Kinomics: characterizing the therapeutically validated kinase space

The role of fragment-based and computational methods in polypharmacology

Polypharmacology-based strategies are gaining increased attention as a novel approach to obtaining potentially innovative medicines for multifactorial diseases. However, some within the pharmaceutical community have resisted these strategies because they can be resource-hungry in the early stages of the drug discovery process. Here, we report on fragment-based and computational methods that might accelerate and optimize the discovery of multitarget drugs. In particular, we illustrate that fragment-based approaches can be particularly suited for polypharmacology, owing to the inherent promiscuous nature of fragments. In parallel, we explain how computer-assisted protocols can provide invaluable insights into how to unveil compounds theoretically able to bind to more than one protein. Furthermore, several pragmatic aspects related to the use of these approaches are covered, thus offering the reader practical insights on multitarget-oriented drug discovery projects.

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.

Profile-QSAR: a novel meta-QSAR method that combines activities across the kinase family to accurately predict affinity, selectivity, and cellular activity

Profile-QSAR is a novel 2D predictive model building method for kinases. This "meta-QSAR" method models the activity of each compound against a new kinase target as a linear combination of its predicted activities against a large panel of 92 previously studied kinases comprised from 115 assays. Profile-QSAR starts with a sparse incomplete kinase by compound (KxC) activity matrix, used to generate Bayesian QSAR models for the 92 "basis-set" kinases. These Bayesian QSARs generate a complete "synthetic" KxC activity matrix of predictions. These synthetic activities are used as "chemical descriptors" to train partial-least squares (PLS) models, from modest amounts of medium-throughput screening data, for predicting activity against new kinases. The Profile-QSAR predictions for the 92 kinases (115 assays) gave a median external R²(ext) = 0.59 on 25% held-out test sets. The method has proven accurate enough to predict pairwise kinase selectivities with a median correlation of R²(ext) = 0.61 for 958 kinase pairs with at least 600 common compounds. It has been further expanded by adding a "C(k)XC" cellular activity matrix to the KxC matrix to predict cellular activity for 42 kinase driven cellular assays with median R²(ext) = 0.58 for 24 target modulation assays and R²(ext) = 0.41 for 18 cell proliferation assays. The 2D Profile-QSAR, along with the 3D Surrogate AutoShim, are the foundations of an internally developed iterative medium-throughput screening (IMTS) methodology for virtual screening (VS) of compound archives as an alternative to experimental high-throughput screening (HTS). The method has been applied to 20 actual prospective kinase projects. Biological results have so far been obtained in eight of them. Q² values ranged from 0.3 to 0.7. Hit-rates at 10 uM for experimentally tested compounds varied from 25% to 80%, except in K5, which was a special case aimed specifically at finding "type II" binders, where none of the compounds were predicted to be active at 10 μM. These overall results are particularly striking as chemical novelty was an important criterion in selecting compounds for testing. The method is completely automated. Predicted activities for nearly 4 million internal and commercial compounds across 115 kinase assays and 42 cellular assays are stored in the corporate database. Like computed physical properties, this predicted kinase activity profile can be computed and stored as each compound is registered.

High throughput screening of a library based on kinase inhibitor scaffolds against Mycobacterium tuberculosis H37Rv

Kinase targets are being pursued in a variety of diseases beyond cancer, including immune and metabolic as well as viral, parasitic, fungal and bacterial. In particular, there is a relatively recent interest in kinase and ATP-binding targets in Mycobacterium tuberculosis in order to identify inhibitors and potential drugs for essential proteins that are not targeted by current drug regimens. Herein, we report the high throughput screening results for a targeted library of approximately 26,000 compounds that was designed based on current kinase inhibitor scaffolds and known kinase binding sites. The phenotypic data presented herein may form the basis for selecting scaffolds/compounds for further enzymatic screens against specific kinase or other ATP-binding targets in Mycobacterium tuberculosis based on the apparent activity against the whole bacteria in vitro.

Correlation between protein function and ligand binding profiles

We report that proteins with the same function bind the same set of small molecules from a standardized chemical library. This observation led to a quantifiable and rapidly adaptable method for protein functional analysis using experimentally derived ligand binding profiles. Ligand binding is measured using a high-throughput NMR ligand affinity screen with a structurally diverse chemical library. The method was demonstrated using a set of 19 proteins with a range of functions. A statistically significant similarity in ligand binding profiles was only observed between the two functionally identical albumins and between the five functionally similar amylases. This new approach is independent of sequence, structure, or evolutionary information and, therefore, extends our ability to analyze and functionally annotate novel genes.

