Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION: Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways. AREAS COVERED: The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques. EXPERT OPINION: The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.     

Fulfilling the promise: drug discovery in the post-genomic era

The genomic era has brought with it a basic change in experimentation, enabling researchers to look more comprehensively at biological systems. The sequencing of the human genome coupled with advances in automation and parallelization technologies have afforded a fundamental transformation in the drug target discovery paradigm, towards systematic whole genome and proteome analyses. In conjunction with novel proteomic techniques, genome-wide annotation of function in cellular models is possible. Overlaying data derived from whole genome sequence, expression and functional analysis will facilitate the identification of causal genes in disease and significantly streamline the target validation process. Moreover, several parallel technological advances in small molecule screening have resulted in the development of expeditious and powerful platforms for elucidating inhibitors of protein or pathway function. Conversely, high-throughput and automated systems are currently being used to identify targets of orphan small molecules. The consolidation of these emerging functional genomics and drug discovery technologies promises to reap the fruits of the genomic revolution.     

Chemical genomics strategy for the discovery of new anticancer agents

Chemical genomics represents a cooperation of biology and chemistry to identify and intervene the biological targets. Small molecules with diverse structural characteristics should be used to validate the target through interfering with the biological processes. Because of the limitation of existing chemical libraries, the diversity can be exploited using both the molecular design techniques; structure-based design and ligand-based design. These methods can guide the selection of small molecules with optimal binding properties to desired biological targets. Studies of potential molecular targets for novel anticancer drug discovery including in silico screening, QSAR, and de novo design demonstrated the importance of chemical genomics strategy to find the chemical probes and drug lead compounds.     

Optimization methods for virtual screening on novel computational architectures

The numerous virtual screening (VS) methods that are used today in drug discovery processes differ mainly by the way they model the receptor and/or ligand and by the approach to perform screening. All these methods have in common that they screen databases of chemical compounds containing up to millions of ligands i.e. ZINC database. Larger databases increase the chances of generating hits or leads, but the computational time needed for the calculations increases not only with the size of the database but also with the accuracy of the VS method and the model. Fast docking methods with atomic resolution require a few minutes per ligand, while molecular dynamics-based approaches still require hundreds or thousands of hours per ligand. Therefore, the limitations of VS predictions are directly related to a lack of computational resources, a major bottleneck that prevents the application of detailed, high-accuracy models to VS. The current increase in available computer power at low cost due to novel computational architectures would enhance considerably the performance of the different VS methods and the quality and quantity of the conclusions we can get from screening. In this review, we will discuss recent trends in modeling techniques which, in combination with novel hardware platforms, yield order-of-magnitude improvements in the processing speeds of VS methods. We show the state of the art of VS methods as applied with novel computational architectures and the current trends of advanced computing.     

Structural bioinformatic approaches to the discovery of new antimycobacterial drugs

Integrated bioinformatic approaches to drug discovery exploit computational techniques to examine the flow of information from genome to structure to function. Informatics is being be used to accelerate and rationalize the process of antimycobacterial drug discovery and design, with the immediate goals to identify viable drug targets and produce a set of critically evaluated protein target models and corresponding set of probable lead compounds. Bioinformatic approaches are being successfully applied in the selection and prioritization of putative mycobacterial drug target genes; computational modelling and x-ray structure validation of protein targets with drug lead compounds; simulated docking and virtual screening of potential lead compounds; and lead validation and optimization using structure-activity and structure-function relationships. By identifying active sites, characterizing patterns of conserved residues and, where relevant, predicting catalytic residues, bioinformatics provides information to aid the design of selective and efficacious pharmacophores. In this review, we describe selected recent progress in antimycobacterial drug design, illustrating the strengths and limitations of current structural bioinformatic approaches as tools in the fight against tuberculosis.     

Structure-based approaches to drug design and virtual screening

Structure-based design has made an important contribution to drug discovery for many years. Recently, the increasing availability of structural data and the affordability of high-performance computing platforms have broadened the applicability of these methods. In particular, virtual screening has been adopted as an effective paradigm for lead discovery that fits in well alongside high-throughput screening programs. Structure-based virtual screening relies on fast and accurate computational methods for the prediction of receptor-ligand binding modes and binding affinities. In this paper, recent technical advances in the field of molecular docking and de novo design are reviewed, in particular, the development of flexible receptor models, docking of combinatorial libraries and novel scoring methods.     

