Multiple-docking and affinity fingerprint methods for protein classification and inhibitors selection

Inhibition of secreted phospholipases A₂ by 2-oxoamides based on α-amino acids: Synthesis, in vitro evaluation and molecular docking calculations

Group IIA secreted phospholipase A₂ (GIIA sPLA₂) is a member of the mammalian sPLA₂ enzyme family and is associated with various inflammatory conditions. In this study, the synthesis of 2-oxoamides based on α-amino acids and the in vitro evaluation against three secreted sPLA₂s (GIIA, GV and GX) are described. The long chain 2-oxoamide GK126 based on the amino acid (S)-leucine displayed inhibition of human and mouse GIIA sPLA₂s (IC₅₀ 300nM and 180nM, respectively). It also inhibited human GV sPLA₂ with similar potency, while it did not inhibit human GX sPLA₂. The elucidation of the stereoelectronic characteristics that affect the in vitro activity of these compounds was achieved by using a combination of simulated annealing to sample low-energy conformations before the docking procedure, and molecular docking calculations.

Relating the shape of protein binding sites to binding affinity profiles: is there an association?

Background

Various pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins. Nevertheless, the connection between the protein affinity profiles and the structural characteristics of the binding sites is still unclear. Our aim was to investigate the association between virtual drug screening results (calculated binding free energy values) and the geometry of protein binding sites. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Protein binding site geometries were characterized by 420 PocketPicker descriptors. The basic underlying component structure of MAFs and binding site geometries, respectively, were examined by principal component analysis; association between principal components extracted from these two sets of variables was then investigated by canonical correlation and redundancy analyses.

Results

PCA analysis of the MAF variables provided 30 factors which explained 71.4% of the total variance of the energy values while 13 factors were obtained from the PocketPicker descriptors which cumulatively explained 94.1% of the total variance. Canonical correlation analysis resulted in 3 statistically significant canonical factor pairs with correlation values of 0.87, 0.84 and 0.77, respectively. Redundancy analysis indicated that PocketPicker descriptor factors explain 6.9% of the variance of the MAF factor set while MAF factors explain 15.9% of the total variance of PocketPicker descriptor factors. Based on the salient structures of the factor pairs, we identified a clear-cut association between the shape and bulkiness of the drug molecules and the protein binding site descriptors.

Conclusions

This is the first study to investigate complex multivariate associations between affinity profiles and the geometric properties of protein binding sites. We found that, except for few specific cases, the shapes of the binding pockets have relatively low weights in the determination of the affinity profiles of proteins. Since the MAF profile is closely related to the target specificity of ligand binding sites we can conclude that the shape of the binding site is not a pivotal factor in selecting drug targets. Nonetheless, based on strong specific associations between certain MAF profiles and specific geometric descriptors we identified, the shapes of the binding sites do have a crucial role in virtual drug design for certain drug categories, including morphine derivatives, benzodiazepines, barbiturates and antihistamines.