Structural approaches to obtain kinase selectivity

Polypharmacological Drug Design Guided by Integrating Phenotypic and Restricted Fragment Docking Strategies

Polypharmacological drugs are of great value for treating complex human diseases by the combinative modulation of several biological targets. However, multitarget drug design with more than two targets is challenging and generally discovered by serendipity. Herein, a restricted fragment docking (RFD) computational method combined with a phenotypic discovery approach was developed for rational polypharmacological drug design. Via genetic and drug combination studies in a microglial phenotype, we first identified novel synergistic effects by triple target modulation toward RIPK1, MAP4K4, and ALK. Drawing on the RFD method to explore virtual chemical spaces in three target pockets, we identified a lead compound, LP-10d, that precisely modulated RIPK1/MAP4K4/ALK for synergistic microglial protection with low nanomolar potency. LP-10d showed polypharmacology against multiple neuropathologies in the 3xTg Alzheimer's disease mouse model. Our study revealed a potential application of the RFD method, which is valuable to further polypharmacological drug discovery involved in clinical studies for treating complex human diseases.

Small Molecular Inhibitors That Target ATM for Drug Discovery: Current Research and Potential Prospective

The protein kinase ataxia telangiectasia mutated (ATM) is a constituent of the phosphatidylinositol 3-kinase-related kinase (PIKK) family, exerting a pivotal influence on diverse cellular processes, notably the signaling of double-strand DNA breaks (DSB) and stress response. The dysregulation of ATM is implicated in the pathogenesis of cancer and other diseases such as neurodegeneration. Hence, ATM is deemed a promising candidate for potential therapeutic interventions across a spectrum of diseases. Presently, while ATM small molecule inhibitors are not commercially available, various selective inhibitors have progressed to the clinical research phase. Specifically, AZD1390, WSD0628, SYH2051, and ZN-B-2262 are under investigation in clinical studies pertaining to glioblastoma multiforme and advanced solid tumors, respectively. In this Perspective, we encapsulate the structure, biological functions, and disease relevance of ATM. Subsequently, we concentrate on the design concepts and structure-activity relationships (SAR) of ATM inhibitors, delineating potential avenues for the development of more efficacious ATM-targeted inhibitors.

GraphEGFR: Multi-task and transfer learning based on molecular graph attention mechanism and fingerprints improving inhibitor bioactivity prediction for EGFR family proteins on data scarcity

The proteins within the human epidermal growth factor receptor (EGFR) family, members of the tyrosine kinase receptor family, play a pivotal role in the molecular mechanisms driving the development of various tumors. Tyrosine kinase inhibitors, key compounds in targeted therapy, encounter challenges in cancer treatment due to emerging drug resistance mutations. Consequently, machine learning has undergone significant evolution to address the challenges of cancer drug discovery related to EGFR family proteins. However, the application of deep learning in this area is hindered by inherent difficulties associated with small-scale data, particularly the risk of overfitting. Moreover, the design of a model architecture that facilitates learning through multi-task and transfer learning, coupled with appropriate molecular representation, poses substantial challenges. In this study, we introduce GraphEGFR, a deep learning regression model designed to enhance molecular representation and model architecture for predicting the bioactivity of inhibitors against both wild-type and mutant EGFR family proteins. GraphEGFR integrates a graph attention mechanism for molecular graphs with deep and convolutional neural networks for molecular fingerprints. We observed that GraphEGFR models employing multi-task and transfer learning strategies generally achieve predictive performance comparable to existing competitive methods. The integration of molecular graphs and fingerprints adeptly captures relationships between atoms and enables both global and local pattern recognition. We further validated potential multi-targeted inhibitors for wild-type and mutant HER1 kinases, exploring key amino acid residues through molecular dynamics simulations to understand molecular interactions. This predictive model offers a robust strategy that could significantly contribute to overcoming the challenges of developing deep learning models for drug discovery with limited data and exploring new frontiers in multi-targeted kinase drug discovery for EGFR family proteins.

Molecular basis for differential recognition of an allosteric inhibitor by receptor tyrosine kinases

Understanding kinase-inhibitor selectivity continues to be a major objective in kinase drug discovery. We probe the molecular basis of selectivity of an allosteric inhibitor (MSC1609119A-1) of the insulin-like growth factor-I receptor kinase (IGF1RK), which has been shown to be ineffective for the homologous insulin receptor kinase (IRK). Specifically, we investigated the structural and energetic basis of the allosteric binding of this inhibitor to each kinase by combining molecular modeling, molecular dynamics (MD) simulations, and thermodynamic calculations. We predict the inhibitor conformation in the binding pocket of IRK and highlight that the charged residues in the histidine-arginine-aspartic acid (HRD) and aspartic acid-phenylalanine-glycine (DFG) motifs and the nonpolar residues in the binding pocket govern inhibitor interactions in the allosteric pocket of each kinase. We suggest that the conformational changes in the IGF1RK residues M1054 and M1079, movement of the ⍺C-helix, and the conformational stabilization of the DFG motif favor the selectivity of the inhibitor toward IGF1RK. Our thermodynamic calculations reveal that the observed selectivity can be rationalized through differences observed in the electrostatic interaction energy of the inhibitor in each inhibitor/kinase complex and the hydrogen bonding interactions of the inhibitor with the residue V1063 in IGF1RK that are not attained with the corresponding residue V1060 in IRK. Overall, our study provides a rationale for the molecular basis of recognition of this allosteric inhibitor by IGF1RK and IRK, which is potentially useful in developing novel inhibitors with improved affinity and selectivity.

Exploring isoindolin-1-ones as potential CDK7 inhibitors using cheminformatic tools

In women who die from cancer, breast cancer is the most common cause of death. The development of small molecular scaffolds as specific Cyclin-dependent kinase (CDK) inhibitors is a promising strategy in the discovery of anti-breast cancer drugs. Isoindolin-1-ones are heterocyclic compounds with useful therapeutic properties. In this study, a library of 48 isoindolinones has been virtually screened by molecular docking that showed high binding affinity up to - 10.1 kcal/mol and conventional hydrogen bonding interactions with active amino acid residues of CDK7. The molecular dynamics simulation (MDS), fragment molecular orbital (FMO), density functional theory (DFT), and pharmacokinetics studies of the best two docked scored ligands 7 and 14 have been studied. Examining the ligand root mean square deviation and hydrogen bonding occupancy of the 100 ns MDS trajectory, both ligands 7 and 14 showed docked pose stability. FMO calculations displayed that LYS139 and LYS41 are majorly contributing to the binding interactions with ligands 7 and 14 in the docked poses. DFT studies of ligands 7 and 14 showed high values of global softness and low values of global hardness and chemical potential thus displaying chemically reactive soft molecules and this influences their anti-cancer activity. Our hits exhibited superior qualities to known CDK7 inhibitors, according to the comprehensive pharmacokinetic parameters that were predicted. The results indicate that isoindolin-1-one moieties are good candidates for anti-cancer action and could serve as effective CDK7 inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00225-0.

