Combinatorial Chemistry & High Throughput Screening - Volume 3, Issue 5, 2000

Finding drugs that inhibit protein-protein interactions is usually difficult. While computer-aided design is used widely to facilitate the drug discovery process for protein targets with well-defined binding pockets, its application to the design of inhibitors targeting a protein surface is very limited. In this mini-review we address two aspects of this issue: firstly, we overview the current state of design methodology for inhibitors specifically targeting protein surfaces, and secondly, we briefly outline recent advances in computational methods for structure-based drug design. These methods are closely related to protein docking and protein recognition, the difference being that in ligand design, ligands are built on a fragment-by-fragment basis. A novel scheme of computational combinatorial ligand design developed for the design of inhibitors that interfere with protein-protein interaction is described in detail. Current applications and limitations of this methodology, as well as its future prospects, are discussed.

Many contemporary applications in computer-aided drug discovery and chemoinformatics depend on representations of molecules by descriptors that capture their structural characteristics and properties. Such applications include, among others, diversity analysis, library design, and virtual screening. Hundreds of molecular descriptors have been reported in the literature, ranging from simple bulk properties to elaborate three-dimensional formulations and complex molecular fingerprints, which sometimes consist of thousands of bit positions. Knowledge-based selection of descriptors that are suitable for specific applications is an important task in chemoinformatics research. If descriptors are to be selected on rational grounds, rather than guesses or chemical intuition, detailed evaluation of their performance is required. A number of studies have been reported that investigate the performance of molecular descriptors in specific applications and/or introduce novel types of descriptors. Progress made in this area is reviewed here in the context of other computational developments in combinatorial chemistry and compound screening.

Phage display libraries offer a strategy to isolate peptide ligands to target proteins and to define potential interaction sites between proteins. Recent studies have indicated a novel utility for phage display in that bacteriophage engineered to express peptide ligands to specific cell surface receptors are internalized by mammalian cells. Thus, reporter genes such as green fluorescent protein and lacZ harbored in the phage genome can be delivered to mammalian cells using targeting peptides displayed on the surface of phage. There is also the possibility to generate novel types of peptide libraries expressed intracellularly using a phage capable of inducing expression of its coding genes in human cells.

Molecular diagnosis of complex inherited disorders, population screening of genetic diseases, studies of the genetic basis of variable drug response (pharmacogenetics) as well as discovery and investigation of new drug targets (pharmacogenomics) involve screening for mutations in multiple DNA samples. Furthermore, the development of a third generation of the human genome map, based on single nucleotide polymorphisms (SNPs), requires screening for allelic variants through all of the three billion basepairs in the human genome. Thus, the need for high throughput mutation screening methods is great and is rapidly increasing. Traditional methods for mutation screening often involve slab-gel electrophoresis analyses which are laborious and difficult to automate. However, recent developments in capillary electrophoresis systems for DNA fragment analysis have made fully automated mutation screening possible and have dramatically increased the possible sample throughput. This review describes the recent advances in capillary electrophoresis of DNA and summarize the various methods for mutation screening based on this technique.

In the past several years, a new set of technologies based on whole genome analysis have revolutionized the study of gene expression. These microarray or "gene chip" technologies, which arose out of the development of large-scale sequencing approaches, are now coming into increasing use, generating a far greater volume of data than the data representing the sequences themselves. This review focuses on the current state of development of these technologies, and the available approaches to manage and analyze the information they generate. The applicability of this technology to general problems in biomedicine is also discussed.

Genomics has caused an explosion in the number of potential therapeutic targets with varying degrees of validated pathophysiology. Among the first applications of combinatorial chemistry in genomics-driven drug discovery is the search for surrogate ligands or substrates. In the event that no surrogate is found for molecular assays, more exotic functional screens in whole cells or model organisms are used. Protein-protein interaction mapping by yeast and mammalian two-hybrid systems dominates empirical functional genomics, and this will lead to a bias for screening projects targeting this type of interaction. Drug discovery for protein-protein interactions has a poor track record, and this will challenge prevailing views on the design of combinatorial libraries. Genomics based on structural homology will yield many putative kinases, receptors, enzymes, transporter proteins, ion channels and GPCRs. Most of these projects will require new surrogate agonists, ligands or substrates, and then pharmaceutically useful agonists or antagonists will need to be found. Again, combinatorial chemistry might be essential to these studies. Given the need to screen hundreds of targets at great risk of irrelevance to pathophysiology, combined with the challenge of finding surrogate or natural ligands for these new targets, there is an urgent need for efficiency. Different groups are addressing these concerns by developing biologically-driven combinatorial libraries in order to achieve a higher density of bioactivity. Early efforts in this regard will be described.

The use of fluorescence polarization (FP) has increased significantly in the development of sensitive and robust assays for high throughput screening of chemical compound libraries during the past few years. In this study, we show that FP is a useful assay miniaturization technology for reagent reduction during high throughput screening. We developed and optimized several FP assays for binding to estrogen receptor ??and two protein kinases with an assay volume of 100 μ. Without any re-optimization, a consistent signal window was maintained in 384- or 1536-well format when the assay volume varied from 2.5-100 μ at constant concentrations of all assay components. In contrast, the signal window decreased with decreasing assay volume at constant reagent concentration in the protein kinase C scintillation proximity assay (SPA) and prompt fluorescence assay. In addition, the effect of evaporation on the signal window was minimal for the FP assays. Our study suggests that FP is superior to SPA and prompt fluorescence in terms of reagent reduction in the miniaturized assay format.