Navigating the kinome

Although it is increasingly being recognized that drug-target interaction networks can be powerful tools for the interrogation of systems biology and the rational design of multitargeted drugs, there is no generalized, statistically validated approach to harmonizing sequence-dependent and pharmacology-dependent networks. Here we demonstrate the creation of a comprehensive kinome interaction network based not only on sequence comparisons but also on multiple pharmacology parameters derived from activity profiling data. The framework described for statistical interpretation of these network connections also enables rigorous investigation of chemotype-specific interaction networks, which is critical for multitargeted drug design.

Computational Modeling of Kinase Inhibitor Selectivity

An exhaustive computational exercise on a comprehensive set of 15 therapeutic kinase inhibitors was undertaken to identify as to which compounds hit which kinase off-targets in the human kinome. Although the kinase selectivity propensity of each inhibitor against ∼480 kinase targets is predicted, we compared our predictions to ∼280 kinase targets for which consistent experimental data are available and demonstrate an overall average prediction accuracy and specificity of ∼90%. A comparison of the predictions was extended to an additional ∼60 kinases for sorafenib and sunitinib as new experimental data were reported recently with similar prediction accuracy. The successful predictive capabilities allowed us to propose predictions on the remaining kinome targets in an effort to repurpose known kinase inhibitors to these new kinase targets that could hold therapeutic potential.

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/ .

Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank

One approach to estimating the "chemical tractability" of a candidate protein target where we know the atomic resolution structure is to examine the physical properties of potential binding sites. A number of other workers have addressed this issue. We characterize ~290,000 "pockets" from ~42,000 protein crystal structures in terms of a three parameter "pocket space": volume, buriedness, and hydrophobicity. A metric DLID (drug-like density) measures how likely a pocket is to bind a drug-like molecule. This is calculated from the count of other pockets in its local neighborhood in pocket space that contain drug-like cocrystallized ligands and the count of total pockets in the neighborhood. Surprisingly, despite being defined locally, a global trend in DLID can be predicted by a simple linear regression on log(volume), buriedness, and hydrophobicity. Two levels of simplification are necessary to relate the DLID of individual pockets to "targets": taking the best DLID per Protein Data Bank (PDB) entry (because any given crystal structure can have many pockets), and taking the median DLID over all PDB entries for the same target (because different crystal structures of the same protein can vary because of artifacts and real conformational changes). We can show that median DLIDs for targets that are detectably homologous in sequence are reasonably similar and that median DLIDs correlate with the "druggability" estimate of Cheng et al. (Nature Biotechnology 2007, 25, 71-75).

In silico analysis of kinase expression identifies WEE1 as a gatekeeper against mitotic catastrophe in glioblastoma

Kinases execute pivotal cellular functions and are therefore widely investigated as potential targets in anticancer treatment. Here we analyze the kinase gene expression profiles of various tumor types and reveal the wee1 kinase to be overexpressed in glioblastomas. We demonstrate that WEE1 is a major regulator of the G(2) checkpoint in glioblastoma cells. Inhibition of WEE1 by siRNA or small molecular compound in cells exposed to DNA damaging agents results in abrogation of the G(2) arrest, premature termination of DNA repair, and cell death. Importantly, we show that the small-molecule inhibitor of WEE1 sensitizes glioblastoma to ionizing radiation in vivo. Our results suggest that inhibition of WEE1 kinase holds potential as a therapeutic approach in treatment of glioblastoma.

Modeling bone marrow toxicity using kinase structural motifs and the inhibition profiles of small molecular kinase inhibitors

The cellular function of kinases combined with the difficulty of designing selective small molecule kinase inhibitors (SMKIs) poses a challenge for drug development. The late-stage attrition of SMKIs could be lessened by integrating safety information of kinases into the lead optimization stage of drug development. Herein, a mathematical model to predict bone marrow toxicity (BMT) is presented which enables the rational design of SMKIs away from this safety liability. A specific example highlights how this model identifies critical structural modifications to avoid BMT. The model was built using a novel algorithm, which selects 19 representative kinases from a panel of 277 based upon their ATP-binding pocket sequences and ability to predict BMT in vivo for 48 SMKIs. A support vector machine classifier was trained on the selected kinases and accurately predicts BMT with 74% accuracy. The model provides an efficient method for understanding SMKI-induced in vivo BMT earlier in drug discovery.

