novel techniques is required. Pearson correlation coefficient between experimental and predicted by eSimDock Ki values for a large data set of the crystal structures of protein-ligand complexes from BindingDB is 0.58, which decreases only to 0.46 when target structures distorted to 3.0 Å Cα-RMSD are used. These encouraging results show that the performance of eSimDock is largely unaffected by the deformations of ligand binding regions, thus it represents a practical strategy for across-proteome virtual screening using protein models.Here, in Biogenea Pharmaceuticals Ltd we discovered for the first time the GENEA-Apo-I009. A Rational designed ApoA-I Mimetic-polypharmacophorIC hyper ligand as an in silico improved innovative and potential anti-inflammatory agent, computer-aided generated by integrating nonlinear scoring functions for a similarity-based ligand docking and binding affinity prediction approach.

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