Doing more than just the structure—structural genomics in kinase drug discovery

A review on structural genomics approach applied for drug discovery against three vector-borne viral diseases: Dengue, Chikungunya and Zika

Structural genomics involves the advent of three-dimensional structures of the genome encoded proteins through various techniques available. Numerous structural genomics research groups have been developed across the globe and they contribute enormously to the identification of three-dimensional structures of various proteins. In this review, we have discussed the applications of the structural genomics approach towards the discovery of potential lead-like molecules against the genomic drug targets of three vector-borne diseases, namely, Dengue, Chikungunya and Zika. Currently, all these three diseases are associated with the most important global public health problems and significant economic burden in tropical countries. Structural genomics has accelerated the identification of novel drug targets and inhibitors for the treatment of these diseases. We start with the current development status of the drug targets and antiviral drugs against these three diseases and conclude by describing challenges that need to be addressed to overcome the shortcomings in the process of drug discovery.

Backbone resonance assignments of the catalytic and regulatory domains of Ca2+/calmodulin-dependent protein kinase 1D

The CaMK subfamily of Ser/Thr kinases are regulated by calmodulin interactions with their C-terminal regions. They are exemplified by Ca²⁺/calmodulin dependent protein kinase 1δ which is known as CaMK1D, CaMKIδ or CKLiK. CaMK1D mediates intracellular signalling downstream of Ca²⁺ influx and thereby exhibits amplifications of Ca²⁺signals and polymorphisms that have been implicated in breast cancer and diabetes. Here we report the backbone ¹H, ¹³C, ¹⁵N assignments of the 38 kDa human CaMK1D protein in its free state, including both the canonical bi-lobed kinase fold as well as the autoinhibitory and calmodulin binding domains.

Conformational analysis of the DFG-out kinase motif and biochemical profiling of structurally validated type II inhibitors

Structural coverage of the human kinome has been steadily increasing over time. The structures provide valuable insights into the molecular basis of kinase function and also provide a foundation for understanding the mechanisms of kinase inhibitors. There are a large number of kinase structures in the PDB for which the Asp and Phe of the DFG motif on the activation loop swap positions, resulting in the formation of a new allosteric pocket. We refer to these structures as “classical DFG-out” conformations in order to distinguish them from conformations that have also been referred to as DFG-out in the literature but that do not have a fully formed allosteric pocket. We have completed a structural analysis of almost 200 small molecule inhibitors bound to classical DFG-out conformations; we find that they are recognized by both type I and type II inhibitors. In contrast, we find that nonclassical DFG-out conformations strongly select against type II inhibitors because these structures have not formed a large enough allosteric pocket to accommodate this type of binding mode. In the course of this study we discovered that the number of structurally validated type II inhibitors that can be found in the PDB and that are also represented in publicly available biochemical profiling studies of kinase inhibitors is very small. We have obtained new profiling results for several additional structurally validated type II inhibitors identified through our conformational analysis. Although the available profiling data for type II inhibitors is still much smaller than for type I inhibitors, a comparison of the two data sets supports the conclusion that type II inhibitors are more selective than type I. We comment on the possible contribution of the DFG-in to DFG-out conformational reorganization to the selectivity.

Kinome Render: a stand-alone and web-accessible tool to annotate the human protein kinome tree

Human protein kinases play fundamental roles mediating the majority of signal transduction pathways in eukaryotic cells as well as a multitude of other processes involved in metabolism, cell-cycle regulation, cellular shape, motility, differentiation and apoptosis. The human protein kinome contains 518 members. Most studies that focus on the human kinome require, at some point, the visualization of large amounts of data. The visualization of such data within the framework of a phylogenetic tree may help identify key relationships between different protein kinases in view of their evolutionary distance and the information used to annotate the kinome tree. For example, studies that focus on the promiscuity of kinase inhibitors can benefit from the annotations to depict binding affinities across kinase groups. Images involving the mapping of information into the kinome tree are common. However, producing such figures manually can be a long arduous process prone to errors. To circumvent this issue, we have developed a web-based tool called Kinome Render (KR) that produces customized annotations on the human kinome tree. KR allows the creation and automatic overlay of customizable text or shape-based annotations of different sizes and colors on the human kinome tree. The web interface can be accessed at: http://bcb.med.usherbrooke.ca/kinomerender. A stand-alone version is also available and can be run locally.