Receptor Interacting Ser/Thr-Protein Kinase 2 as a New Therapeutic Target

Receptor interacting serine/threonine protein kinase 2 (RIPK2) is a downstream signaling molecule essential for the activation of several innate immune receptors, including the NOD-like receptors (NOD1 and NOD2). Recognition of pathogen-associated molecular pattern proteins by NOD1/2 leads to their interaction with RIPK2, which induces release of pro-inflammatory cytokines through the activation of NF-κB and MAPK pathways, among others. Thus, RIPK2 has emerged as a key mediator of intracellular signal transduction and represents a new potential therapeutic target for the treatment of various conditions, including inflammatory diseases and cancer. In this Perspective, first, an overview of the mechanisms that underlie RIPK2 function will be presented along with its role in several diseases. Then, the existing inhibitors that target RIPK2 and different therapeutic strategies will be reviewed, followed by a discussion on current challenges and outlook.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

How many kinases are druggable? A review of our current understanding

There are over 500 human kinases ranging from very well-studied to almost completely ignored. Kinases are tractable and implicated in many diseases, making them ideal targets for medicinal chemistry campaigns, but is it possible to discover a drug for each individual kinase? For every human kinase, we gathered data on their citation count, availability of chemical probes, approved and investigational drugs, PDB structures, and biochemical and cellular assays. Analysis of these factors highlights which kinase groups have a wealth of information available, and which groups still have room for progress. The data suggest a disproportionate focus on the more well characterized kinases while much of the kinome remains comparatively understudied. It is noteworthy that tool compounds for understudied kinases have already been developed, and there is still untapped potential for further development in this chemical space. Finally, this review discusses many of the different strategies employed to generate selectivity between kinases. Given the large volume of information available and the progress made over the past 20 years when it comes to drugging kinases, we believe it is possible to develop a tool compound for every human kinase. We hope this review will prove to be both a useful resource as well as inspire the discovery of a tool for every kinase.

Advances in CD73 inhibitors for immunotherapy: Antibodies, synthetic small molecule compounds, and natural compounds

Tumors, a disease with a high mortality rate worldwide, have become a serious threat to human health. Exonucleotide-5'-nucleotidase (CD73) is an emerging target for tumor therapy. Its inhibition can significantly reduce adenosine levels in the tumor microenvironment. It has a better therapeutic effect on adenosine-induced immunosuppression. In the immune response, extracellular ATP exerts immune efficacy by activating T cells. However, dead tumor cells release excess ATP, overexpress CD39 and CD73 on the cell membrane and catabolize this ATP to adenosine. This leads to further immunosuppression. There are a number of inhibitors of CD73 currently under investigation. These include antibodies, synthetic small molecule inhibitors and a number of natural compounds with prominent roles in the anti-tumor field. However, only a small proportion of the CD73 inhibitors studied to date have successfully reached the clinical stage. Therefore, effective and safe inhibition of CD73 in oncology therapy still holds great therapeutic potential. This review summarizes the currently reported CD73 inhibitors, describes their inhibitory effects and pharmacological mechanisms, and provides a brief review of them. It aims to provide more information for further research and development of CD73 inhibitors.

Emerging Data on the Safety and Efficacy of Ripretinib for the Treatment of Gastrointestinal Stromal Tumors

In patients with gastrointestinal stromal tumors (GIST), systemic treatment after disease progression on imatinib is challenging. Sunitinib and regorafenib are approved in the second- and third-line setting, respectively, with activity against certain secondary mutations with comparatively much lower response rates and survival increment compared to imatinib. All three of these drugs were serendipitously found to have activity in GIST, starting with imatinib, which was formulated for its ability to inhibit BCR-ABL in chronic myelogenous leukemia. Ripretinib is a drug that was specifically developed as a more potent KIT tyrosine kinase inhibitor (TKI), with broad-spectrum activity against the mutations encountered in GIST. Encouraging responses in early and later lines of treatment in the Phase 1 trial of ripretinib in GIST led to the rapid development of this novel drug. In a Phase 3 randomized clinical trial with cross-over, ripretinib demonstrated superior PFS and overall survival (OS) in 4th-line treatment and beyond compared to placebo. This established 150 mg once daily ripretinib as the standard of care in this setting. Ripretinib is generally well tolerated, with common adverse effects of hair loss, diarrhea, cramps, fatigue and nausea. The favorable safety profile and efficacy of ripretinib prompted its evaluation in a randomized phase 3 trial in the 2nd-line treatment setting. However, it did not result in a longer PFS duration than sunitinib. Although the efficacy of ripretinib in this unselected patient population was not significantly different from that of sunitinib, the tolerability profile was better. This review article aims to review the efficacy and tolerability profile of ripretinib, together with its role in the setting of unresectable or metastatic GIST.

Illuminating the Dark: Highly Selective Inhibition of Serine/Threonine Kinase 17A with Pyrazolo[1,5-a]pyrimidine-Based Macrocycles

Serine/threonine kinase 17A (death-associated protein kinase-related apoptosis-inducing protein kinase 1─DRAK1) is a part of the death-associated protein kinase (DAPK) family and belongs to the so-called dark kinome. Thus, the current state of knowledge of the cellular function of DRAK1 and its involvement in pathophysiological processes is very limited. Recently, DRAK1 has been implicated in tumorigenesis of glioblastoma multiforme (GBM) and other cancers, but no selective inhibitors of DRAK1 are available yet. To this end, we optimized a pyrazolo[1,5-a]pyrimidine-based macrocyclic scaffold. Structure-guided optimization of this macrocyclic scaffold led to the development of CK156 (34), which displayed high in vitro potency (K_D = 21 nM) and selectivity in kinomewide screens. Crystal structures demonstrated that CK156 (34) acts as a type I inhibitor. However, contrary to studies using genetic knockdown of DRAK1, we have seen the inhibition of cell growth of glioma cells in 2D and 3D culture only at low micromolar concentrations.

Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations

The cyclin-dependent kinase 7 (CDK7) plays a crucial role in regulating the cell cycle and RNA polymerase-based transcription. Overexpression of this kinase is linked with various cancers in humans due to its dual involvement in cell development. Furthermore, emerging evidence has revealed that inhibiting CDK7 has anti-cancer effects, driving the development of novel and more cost-effective inhibitors with enhanced selectivity for CDK7 over other CDKs. In the present investigation, a pharmacophore-based approach was utilized to identify potential hit compounds against CDK7. The generated pharmacophore models were validated and used as 3D queries to screen 55,578 natural drug-like compounds. The obtained compounds were then subjected to molecular docking and molecular dynamics simulations to predict their binding mode with CDK7. The molecular dynamics simulation trajectories were subsequently used to calculate binding affinity, revealing four hits-ZINC20392430, SN00112175, SN00004718, and SN00262261-having a better binding affinity towards CDK7 than the reference inhibitors (CT7001 and THZ1). The binding mode analysis displayed hydrogen bond interactions with the hinge region residues Met94 and Glu95, DFG motif residue Asp155, ATP-binding site residues Thr96, Asp97, and Gln141, and quintessential residue outside the kinase domain, Cys312 of CDK7. The in silico selectivity of the hits was further checked by docking with CDK2, the close homolog structure of CDK7. Additionally, the detailed pharmacokinetic properties were predicted, revealing that our hits have better properties than established CDK7 inhibitors CT7001 and THZ1. Hence, we argue that proposed hits may be crucial against CDK7-related malignancies.

Identifying Novel Drug Targets by iDTPnd: A Case Study of Kinase Inhibitors

Current FDA-approved kinase inhibitors cause diverse adverse effects, some of which are due to the mechanism-independent effects of these drugs. Identifying these mechanism-independent interactions could improve drug safety and support drug repurposing. Here, we develop iDTPnd (integrated Drug Target Predictor with negative dataset), a computational approach for large-scale discovery of novel targets for known drugs. For a given drug, we construct a positive structural signature as well as a negative structural signature that captures the weakly conserved structural features of drug-binding sites. To facilitate assessment of unintended targets, iDTPnd also provides a docking-based interaction score and its statistical significance. We confirm the interactions of sorafenib, imatinib, dasatinib, sunitinib, and pazopanib with their known targets at a sensitivity of 52% and a specificity of 55%. We also validate 10 predicted novel targets by using in vitro experiments. Our results suggest that proteins other than kinases, such as nuclear receptors, cytochrome P450, and MHC class I molecules, can also be physiologically relevant targets of kinase inhibitors. Our method is general and broadly applicable for the identification of protein–small molecule interactions, when sufficient drug–target 3D data are available. The code for constructing the structural signatures is available at https://sfb.kaust.edu.sa/Documents/iDTP.zip.

Structure-Based Design of Potent Selective Nanomolar Type-II Inhibitors of Glycogen Synthase Kinase-3β

For the first time, the in silico design, screening, and in vitro validation of potent GSK-3β type-II inhibitors are presented. In the absence of crystallographic evidence for a DFG-out GSK-3β activation loop conformation, computational models were designed using an adapted DOLPHIN approach and a method consisting of Prime loop refinement, induced-fit docking, and molecular dynamics. Virtual screening of the Biogenics subset from the ZINC database led to an initial selection of 20 Phase I compounds revealing two low micromolar inhibitors in an isolated enzyme assay. Twenty more analogues (Phase II compounds) related to the hit [pyrimidin-2-yl]amino-furo[3,2-b]furyl-urea scaffold were selected for structure-activity relationship analysis. The Phase II studies led to five highly potent nanomolar inhibitors, with compound 23 (IC₅₀ =0.087 μM) > 100 times more potent than the best Phase I inhibitor, and selectivity for GSK-3β inhibition compared to homologous kinases was observed. Ex vivo experiments (SH-SY5Y cell lines) for tau hyperphosphorylation revealed promising neuroprotective effects at low micromolar concentrations. The type-II inhibitor design has been unraveled as a potential route toward more clinically effective GSK-3β inhibitors.

Modeling conformational flexibility of kinases in inactive states

Kinase structures in the inactive "DFG-out" state provide a wealth of druggable binding site variants. The conformational plasticity of this state can be mainly described by different conformations of binding site-forming elements such as DFG motif, A-loop, P-loop, and αC-helix. Compared to DFG-in structures, DFG-out structures are largely underrepresented in the Protein Data Bank (PDB). Thus, structure-based drug design efforts for DFG-out inhibitors may benefit from an efficient approach to generate an ensemble of DFG-out structures. Accordingly, the presented modeling pipeline systematically generates homology models of kinases in several DFG-out conformations based on a sophisticated creation of template structures that represent the major states of the flexible structural elements. Eighteen template classes were initially selected from all available kinase structures in the PDB and subsequently employed for modeling the entire kinome in different DFG-out variants by fusing individual structural elements to multiple chimeric template structures. Molecular dynamics simulations revealed that conformational transitions between the different DFG-out states generally do not occur within trajectories of a few hundred nanoseconds length. This underlines the benefits of the presented homology modeling pipeline to generate relevant conformations of "DFG-out" kinase structures for subsequent in silico screening or binding site analysis studies.

How to design potent and selective DYRK1B inhibitors? Molecular modeling study

DYRK1B protein kinase is an emerging anticancer target due to its overexpression in a variety of cancers and its role in cancer chemoresistance through maintaining cancer cells in the G0 (quiescent) state. Consequently, there is a growing interest in the development of potent and selective DYRK1B inhibitors for anticancer therapy. One of the major off-targets is another protein kinase, GSK3β, which phosphorylates an important regulator of cell cycle progression on the same residue as DYRK1B and is involved in multiple signaling pathways. In the current work, we performed a detailed comparative structural analysis of DYRK1B and GSK3β ATP-binding sites and identified key regions responsible for selectivity. As the crystal structure of DYRK1B has never been reported, we built and optimized a homology model by comparative modeling and metadynamics simulations. Calculation of interaction energies between docked ligands in the ATP-binding sites of both kinases allowed us to pinpoint key residues responsible for potency and selectivity. Specifically, the role of the gatekeeper residues in DYRK1B and GSK3β is discussed in detail, and two other residues are identified as key to selectivity of DYRK1B inhibition versus GSK3β. The analysis presented in this work was used to support the design of potent and selective azaindole-quinoline-based DYRK1B inhibitors and can facilitate development of more selective inhibitors for DYRK kinases.

Data Mining and Computational Modeling of High-Throughput Screening Datasets

We are now seeing the benefit of investments made over the last decade in high-throughput screening (HTS) that is resulting in large structure activity datasets entering public and open databases such as ChEMBL and PubChem. The growth of academic HTS screening centers and the increasing move to academia for early stage drug discovery suggests a great need for the informatics tools and methods to mine such data and learn from it. Collaborative Drug Discovery, Inc. (CDD) has developed a number of tools for storing, mining, securely and selectively sharing, as well as learning from such HTS data. We present a new web based data mining and visualization module directly within the CDD Vault platform for high-throughput drug discovery data that makes use of a novel technology stack following modern reactive design principles. We also describe CDD Models within the CDD Vault platform that enables researchers to share models, share predictions from models, and create models from distributed, heterogeneous data. Our system is built on top of the Collaborative Drug Discovery Vault Activity and Registration data repository ecosystem which allows users to manipulate and visualize thousands of molecules in real time. This can be performed in any browser on any platform. In this chapter we present examples of its use with public datasets in CDD Vault. Such approaches can complement other cheminformatics tools, whether open source or commercial, in providing approaches for data mining and modeling of HTS data.

