Integrated Approach Using Protein and Ligand Information to Analyze Selectivity‐ and Affinity‐Determining Features of Carbonic Anhydrase Isozymes

Abstract

The application and comparison of selected protein- and ligand-based approaches to elucidate factors important for affinity and selectivity towards the carbonic anhydrase isozymes I, II, and IV are described. Carbonic anhydrases are abundant in pro- and eukaryotes. These enzymes catalyze the reversible hydration of carbon dioxide to bicarbonate and H(+) ions and are thus involved in many important physiological and pathophysiological processes. Due to the fact that the human carbonic anhydrase family consists of 16 closely related isozymes, the design of selective inhibitors is a special challenge for medicinal chemists. In order to extract selectivity-determining features, we applied purely ligand-based 3D QSAR techniques as well as qualitative comparative molecular field analyses of the targets' binding sites using consensus principal component analysis (CPCA). The dataset for the QSAR studies was deliberately compiled from 1,748 inhibitors and comprises about 140 ligands, mainly of the sulfonamide type. Additionally, we employed the novel AFMoC approach, which intrinsically combines protein and ligand information. The simultaneous use of these different techniques gives deeper insight into selectivity and affinity-determining features and provides quantitative models for prediction.

3D-QSAR in Drug Design - A Review

“…• In addition of the enthalpic contribution, the methodology is also expected to include the entropic effects resulting from (de-)solvation, since structural knowledge from experimentally determined complexes is converted into statistical pair potentials Some of the thriving applications of AFMoC include building predictive 3D-QSAR models for 1-deoxyxylulose-5-phosphate (DOXP)-reductoisomerase inhibitors [87], 3-oxybenzamides as potent inhibitors of the coagulation protease factor Xa [88], thermolysin and glycogen phosphorylase b inhibitors [86], and for analyzing selectivity-and affinity-determining features of carbonic anhydrase isozymes [89]. Recently the methodology has been modified to account for the multiple ligand conformations in an ensemble of protein configurations.…”

From Hansch‐Fujita Analysis to AFMoC: A Road to Structure‐Based QSAR

“…So far 3D‐QSAR models have been derived by AFMoC analyses for ligands of the DOXP‐reductoisomerase,22 the carbonic anhydrase isoenzymes,23 and factor Xa 24. In the first case, a predictive AFMoC model was obtained despite a small set of ligands and a heterogeneous set of crystal structures to work with: The crystal structures either had different loop conformations or missing metal ions or co‐substrates resulting in different orientations of co‐crystallized antagonists.…”

“…For this, classical 3D‐QSAR techniques (CoMFA, CoMSIA), protein‐based consensus principal component analysis (CPCA), and AFMoC was applied. Encouragingly, the AFMoC approach showed regions for enhancing ligand selectivity that purely ligand‐based methods were unable to detect; this was attributed to the fact that AFMoC, in addition to ligand information, also exploits information on structural differences between the isoenzymes 23…”

Identification and Visualization of Kinase-Specific Subpockets

“…Several computational approaches already deal with the prediction of selectivity determining features. The most prominent ones include the physicochemical analysis of binding sites, − docking calculations, matched molecular pair analysis, − QSAR, proteochemometric approaches, , analysis of protein ligand interactions, , and rule-based methods …”

“…Most of the initial binding site comparison studies focused on the identification of similarities in order to functionally classify proteins. − Nevertheless, several approaches to identify selectivity determining features in binding sites also exist. ,,,, To highlight physicochemical hot spots within the binding site, many grid-based approaches use molecular interaction fields (MIFs) , or knowledge-based potentials. , BioGPS, , for instance, uses MIFs to identify and compare hydrophilic as well as hydrophobic areas in the binding sites of interest. While many previous GRID-based methods ,, only allowed calculation of hot-spots for one structure at a time, BioGPS introduces so-called pharmacophore interaction points for structural ensembles.…”