Knowledge based prediction of ligand binding modes and rational inhibitor design for kinase drug discovery

Discovery of imidazo[1,2-b]pyridazine derivatives as IKKbeta inhibitors. Part 1: Hit-to-lead study and structure-activity relationship

Imidazo[1,2-b]pyridazine derivatives from high-throughput screening were developed as IKKbeta inhibitors. By the optimization of the 3- and 6-position of imidazo[1,2-b]pyridazine scaffold, cell-free IKKbeta inhibitory activity and TNFalpha inhibitory activity in THP-1 cell increased. Also, these compounds showed high kinase selectivity. The structure-activity relationship was revealed and the interaction model of imidazo[1,2-b]pyridazine compounds with IKKbeta was constructed.

The use of machine learning and nonlinear statistical tools for ADME prediction

Absorption, distribution, metabolism and excretion (ADME)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of ADME by in silico tools has now become an inevitable paradigm to reduce cost and enhance efficiency in pharmaceutical research. Recently, machine learning as well as nonlinear statistical tools has been widely applied to predict routine ADME end points. To achieve accurate and reliable predictions, it would be a prerequisite to understand the concepts, mechanisms and limitations of these tools. Here, we have devised a small synthetic nonlinear data set to help understand the mechanism of machine learning by 2D-visualisation. We applied six new machine learning methods to four different data sets. The methods include Naive Bayes classifier, classification and regression tree, random forest, Gaussian process, support vector machine and k nearest neighbour. The results demonstrated that ensemble learning and kernel machine displayed greater accuracy of prediction than classical methods irrespective of the data set size. The importance of interaction with the engineering field is also addressed. The results described here provide insights into the mechanism of machine learning, which will enable appropriate usage in the future.

Identification of 2-anilino-9-methoxy-5,7-dihydro-6H-pyrimido[5,4-d][1]benzazepin-6-ones as dual PLK1/VEGF-R2 kinase inhibitor chemotypes by structure-based lead generation

To develop multikinase inhibitors with dual PLK1/VEGF-R2 inhibitory activity, the d-annulated 1-benzazepin-2-one scaffold present in the paullone family of kinase inhibitors was investigated as a general structure template suitable for anchoring annulated heterocycles at the hinge region of the ATP binding site. For this purpose, the indole substructure of the paullones was replaced by other nitrogen containing heteroaromatics. The designed scaffolds were synthesized and tested on the indicated kinases. The 2-anilino-5,7-dihydro-6H-pyrimido[5,4-d][1]benzazepin-6-ones were found to be VEGF-R2 inhibitors with selectivity against the insulin receptor kinase. The attachment of a methoxy group to the 9-position of the scaffold led to additional PLK1 inhibitory activity, which was explained by an alternative binding mode of the 9-methoxy derivatives. Selected members of the compound class inhibited the VEGF-R2 autophosphorylation in human umbilical vein endothelial cells, the sprouting of human umbilical vein endothelial cell speroids, and the proliferation of diverse cancer cell lines.

From imide to lactam metallo-pyridocarbazoles: distinct scaffolds for the design of selective protein kinase inhibitors

Organometallic pyridocarbazole scaffolds are investigated as protein kinase inhibitors. Whereas our previous designs employed solely a maleimide pharmacophore for achieving the two crucial canonical hydrogen bonds to the hinge region of the ATP binding site, we have now extended our investigations to include the related lactam metallo-pyridocarbazoles. The synthetic access of the two regioisomeric lactam pyridocarbazoles is described, and the distinct biological properties of the two lactam scaffolds are revealed by employing a ruthenium half sandwich complex as a model system, resulting in organometallic lead structures for the inhibition of the protein kinases TrkA and CLK2. These new lactam metallo-pyridocarbazoles expand our existing molecular toolbox and assist toward the generation of metal complex scaffolds as lead structures for the design of selective inhibitors for numerous kinases of the human kinome.

Analysis of c-Met kinase domain complexes: a new specific catalytic site receptor model for defining binding modes of ATP-competitive ligands

The receptor tyrosine kinase c-Met have multiple roles during cancer development and is currently considered as an important target for molecularly targeted therapies. Structural knowledge of how compounds interact on c-Met catalytic site could guide structure-based drug design strategies towards more effective and selective anticancer drug candidates. However, although 17 crystal structures of c-Met complexed with adenosine triphosphate (ATP)-competitive kinase inhibitors are publicly available (August 2009), there are still open questions regarding the prediction of ligand binding modes. We have applied molecular modeling and molecular mechanics to analyze the distribution of ligands interaction energy on c-Met residues, and deduced a new model of the active site allowing for an unambiguous identification of ligand binding modes. We demonstrate that the binding of known ligands on the c-Met catalytic site involves seven identified structurally-distinct areas. Five of these match the generic kinase ATP binding site model built by Novartis scientists in the 1990s, while the two others are distinct allosteric regions that can be exploited by second generation kinase inhibitors such as Gleevec. We show here that c-Met can accept both such kinds of allosteric inhibitors, a very unusual feature in the kinase family that opens new grounds for highly specific drug design.