Inhibitors of the Abl kinase directed at either the ATP- or myristate-binding site

The ATP-competitive inhibitors dasatinib and nilotinib, which bind to catalytically different conformations of the Abl kinase domain, have recently been approved for the treatment of imatinib-resistant CML. These two new drugs, albeit very efficient against most of the imatinib-resistant mutants of Bcr-Abl, fail to effectively suppress the Bcr-Abl activity of the T315I (or gatekeeper) mutation. Generating new ATP site-binding drugs that target the T315I in Abl has been hampered, amongst others, by target selectivity, which is frequently an issue when developing ATP-competitive inhibitors. Recently, using an unbiased cellular screening approach, GNF-2, a non-ATP-competitive inhibitor, has been identified that demonstrates cellular activity against Bcr-Abl transformed cells. The exquisite selectivity of GNF-2 is due to the finding that it targets the myristate binding site located near the C-terminus of the Abl kinase domain, as demonstrated by genetic approaches, solution NMR and X-ray crystallography. GNF-2, like myristate, is able to induce and/or stabilize the clamped inactive conformation of Abl analogous to the SH2-Y527 interaction of Src. The molecular mechanism for allosteric inhibition by the GNF-2 inhibitor class, and the combined effects with ATP-competitive inhibitors such as nilotinib and imatinib on wild-type Abl and imatinib-resistant mutants, in particular the T315I gatekeeper mutant, are reviewed.

Small molecule kinase inhibitors in glioblastoma: a systematic review of clinical studies

The efficacy of small-molecule kinase inhibitors has recently changed standard clinical practice for several solid cancers. Glioblastoma is a solid cancer that universally recurs and unrelentingly results in death despite maximal surgery and radiotherapy with concomitant and adjuvant temozolomide. Several clinical studies using kinase inhibitors in glioblastoma have been reported. The present study systematically reviews the efficacy, toxicity, and tissue analysis of small-molecule kinase inhibitors in adult patients with glioblastoma as reported in published clinical studies and determines which kinases have been targeted by the inhibitors used in these studies. Publications were retrieved using a MEDLINE search and by screening meeting abstracts. A total of 60 studies qualified for inclusion, of which 25 were original reports. A total of 2385 glioblastoma patients receiving kinase inhibitors could be evaluated. The study designs included 2 phase III studies and 37 phase II studies. Extracted data included radiological response, progression-free survival, overall survival, toxicity, and biomarker analysis. The main findings were that (i) efficacy of small-molecule kinase inhibitors in clinical studies with glioblastoma patients does not yet warrant a change in standard clinical practice and (ii) 6 main kinase targets for inhibitors have been evaluated in these studies: EGFR, mTOR, KDR, FLT1, PKCbeta, and PDGFR.

Biochemical mechanisms of resistance to small-molecule protein kinase inhibitors

Protein kinases have emerged as one of the most frequently targeted families of proteins in drug discovery. While the development of small-molecule inhibitors that have the potency and selectivity necessary to be effective cancer drugs is still a formidable challenge, there have been several notable successes in this area over the past decade. However, in the course of the clinical use of these inhibitors, it has become apparent that drug resistance is a recurring problem. Because kinase inhibitors act by targeting a specific kinase or set of kinases, there is a strong selective pressure for the development of mutations that hinder drug binding but preserve the catalytic activity of these enzymes. To date, resistance mutations to clinically approved kinase inhibitors have been identified in a number of kinases. This review will highlight recent work that has been performed to understand how mutations in the kinase catalytic domain confer drug resistance. In addition, recent experimental efforts to predict potential sites of clinical drug resistance will be discussed.

Therapeutic prospect of Syk inhibitors

BACKGROUND

The non-receptor spleen tyrosine kinase (Syk; EC 2.7.10.2) is involved in signal transduction in a variety of cell types. In particular, it is a key mediator of immune receptors signaling in host inflammatory cells (B cells, mast cells, macrophages and neutrophils), important for both allergic and antibody-mediated autoimmune diseases. Deregulated Syk kinase activity also allows growth factor-independent proliferation and transforms bone marrow-derived pre-B cells that are able to induce leukemia. Consequently, the development of Syk kinase inhibitors could conceivably treat these disorders and so they have became a major focus in the pharmaceutical and biotech industry.

