Accuracy of binding mode prediction with a cascadic stochastic tunneling method

7-Alkyl-3-benzylcoumarins: a versatile scaffold for the development of potent and selective cannabinoid receptor agonists and antagonists

A series of 7-alkyl-3-benzylcoumarins was designed, synthesized, and tested at cannabinoid CB(1) and CB(2) receptors in radioligand binding and cAMP accumulation studies. 7-Alkyl-3-benzylcoumarins were found to constitute a versatile scaffold for obtaining potent CB receptor ligands with high potency at either CB(1) or CB(2) and a broad spectrum of efficacies. Fine-tuning of compound properties was achieved by small modifications of the substitution pattern. The most potent compounds of the present series include 5-methoxy-3-(2-methylbenzyl)-7-pentyl-2H-chromen-2-one (19a, PSB-SB-1201), a selective CB(1)antagonist (K(i) CB(1) 0.022 μM), 5-methoxy-3-(2-methoxybenzyl)-7-pentyl-2H-chromen-2-one (21a, PSB-SB-1202), a dual CB(1)/CB(2)agonist (CB(1)K(i) 0.032 μM, EC(50) 0.056 μM; CB(2)K(i) 0.049 μM, EC(50) 0.014 μM), 5-hydroxy-3-(2-hydroxybenzyl)-7-(2-methyloct-2-yl)-2H-chromen-2-one (25b, PSB-SB-1203), a dual CB(1)/CB(2) ligand that blocks CB(1) but activates CB(2) receptors (CB(1)K(i) 0.244 μM; CB(2)K(i) 0.210 μM, EC(50) 0.054 μM), and 7-(1-butylcyclopentyl)-5-hydroxy-3-(2-hydroxybenzyl)-2H-chromen-2-one (27b, PSB-SB-1204), a selective CB(2) receptor agonist (CB(1)K(i) 1.59 μM; CB(2)K(i) 0.068 μM, EC(50) 0.048 μM).

In silico discovery of a compound with nanomolar affinity to antithrombin causing partial activation and increased heparin affinity

The medical and socioeconomic relevance of thromboembolic disorders promotes an ongoing effort to develop new anticoagulants. Heparin is widely used as activator of antithrombin but incurs side effects. We screened a large database in silico to find alternative molecules and predicted d-myo-inositol 3,4,5,6-tetrakisphosphate (TMI) to strongly interact with antithrombin. Isothermal titration calorimetry confirmed a TMI affinity of 45 nM, higher than the heparin affinity (273 nM). Functional studies, fluorescence analysis, and citrullination experiments revealed that TMI induced a partial activation of antithrombin that facilitated the interaction with heparin and low affinity heparins. TMI improved antithrombin inhibitory function of plasma from homozygous patients with antithrombin deficiency with a heparin binding defect and also in a model with endothelial cells. Our in silico screen identified a new, non-polysaccharide scaffold able to interact with the heparin binding domain of antithrombin. The functional consequences of this interaction were experimentally characterized and suggest potential anticoagulant therapeutic applications.

Automated site preparation in physics-based rescoring of receptor ligand complexes

Hydrogen atoms are not typically observable in X-ray crystal structures, but inferring their locations is often important in structure-based drug design. In addition, protonation states of the protein can change in response to ligand binding, as can the orientations of OH groups, a subtle form of "induced fit." We implement and evaluate an automated procedure for optimizing polar hydrogens in protein-binding sites in complex with ligands. Specifically, we apply the previously described Independent Cluster Decomposition Algorithm (ICDA) algorithm (Li et al., Proteins 2007;66:824-837), which assigns the ionization states of titratable residues, the amide orientations of Asn/Gln side chains, the imidazole ring orientation in His, and the orientations of OH/SH groups, in a unified algorithm. We test the utility of this method for identifying nativelike ligand poses using 247 protein-ligand complexes from an established database of docked decoys. Pose selection is performed with a physics-based scoring function based on a molecular mechanics energy function and a Generalized Born implicit solvent model. The use of the ICDA receptor preparation protocol, implemented with no knowledge of the native ligand pose, increases the accuracy of pose selection significantly, with the average RMSD over all complexes decreasing from 2.7 to 1.5 A when applying ICDA. Large improvements are seen for specific classes of binding sites with titratable groups, such as aspartyl proteases.

Structure-based approaches to antibiotic drug discovery

The development of antimicrobials has advanced tremendously over the past century. However, as our production capacity increases, the threat of resistance is ever-present. To combat this resistance, two main avenues of drug discovery are being pursued: identifying new microbial proteins for which to direct drug discovery efforts, and designing innovative drugs that target existing proteins. The advent of structural genomics research has advanced to the point of rapidly discovering novel microbial protein targets. In addition, modern tools of computational biology greatly enhance the speed and reliability of antimicrobial discovery. The various steps of this process are outlined and discussed, including virtual ligand screening, pocket identification, and compound optimization.

Receptor-specific scoring functions derived from quantum chemical models improve affinity estimates for in-silico drug discovery

The adaptation of forcefield-based scoring function to specific receptors remains an important challenge for in-silico drug discovery. Here we compare binding energies of forcefield-based scoring functions with models that are reparameterized on the basis of large-scale quantum calculations of the receptor. We compute binding energies of eleven ligands to the human estrogen receptor subtype alpha (ERalpha) and four ligands to the human retinoic acid receptor of isotype gamma (RARgamma). Using the FlexScreen all-atom receptor-ligand docking approach, we compare docking simulations parameterized by quantum-mechanical calculation of a large protein fragment with purely forcefield-based models. The use of receptor flexibility in the FlexScreen permits the treatment of all ligands in the same receptor model. We find a high correlation between the classical binding energy obtained in the docking simulation and quantum mechanical binding energies and a good correlation with experimental affinities R=0.81 for ERalpha and R=0.95 for RARgamma using the quantum derived scoring functions. A significant part of this improvement is retained, when only the receptor is treated with quantum-based parameters, while the ligands are parameterized with a purely classical model.