The role of emerging genomics and proteomics technologies in cancer drug target discovery

Cancer drugs have traditionally been identified in screens designed to produce broad biological end points such as cell death. A serious undesired outcome of drugs discovered in these screens is that the mechanism of drug action is unknown and such drugs often have adverse side effects. Designing cancer drugs that act on specific targets offer the advantage that the mechanism of drug action can be understood and accurately monitored in clinical trials leading to development of better drugs. The pharmacological industry has recently shifted to a target directed drug discovery model. However, until recently potential cancer drug targets comprised of only a small fraction of the human genome. The human genome project and high-throughput structural and functional genomics have dramatically increased the number of cancer drug targets. Deciphering cancer drug targets requires the understanding of biochemical pathways that are affected in the cancer genome. It has been suggested that utilization of Single-nucleotide polymorphisms (SNPs) will aid in identifying individuals at high risk of developing certain cancers, and will also help in development of tailored medication or identify genetic profiles of specific drug action and toxicity. Achieving successful new cancer drug development schemes will require a merger of research disciplines that include pharmacology, genomics, comparative genomics, functional genomics, proteomics and bioinformatics. In this review the significance and challenges of these rapidly evolving technologies in cancer drug target discovery are discussed.     

The role of structure based virtual screening in the early phase of drug discovery

Identification of a viable lead is a critical step in drug discovery. The qualities of the lead set the stage for subsequent efforts to ameliorate therapeutic efficacy through potency, selectivity, pharmacokinetics, toxicity and side effects. In a retrospective view of drug research the lead identification has been realised mainly by in vivo methodologies. However, limitations of in vivo models were found to be critical factors when analysing attrition rates that prompted research groups to introduce in vitro tests and rational approaches at the frontline of discovery programs. Virtual screening (VS) methods merge in vitro high-throughput (HTS) and rational approaches. The VS methods can be classified as ligand and structure based techniques. Structure based approaches depart from the structural information of the target to identify potential interactions between the ligands and the protein. The advantages and disadvantages and the applicability of the structure based virtual screening approaches constituted the main aim of my studies. The glycogen synthase kinase 3beta (GSK-3beta), the beta-secretase and the c-jun N-terminal kinase 3 (JNK-3) were selected as primary targets for virtual screening. The performance of virtual screens can only be validated in parallel with HTS, therefore a head to head comparative analysis was my next goal.     

Genomics strategies for antifungal drug discovery--from gene discovery to compound screening

The use of genomics tools to discover new genes, to decipher pathways or to assign a function to a gene is just beginning to have an impact. Genomics approaches have been applied to both antibacterial and antifungal target discovery in order to identify a new generation of antibiotics. This review discusses genomics approaches for antifungal drug discovery, focusing on the areas of gene discovery, target validation, and compound screening. A variety of methods to identify fungal genes of interest are discussed, as well as methods for obtaining full-length sequences of these genes. One approach is well-suited to organisms having few introns (Candida albicans), and another for organisms with many introns (Aspergillus fumigatus). To validate broad spectrum fungal targets, the yeast Saccharomyces cerevisiae was used as a model system to rapidly identify genes essential for growth and viability of the organism. Validated targets were then exploited for high-throughput compound screening.     

Genomics in the real world

The term genomics has evolved into a catch-all term for a variety of information intensive biological methodologies. While the promise of genomics in the bio/pharmaceutical industry is great, its impact on the drug discovery pipeline has not yet been realized, excluding a few notable exceptions. As companies acquire several years of experience in working with genomic data, it is likely that the impact on the discovery process will slowly emerge as we learn to integrate these new technologies into individual discovery programs. It is clear that extracting novel biologically valid targets targets from exponentially growing amounts of sequence data requires time and considerable investment in biological research infrastructure. In order to accelerate the process of target validation, a variety of functional genomics technologies are also being developed to try to predict the effect of inhibitory compounds in advance of development. Resources spent on early stage exploratory efforts such as these can pay off by improving the success rate for screening and medicinal chemistry.     

High-throughput screening and structure-based approaches to hit discovery: is there a clear winner?

Introduction: There are currently many lead discovery platforms available for drug discovery. Yet, it is often debated whether any of the available platforms are superior or standout to the other vast number of available technologies.