OBJECTIVE

In this review, we analyze the structure and role of Syk kinase, the use of small molecules, interacting with ATP-binding site, as inhibitors of kinase activity and finally the potential of using inhibitors of Syk kinase expression to attenuate pathological conditions.

CONCLUSION

Syk kinase inhibition is suggested as a powerful tool for the therapy of different pathologies.

Kinase selectivity potential for inhibitors targeting the ATP binding site: a network analysis

MOTIVATION AND METHOD: Small-molecule inhibitors targeting the adenosine triphosphate (ATP) binding pocket of the catalytic domain of protein kinases have potential to become drugs devoid of (major) side effects, particularly if they bind selectively. Here, the sequences of the 518 human kinases are first mapped onto the structural alignment of 116 kinases of known three-dimensional structure. The multiple structure alignment is then used to encode the known strategies for developing selective inhibitors into a fingerprint. Finally, a network analysis is used to partition the kinases into clusters according to similarity of their fingerprints, i.e. physico-chemical characteristics of the residues responsible for selective binding.

RESULTS

For each kinase the network analysis reveals the likelihood to find selective inhibitors targeting the ATP binding site. Systematic guidelines are proposed to develop selective inhibitors. Importantly, the network analysis suggests that the tyrosine kinase EphB4 has high selectivity potential, which is consistent with the selectivity profile of two novel EphB4 inhibitors.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Safety assessment considerations and strategies for targeted small molecule cancer therapeutics in drug discovery

Less than 10% of all experimental drugs introduced into clinical trials ever achieve the approval of regulatory agencies for marketing. For experimental small molecule oncology therapeutics, the approval rate is even less (5%). Clinical safety and efficacy are the two main causes of failure for oncologic drugs in development. Because these failures of experimental drugs are tremendously expensive for pharmaceutical companies, strategies have been developed and refined for reducing this attrition. While these strategic activities can take place in drug development, more benefit may be gained by increasing efforts in drug discovery in the form of (1) target validation; (2) compound selectivity analysis from the perspective of balancing efficacy and toxicity; and (3) investigation of ancillary means to abrogate toxicity, especially with respect to undesirable target-related effects. Most pharmaceutical companies recognize the benefit of lead compound optimization, but the degree to which it is applied seems to vary greatly. This article presents concepts and strategies to reduce the attrition of small molecule oncology therapeutic drug candidates due to toxicity.

QSAR models for predicting the similarity in binding profiles for pairs of protein kinases and the variation of models between experimental data sets

We propose a direct QSAR methodology to predict how similar the inhibitor-binding profiles of two protein kinases are likely to be, based on the properties of the residues surrounding the ATP-binding site. We produce a random forest model for each of five data sets (one in-house, four from the literature) where multiple compounds are tested on many kinases. Each model is self-consistent by cross-validation, and all models point to only a few residues in the active site controlling the binding profiles. While all models include the "gatekeeper" as one of the important residues, consistent with previous literature, some models suggest other residues as being more important. We apply each model to predict the similarity in binding profile to all pairs in a set of 411 kinases from the human genome and get very different predictions from each model. This turns out not to be an issue with model-building but with the fact that the experimental data sets disagree about which kinases are similar to which others. It is possible to build a model combining all the data from the five data sets that is reasonably self-consistent but not surprisingly, given the disagreement between data sets, less self-consistent than the individual models.

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.

Correction for Interference by Test Samples in High-Throughput Assays

In high-throughput biochemical assays performed in multiwell plates, the effect of test samples on the activity of the biochemical system is usually measured by optical means such as absorbance, fluorescence, luminescence, or scintillation counting. The test sample often causes detection interference when it remains in the well during the measurement. Interference may be due to light absorption, fluorescence quenching, sample fluorescence, chemical interaction of the sample with a detection reagent, or depression of the meniscus. A simple method is described that corrects for such interference well by well. The interference is measured in a separate artifact assay plate. An appropriate arithmetic correction is then applied to the measurement in the corresponding well of the activity assay plate. The correction procedure can be used for single-point screening or potency measurements on serial dilutions of test samples.