Identification of allosteric inhibitors of the ecto-5'-nucleotidase (CD73) targeting the dimer interface

The ecto-5'-nucleotidase CD73 plays an important role in the production of immune-suppressive adenosine in tumor micro-environment, and has become a validated drug target in oncology. Indeed, the anticancer immune response involves extracellular ATP to block cell proliferation through T-cell activation. However, in the tumor micro-environment, two extracellular membrane-bound enzymes (CD39 and CD73) are overexpressed and hydrolyze efficiently ATP into AMP then further into immune-suppressive adenosine. To circumvent the impact of CD73-generated adenosine, we applied an original bioinformatics approach to identify new allosteric inhibitors targeting the dimerization interface of CD73, which should impair the large dynamic motions required for its enzymatic function. Several hit compounds issued from virtual screening campaigns showed a potent inhibition of recombinant CD73 with inhibition constants in the low micromolar range and exhibited a non-competitive inhibition mode. The structure-activity relationships studies indicated that several amino acid residues (D366, H456, K471, Y484 and E543 for polar interactions and G453-454, I455, H456, L475, V542 and G544 for hydrophobic contacts) located at the dimerization interface are involved in the tight binding of hit compounds and likely contributed for their inhibitory activity. Overall, the gathered information will guide the upcoming lead optimization phase that may lead to potent and selective CD73 inhibitors, able to restore the anticancer immune response.

A screen for kinase inhibitors identifies antimicrobial imidazopyridine aminofurazans as specific inhibitors of the Listeria monocytogenes PASTA kinase PrkA

Bacterial signaling systems such as protein kinases and quorum sensing have become increasingly attractive targets for the development of novel antimicrobial agents in a time of rising antibiotic resistance. The family of bacterial Penicillin-binding-protein And Serine/Threonine kinase-Associated (PASTA) kinases is of particular interest due to the role of these kinases in regulating resistance to β-lactam antibiotics. As such, small-molecule kinase inhibitors that target PASTA kinases may prove beneficial as treatments adjunctive to β-lactam therapy. Despite this interest, only limited progress has been made in identifying functional inhibitors of the PASTA kinases that have both activity against the intact microbe and high kinase specificity. Here, we report the results of a small-molecule screen that identified GSK690693, an imidazopyridine aminofurazan-type kinase inhibitor that increases the sensitivity of the intracellular pathogen Listeria monocytogenes to various β-lactams by inhibiting the PASTA kinase PrkA. GSK690693 potently inhibited PrkA kinase activity biochemically and exhibited significant selectivity for PrkA relative to the Staphylococcus aureus PASTA kinase Stk1. Furthermore, other imidazopyridine aminofurazans could effectively inhibit PrkA and potentiate β-lactam antibiotic activity to varying degrees. The presence of the 2-methyl-3-butyn-2-ol (alkynol) moiety was important for both biochemical and antimicrobial activity. Finally, mutagenesis studies demonstrated residues in the back pocket of the active site are important for GSK690693 selectivity. These data suggest that targeted screens can successfully identify PASTA kinase inhibitors with both biochemical and antimicrobial specificity. Moreover, the imidazopyridine aminofurazans represent a family of PASTA kinase inhibitors that have the potential to be optimized for selective PASTA kinase inhibition.

The Role of Kinase Modulators in Cellular Senescence for Use in Cancer Treatment

Recently, more than 30 small molecules and eight monoclonal antibodies that modulate kinase signaling have been approved for the treatment of several pathological conditions, including cancer, idiopathic pulmonary fibrosis, and rheumatoid arthritis. Among them, kinase modulators have been a primary focus for use in cancer treatment. Cellular senescence is believed to protect cells from tumorigenesis by irreversibly halting cell cycle progression and avoiding the growth of damaged cells and tissues. Senescence can also contribute to tumor suppression and be utilized as a mechanism by anti-cancer agents. Although the role of kinase modulators in cancer treatment and their effects on senescence in tumor development have been extensively studied, the relationship between kinase modulators for cancer treatment and senescence has not been fully discussed. In this review, we discuss the pro- and anti-tumorigenesis functions of senescence and summarize the key roles of kinase modulators in the regulation of senescence against tumors.

Fragment-based drug discovery as alternative strategy to the drug development for neglected diseases

Neglected diseases (NDs) affect large populations and almost whole continents, representing 12% of the global health burden. In contrast, the treatment available today is limited and sometimes ineffective. Under this scenery, the Fragment-Based Drug Discovery emerged as one of the most promising alternatives to the traditional methods of drug development. This method allows achieving new lead compounds with smaller size of fragment libraries. Even with the wide Fragment-Based Drug Discovery success resulting in new effective therapeutic agents against different diseases, until this moment few studies have been applied this approach for NDs area. In this article, we discuss the basic Fragment-Based Drug Discovery process, brief successful ideas of general applications and show a landscape of its use in NDs, encouraging the implementation of this strategy as an interesting way to optimize the development of new drugs to NDs.

Kinases as Novel Therapeutic Targets in Asthma and Chronic Obstructive Pulmonary Disease

Multiple kinases play a critical role in orchestrating the chronic inflammation and structural changes in the respiratory tract of patients with asthma and chronic obstructive pulmonary disease (COPD). Kinases activate signaling pathways that lead to contraction of airway smooth muscle and release of inflammatory mediators (such as cytokines, chemokines, growth factors) as well as cell migration, activation, and proliferation. For this reason there has been great interest in the development of kinase inhibitors as anti-inflammatory therapies, particular where corticosteroids are less effective, as in severe asthma and COPD. However, it has proven difficult to develop selective kinase inhibitors that are both effective and safe after oral administration and this has led to a search for inhaled kinase inhibitors, which would reduce systemic exposure. Although many kinases have been implicated in inflammation and remodeling of airway disease, very few classes of drug have reached the stage of clinical studies in these diseases. The most promising drugs are p38 MAP kinases, isoenzyme-selective PI3-kinases, Janus-activated kinases, and Syk-kinases, and inhaled formulations of these drugs are now in development. There has also been interest in developing inhibitors that block more than one kinase, because these drugs may be more effective and with less risk of losing efficacy with time. No kinase inhibitors are yet on the market for the treatment of airway diseases, but as kinase inhibitors are improved from other therapeutic areas there is hope that these drugs may eventually prove useful in treating refractory asthma and COPD.

