ABSTRACT: Protein and lipid kinases play key regulatory roles in a number of biological processes. Unsurprisingly, activating mutations in kinases have been linked to a number of disorders and diseases, most notably cancers. Thus, kinases have emerged as promising clinical targets. There are more than 500 human protein kinases and about 20 lipid kinases. Most protein kinases share a highly conserved domain, the eukaryotic protein kinase (ePK) domain, which contains the ATP and substrate-binding sites. Many inhibitors in clinical use bind to the highly conserved ATP binding site. For this reason, many kinase inhibitors are not exclusively selective for their intended targets. Furthermore, despite the current interest in kinase inhibitors, very few kinases implicated in disease have validated inhibitors. This unit describes the human kinome, ePK structure, and types of kinase inhibitors, focusing on methods to identify potent and selective kinase inhibitors.
Project description:The eukaryotic protein kinase (ePK) domain mediates the majority of signaling and coordination of complex events in eukaryotes. By contrast, most bacterial signaling is thought to occur through structurally unrelated histidine kinases, though some ePK-like kinases (ELKs) and small molecule kinases are known in bacteria. Our analysis of the Global Ocean Sampling (GOS) dataset reveals that ELKs are as prevalent as histidine kinases and may play an equally important role in prokaryotic behavior. By combining GOS and public databases, we show that the ePK is just one subset of a diverse superfamily of enzymes built on a common protein kinase-like (PKL) fold. We explored this huge phylogenetic and functional space to cast light on the ancient evolution of this superfamily, its mechanistic core, and the structural basis for its observed diversity. We cataloged 27,677 ePKs and 18,699 ELKs, and classified them into 20 highly distinct families whose known members suggest regulatory functions. GOS data more than tripled the count of ELK sequences and enabled the discovery of novel families and classification and analysis of all ELKs. Comparison between and within families revealed ten key residues that are highly conserved across families. However, all but one of the ten residues has been eliminated in one family or another, indicating great functional plasticity. We show that loss of a catalytic lysine in two families is compensated by distinct mechanisms both involving other key motifs. This diverse superfamily serves as a model for further structural and functional analysis of enzyme evolution.
Project description:BACKGROUND: Malaria, caused by the parasitic protist Plasmodium falciparum, represents a major public health problem in the developing world. The P. falciparum genome has been sequenced, which provides new opportunities for the identification of novel drug targets. Eukaryotic protein kinases (ePKs) form a large family of enzymes with crucial roles in most cellular processes; hence malarial ePKS represent potential drug targets. We report an exhaustive analysis of the P. falciparum genomic database (PlasmoDB) aimed at identifying and classifying all ePKs in this organism. RESULTS: Using a variety of bioinformatics tools, we identified 65 malarial ePK sequences and constructed a phylogenetic tree to position these sequences relative to the seven established ePK groups. Predominant features of the tree were: (i) that several malarial sequences did not cluster within any of the known ePK groups; (ii) that the CMGC group, whose members are usually involved in the control of cell proliferation, had the highest number of malarial ePKs; and (iii) that no malarial ePK clustered with the tyrosine kinase (TyrK) or STE groups, pointing to the absence of three-component MAPK modules in the parasite. A novel family of 20 ePK-related sequences was identified and called FIKK, on the basis of a conserved amino acid motif. The FIKK family seems restricted to Apicomplexa, with 20 members in P. falciparum and just one member in some other Apicomplexan species. CONCLUSION: The considerable phylogenetic distance between Apicomplexa and other Eukaryotes is reflected by profound divergences between the kinome of malaria parasites and that of yeast or mammalian cells.