Identification of a kinase profile that predicts chromosome damage induced by small molecule kinase inhibitors

Kinases are heavily pursued pharmaceutical targets because of their mechanistic role in many diseases. Small molecule kinase inhibitors (SMKIs) are a compound class that includes marketed drugs and compounds in various stages of drug development. While effective, many SMKIs have been associated with toxicity including chromosomal damage. Screening for kinase-mediated toxicity as early as possible is crucial, as is a better understanding of how off-target kinase inhibition may give rise to chromosomal damage. To that end, we employed a competitive binding assay and an analytical method to predict the toxicity of SMKIs. Specifically, we developed a model based on the binding affinity of SMKIs to a panel of kinases to predict whether a compound tests positive for chromosome damage. As training data, we used the binding affinity of 113 SMKIs against a representative subset of all kinases (290 kinases), yielding a 113x290 data matrix. Additionally, these 113 SMKIs were tested for genotoxicity in an in vitro micronucleus test (MNT). Among a variety of models from our analytical toolbox, we selected using cross-validation a combination of feature selection and pattern recognition techniques: Kolmogorov-Smirnov/T-test hybrid as a univariate filter, followed by Random Forests for feature selection and Support Vector Machines (SVM) for pattern recognition. Feature selection identified 21 kinases predictive of MNT. Using the corresponding binding affinities, the SVM could accurately predict MNT results with 85% accuracy (68% sensitivity, 91% specificity). This indicates that kinase inhibition profiles are predictive of SMKI genotoxicity. While in vitro testing is required for regulatory review, our analysis identified a fast and cost-efficient method for screening out compounds earlier in drug development. Equally important, by identifying a panel of kinases predictive of genotoxicity, we provide medicinal chemists a set of kinases to avoid when designing compounds, thereby providing a basis for rational drug design away from genotoxicity.

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.

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.

Prediction of specificity-determining residues for small-molecule kinase inhibitors

Background

Designing small-molecule kinase inhibitors with desirable selectivity profiles is a major challenge in drug discovery. A high-throughput screen for inhibitors of a given kinase will typically yield many compounds that inhibit more than one kinase. A series of chemical modifications are usually required before a compound exhibits an acceptable selectivity profile. Rationalizing the selectivity profile for a small-molecule inhibitor in terms of the specificity-determining kinase residues for that molecule can be an important step toward the goal of developing selective kinase inhibitors.

Results

Here we describe S-Filter, a method that combines sequence and structural information to predict specificity-determining residues for a small molecule and its kinase selectivity profile. Analysis was performed on seven selective kinase inhibitors where a structural basis for selectivity is known. S-Filter correctly predicts specificity determinants that were described by independent groups. S-Filter also predicts a number of novel specificity determinants that can often be justified by further structural comparison.

Conclusion

S-Filter is a valuable tool for analyzing kinase selectivity profiles. The method identifies potential specificity determinants that are not readily apparent, and provokes further investigation at the structural level.

Activity-based protein profiling: from enzyme chemistry to proteomic chemistry

Genome sequencing projects have provided researchers with a complete inventory of the predicted proteins produced by eukaryotic and prokaryotic organisms. Assignment of functions to these proteins represents one of the principal challenges for the field of proteomics. Activity-based protein profiling (ABPP) has emerged as a powerful chemical proteomic strategy to characterize enzyme function directly in native biological systems on a global scale. Here, we review the basic technology of ABPP, the enzyme classes addressable by this method, and the biological discoveries attributable to its application.

Functional classification of protein kinase binding sites using Cavbase

Increasingly, drug-discovery processes focus on complete gene families. Tools for analyzing similarities and differences across protein families are important for the understanding of key functional features of proteins. Herein we present a method for classifying protein families on the basis of the properties of their active sites. We have developed Cavbase, a method for describing and comparing protein binding pockets, and show its application to the functional classification of the binding pockets of the protein family of protein kinases. A diverse set of kinase cavities is mutually compared and analyzed in terms of recurring functional recognition patterns in the active sites. We are able to propose a relevant classification based on the binding motifs in the active sites. The obtained classification provides a novel perspective on functional properties across protein space. The classification of the MAP and the c-Abl kinases is analyzed in detail, showing a clear separation of the respective kinase subfamilies. Remarkable cross-relations among protein kinases are detected, in contrast to sequence-based classifications, which are not able to detect these relations. Furthermore, our classification is able to highlight features important in the optimization of protein kinase inhibitors. Using small-molecule inhibition data we could rationalize cross-reactivities between unrelated kinases which become apparent in the structural comparison of their binding sites. This procedure helps in the identification of other possible kinase targets that behave similarly in "binding pocket space" to the kinase under consideration.