From on-target to off-target activity: identification and optimisation of Trypanosoma brucei GSK3 inhibitors and their characterisation as anti-Trypanosoma brucei drug discovery lead molecules

Human African trypanosomiasis (HAT) is a life-threatening disease with approximately 30 000–40 000 new cases each year. Trypanosoma brucei protein kinase GSK3 short (TbGSK3) is required for parasite growth and survival. Herein we report a screen of a focused kinase library against T. brucei GSK3. From this we identified a series of several highly ligand-efficient TbGSK3 inhibitors. Following the hit validation process, we optimised a series of diaminothiazoles, identifying low-nanomolar inhibitors of TbGSK3 that are potent in vitro inhibitors of T. brucei proliferation. We show that the TbGSK3 pharmacophore overlaps with that of one or more additional molecular targets.

Identification and characterization of bi-thiazole-2,2'-diamines as kinase inhibitory scaffolds

Based on bioinformatics interrogation of the genome, >500 mammalian protein kinases can be clustered within seven different groups. Of these kinases, the mitogen-activated protein kinase (MAPK) family forms part of the CMGC group of serine/threonine kinases that includes extracellular signal regulated kinases (ERKs), cJun N-terminal kinases (JNKs), and p38 MAPKs. With the JNKs considered attractive targets in the treatment of pathologies including diabetes and stroke, efforts have been directed to the discovery of new JNK inhibitory molecules that can be further developed as new therapeutics. Capitalizing on our biochemical understanding of JNK, we performed in silico screens of commercially available chemical databases to identify JNK1-interacting compounds and tested their in vitro JNK inhibitory activity. With in vitro and cell culture studies, we showed that the compound, 4'-methyl-N(2)-3-pyridinyl-4,5'-bi-1,3-thiazole-2,2'-diamine (JNK Docking (JD) compound 123, but not the related compound (4'-methyl-N~2~-(6-methyl-2-pyridinyl)-4,5'-bi-1,3-thiazole-2,2'-diamine (JD124), inhibited JNK1 activity towards a range of substrates. Molecular docking, saturation transfer difference NMR experiments and enzyme kinetic analyses revealed both ATP- and substrate-competitive inhibition of JNK by JD123. In characterizing JD123 further, we noted its ATP-competitive inhibition of the related p38-γ MAPK, but not ERK1, ERK2, or p38-α, p38-β or p38-δ. Further screening of a broad panel of kinases using 10μM JD123, identified inhibition of kinases including protein kinase Bβ (PKBβ/Aktβ). Appropriately modified thiazole diamines, as typified by JD123, thus provide a new chemical scaffold for development of inhibitors for the JNK and p38-γ MAPKs as well as other kinases that are also potential therapeutic targets such as PKBβ/Aktβ.

Mechanisms of drug resistance in kinases

INTRODUCTION

because of their important roles in disease and excellent 'druggability', kinases have become the second largest drug target family. The great success of the BCR-ABL inhibitor imatinib in treating chronic myelogenous leukemia illustrates the high potential of kinase inhibitor (KI) therapeutics, but also unveils a major limitation: the development of drug resistance. This is a significant concern as KIs reach large patient populations for an expanding array of indications.

AREAS COVERED

we provide an up-to-date understanding of the mechanisms through which KIs function and through which cells can become KI-resistant. We review current and future approaches to overcome KI resistance, focusing on currently approved KIs and KIs in clinical trials. We then discuss approaches to improve KI efficacy and overcome drug resistance and novel approaches to develop less drug resistance-prone KI therapeutics.

