Current Protein and Peptide Science - Volume 7, Issue 5, 2006

The last two decades have witnessed the dawn of a new era of “in silico-based” biology. These methods have been playing a major role, from investigation of the genomes to the design of new therapeutic compounds or prediction of protein structures. Lately, as computer technology has become cost-effective, new ideas and concepts have emerged, such as in silico virtual ligand screening with considerations of ligand and/or receptor flexibility. These computer-assisted drug discovery methods have been important in the past and are now part of most drug discovery campaigns. There are many reasons for that, for instance, Structural Genomics projects have enabled the determination of many high quality protein structures, and several of them are indeed potential drug targets. Further, parallel synthesis allows for the production of millions of “drug-like” molecules, thus potential drug candidates that could obstruct active sites, impede macromolecular interactions or induce conformational changes. In addition, Genome Projects have identified over 10,000 targets believed to be involved in the pathogenesis of diseases; some of these targets should be investigated rapidly as there obviously remains a significant number of unmet clinical needs in many disease indications. Many scientists around the World believe that, to use available data most effectively for drug discovery projects, it is essential to develop/apply reliable in silico high throughput screening methods. Thus, in 2005, I thought that it could be interesting to compile an issue about in silico screening methods based on knowledge about the 3D structure of protein targets. I am now delighted to present in this issue of Current Protein and Peptide Science review papers pertaining to the continuously evolving field of in silico screening and drug design. The contributions cover a broad range of topics, from pocket definition to docking, scoring and applications to homology models. There should be something to interest everyone who is involved with drug design. Because of the freedom of style and subject matter afforded to the contributors, it was felt necessary to open this issue with an introduction to the field. Thus, in my laboratory, we wrote a review introducing structure-based in silico screening and it is my hope that we have met this requirement in full. Then, Laurie and Jackson provide a highly readable introduction to pocket prediction while Jain describes the science (art) of scoring including his new approach as integrated in Surflex. Zsoldos et al. report for the first time on the exciting use of eHiTS and compare their approach with other tools. To the end of this issue, Rockey and Elcock discuss the use of homology models for receptor-based in silico screening with a special emphasis on the protein kinase family. I believe that this special issue contains much valuable information for protein scientists engaged in drug discovery campaigns. Not all topics could be covered but the readers should be able to find additional information in the references cited in each review article. I would like here again to thank the contributors for their work and the reviewers for their suggestions. It has been great pleasure to put this issue together. I am grateful to Dr. Maria Miteva (Inserm, Paris, France), Dr. Frederic Cazals (Inria, Sophia-Antipolis, France), Dr. Ajay Jain (San Francisco, USA), Dr. Xueliang Fang (Ann Arbor, USA), Dr. Wen Lee (Oxford, UK) and Dr. Anthony Nicholls (OpenEye, USA) for comments about this special issue. Finally, last but not least, I would like to give special thanks to Dr. Ben Dunn and Mr. M. Ilyas (Senior Manager Publications, Bentham Science Publishers Ltd.) for helping me bringing this special issue to completion.

The processes used by academic and industrial scientists to discover new drugs have recently experienced a true renaissance with many new and exciting techniques. The number of protein structures and/or chemical ligands is constantly growing, through the use of parallel chemistry, X-ray crystallography, NMR or homology modeling methods and so is the theoretical understanding of protein-ligand interactions. As such, structure-based approaches to drug-design and in silico screening are becoming routine part of most modern lead discovery programs. Prioritization of compound libraries is an extremely important task that aims at the rapid identification of tight-binding ligands and ultimately new therapeutic compounds. These in silico approaches combined with other experimental methods facilitate the design of new medicines to treat cardiovascular, degenerative, infectious, and neoplastic diseases, among others. Here, we review key concepts and specific features of several selected ligand-receptor docking/scoring methods while several other topics pertaining to the field of in silico screening are reviewed in the following articles of this special issue of Current Protein and Peptide Science.

Structure Based Drug Design (SBDD) is a computational approach to lead discovery that uses the threedimensional structure of a protein to fit drug-like molecules into a ligand binding site to modulate function. Identifying the location of the binding site is therefore a vital first step in this process, restricting the search space for SBDD or virtual screening studies. The detection and characterisation of functional sites on proteins has increasingly become an area of interest. Structural genomics projects are increasingly yielding protein structures with unknown functions and binding sites. Binding site prediction was pioneered by pocket detection, since the binding site is often found in the largest pocket. More recent methods involve phylogenetic analysis, identifying structural similarity with proteins of known function and identifying regions on the protein surface with a potential for high binding affinity. Binding site prediction has been used in several SBDD projects and has been incorporated into several docking tools. We discuss different methods of ligand binding site prediction, their strengths and weaknesses, and how they have been used in SBDD.