Multi-step virtual screening to develop selective DYRK1A inhibitors

Developing selective inhibitors for a particular kinase remains a major challenge in kinase-targeted drug discovery. Here we performed a multi-step virtual screening for dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors by focusing on the selectivity for DYRK1A over cyclin-dependent kinase 5 (CDK5). To examine the key factors contributing to the selectivity, we constructed logistic regression models to discriminate between actives and inactives for DYRK1A and CDK5, respectively, using residue-based binding free energies. The residue-based parameters were calculated by molecular mechanics-generalized Born surface area (MM-GBSA) decomposition methods for kinase-ligand complexes modeled by computer ligand docking. Based on the findings from the logistic regression models, we built a three-dimensional (3D) pharmacophore model and chose filter criteria for the multi-step virtual screening. The virtual hit compounds obtained from the screening were assessed for their inhibitory activities against DYRK1A and CDK5 by in vitro assay. Our screening identified two novel selective DYRK1A inhibitors with IC50 values of several μM for DYRK1A and >100μM for CDK5, which can be further optimized to develop more potent selective DYRK1A inhibitors.

Targeting pathogen metabolism without collateral damage to the host

The development of drugs that can inactivate disease-causing cells (e.g. cancer cells or parasites) without causing collateral damage to healthy or to host cells is complicated by the fact that many proteins are very similar between organisms. Nevertheless, due to subtle, quantitative differences between the biochemical reaction networks of target cell and host, a drug can limit the flux of the same essential process in one organism more than in another. We identified precise criteria for this ‘network-based’ drug selectivity, which can serve as an alternative or additive to structural differences. We combined computational and experimental approaches to compare energy metabolism in the causative agent of sleeping sickness, Trypanosoma brucei, with that of human erythrocytes, and identified glucose transport and glyceraldehyde-3-phosphate dehydrogenase as the most selective antiparasitic targets. Computational predictions were validated experimentally in a novel parasite-erythrocytes co-culture system. Glucose-transport inhibitors killed trypanosomes without killing erythrocytes, neurons or liver cells.

Evaluation of a tyrosine kinase peptide microarray for tyrosine kinase inhibitor therapy selection in cancer

Personalized cancer medicine aims to accurately predict the response of individual patients to targeted therapies, including tyrosine kinase inhibitors (TKIs). Clinical implementation of this concept requires a robust selection tool. Here, using both cancer cell lines and tumor tissue from patients, we evaluated a high-throughput tyrosine kinase peptide substrate array to determine its readiness as a selection tool for TKI therapy. We found linearly increasing phosphorylation signal intensities of peptides representing kinase activity along the kinetic curve of the assay with 7.5–10 μg of lysate protein and up to 400 μM adenosine triphosphate (ATP). Basal kinase activity profiles were reproducible with intra- and inter-experiment coefficients of variation of <15% and <20%, respectively. Evaluation of 14 tumor cell lines and tissues showed similar consistently high phosphorylated peptides in their basal profiles. Incubation of four patient-derived tumor lysates with the TKIs dasatinib, sunitinib, sorafenib and erlotinib primarily caused inhibition of substrates that were highly phosphorylated in the basal profile analyses. Using recombinant Src and Axl kinase, relative substrate specificity was demonstrated for a subset of peptides, as their phosphorylation was reverted by co-incubation with a specific inhibitor. In conclusion, we demonstrated robust technical specifications of this high-throughput tyrosine kinase peptide microarray. These features required as little as 5–7 μg of protein per sample, facilitating clinical implementation as a TKI selection tool. However, currently available peptide substrates can benefit from an enhancement of the differential potential for complex samples such as tumor lysates. We propose that mass spectrometry-based phosphoproteomics may provide such an enhancement by identifying more discriminative peptides.

From Type I to Type II: Design, Synthesis, and Characterization of Potent Pyrazin-2-ones as DFG-Out Inhibitors of PDGFRβ

Reversible protein kinase inhibitors that bind in the ATP cleft can be classified as type I or type II binders. Of these, type I inhibitors address the active form, whereas type II inhibitors typically lock the kinase in an inactive form. At the molecular level, the conformation of the flexible activation loop holding the key DFG motif controls access to the ATP site, thereby determining an active or inactive kinase state. Accordingly, type I and type II kinase inhibitors bind to so-called DFG-in or DFG-out conformations, respectively. Based on our former study on highly selective platelet-derived growth factor receptor β (PDGFRβ) pyrazin-2-one type I inhibitors, we expanded this scaffold toward the deep pocket, yielding the highly potent and effective type II inhibitor 5 (4-[(4-methylpiperazin-1-yl)methyl]-N-[3-[[6-oxo-5-(3,4,5-trimethoxyphenyl)-1H-pyrazin-3-yl]methyl]phenyl]benzamide). In vitro characterization, including selectivity panel data from activity-based assays (300 kinases) and affinity-based assays (97 kinases) of these PDGFRβ type I (1; 5-(4-hydroxy-3-methoxy-phenyl)-3-(3,4,5-trimethoxyphenyl)-1H-pyrazin-2-one) and II (5) inhibitors showing the same pyrazin-2-one chemotype are compared. Implications are discussed regarding the data for selectivity and efficacy of type I and type II ligands.

High quality, small molecule-activity datasets for kinase research

Kinases regulate cell growth, movement, and death. Deregulated kinase activity is a frequent cause of disease. The therapeutic potential of kinase inhibitors has led to large amounts of published structure activity relationship (SAR) data. Bioactivity databases such as the Kinase Knowledgebase (KKB), WOMBAT, GOSTAR, and ChEMBL provide researchers with quantitative data characterizing the activity of compounds across many biological assays. The KKB, for example, contains over 1.8M kinase structure-activity data points reported in peer-reviewed journals and patents. In the spirit of fostering methods development and validation worldwide, we have extracted and have made available from the KKB 258K structure activity data points and 76K associated unique chemical structures across eight kinase targets. These data are freely available for download within this data note.

Discovery of pan-VEGF inhibitory peptides directed to the extracellular ligand-binding domains of the VEGF receptors

Receptor tyrosine kinases (RTKs) are key molecules in numerous cellular processes, the inhibitors of which play an important role in the clinic. Among them are the vascular endothelial growth factor (VEGF) family members and their receptors (VEGFR), which are essential in the formation of new blood vessels by angiogenesis. Anti-VEGF therapy has already shown promising results in oncology and ophthalmology, but one of the challenges in the field is the design of specific small-molecule inhibitors for these receptors. We show the identification and characterization of small 6-mer peptides that target the extracellular ligand-binding domain of all three VEGF receptors. These peptides specifically prevent the binding of VEGF family members to all three receptors and downstream signaling but do not affect other angiogenic RTKs and their ligands. One of the selected peptides was also very effective at preventing pathological angiogenesis in a mouse model of retinopathy, normalizing the vasculature to levels similar to those of a normal developing retina. Collectively, our results suggest that these peptides are pan-VEGF inhibitors directed at a common binding pocket shared by all three VEGFRs. These peptides and the druggable binding site they target might be important for the development of novel and selective small-molecule, extracellular ligand-binding inhibitors of RTKs (eTKIs) for angiogenic-dependent diseases.