EXPERT OPINION

although drug resistance is a concern for current KI therapeutics, recent progress in our understanding of the underlying mechanisms and promising technological advances may overcome this limitation and provide powerful new therapeutics.

Predicting inactive conformations of protein kinases using active structures: conformational selection of type-II inhibitors

Protein kinases have been found to possess two characteristic conformations in their activation-loops: the active DFG-in conformation and the inactive DFG-out conformation. Recently, it has been very interesting to develop type-II inhibitors which target the DFG-out conformation and are more specific than the type-I inhibitors binding to the active DFG-in conformation. However, solving crystal structures of kinases with the DFG-out conformation remains a challenge, and this seriously hampers the application of the structure-based approaches in development of novel type-II inhibitors. To overcome this limitation, here we present a computational approach for predicting the DFG-out inactive conformation using the DFG-in active structures, and develop related conformational selection protocols for the uses of the predicted DFG-out models in the binding pose prediction and virtual screening of type-II ligands. With the DFG-out models, we predicted the binding poses for known type-II inhibitors, and the results were found in good agreement with the X-ray crystal structures. We also tested the abilities of the DFG-out models to recognize their specific type-II inhibitors by screening a database of small molecules. The AUC (area under curve) results indicated that the predicted DFG-out models were selective toward their specific type-II inhibitors. Therefore, the computational approach and protocols presented in this study are very promising for the structure-based design and screening of novel type-II kinase inhibitors.

Investigating combinatorial approaches in virtual screening on human inducible 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase (PFKFB3): a case study for small molecule kinases

Efforts toward improving the predictiveness in tier-based approaches to virtual screening (VS) have mainly focused on protein kinases. Despite their significance as drug targets, small molecule kinases have been rarely tested with these approaches. In this paper, we investigate the efficacy of a pharmacophore screening-combined structure-based docking approach on the human inducible 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase, an emerging target for cancer chemotherapy. Six out of a total 1364 compounds from NCI's Diversity Set II were selected as true actives via throughput screening. Using a database constructed from these compounds, five programs were tested for structure-based docking (SBD) performance, the MOE of which showed the highest enrichments and second highest screening rates. Separately, using the same database, pharmacophore screening was performed, reducing 1364 compounds to 287 with no loss in true actives, yielding an enrichment of 4.75. When SBD was retested with the pharmacophore filtered database, 4 of the 5 SBD programs showed significant improvements to enrichment rates at only 2.5% of the database, with a 7-fold decrease in an average VS time. Our results altogether suggest that combinatorial approaches of VS technologies are easily applicable to small molecule kinases and, moreover, that such methods can decrease the variability associated with single-method SBD approaches.

Comprehensive structural and functional characterization of the human kinome by protein structure modeling and ligand virtual screening

The growing interest in the identification of kinase inhibitors, promising therapeutics in the treatment of many diseases, has created a demand for the structural characterization of the entire human kinome. At the outset of the drug development process, the lead-finding stage, approaches that enrich the screening library with bioactive compounds are needed. Here, protein structure based methods can play an important role, but despite structural genomics efforts, it is unlikely that the three-dimensional structures of the entire kinome will be available soon. Therefore, at the proteome level, structure-based approaches must rely on predicted models, with a key issue being their utility in virtual ligand screening. In this study, we employ the recently developed FINDSITE/Q-Dock ligand homology modeling approach, which is well-suited for proteome-scale applications using predicted structures, to provide extensive structural and functional characterization of the human kinome. Specifically, we construct structure models for the human kinome; these are subsequently subject to virtual screening against a library of more than 2 million compounds. To rank the compounds, we employ a hierarchical approach that combines ligand- and structure-based filters. Modeling accuracy is carefully validated using available experimental data with particularly encouraging results found for the ability to identify, without prior knowledge, specific kinase inhibitors. More generally, the modeling procedure results in a large number of predicted molecular interactions between kinases and small ligands that should be of practical use in the development of novel inhibitors. The data set is freely available to the academic community via a user-friendly Web interface at http://cssb.biology.gatech.edu/kinomelhm/ as well as at the ZINC Web site ( http://zinc.docking.org/applications/2010Apr/Brylinski-2010.tar.gz ).