Development of anaplastic lymphoma kinase (ALK) small-molecule inhibitors for cancer therapy

Anaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase (RTK) involved in the genesis of several human cancers; indeed, ALK was initially identified in constitutively activated and oncogenic fusion forms--the most common being nucleophosmin (NPM)-ALK--in a non-Hodgkin's lymphoma (NHL) known as anaplastic large-cell lymphoma (ALCL) and subsequent studies identified ALK fusions in the human sarcomas called inflammatory myofibroblastic tumors (IMTs). In addition, two recent reports have suggested that the ALK fusion, TPM4-ALK, may be involved in the genesis of a subset of esophageal squamous cell carcinomas. While the cause-effect relationship between ALK fusions and malignancies such as ALCL and IMT is very well established, more circumstantial links implicate the involvement of the full-length, normal ALK receptor in the genesis of additional malignancies including glioblastoma, neuroblastoma, breast cancer, and others; in these instances, ALK is believed to foster tumorigenesis following activation by autocrine and/or paracrine growth loops involving the reported ALK ligands, pleiotrophin (PTN) and midkine (MK). There are no currently available ALK small-molecule inhibitors approved for clinical cancer therapy; however, recognition of the variety of malignancies in which ALK may play a causative role has recently begun to prompt developmental efforts in this area. This review provides a succinct summary of normal ALK biology, the confirmed and putative roles of ALK fusions and the full-length ALK receptor in the development of human cancers, and efforts to target ALK using small-molecule kinase inhibitors.

Enzyme microarrays assembled by acoustic dispensing technology

Miniaturizing bioassays to the nanoliter scale for high-throughput screening reduces the consumption of reagents that are expensive or difficult to handle. Through the use of acoustic dispensing technology, nanodroplets containing 10 microM ATP (3 microCi/microL (32)P) and reaction buffer in 10% glycerol were positionally dispensed to the surface of glass slides to form 40-nL compartments (100 droplets/slide) for Pim1 (proviral integration site 1) kinase reactions. The reactions were activated by dispensing 4 nL of various levels of a pyridocarbazolo-cyclopentadienyl ruthenium complex Pim1 inhibitor, followed by dispensing 4 nL of a Pim1 kinase and peptide substrate solution to achieve final concentrations of 150 nM enzyme and 10 microM substrate. The microarray was incubated at 30 degrees C (97% R(h)) for 1.5 h. The spots were then blotted to phosphocellulose membranes to capture phosphorylated substrate. With phosphor imaging to quantify the washed membranes, the assay showed that, for doses of inhibitor from 0.75 to 3 microM, Pim1 was increasingly inhibited. Signal-to-background ratios were as high as 165, and average coefficients of variation for the assay were approximately 20%. Coefficients of variation for dispensing typical working buffers were under 5%. Thus, microarrays assembled by acoustic dispensing are promising as cost-effective tools that can be used in protein assay development.

The challenge of selecting protein kinase assays for lead discovery optimization

BACKGROUND: Protein kinases represent one of the most promising groups of drug targets owing to their involvement in such pathological conditions as cancer, inflammatory diseases, neural disorders, and metabolism problems. In the last few years, numerous pharmaceutical and biotech companies have established kinase high-throughput screening (HTS) programs, and the reagent and service industries for kinase assay platforms, kits, and profiling services have begun to thrive. OBJECTIVE: The plethora of different assay formats available today poses a great challenge to scientists who want to select a technology that will be cost efficient, convenient to use, and have low false positive and false negative rates. METHODS: In the current review, we summarize the most commonly used kinase assay methods in the drug discovery process, present the advantages and disadvantages of each of these methods, and discuss the challenges of discovering kinase inhibitors by using these technologies. CONCLUSIONS: The decision of selecting the assay formats for HTS or service platform for profiling should take into account not only the final goals of the screens but also the limitation of resources.

