Abstract

BackgroundVarious 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.ResultsPCA 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.ConclusionsThis 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.

Highlights

  • Various pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins

  • principal component analysis (PCA) of Molecular Affinity Profiles of target proteins As described in the Methods, PCA with ORTHOMAX/ PARSIMAX rotation of the molecular affinity fingerprints was conducted in order to determine the underlying factor structure of the Molecular Affinity Fingerprints (MAFs) profiles, characterizing the set of 154 proteins used for the purpose of our study

  • The first 40 factors obtained from the factor analysis of the MAF profiles of 154 target proteins are displayed. 30 factors were retained in accordance with the average variance criterion

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Summary

Introduction

Various pattern-based methods exist that use in vitro or in silico affinity profiles for classification and functional examination of proteins. Molecular Affinity Fingerprints (MAFs) were determined for 154 proteins based on their molecular docking energy results for 1,255 FDA-approved drugs. Finding complementary shapes for the active site of a druggable protein is a starting point of de novo drug design if the target structure is previously determined [1]. It should be noted that shape-based techniques play an important role in the simulation of protein-protein interactions. From this area of research we mention a recent publication by Venkatraman et al which reports on the development of a docking algorithm based on 3D Zernike Descriptors (i.e., 3D function representations of protein surface) that produced outstanding performance compared to other methods [4]

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