Project description:An intensive recent effort to develop ATP-competitive mTOR inhibitors has resulted in several potent and selective molecules such as Torin1, PP242, KU63794, and WYE354. These inhibitors are being widely used as pharmacological probes of mTOR-dependent biology. To determine the potency and specificity of these agents, we have undertaken a systematic kinome-wide effort to profile their selectivity and potency using chemical proteomics and assays for enzymatic activity, protein binding, and disruption of cellular signaling. Enzymatic and cellular assays revealed that all four compounds are potent inhibitors of mTORC1 and mTORC2, with Torin1 exhibiting ?20-fold greater potency for inhibition of Thr-389 phosphorylation on S6 kinases (EC(50) = 2 nM) relative to other inhibitors. In vitro biochemical profiling at 10 ?M revealed binding of PP242 to numerous kinases, although WYE354 and KU63794 bound only to p38 kinases and PI3K isoforms and Torin1 to ataxia telangiectasia mutated, ATM and Rad3-related protein, and DNA-PK. Analysis of these protein targets in cellular assays did not reveal any off-target activities for Torin1, WYE354, and KU63794 at concentrations below 1 ?M but did show that PP242 efficiently inhibited the RET receptor (EC(50), 42 nM) and JAK1/2/3 kinases (EC(50), 780 nM). In addition, Torin1 displayed unusually slow kinetics for inhibition of the mTORC1/2 complex, a property likely to contribute to the pharmacology of this inhibitor. Our results demonstrated that, with the exception of PP242, available ATP-competitive compounds are highly selective mTOR inhibitors when applied to cells at concentrations below 1 ?M and that the compounds may represent a starting point for medicinal chemistry efforts aimed at developing inhibitors of other PI3K kinase-related kinases.
Project description:Selectivity of kinase inhibitors, or the lack thereof, continues to be an intensely debated topic in drug discovery research. Especially, type I inhibitors, which represent most of the currently available kinase inhibitors, are often thought to lack selectivity because they target the largely conserved adenosine triphosphate-binding site in kinases. Herein, we present a large-scale analysis of potential selectivity among multikinase inhibitors, covering 141 human kinases and more than 10?000 qualifying compounds. By design, the analysis was focused on type I inhibitors and carried out at the level of systematically generated kinase pairs sharing inhibitors. Kinase pair category- and compound-based selectivity profiles identified in part highly selective inhibitors for many kinases. Sets of inhibitors associated with kinase pairs frequently contained nonselective as well as increasingly selective compounds. Selectivity of inhibitors did not result from gatekeeper residues settings or phylogenetic distance of kinases. Rather, it was most likely attributable to subtle differences between binding regions in kinases. Taken together, the results of our study reveal that many multikinase inhibitors are more selective than one might assume.
Project description:Oncogenic BRAF mutations contribute to the development of a number of cancers, and small-molecule BRAF inhibitors have been approved by the Food and Drug Administration (FDA) for anticancer therapy. In this study, we employed two targeted quantitative proteomics approaches for monitoring separately the alterations in protein expression and ATP binding affinities of kinases in cultured human melanoma cells elicited by two FDA-approved small-molecule BRAF inhibitors, dabrafenib and vemurafenib. Our results showed that treatment with the two inhibitors led to markedly different reprograming of the human kinome. Furthermore, we confirmed that vemurafenib could compromise the ATP binding capacity of MAP2K5 in vitro and inhibit its kinase activity in cells. Together, our targeted quantitative proteomic methods revealed profound changes in expression levels of kinase proteins in cultured melanoma cells upon treatment with clinically used BRAF inhibitors and led to the discovery of novel putative target kinases for these inhibitors.
Project description:The protein kinases are a large family of enzymes that play fundamental roles in propagating signals within the cell. Because of the high degree of binding site similarity shared among protein kinases, designing drug compounds with high specificity among the kinases has proven difficult. However, computational approaches to comparing the 3-dimensional geometry and physicochemical properties of key binding site residue positions have been shown to be informative of inhibitor selectivity. The Combinatorial Clustering Of Residue Position Subsets (ccorps) method, introduced here, provides a semi-supervised learning approach for identifying structural features that are correlated with a given set of annotation labels. Here, ccorps is applied to the problem of identifying structural features of the kinase atp binding site that are informative of inhibitor binding. ccorps is demonstrated to make perfect or near-perfect predictions for the binding affinity profile of 8 of the 38 kinase inhibitors studied, while only having overall poor predictive ability for 1 of the 38 compounds. Additionally, ccorps is shown to identify shared structural features across phylogenetically diverse groups of kinases that are correlated with binding affinity for particular inhibitors; such instances of structural similarity among phylogenetically diverse kinases are also shown to not be rare among kinases. Finally, these function-specific structural features may serve as potential starting points for the development of highly specific kinase inhibitors.