Metalloproteases as potential therapeutic targets in arthritis treatment

Dysregulated proteolysis of the extracellular matrix of articular cartilage represents a unifying hallmark of the arthritides, and has been a target for therapeutic intervention for some time, although clinical efficacy has been elusive. Members of the 'A disintegrin and metalloprotease with thrombospondin motifs' and matrix metalloprotease families are considered to be collectively responsible for cartilage catabolism, such that inhibition of these activities is theoretically a highly attractive strategy for preventing further proteolytic damage. This review outlines the biology of these metalloproteases and what we have learnt from inhibition studies and transgenics, and highlights the important questions that this information raises for the future development of therapeutics directed towards metalloproteases for arthritis treatment.

A systematic interaction map of validated kinase inhibitors with Ser/Thr kinases

Protein kinases play a pivotal role in cell signaling, and dysregulation of many kinases has been linked to disease development. A large number of kinase inhibitors are therefore currently under investigation in clinical trials, and so far seven inhibitors have been approved as anti-cancer drugs. In addition, kinase inhibitors are widely used as specific probes to study cell signaling, but systematic studies describing selectivity of these reagents across a panel of diverse kinases are largely lacking. Here we evaluated the specificity of 156 validated kinase inhibitors, including inhibitors used in clinical trials, against 60 human Ser/Thr kinases using a thermal stability shift assay. Our analysis revealed many unexpected cross-reactivities for inhibitors thought to be specific for certain targets. We also found that certain combinations of active-site residues in the ATP-binding site correlated with the detected ligand promiscuity and that some kinases are highly sensitive to inhibition using diverse chemotypes, suggesting them as preferred intervention points. Our results uncovered also inhibitor cross-reactivities that may lead to alternate clinical applications. For example, LY333'531, a PKCbeta inhibitor currently in phase III clinical trials, efficiently inhibited PIM1 kinase in our screen, a suggested target for treatment of leukemia. We determined the binding mode of this inhibitor by x-ray crystallography and in addition showed that LY333'531 induced cell death and significantly suppressed growth of leukemic cells from acute myeloid leukemia patients.

Analysis of protein phosphorylation on a proteome-scale

Phosphorylation, the most intensively studied and common PTM on proteins, is a complex biological phenomenon. Its complexity manifests itself in the large numbers of proteins that attach it, remove it and recognise it as a protein code. Since the first report of protein phosphorylation on vitellin 100 years ago, a wide variety of biochemical and analytical chemical approaches have been developed to enrich and detect protein phosphorylation. The last 5 years have witnessed a renaissance in methodologies capable of characterising protein phosphorylation on a proteome-scale. These technological advances have allowed identification of hundreds to thousands of phosphorylation sites in a proteome and have resulted in a profound paradigm shift. For the first time, using quantitative MS, the topology and significance of global phosphorylation networks may be investigated, marking a new era of cell signalling research. This review addresses recent technological advances in the purification of phosphorylated proteins and peptides and current MS-based strategies used to qualitatively and quantitatively probe these enriched phosphoproteomes. In addition, we review the application of complementary array-based technologies to derive signalling networks from kinase-substrate interactions and discuss future challenges in the field.

The scientific impact of the Structural Genomics Consortium: a protein family and ligand-centered approach to medically-relevant human proteins

As many of the structural genomics centers have ended their first phase of operation, it is a good point to evaluate the scientific impact of this endeavour. The Structural Genomics Consortium (SGC), operating from three centers across the Atlantic, investigates human proteins involved in disease processes and proteins from Plasmodium falciparum and related organisms. We present here some of the scientific output of the Oxford node of the SGC, where the target areas include protein kinases, phosphatases, oxidoreductases and other metabolic enzymes, as well as signal transduction proteins. The SGC has aimed to achieve extensive coverage of human gene families with a focus on protein–ligand interactions. The methods employed for effective protein expression, crystallization and structure determination by X-ray crystallography are summarized. In addition to the cumulative impact of accelerated delivery of protein structures, we demonstrate how family coverage, generic screening methodology, and the availability of abundant purified protein samples, allow a level of discovery that is difficult to achieve otherwise. The contribution of NMR to structure determination and protein characterization is discussed. To make this information available to a wide scientific audience, a new tool for disseminating annotated structural information was created that also represents an interactive platform allowing for a continuous update of the annotation by the scientific community.