High quality, small molecule-activity datasets for kinase research

Kinases regulate cell growth, movement, and death. Deregulated kinase activity is a frequent cause of disease. The therapeutic potential of kinase inhibitors has led to large amounts of published structure activity relationship (SAR) data. Bioactivity databases such as the Kinase Knowledgebase (KKB), WOMBAT, GOSTAR, and ChEMBL provide researchers with quantitative data characterizing the activity of compounds across many biological assays. The KKB, for example, contains over 1.8M kinase structure-activity data points reported in peer-reviewed journals and patents. In the spirit of fostering methods development and validation worldwide, we have extracted and have made available from the KKB 258K structure activity data points and 76K associated unique chemical structures across eight kinase targets. These data are freely available for download within this data note.

High quality, small molecule-activity datasets for kinase research

Kinases regulate cell growth, movement, and death. Deregulated kinase activity is a frequent cause of disease. The therapeutic potential of kinase inhibitors has led to large amounts of published structure activity relationship (SAR) data. Bioactivity databases such as the Kinase Knowledgebase (KKB), WOMBAT, GOSTAR, and ChEMBL provide researchers with quantitative data characterizing the activity of compounds across many biological assays. The KKB, for example, contains over 1.8M kinase structure-activity data points reported in peer-reviewed journals and patents. In the spirit of fostering methods development and validation worldwide, we have extracted and have made available from the KKB 258K structure activity data points and 76K associated unique chemical structures across eight kinase targets. These data are freely available for download within this data note.

Allosteric small-molecule kinase inhibitors

Small-molecule kinase inhibitors are invaluable targeted therapeutics for the treatment of various human diseases, especially cancers. While the majority of approved and developed preclinical small-molecule inhibitors are characterized as type I or type II inhibitors that target the ATP-binding pocket of kinases, the remarkable sequential and structural similarity among ATP pockets renders the selective inhibition of kinases a daunting challenge. Therefore, targeting allosteric pockets of kinases outside the highly conversed ATP pocket has been proposed as a promising alternative to overcome current barriers of kinase inhibitors, including poor selectivity and emergence of drug resistance. In spite of the small number of identified allosteric inhibitors in comparison with that of inhibitors targeting the ATP pocket, encouraging results, such as the FDA-approval of the first small-molecule allosteric inhibitor trametinib in 2013, the progress of more than 10 other allosteric inhibitors in clinical trials, and the emergence of a pipeline of highly selective and potent preclinical molecules, have been reported in the past decade. In this article, we present the current knowledge on allosteric inhibition in terms of conception, classification, potential advantages, and summarized debatable topics in the field. Recent progress and allosteric inhibitors that were identified in the past three years are highlighted in this paper.

Structural and dynamic insights into the energetics of activation loop rearrangement in FGFR1 kinase

Protein tyrosine kinases differ widely in their propensity to undergo rearrangements of the N-terminal Asp–Phe–Gly (DFG) motif of the activation loop, with some, including FGFR1 kinase, appearing refractory to this so-called ‘DFG flip'. Recent inhibitor-bound structures have unexpectedly revealed FGFR1 for the first time in a ‘DFG-out' state. Here we use conformationally selective inhibitors as chemical probes for interrogation of the structural and dynamic features that appear to govern the DFG flip in FGFR1. Our detailed structural and biophysical insights identify contributions from altered dynamics in distal elements, including the αH helix, towards the outstanding stability of the DFG-out complex with the inhibitor ponatinib. We conclude that the αC-β4 loop and ‘molecular brake' regions together impose a high energy barrier for this conformational rearrangement, and that this may have significance for maintaining autoinhibition in the non-phosphorylated basal state of FGFR1.

Receptor tyrosine kinases are key mediators of cell proliferation that have been implicated in several disease states for which they represent promising drug targets. Here the authors determine the thermodynamic basis for the low propensity of FGFR1 to access the DFG-Phe-out conformation required to bind type-II inhibitors.

Small-molecule kinase inhibitors: an analysis of FDA-approved drugs

Small-molecule kinase inhibitors (SMKIs), 28 of which are approved by the US Food and Drug Administration (FDA), have been actively pursued as promising targeted therapeutics. Here, we assess the key structural and physicochemical properties, target selectivity and mechanism of function, and therapeutic indications of these approved inhibitors. Our analysis showed that >30% of approved SMKIs have a molecule weight (MW) exceeding 500 and all have a total ring count of between three and five. The assumption that type II inhibitors tend to be more selective than type I inhibitors has been proved to be unreliable. Although previous SMKI research was concentrated on tyrosine kinase inhibitors for cancer treatment, recent progress indicates diversification of SMKI research in terms of new targets, mechanistic types, and therapeutic indications.

Illuminating the molecular mechanisms of tyrosine kinase inhibitor resistance for the FGFR1 gatekeeper mutation: the Achilles' heel of targeted therapy

Human fibroblast growth factor receptors (FGFRs) 1-4 are a family of receptor tyrosine kinases that can serve as drivers of tumorigenesis. In particular, FGFR1 gene amplification has been implicated in squamous cell lung and breast cancers. Tyrosine kinase inhibitors (TKIs) targeting FGFR1, including AZD4547 and E3810 (Lucitanib), are currently in early phase clinical trials. Unfortunately, drug resistance limits the long-term success of TKIs, with mutations at the "gatekeeper" residue leading to tumor progression. Here we show the first structural and kinetic characterization of the FGFR1 gatekeeper mutation, V561M FGFR1. The V561M mutation confers a 38-fold increase in autophosphorylation achieved at least in part by a network of interacting residues forming a hydrophobic spine to stabilize the active conformation. Moreover, kinetic assays established that the V561M mutation confers significant resistance to E3810, while retaining affinity for AZD4547. Structural analyses of these TKIs with wild type (WT) and gatekeeper mutant forms of FGFR1 offer clues to developing inhibitors that maintain potency against gatekeeper mutations. We show that AZD4547 affinity is preserved by V561M FGFR1 due to a flexible linker that allows multiple inhibitor binding modes. This is the first example of a TKI binding in distinct conformations to WT and gatekeeper mutant forms of FGFR, highlighting adaptable regions in both the inhibitor and binding pocket crucial for drug design. Exploiting inhibitor flexibility to overcome drug resistance has been a successful strategy for combatting diseases such as AIDS and may be an important approach for designing inhibitors effective against kinase gatekeeper mutations.

FDA-approved small-molecule kinase inhibitors

Kinases have emerged as one of the most intensively pursued targets in current pharmacological research, especially for cancer, due to their critical roles in cellular signaling. To date, the US FDA has approved 28 small-molecule kinase inhibitors, half of which were approved in the past 3 years. While the clinical data of these approved molecules are widely presented and structure-activity relationship (SAR) has been reported for individual molecules, an updated review that analyzes all approved molecules and summarizes current achievements and trends in the field has yet to be found. Here we present all approved small-molecule kinase inhibitors with an emphasis on binding mechanism and structural features, summarize current challenges, and discuss future directions in this field.