Areas covered: The authors comment, in this editorial, on the use and current state of the art of diversity-based high-throughput screening and how this has evolved and been improved from its earliest manifestations. They also describe structure- and computational-based drug discovery strategies and reflect on the differences between these two approaches.

Expert opinion: Looking to the future, success in drug discovery is likely to depend on the intelligent deployment of multiple hit identification techniques, appropriate to the drug target, to identify and optimise novel drug leads. The authors' opinion is that there is no clear winner, but that each platform has its own particular strengths and different targets may be more amenable to one platform over another. The authors suggest that the most appropriate platform should be used on a case-by-case basis.     

Understanding the challenges of protein flexibility in drug design

Introduction: Protein–ligand interactions play key roles in various metabolic pathways, and the proteins involved in these interactions represent major targets for drug discovery. Molecular docking is widely used to predict the structure of protein–ligand complexes, and protein flexibility stands out as one of the most important and challenging issues for binding mode prediction. Various docking methods accounting for protein flexibility have been proposed, tackling problems of ever-increasing dimensionality.

Areas covered: This paper presents an overview of conformational sampling methods treating target flexibility during molecular docking. Special attention is given to approaches considering full protein flexibility. Contrary to what is frequently done, this review does not rely on classical biomolecular recognition models to classify existing docking methods. Instead, it applies algorithmic considerations, focusing on the level of flexibility accounted for. This review also discusses the diversity of docking applications, from virtual screening (VS) of small drug-like compounds to geometry prediction (GP) of protein–peptide complexes.

Expert opinion: Considering the diversity of docking methods presented here, deciding which one is the best at treating protein flexibility depends on the system under study and the research application. In VS experiments, ensemble docking can be used to implicitly account for large-scale conformational changes, and selective docking can additionally consider local binding-site rearrangements. In other cases, on-the-fly exploration of the whole protein–ligand complex might be needed for accurate GP of the binding mode. Among other things, future methods are expected to provide alternative binding modes, which will better reflect the dynamic nature of protein–ligand interactions.     

Evaluation and validation of drug targets

Drug target is one of the key factors for discovering and developing new drugs. To find and validate drug targets is a crucial technique required in drug discovery by the strategy of high throughput screening. Based on the knowledge of molecular biology, human genomics and proteomics, it has been predicted that 5000 to 10000 drug targets exist in human. So, it is important orocedure to evaluate and validate the drug targets.     

Challenges of target/compound data integration from disease to chemistry: a case study of dihydrofolate reductase inhibitors

Despite the improvements in informatics associated with initiatives in the structure-based design and genomics fields, no straight-forward links are available between a given disease class and drug chemistry. This involves effective linking of disease to protein targets, and then mapping these targets to drug chemistry. In practice, protein-ligand structural analyses and high-throughput screening experiments generate the links between targets implicated in disease and chemical leads. Additionally, large volumes of relevant data are also being produced by high-throughput X-ray crystallography and in-silico docking initiatives. Each of these efforts takes a distinctly different approach to how data is managed and mined, resulting in difficulties in sharing data across each area. This review discusses the diverse approaches taken to data management in these areas, and the challenges associated with the construction of a data warehouse that meets all of the needs of each data type. Using the current work available for dihydrofolate reductase inhibitors, we demonstrate the challenges and opportunities associated with data mining from disease to drug chemistry.     

Virtual screening for kinase targets

Kinases have become a major area of drug discovery and structure-based design. Hundreds of 3D structures for more than thirty different kinases are available to the public. High structural and sequence homology within the kinase gene family makes the remaining kinases ideal targets for homology modeling and virtual screening. Somewhat surprisingly, however, the number of publications about virtual screening of kinases is very low. Therefore, rather than reviewing the field of virtual screening for kinases, we attempt here a hybrid approach of presenting what is known and common practice together with new studies on CDK2 and SRC kinase. To illustrate the challenges and pitfalls of virtual screening for kinase targets we focus on the question of how ranking is influenced by the database screened, the docking scheme, the scoring function, the activity of the compounds used for testing, and small changes in the binding pocket. In addition, a case study of finding irreversible inhibitors of ErbB2 through in silico screening is presented.     