Cross-reactivity virtual profiling of the human kinome by X-react(KIN): a chemical systems biology approach

Many drug candidates fail in clinical development due to their insufficient selectivity that may cause undesired side effects. Therefore, modern drug discovery is routinely supported by computational techniques, which can identify alternate molecular targets with a significant potential for cross-reactivity. In particular, the development of highly selective kinase inhibitors is complicated by the strong conservation of the ATP-binding site across the kinase family. In this paper, we describe X-React(KIN), a new machine learning approach that extends the modeling and virtual screening of individual protein kinases to a system level in order to construct a cross-reactivity virtual profile for the human kinome. To maximize the coverage of the kinome, X-React(KIN) relies solely on the predicted target structures and employs state-of-the-art modeling techniques. Benchmark tests carried out against available selectivity data from high-throughput kinase profiling experiments demonstrate that, for almost 70% of the inhibitors, their alternate molecular targets can be effectively identified in the human kinome with a high (>0.5) sensitivity at the expense of a relatively low false positive rate (<0.5). Furthermore, in a case study, we demonstrate how X-React(KIN) can support the development of selective inhibitors by optimizing the selection of kinase targets for small-scale counter-screen experiments. The constructed cross-reactivity profiles for the human kinome are freely available to the academic community at http://cssb.biology.gatech.edu/kinomelhm/ .

Human kinome drug discovery and the emerging importance of atypical allosteric inhibitors

IMPORTANCE OF THE FIELD

Protein kinases are important targets for drug discovery because they possess critical roles in many human diseases. Several protein kinase inhibitors have entered clinical development with others having already been approved for treating a host of diseases. However, many kinase inhibitors suffer from non-selectivity because they interact with the ATP binding region which has similar structures amongst the protein kinases and this non-selectivity sometimes can cause side effects. As a consequence, there is much interest in developing drugs that inhibit kinases through non-classical mechanisms with the hope of avoiding the side effects of previous kinase drugs.

AREAS COVERED IN THIS REVIEW

This review covers emerging information on kinase biology and discusses new approaches to design selective inhibitors that do not compete with ATP.

WHAT THE READER WILL GAIN

The reader will gain a better understanding of the importance of the field of allosteric inhibitor drug discovery and how this has required the adoption of a new generation of high-throughput screening techniques.

TAKE HOME MESSAGE

Discovery and development of allosteric modulators will result in a family of novel kinase therapies with greater selectivity and more varied ways to control activity of disease causing kinase targets.

Parallel methods for expression and purification

Protein properties are highly diverse, making parallel expression and purification a particular challenge. Parallel methods are typically used when a number derivatives of a target protein are desired or when multiple homologs are needed. A typical scenario involves target evaluation, cloning and mutagenesis of the target, expression screening, large-scale expression and purification, and analytical and biophysical testing of the resulting protein. This chapter describes some of the strategies and methods employed for parallel protein expression and purification.

An overview of the PubChem BioAssay resource

The PubChem BioAssay database (http://pubchem.ncbi.nlm.nih.gov) is a public repository for biological activities of small molecules and small interfering RNAs (siRNAs) hosted by the US National Institutes of Health (NIH). It archives experimental descriptions of assays and biological test results and makes the information freely accessible to the public. A PubChem BioAssay data entry includes an assay description, a summary and detailed test results. Each assay record is linked to the molecular target, whenever possible, and is cross-referenced to other National Center for Biotechnology Information (NCBI) database records. ‘Related BioAssays’ are identified by examining the assay target relationship and activity profile of commonly tested compounds. A key goal of PubChem BioAssay is to make the biological activity information easily accessible through the NCBI information retrieval system-Entrez, and various web-based PubChem services. An integrated suite of data analysis tools are available to optimize the utility of the chemical structure and biological activity information within PubChem, enabling researchers to aggregate, compare and analyze biological test results contributed by multiple organizations. In this work, we describe the PubChem BioAssay database, including data model, bioassay deposition and utilities that PubChem provides for searching, downloading and analyzing the biological activity information contained therein.