Identification and characterisation of 2-aminopyridine inhibitors of checkpoint kinase 2

5-(Hetero)aryl-3-(4-carboxamidophenyl)-2-aminopyridine inhibitors of CHK2 were identified from high throughput screening of a kinase-focussed compound library. Rapid exploration of the hits through straightforward chemistry established structure-activity relationships and a proposed ATP-competitive binding mode which was verified by X-ray crystallography of several analogues bound to CHK2. Variation of the 5-(hetero)aryl substituent identified bicyclic dioxolane and dioxane groups which improved the affinity and the selectivity of the compounds for CHK2 versus CHK1. The 3-(4-carboxamidophenyl) substituent could be successfully replaced by acyclic omega-aminoalkylamides, which made additional polar interactions within the binding site and led to more potent inhibitors of CHK2. Compounds from this series showed activity in cell-based mechanistic assays for inhibition of CHK2.

The impact of GPCR structures on pharmacology and structure-based drug design

After many years of effort, recent technical breakthroughs have enabled the X-ray crystal structures of three G-protein-coupled receptors (GPCRs) (beta1 and beta2 adrenergic and adenosine A(2a)) to be solved in addition to rhodopsin. GPCRs, like other membrane proteins, have lagged behind soluble drug targets such as kinases and proteases in the number of structures available and the level of understanding of these targets and their interaction with drugs. The availability of increasing numbers of structures of GPCRs is set to greatly increase our understanding of some of the key issues in GPCR biology. In particular, what constitutes the different receptor conformations that are involved in signalling and the molecular changes which occur upon receptor activation. How future GPCR structures might alter our views on areas such as agonist-directed signalling and allosteric regulation as well as dimerization is discussed. Knowledge of crystal structures in complex with small molecules will enable techniques in drug discovery and design, which have previously only been applied to soluble targets, to now be used for GPCR targets. These methods include structure-based drug design, virtual screening and fragment screening. This review considers how these methods have been used to address problems in drug discovery for kinase and protease targets and therefore how such methods are likely to impact GPCR drug discovery in the future.

An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics

Incorporating receptor flexibility into molecular docking should improve results for flexible proteins. However, the incorporation of explicit all-atom flexibility with molecular dynamics for the entire protein chain may also introduce significant error and "noise" that could decrease docking accuracy and deteriorate the ability of a scoring function to rank native-like poses. We address this apparent paradox by comparing the success of several flexible receptor models in cross-docking and multiple receptor ensemble docking for p38α mitogen-activated protein (MAP) kinase. Explicit all-atom receptor flexibility has been incorporated into a CHARMM-based molecular docking method (CDOCKER) using both molecular dynamics (MD) and torsion angle molecular dynamics (TAMD) for the refinement of predicted protein-ligand binding geometries. These flexible receptor models have been evaluated, and the accuracy and efficiency of TAMD sampling is directly compared to MD sampling. Several flexible receptor models are compared, encompassing flexible side chains, flexible loops, multiple flexible backbone segments, and treatment of the entire chain as flexible. We find that although including side chain and some backbone flexibility is required for improved docking accuracy as expected, docking accuracy also diminishes as additional and unnecessary receptor flexibility is included into the conformational search space. Ensemble docking results demonstrate that including protein flexibility leads to to improved agreement with binding data for 227 active compounds. This comparison also demonstrates that a flexible receptor model enriches high affinity compound identification without significantly increasing the number of false positives from low affinity compounds.

Blocking UV-induced eIF2alpha phosphorylation with small molecule inhibitors of GCN2

The eIF2alpha kinase general control non-depressible 2 integrates translation initiation rates to amino acid availability. General control non-depressible 2 also regulates translation initiation during synaptic plasticity and GCN2(-/-) mice show improved memory compared with wild-type mice with a reduced threshold for triggering late long-term potentiation. This property suggests that inhibiting general control non-depressible 2 function might represent a therapeutic avenue for improving memory. We screened for general control non-depressible 2 inhibitors using a small library of known kinase inhibitors and ATP-analogs and identified three compounds--indirubin-3'-monoxime, SP600125 and a SyK inhibitor with activity against general control non-depressible 2. All three compounds inhibit the ability of general control non-depressible 2 to phosphorylate eIF2alphain vitro as well as in vivo following UV-treatment of mouse embryonic fibroblasts. Using computer-assisted modeling, we modeled the binding of the inhibitors in the ATP-binding site of general control non-depressible 2. This work provides the molecular basis for undertaking structure-activity relationships of these compounds in order to develop specific inhibitors of general control non-depressible 2.

p3d--Python module for structural bioinformatics

Background

High-throughput bioinformatic analysis tools are needed to mine the large amount of structural data via knowledge based approaches. The development of such tools requires a robust interface to access the structural data in an easy way. For this the Python scripting language is the optimal choice since its philosophy is to write an understandable source code.