Chemical genomics and emerging DNA technologies in the identification of drug mechanisms and drug targets

Chemical genomics combines chemistry with molecular biology as a means of exploring the function of unknown proteins or identifying the proteins responsible for a particular phenotype induced by a small cell-permeable bioactive molecule. Chemical genomics therefore has the potential to identify and validate therapeutic targets and to discover drug candidates for rapidly and effectively generating new interventions for human diseases. The recent emergence of genomic technologies and their application on genetically tractable model organisms like Drosophila melanogaster, Caenorhabditis elegans and Saccharomyces cerevisiae have provided momentum to cell biological and biomedical research, particularly in the functional characterization of gene functions and the identification of novel drug targets. We therefore anticipate that chemical genomics and the vast development of genomic technologies will play critical roles in the genomic age of biological research and drug discovery. In the present review we discuss how simple biological model organisms can be used as screening platforms in combination with emerging genomic technologies to advance the identification of potential drugs and their molecular mechanisms of action.     

Discovery of estrogen receptor modulators: a review of virtual screening and SAR efforts

Importance of the field: Virtual screening (VS) coupled with structural biology is a significantly important approach to increase the number and enhance the success of projects in lead identification stage of drug discovery process. Recent advances and future directions in estrogen therapy have resulted in great demand for identifying the potential estrogen receptor (ER) modulators with more activity and selectivity. Areas covered in this review: This review presents the current state of the art in VS and structure-activity relationship of ER modulators in recent discovery, and discusses the strengths and weaknesses of the technology. What the reader will gain: Readers will gain an overview of the current platforms of in silico screening for discovery of ER modulators; they will learn which structural information is significantly correlated with the bioactivity of ER modulators and what novel strategies should be considered for the creation of more effective chemical structures. Take home message: With the goal of reducing toxicity and/or improving efficacy, challenges to the successful modeling of endocrine agents are proposed, providing new paradigms for the design of ER inhibitors.     

Computational‐based discovery of FAK FERM domain chemical probes that inhibit HER2‐FAK cancer signaling

The N‐terminal FERM domain of focal adhesion kinase (FAK) contributes to FAK scaffolding and interacts with HER2, an oncogene and receptor tyrosine kinase. The interaction between HER2 and FAK drives resistance to FAK‐kinase domain inhibitors through FAK Y397 transphosphorylation and FAK re‐activation upon inhibition. As such, FAK FERM remains an attractive drug discovery target. In this report, we detail an alternative approach to targeting FAK through virtual screening‐based discovery of chemical probes that target FAK FERM. We validated the binding interface between HER2 and FAK using site‐directed mutagenesis and GST pull‐down experiments. We assessed the ligandability of key‐binding residues of HER2 and FAK utilizing computational tools. We developed a virtual screening method to screen ~200,000 compounds against the FAK FERM domain, identifying 20 virtual chemical probes. We performed GST pull‐down screening on these compounds, discovering two hits, VS4 and VS14, with nanomolar IC₅₀s in disrupting HER2‐FAK. We performed further testing, including molecular docking, immunofluorescence, phosphorylation, and cellular invasion assays to evaluate the compounds’ biological effects. One probe, VS14, was identified with the ability to block both auto‐ and transphosphorylation of Y397. In all, these studies identify two new probes that target FAK FERM, enabling future investigation of this domain.     

Advances in capillary electrophoresis and the implications for drug discovery

Introduction: Many screening platforms are prone to assay interferences that can be avoided by directly measuring the target or enzymatic product. Capillary electrophoresis (CE) and microchip electrophoresis (MCE) have been applied in a variety of formats to drug discovery. CE provides direct detection of the product allowing for the identification of some forms of assay interference. The high efficiency, rapid separations, and low volume requirements make CE amenable to drug discovery.

Areas covered: This article describes advances in capillary electrophoresis throughput, sample introduction, and target assays as they pertain to drug discovery and screening. Instrumental advances discussed include integrated droplet microfluidics platforms and multiplexed arrays. Applications of CE to assays of diverse drug discovery targets, including enzymes and affinity interactions are also described.

Expert opinion: Current screening with CE does not fully take advantage of the throughputs or low sample volumes possible with CE and is most suitable as a secondary screening method or for screens that are inaccessible with more common platforms. With further development, droplet microfluidics coupled to MCE could take advantage of the low sample requirements by performing assays on the nanoliter scale at high throughput.