Discoidin domain receptor 1 (DDR1) kinase as target for structure-based drug discovery

Discoidin domain receptor (DDR) 1 and 2 are transmembrane receptors that belong to the family of receptor tyrosine kinases (RTK). Upon collagen binding, DDRs transduce cellular signaling involved in various cell functions, including cell adhesion, proliferation, differentiation, migration, and matrix homeostasis. Altered DDR function resulting from either mutations or overexpression has been implicated in several types of disease, including atherosclerosis, inflammation, cancer, and tissue fibrosis. Several established inhibitors, such as imatinib, dasatinib, and nilotinib, originally developed as Abelson murine leukemia (Abl) kinase inhibitors, have been found to inhibit DDR kinase activity. As we review here, recent discoveries of novel inhibitors and their co-crystal structure with the DDR1 kinase domain have made structure-based drug discovery for DDR1 amenable.

Physicochemical properties of novel protein kinase inhibitors in relation to their substrate specificity for drug transporters

INTRODUCTION

Small molecule tyrosine and serine-threonine kinase inhibitors (TKIs and STKIs) are emerging drugs that interfere with downstream signaling pathways involved in cancer proliferation, invasion, metastasis and angiogenesis. The understanding of their pharmacokinetics, the identification of their transporters and the modulating activity exerted on transporters is pivotal to predict therapy efficacy and to avoid unwarranted drug treatment combinations.

AREAS COVERED

Experimental or in silico data were collected and summarized on TKIs and STKIs physico-chemical properties, which influence their transport, metabolism and efficacy, and TKIs and STKIs as influx transporter substrates and inhibitors. In addition, the uptake by tumor cell influx transporters and some factors in the tumor microenvironment affecting the uptake of TKIs and STKIs by cancer cells are briefly covered.

EXPERT OPINION

Membrane transporters play an important role in the pharmacokinetics and hence the efficacy of anticancer drugs, including TKIs and STKIs. These drugs are substrates and inhibitors of various transporters. Drug resistance may be bypassed not only by identifying the proper transporter but also by selective combinations, which may either downregulate or increase transporter activity. However, care has to be taken because this profile might be disease, drug and patient specific.

Homopharma: a new concept for exploring the molecular binding mechanisms and drug repurposing

Background

Drugs that simultaneously target multiple proteins often improve efficacy, particularly in the treatment of complex diseases such as cancers and central nervous system disorders. Many approaches have been proposed to identify the potential targets of a drug. Recently, we have introduced Space-Related Pharmamotif (SRPmotif) method to recognize the proteins that share similar binding environments. In addition, compounds with similar topology may bind to similar proteins and have similar protein-compound interactions. However, few studies have focused on exploring the relationships between binding environments and protein-compound interactions, which is important for understanding molecular binding mechanisms and helpful to be used in discovering drug repurposing.

Results

In this study, we propose a new concept of "Homopharma", combining similar binding environments and protein-compound interaction profiles, to explore the molecular binding mechanisms and drug repurposing. A Homopharma consists of a set of proteins which have the conserved binding environment and a set of compounds that share similar structures and functional groups. These proteins and compounds present conserved interactions and similar physicochemical properties. Therefore, these compounds are often able to inhibit the proteins in a Homopharma. Our experimental results show that the proteins and compounds in a Homopharma often have similar protein-compound interactions, comprising conserved specific residues and functional sites. Based on the Homopharma concept, we selected four flavonoid derivatives and 32 human protein kinases for enzymatic profiling. Among these 128 bioassays, the IC₅₀ of 56 and 25 flavonoid-kinase inhibitions are less than 10 μM and 1 μM, respectively. Furthermore, these experimental results suggest that these flavonoids can be used as anticancer compounds, such as oral and colorectal cancer drugs.

Conclusions

The experimental results show that the Homopharma is useful for identifying key binding environments of proteins and compounds and discovering new inhibitory effects. We believe that the Homopharma concept can have the potential for understanding molecular binding mechanisms and providing new clues for drug development.

Structural insights into FGFR kinase isoform selectivity: diverse binding modes of AZD4547 and ponatinib in complex with FGFR1 and FGFR4

The fibroblast growth factor receptor (FGFR) family of receptor tyrosine kinases has been implicated in a wide variety of cancers. Despite a high level of sequence homology in the ATP-binding site, the majority of reported inhibitors are selective for the FGFR1-3 isoforms and display much reduced potency toward FGFR4, an exception being the Bcr-Abl inhibitor ponatinib. Here we present the crystal structure of the FGFR4 kinase domain and show that both FGFR1 and FGFR4 kinase domains in complex with ponatinib adopt a DFG-out activation loop conformation. Comparison with the structure of FGFR1 in complex with the candidate drug AZD4547, combined with kinetic characterization of the binding of ponatinib and AZD4547 to FGFR1 and FGFR4, sheds light on the observed differences in selectivity profiles and provides a rationale for developing FGFR4-selective inhibitors.

Constitutive PKA activity is essential for maintaining the excitability and contractility in guinea pig urinary bladder smooth muscle: role of the BK channel

The elevation of protein kinase A (PKA) activity activates the large-conductance voltage- and Ca(2+)-activated K(+) (BK) channels in urinary bladder smooth muscle (UBSM) cells and consequently attenuates spontaneous phasic contractions of UBSM. However, the role of constitutive PKA activity in UBSM function has not been studied. Here, we tested the hypothesis that constitutive PKA activity is essential for controlling the excitability and contractility of UBSM. We used patch clamp electrophysiology, line-scanning confocal and ratiometric fluorescence microscopy on freshly isolated guinea pig UBSM cells, and isometric tension recordings on freshly isolated UBSM strips. Pharmacological inhibition of the constitutive PKA activity with H-89 or PKI 14-22 significantly reduced the frequency and amplitude of spontaneous transient BK channel currents (TBKCs) in UBSM cells. Confocal and ratiometric fluorescence microscopy studies revealed that inhibition of constitutive PKA activity with H-89 reduced the frequency and amplitude of the localized Ca(2+) sparks but increased global Ca(2+) levels and the magnitude of Ca(2+) oscillations in UBSM cells. H-89 abolished the spontaneous transient membrane hyperpolarizations and depolarized the membrane potential in UBSM cells. Inhibition of PKA with H-89 or KT-5720 also increased the amplitude and muscle force of UBSM spontaneous phasic contractions. This study reveals the novel concept that constitutive PKA activity is essential for controlling localized Ca(2+) signals generated by intracellular Ca(2+) stores and cytosolic Ca(2+) levels. Furthermore, constitutive PKA activity is critical for mediating the spontaneous TBKCs in UBSM cells, where it plays a key role in regulating spontaneous phasic contractions in UBSM.