Virtual screening by molecular docking has become established as a method for drug lead discovery and optimization. All docking algorithms make use of a scoring function in combination with a method of search. Two theoretical aspects of scoring function performance dominate operational performance. The first is the degree to which a scoring function has a global extremum within the ligand pose landscape at the proper location. The second is the degree to which the magnitude of the function at the extremum is accurate. Presuming adequate search strategies, a scoring function's location performance will dominate behavior with respect to docking accuracy: the degree to which a predicted pose of a ligand matches experimental observation. A scoring function 's magnitude performance will dominate behavior with respect to screening utility: enrichment of true ligands over non-ligands. Magnitude estimation also controls pure scoring accuracy: the degree to which bona fide ligands of a particular protein may be correctly ranked. Approaches to the development of scoring functions have varied widely, with a number of functions yielding similarly high levels of performance relating to the location issue. However, even among functions performing equally well on location, widely varying performance is observed on the question of magnitude. In many cases, performance is good enough to yield high enrichments of true ligands versus non-ligands in screening across a wide variety of protein types. Generally, performance is not good enough to correctly rank among true ligands. Strategies for improvement are discussed.

Virtual Ligand Screening (VLS) has become an integral part of the drug design process for many pharmaceutical companies. In protein structure based VLS the aim is to find a ligand that has a high binding affinity to the target receptor whose 3D structure is known. This review will describe the docking tool eHiTS. eHiTS is an exhaustive and systematic docking tool which contains many automated features that simplify the drug design workflow. A description of the unique docking algorithm and novel approach to scoring used within eHiTS is presented. In addition a validation study is presented that demonstrates the accuracy and wide applicability of eHiTS in re-docking bound ligands into their receptors.

There is currently far more sequence information than structural information available, and the ability to use homology models for virtual screening applications is desirable in many cases where structures have not yet been solved. This review focuses on the application of protein kinase homology models for virtual screening use. In addition to reviewing previous cases in which kinase homology models have been used in inhibitor design, we present new data - useful for template selection in homology modeling applications - indicating that the template structure with the highest sequence or structural similarity with the target structure may not always be the best choice. This new work explored the simple hypothesis that better results might be obtained for docking a ligand to a target receptor using a homology model of the target created from a different kinase template co-crystallized with the ligand, than from a crystal structure of the actual kinase target that is unliganded or bound to an unrelated ligand. This hypothesis was tested in docking studies of staurosporine with eight different kinases: AutoDock was used to dock staurosporine to homology models of each kinase created from staurosporine-bound template structures, and the results were compared with docking staurosporine to crystal structures of the target kinase that were obtained in complex with a non-staurosporine ligand or no ligand. It was found that the homology models performed as well as or better than the crystal structures, suggesting that using a homology model created from a template crystallized with a representative ligand may in some cases be a preferred approach, especially in virtual screening experiments that focus on enriching for members of a particular inhibitor class.

The G-protein coupled receptor (GPCR) superfamily is one of the most important drug target classes for the pharmaceutical industry. The completion of the human genome project has revealed that there are more than 300 potential GPCR targets of interest. The identification of their natural ligands can gain significant insights into regulatory mechanisms of cellular signaling networks and provide unprecedented opportunities for drug discovery. Much effort has been directed towards the GPCR ligand discovery study by both academic institutions and pharmaceutical industries. However, the endogenous ligands still remain unknown for about 150 GPCRs in the human genome. It is necessary to develop new strategies to predict candidate ligands for these so-called orphan receptors. Computational techniques are playing an increasingly important role in finding and validating novel ligands for orphan GPCRs (oGPCRs). In this paper, we focus on recent development in applying bioinformatics approaches for the discovery of GPCR ligands. In addition, some of the data resources for ligand identification are also provided.

G protein-coupled receptors (GPCRs) represent the family of proteins with the highest impact from social, therapeutic and economic point of view. Today, more than 50% of drug targets are based on GPCRs and the annual worldwide sales exceeds $50 billion. GPCRs are involved in all major disease areas such as cardiovascular, metabolic, neurodegenerative, psychiatric, cancer and infectious diseases. The classical drug discovery process has relied on screening compounds, which interact favorably with the GPCR of interest followed by further chemical engineering as a mean of improving efficacy and selectivity. In this review, methods for sophisticated chemical library screening procedures will be presented. Furthermore, development of cell-based assays for functional coupling of GPCRs to G proteins will be discussed. Finally, the possibility of applying structure-based drug design will be summarized. This includes the application of bioinformatics knowledge and molecular modeling approaches in drug development programs. The major efforts established through large networks of structural genomics on GPCRs, where recombinantly expressed GPCRs are subjected to purification and crystallization attempts with the intention of obtaining high-resolution structures, are presented as a promising future approach for tailor-made drug development.