Results

p3d is an object oriented Python module that adds a simple yet powerful interface to the Python interpreter to process and analyse three dimensional protein structure files (PDB files). p3d's strength arises from the combination of a) very fast spatial access to the structural data due to the implementation of a binary space partitioning (BSP) tree, b) set theory and c) functions that allow to combine a and b and that use human readable language in the search queries rather than complex computer language. All these factors combined facilitate the rapid development of bioinformatic tools that can perform quick and complex analyses of protein structures.

Conclusion

p3d is the perfect tool to quickly develop tools for structural bioinformatics using the Python scripting language.

Characterizing the effects of the juxtamembrane domain on vascular endothelial growth factor receptor-2 enzymatic activity, autophosphorylation, and inhibition by axitinib

The catalytic domains of protein kinases are commonly treated as independent modular units with distinct biological functions. Here, the interactions between the catalytic and juxtamembrane domains of VEGFR2 are studied. Highly purified preparations of the receptor tyrosine kinase VEGFR2 catalytic domain without (VEGFR2-CD) and with (VEGFR2-CD/JM) the juxtamembrane (JM) domain were characterized by kinetic, biophysical, and structural methods. Although the catalytic parameters for both constructs were similar, the autophosphorylation rate of VEGFR2-CD/JM was substantially faster than VEGFR2-CD. The first event in the autophosphorylation reaction was phosphorylation of JM residue Y801 followed by phosphorylation of activation loop residues in the CD. The rates of activation loop autophosphorylation for the two constructs were determined to be similar. The autophosphorylation rate of Y801 was invariant on enzyme concentration, which is consistent with an intramolecular reaction. In addition, the first biochemical characterization of the advanced clinical compound axitinib is reported. Axitinib was found to have 40-fold enhanced biochemical potency toward VEGFR2-CD/JM (K(i) = 28 pM) compared to VEGFR2-CD, which correlates better with cellular potency. Calorimetric studies, including a novel ITC compound displacement method, confirmed the potency and provided insight into the thermodynamic origin of the potency differences. A structural model for the VEGFR2-CD/JM is proposed based on the experimental findings reported here and on the JM position in c-Kit, FLT3, and CSF1/cFMS. The described studies identify potential functions of the VEGFR2 JM domain with implications to both receptor biology and inhibitor design.

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.

Trk kinase inhibitors as new treatments for cancer and pain

BACKGROUND

Tropomyosin-related kinases (Trks) are a family of receptor tyrosine kinases activated by neurotrophins. Trks play important roles in pain sensation as well as tumour cell growth and survival signaling. Thus, inhibitors of Trk receptor kinases might provide targeted treatments for pain and cancer.

OBJECTIVE

This paper reviews those patent applications since 2002 claiming small-molecule inhibitors of Trk receptor kinases.

METHODS

Primary literature and patents were searched with SciFinder and Google Scholar. Patents were selected based on their relevance to Trks and were evaluated and representative compounds were listed as examples.

RESULTS/CONCLUSION

Several series of Trk inhibitors with excellent in vitro potencies have been reported and a number of compounds have gone into the clinic. It should be noted that few of these inhibitors are Trk selective, demonstrating that targeting Trk kinases for treatment of pain and/or cancer offers a promising but also challenging approach.

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.

Targeting the purinome

Purines are critical cofactors in the enzymatic reactions that create and maintain living organisms. In humans, there are approximately 3,266 proteins that utilize purine cofactors and these proteins constitute the so-called purinome. The human purinome encompasses a wide-ranging functional repertoire and many of these proteins are attractive drug targets. For example, it is estimated that 30% of modern drug discovery projects target protein kinases and that modulators of small G-proteins comprise more than 50% of currently marketed drugs. Given the importance of purine-binding proteins to drug discovery, the following review will discuss the forces that mediate protein:purine recognition, the factors that determine druggability of a protein target, and the process of structure-based drug design. A review of purine recognition in representatives of the various purine-binding protein families, as well as the challenges faced in targeting members of the purinome in drug discovery campaigns will also be given.