A QSAR Model Derived from a Homology Model: A Strategy to Include Structural Information in Ligand-based Design

Generation and evaluation of a homology model of PfGSK-3

Plasmodial GSK-3 is a potential new target for malaria therapy. For a structure-based design project, the three-dimensional information of the designated target is needed. Unfortunately, experimental structure data for plasmodial GSK-3 is not yet available. Homology building can be used to generate such three-dimensional structure data using structure information of a homologous protein. GSK-3 possesses a very flexible ATP-binding site, a fact reflected in the variety of X-ray structures of the human GSK-3beta which are deposited in the protein data base and are crystallized with different ligands. We used ten different HsGSK-3beta templates for the model building of plasmodial GSK-3 and generated 200 models for each template with different modeling protocols. The quality of the models was evaluated with different tools. The results of these evaluations were used to calculate a rank-by-rank consensus score. The top models of this were used to compile an ensemble of PfGSK-3 models that reflect the flexibility of the ATP-binding site and that will be used for the structure-based design of potential ATP-binding site inhibitors of PfGSK-3.

Computational chemistry approaches to drug discovery in signal transduction

The advent of therapeutic strategies aimed at targeting specific macromolecular components of deregulated signaling pathways associated with particular disease states has given rise to the idea that it should be possible to design ligands as drug candidates to these targets from first principles. This concept has been beckoning for a long time but structure-based ligand design only became feasible once it was possible to determine the 3-D structures of molecular targets at atomic resolution. However, structure-based design turned out to be difficult, chiefly because under physiological conditions both receptors and ligands are not static but they behave dynamically. While it is possible to design ligands with high steric and electronic complementarity to a receptor site, it is always uncertain how biologically relevant the assumed conformations of both ligand and receptor actually are. The fact that it remains beyond our current abilities to predict with sufficient accuracy the affinity between hypothetical ligand and receptor poses is in part connected with this problem and continues to confound the reliable prediction of drug-like ligands for therapeutic targets. Nevertheless, significant progress has been made and so-called virtual screening methods that use computational methods to dock candidate ligands into receptor sites and to score the resulting complexes are now used routinely as one of the components in drug discovery screening campaigns. Here an overview is given of the underlying principles, implementations, and applications of structure-guided computational design technologies. Although the emphasis is on receptor-based strategies, mention will also be made of some of the more established ligand-based approaches, such as similarity analyses and quantitative structure-activity relationship methods.