Insights into protein kinase regulation and inhibition by large scale structural comparison

Protein structure determination of soluble globular protein domains has developed into an efficient routine technology which can now be applied to generate and analyze structures of entire human protein families. In the kinase area, several kinase families still lack comprehensive structural analysis. Nevertheless, Structural Genomics (SG) efforts contributed more than 40 kinase catalytic domain structures during the past 4 years providing a rich resource of information for large scale comparisons of kinase active sites. Moreover, many of the released structures are inhibitor complexes that offer chemical starting points for development of selective and potent inhibitors. Here we discuss the currently available structural data and strategies that can be utilized for the development of highly selective inhibitors.

Measuring and interpreting the selectivity of protein kinase inhibitors

Protein kinase inhibitors are a well-established class of clinically useful drugs, particularly for the treatment of cancer. Achieving inhibitor selectivity for particular protein kinases often remains a significant challenge in the development of new small molecules as drugs or as tools for chemical biology research. This review summarises the methodologies available for measuring kinase inhibitor selectivity, both in vitro and in cells. The interpretation of kinase inhibitor selectivity data is discussed, particularly with reference to the structural biology of the protein targets. Measurement and prediction of kinase inhibitor selectivity will be important for the development of new multi-targeted kinase inhibitors.

The regulation of protein phosphorylation

Phosphorylation plays essential roles in nearly every aspect of cell life. Protein kinases regulate signalling pathways and cellular processes that mediate metabolism, transcription, cell-cycle progression, differentiation, cytoskeleton arrangement and cell movement, apoptosis, intercellular communication, and neuronal and immunological functions. Protein kinases share a conserved catalytic domain, which catalyses the transfer of the γ-phosphate of ATP to a serine, threonine or tyrosine residue in protein substrates. The kinase can exist in an active or inactive state regulated by a variety of mechanisms in different kinases that include control by phosphorylation, regulation by additional domains that may target other molecules, binding and regulation by additional subunits, and control by protein–protein association. This Novartis Medal Lecture was delivered at a meeting on protein evolution celebrating the 200th anniversary of Charles Darwin's birth. I begin with a summary of current observations from protein sequences of kinase phylogeny. I then review the structural consequences of protein phosphorylation using our work on glycogen phosphorylase to illustrate one of the more dramatic consequences of phosphorylation. Regulation of protein phosphorylation is frequently disrupted in the diseased state, and protein kinases have become high-profile targets for drug development. Finally, I consider recent advances on protein kinases as drug targets and describe some of our recent work with CDK9 (cyclin-dependent kinase 9)–cyclin T, a regulator of transcription.

Large-Scale Structural Biology of the Human Proteome

The large-scale structural biology projects that target human proteins focus predominantly on the catalytic domains of potential therapeutic targets and the domains of human proteins that mediate protein-protein and protein-small-molecule interactions. Their main scientific objective is to elucidate the molecular basis for specificity and selectivity of function within large protein families of therapeutic interest, such as kinases, phosphatases, and proteins involved in epigenetic regulation. Half of the unique human protein structures determined in the past three years derive from these initiatives.

Structure-based design of molecular cancer therapeutics

Structure-based approaches now impact across the whole continuum of drug discovery, from new target selection through the identification of hits to the optimization of lead compounds. Optimal application of structure-based design involves close integration with other discovery technologies, including fragment-based and virtual screening. Here, we illustrate the use of structural information and of structure-based drug design approaches in the discovery of small-molecule inhibitors for cancer drug targets and provide an outlook on the exploitation of structural information in future cancer drug discovery. Examples include high profile protein kinase targets and structurally related PI3 kinases, histone deacetylases, poly(ADP-ribose)polymerase and the molecular chaperone HSP90. Structure-based design approaches have also been successfully applied to the protein-protein interaction targets p53-MDM2 and the Bcl-2 family.

