Search

[Image: see text] In order to assess the potential of sPLA(2)-X as a therapeutic target for atherosclerosis, novel sPLA(2) inhibitors with improved type X selectivity are required. To achieve the objective of identifying such compounds, we embarked on a lead generation effort that resulted in the identification of a novel series of indole-2-carboxamides as selective sPLA2-X inhibitors with excellent potential for further optimization.

Structure-based virtual screening was performed against the target dipeptidyl peptidase IV (DPP-IV) to identify good chemical starting points for medicinal chemistry. A database of available compounds was filtered by calculated physical properties and undesired chemistry. This database was matched against two in-house designed DPP-IV pharmacophores, and the hits from these pharmacophore searches were docked into a DPP-IV crystal structure. Compounds were then selected for testing and 51 active compounds were identified from a list of 4000 compounds tested. These had activities ranging from 30% to 82% when tested at a concentration of 30 microM in an enzyme inhibition assay.

New molecular modeling tools were developed to construct a qualitative pharmacophore model for histamine H3 receptor antagonists. The program SLATE superposes ligands assuming optimum hydrogen bond geometry. One or two ligands are allowed to flex in the procedure, thereby enabling the determination of the bioactive conformation of flexible H3 antagonists. In the derived model, four hydrogen-bonding site points and two hydrophobic pockets available for binding antagonists are revealed. The model results in a better understanding of the structure-activity relationships of H3 antagonists. To validate the model, a series of new antagonists was synthesized. The compounds were designed to interact with all four hydrogen-bonding site points and the two hydrophobic pockets simultaneously. These ligands have high H3 receptor affinity, thereby illustrating how the model can be used in the design of new classes of H3 antagonists.

A novel program for the superposition of flexible molecules, slate, is presented. It uses simulated annealing to minimise the difference between the distance matrices calculated from the hydrogen-bonding and aromatic-ring properties of two ligands. A method for generating a molecular stack using multiple pairwise matches is illustrated. These stacks are used by the program doh to predict the relative positions of receptor atoms that could form hydrogen bonds to two or more ligands in the dataset. The methodology has been applied to ligands binding to dihydrofolate reductase, thermolysin, H3 histamine receptors, α2 adrenoceptors and 5-HT1D receptors. When there are sufficient numbers and diversity of molecules in the dataset, the prediction of receptor-atom positions is applicable to compound design.

Hydrogen bonds are the most specific, and therefore predictable of the intermolecular interactions involved in ligand-protein binding. Given the structure of a molecule, it is possible to estimate the positions at which complementary hydrogen-bonding atoms could be found. Crystal-survey data are used in the design of a program, HBMAP, that generates a hydrogen-bond map for any given ligand, which contains all the feasible positions at which a complementary atom could be found. On superposition of ligands, the overlapping regions of their maps represent positions of receptor atoms to which each molecule can bind. The certainty of these positions is increased by the incorporation of a larger number and diversity of molecules. In this work, superposition is achieved using the program HBMATCH, which uses simulated annealing to generate the correspondence between points from the hydrogen-bonding maps of the two molecules. Equivalent matches are distinguished on the basis of their steric similarity. The strategy is tested on a number of ligands for which ligand-protein complexes have been solved crystallographically, which allows validation of the techniques. The receptor atom positions of thermolysin are successfully predicted when the correct superposition is obtained.

Steric complementarity is a prerequisite for ligand-receptor recognition; this implies that drugs with a common receptor binding site should possess sterically similar binding surfaces. This principle is used as the basis for an automatic and unbiased method that superposes molecules. One molecule is rotated and translated to maximize the overlap between the two molecular surface volumes. A fast grid-based method is used to determine the extent of this overlap, and this is optimized using simulated annealing. Matches with high steric similarity scores are then sorted on the basis of both hydrogen-bond and electrostatic similarity between the matched molecules. Flexible molecules are treated as a set of rigid representative conformers. The algorithm has correctly predicted superpositions between a number of paris of molecules, according to crystallographic data from ligands that have been co-crystallized at common enzyme binding sites.

Focused combinatorial libraries are a useful way of approaching structure-based drug design, but they may show unexpected bias in exploring the receptor site. One way to monitor this coverage is by assessing which hydrogen-bonding groups at the receptor site are used by each ligand in the library. In this communication, we present an analysis of the hydrogen bonds formed between inhibitor and enzyme in a set of HIV protease complexes. These data are a model for a larger combinatorial library, and have allowed us to develop methods for receptor-based diversity analysis.

This paper describes a computational strategy for the superposition of a set of flexible molecules. The combinatorial problems of searching conformational space and molecular matching are reduced drastically by the combined use of simulated annealing methods and cluster analysis. For each molecule, the global minimum of the conformational energy is determined by annealing and the search trajectory is retained in a history file. All the significantly different low-energy conformations are extracted by cluster analysis of data in the history file. Each pair of molecules, in each of their significantly different conformations, is then matched by simulated annealing, using the difference-distance matrix as the objective function. A set of match statistics is then obtained, from which the best match taken from all different conformations can be found. The molecules are then superposed either by reference to a base molecule or by a consensus method. This strategy ensures that as wide a range of conformations as possible is considered, but at the same time that the smallest number of significantly different conformations is used. The method has been tested on a set of six angiotensin II antagonists with between 7–11 rotatable bonds.