Project description:ATP-competitive protein kinase inhibitors are important research tools and therapeutic agents. Because there are >500 human kinases that contain highly conserved active sites, the development of selective inhibitors is extremely challenging. Methods to rapidly and efficiently profile kinase inhibitor targets in cell lysates are urgently needed to discover selective compounds and to elucidate the mechanisms of action for polypharmacological inhibitors. Here, we describe a protocol for microgram-scale chemoproteomic profiling of ATP-competitive kinase inhibitors using kinobeads. We employed a gel-free in situ digestion protocol coupled to nanoflow liquid chromatography-mass spectrometry to profile ?200 kinases in single analytical runs using as little as 5 ?L of kinobeads and 300 ?g of protein. With our kinobead reagents, we obtained broad coverage of the kinome, monitoring the relative expression levels of 312 kinases in a diverse panel of 11 cancer cell lines. Further, we profiled a set of pyrrolopyrimidine- and pyrazolopyrimidine-based kinase inhibitors in competition-binding experiments with label-free quantification, leading to the discovery of a novel selective and potent inhibitor of protein kinase D (PKD) 1, 2, and 3. Our protocol is useful for rapid and sensitive profiling of kinase expression levels and ATP-competitive kinase inhibitor selectivity in native proteomes.
Project description:Protein kinases achieve substrate selective phosphorylation through their conformational flexibility and dynamic interaction with the substrate. Designing substrate selective or kinase selective small molecule inhibitors remains a challenge because of a lack of understanding of the dynamic mechanism by which substrates are selected by the kinase. Using a combination of all-atom molecular dynamics simulations and FRET sensors, we have delineated an allosteric mechanism that results in interaction among the DFG motif, G-loop, and activation loop and structurally links the nucleotide and substrate binding interfaces in protein kinase C? and three other Ser/Thr kinases. ATP-competitive staurosporine analogues engage this allosteric switch region located just outside the ATP binding site to displace substrate binding to varying degrees. These inhibitors function as bitopic ligands by occupying the ATP binding site and interacting with the allosteric switch region. The conserved mechanism identified in this study can be exploited to select and design bitopic inhibitors for kinases.
Project description: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/ .
Project description:The PKN (protein kinase N) family of Ser/Thr protein kinases regulates a diverse set of cellular functions, such as cell migration and cytoskeletal organization. Inhibition of tumour PKN activity has been explored as an oncology therapeutic approach, with a PKN3-targeted RNAi (RNA interference)-derived therapeutic agent in Phase I clinical trials. To better understand this important family of kinases, we performed detailed enzymatic characterization, determining the kinetic mechanism and lipid sensitivity of each PKN isoform using full-length enzymes and synthetic peptide substrate. Steady-state kinetic analysis revealed that PKN1-3 follows a sequential ordered Bi-Bi kinetic mechanism, where peptide substrate binding is preceded by ATP binding. This kinetic mechanism was confirmed by additional kinetic studies for product inhibition and affinity of small molecule inhibitors. The known lipid effector, arachidonic acid, increased the catalytic efficiency of each isoform, mainly through an increase in kcat for PKN1 and PKN2, and a decrease in peptide KM for PKN3. In addition, a number of PKN inhibitors with various degrees of isoform selectivity, including potent (Ki<10 nM) and selective PKN3 inhibitors, were identified by testing commercial libraries of small molecule kinase inhibitors. This study provides a kinetic framework and useful chemical probes for understanding PKN biology and the discovery of isoform-selective PKN-targeted inhibitors.