Protein kinase inhibitors: contributions from structure to clinical compounds

Protein kinases catalyse key phosphorylation reactions in signalling cascades that affect every aspect of cell growth, differentiation and metabolism. The kinases have become prime targets for drug intervention in the diseased state, especially in cancer. There are currently 10 drugs that have been approved for clinical use and many more in clinical trials. This review summarises the structural basis for protein kinase inhibition and discusses the mode of action for each of the approved drugs in the light of structural results. All but one of the approved compounds target the ATP binding site on the kinase. Both the active and inactive conformations of protein kinases have been used in strategies to produce potent and selective compounds. Targeting the inactive conformation can give high specificity. Targeting the active conformation is favourable where the diseased state has arisen from activating mutations, but such inhibitors generally target several protein kinases. Drug resistance mutations are a potential risk for both conformational states, where drug-binding regions are not directly involved in catalysis. Imatinib (Glivec), the most successful of protein kinase inhibitors, targets the inactive conformation of ABL tyrosine kinase. Newer compounds, such as dasatinib, which targets the ABL active state, have been developed to increase potency and have proved effective for some, but not all, drug-resistant mutations. The first epidermal growth factor receptor (EGFR) inhibitors in clinical use [gefitinib (Iressa) and erlotinib (Tarceva)] targeted the active form of the kinase, and this proved advantageous for patients whose cancer was caused by mutations that resulted in a constitutively active EGFR kinase domain. Newer approved compounds, such as lapatinib (Tykerb), target the inactive conformation with high potency. A further compound that forms a covalent attachment to the kinase has been found to overcome one of the major drug resistance mutations, where the effectiveness of the drug in vivo is dependent on its ability to compete successfully in the presence of cellular concentrations of ATP. Inhibitors of vascular endothelial growth factor receptor (VEGFR) kinase against cancer angiogenesis show the advantage of some relaxation in specificity. Sorafenib, originally developed as RAF inhibitor, is now in clinical use as a VEGFR inhibitor. Temsirolimus (a derivative of rapamycin) is the only example of a drug in clinical use that does not target the kinase ATP site. Instead rapamycin, when in complex with the protein FKBP12, effectively targets mTOR kinase at a site located on a domain, the FRB domain, that appears to be involved in localisation or substrate docking.

Structural biology contributions to tyrosine kinase drug discovery

Successful kinase inhibitor drug discovery relies heavily on the structural knowledge of the interaction of inhibitors with the target. Structural biology of kinases and in particular of tyrosine kinases has given detailed insights into the intrinsic flexibility of the catalytic domain and has provided a rational basis for obtaining selective inhibitors. Important progress has been made recently, both in academia and in the pharmaceutical industry, with respect to solving structures of inactive, multidomain or protein-protein complexes of kinases, which helps our understanding of the dynamics of regulation of kinase activity. This leads to a better understanding of how mutations lead to activation of kinases and resistance, in addition to providing opportunities for novel modes of targeting kinases.

Structural genomics and drug discovery: all in the family

Structural genomics is starting to have an impact on the early stages of drug discovery and target validation through the contribution of new structures of known and potential drug targets, their complexes with ligands and protocols and reagents for additional structural work within a drug discovery program. Recent progress includes structures of targets from bacterial, viral and protozoan human pathogens, and human targets from known or potential druggable protein families such as, kinases, phosphatases, dehydrogenases/oxidoreductases, sulfo-, acetyl- and methyl-transferases, and a number of other key metabolic enzymes. Importantly, many of these structures contained ligands in the active sites, including for example, the first structures of target-bound therapeutics. Structural genomics of protein families combined with ligand discovery holds particular promise for advancing early stage discovery programs.