No abstract

Predicting druggability and prioritizing certain disease modifying targets for the drug development process is of high practical relevance in pharmaceutical research. DoGSiteScorer is a fully automatic algorithm for pocket and druggability prediction. Besides consideration of global properties of the pocket, also local similarities shared between pockets are reflected. Druggability scores are predicted by means of a support vector machine (SVM), trained, and tested on the druggability data set (DD) and its nonredundant version (NRDD). The DD consists of 1069 targets with assigned druggable, difficult, and undruggable classes. In 90% of the NRDD, the SVM model based on global descriptors correctly classifies a target as either druggable or undruggable. Nevertheless, global properties suffer from binding site changes due to ligand binding and from the pocket boundary definition. Therefore, local pocket properties are additionally investigated in terms of a nearest neighbor search. Local similarities are described by distance dependent histograms between atom pairs. In 88% of the DD pocket set, the nearest neighbor and the structure itself conform with their druggability type. A discriminant feature between druggable and undruggable pockets is having less short-range hydrophilic-hydrophilic pairs and more short-range lipophilic-lipophilic pairs. Our findings for global pocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction. Nevertheless, the variety of pocket shapes and their flexibility upon ligand binding limit the automatic projection of druggable features onto descriptors. Incorporating local pocket properties is another step toward a reliable descriptor-based druggability prediction.

rarey@zbh.uni-hamburg.de.

By optimizing binding to a selected target protein, modern drug research strives to develop safe and efficacious agents for the treatment of disease. Selective drug action is intended to minimize undesirable side effects from scatter pharmacology. Celecoxib (Celebrex), valdecoxib (Bextra), and rofecoxib (Vioxx) are nonsteroidal antiinflammatory drugs (NSAIDs) due to selective inhibition of inducible cyclooxygenase COX-2 while sparing inhibition of constitutive COX-1. While rofecoxib contains a methyl sulfone constituent, celecoxib and valdecoxib possess an unsubstituted arylsulfonamide moiety. The latter group is common to many carbonic anhydrase (CA) inhibitors. Using enzyme kinetics and X-ray crystallography, we demonstrate an unexpected nanomolar affinity of the COX-2 specific arylsulfonamide-type celecoxib and valdecoxib for isoenzymes of the totally unrelated carbonic anhydrase (CA) family, such as CA I, II, IV, and IX, whereas the rofecoxib methyl sulfone-type has no effect. When administered orally to glaucomatous rabbits, celecoxib and valdecoxib lowered intraocular pressure, suggesting that these agents may have utility in the treatment of this disorder. The crystal structure of celecoxib in complex with CA II reveals part of this inhibition to be mediated via binding of the sulfonamide group to the catalytic zinc of CA II. To investigate the structural basis for cross-reactivity of these compounds between COX-2 and CA II, we compared the molecular recognition properties of both protein binding pockets in terms of local physicochemical similarities among binding site-exposed amino acids accommodating different portions of the drug molecules. Our approach Cavbase, implemented into Relibase, detects similarities between the sites, suggesting some potential to predict unexpected cross-reactivity of drugs among functionally unrelated target proteins. The observed cross-reactivity with CAs may also contribute to differences in the pharmacological profiles, in particular with respect to glaucoma and anticancer therapy and may suggest new opportunities of these COX-2 selective NSAIDs.

Here we present an evaluation of the binding affinity prediction accuracy of the free energy calculation method FEP+ on internal active drug discovery projects and on a large new public benchmark set. File list (3) download file view on ChemRxiv manuscript.pdf (4.23 MiB) download file view on ChemRxiv supplementary.pdf (0.92 MiB) download file view on ChemRxiv tables.zip (5.99 KiB)