In silico screening and study of novel ERK2 inhibitors using 3D QSAR, docking and molecular dynamics

ERK2 is a dual specificity protein kinase, part of the Ras/Raf/MEK/ERK signal transduction cascade. It forms an interesting target for inhibition based on its relationship with cell proliferation and oncogenesis. A 3D QSAR pharmacophore model (Hypo1) with high correlation (r=0.938) was developed for ERK2 ATP site on the basis of experimentally known inhibitors. The model included three hydrogen bonds, and one hydrophobic site. Assessment of Hypo1 through Fisher randomization, cost analysis, leave one out method and decoy test suggested that the model can reliably detect ERK2 inhibitors. Hypo1 has been used for virtual screening of potential inhibitors from ZINC, Drug Bank, NCI, Maybridge and Chembank databases. Using Hypo1 as a query, databases have been interrogated for compounds who meet the pharmacophore features. The resulting hit compounds were subject to docking and analysis. Docking and molecular dynamics analysis showed that in order to achieve a higher potency compounds have to interact with catalytic site, glycine rich loop, Hinge region, Gatekeeper region and ATP site entrance residues. We also identified catalytic site and Glycine rich loop as important regions to bind by molecules for better potency and selectivity.

Exploration of type II binding mode: A privileged approach for kinase inhibitor focused drug discovery?

The ATP site of kinases displays remarkable conformational flexibility when accommodating chemically diverse small molecule inhibitors. The so-called activation segment, whose conformation controls catalytic activity and access to the substrate binding pocket, can undergo a large conformational change with the active state assuming a ‘DFG-in’ and an inactive state assuming a ‘DFG-out’ conformation. Compounds that preferentially bind to the DFG-out conformation are typically called ‘type II’ inhibitors in contrast to ‘type I’ inhibitors that bind to the DFG-in conformation. This review surveys the large number of type II inhibitors that have been developed and provides an analysis of their crystallographically determined binding modes. Using a small library of type II inhibitors, we demonstrate that more than 200 kinases can be targeted, suggesting that type II inhibitors may not be intrinsically more selective than type I inhibitors.

All-atomic molecular dynamic studies of human CDK8: insight into the A-loop, point mutations and binding with its partner CycC

The Mediator, a conserved multisubunit protein complex in eukaryotic organisms, regulates gene expression by bridging sequence-specific DNA-binding transcription factors to the general RNA polymerase II machinery. In yeast, Mediator complex is organized in three core modules (head, middle and tail) and a separable 'CDK8 submodule' consisting of four subunits including Cyclin-dependent kinase CDK8 (CDK8), Cyclin C (CycC), MED12, and MED13. The 3-D structure of human CDK8-CycC complex has been recently experimentally determined. To take advantage of this structure and the improved theoretical calculation methods, we have performed molecular dynamic simulations to study dynamics of CDK8 and two CDK8 point mutations (D173A and D189N), which have been identified in human cancers, with and without full length of the A-loop, as well as the binding between CDK8 and CycC. We found that CDK8 structure gradually loses two helical structures during the 50-ns molecular dynamic simulation, likely due to the presence of the full-length A-loop. In addition, our studies showed the hydrogen bond occupation of the CDK8 A-loop increases during the first 20-ns MD simulation and stays stable during the later 30-ns MD simulation. Four residues in the A-loop of CDK8 have high hydrogen bond occupation, while the rest residues have low or no hydrogen bond occupation. The hydrogen bond dynamic study of the A-loop residues exhibits three types of changes: increasing, decreasing, and stable. Furthermore, the 3-D structures of CDK8 point mutations D173A, D189N, T196A and T196D have been built by molecular modeling and further investigated by 50-ns molecular dynamic simulations. D173A has the highest average potential energy, while T196D has the lowest average potential energy, indicating that T196D is the most stable structure. Finally, we calculated theoretical binding energy of CDK8 and CycC by MM/PBSA and MM/GBSA methods, and the negative values obtained from both methods demonstrate stability of CDK8-CycC complex. Taken together, these analyses will improve our understanding of the exact functions of CDK8 and the interaction with its partner CycC.

Are phylogenetic trees suitable for chemogenomics analyses of bioactivity data sets: the importance of shared active compounds and choosing a suitable data embedding method, as exemplified on Kinases

Background

‘Phylogenetic trees’ are commonly used for the analysis of chemogenomics datasets and to relate protein targets to each other, based on the (shared) bioactivities of their ligands. However, no real assessment as to the suitability of this representation has been performed yet in this area. We aimed to address this shortcoming in the current work, as exemplified by a kinase data set, given the importance of kinases in many diseases as well as the availability of large-scale datasets for analysis. In this work, we analyzed a dataset comprising 157 compounds, which have been tested at concentrations of 1 μM and 10 μM against a panel of 225 human protein kinases in full-matrix experiments, aiming to explain kinase promiscuity and selectivity against inhibitors. Compounds were described by chemical features, which were used to represent kinases (i.e. each kinase had an active set of features and an inactive set).

Results

Using this representation, a bioactivity-based classification was made of the kinome, which partially resembles previous sequence-based classifications, where particularly kinases from the TK, CDK, CLK and AGC branches cluster together. However, we were also able to show that in approximately 57% of cases, on average 6 kinase inhibitors exhibit activity against kinases which are located at a large distance in the sequence-based classification (at a relative distance of 0.6 – 0.8 on a scale from 0 to 1), but are correctly located closer to each other in our bioactivity-based tree (distance 0 – 0.4). Despite this improvement on sequence-based classification, also the bioactivity-based classification needed further attention: for approximately 80% of all analyzed kinases, kinases classified as neighbors according to the bioactivity-based classification also show high SAR similarity (i.e. a high fraction of shared active compounds and therefore, interaction with similar inhibitors). However, in the remaining ~20% of cases a clear relationship between kinase bioactivity profile similarity and shared active compounds could not be established, which is in agreement with previously published atypical SAR (such as for LCK, FGFR1, AKT2, DAPK1, TGFR1, MK12 and AKT1).

Conclusions

In this work we were hence able to show that (1) targets (here kinases) with few shared activities are difficult to establish neighborhood relationships for, and (2) phylogenetic tree representations make implicit assumptions (i.e. that neighboring kinases exhibit similar interaction profiles with inhibitors) that are not always suitable for analyses of bioactivity space. While both points have been implicitly alluded to before, this is to the information of the authors the first study that explores both points on a comprehensive basis. Excluding kinases with few shared activities improved the situation greatly (the percentage of kinases for which no neighborhood relationship could be established dropped from 20% to only 4%). We can conclude that all of the above findings need to be taken into account when performing chemogenomics analyses, also for other target classes.

The two faces of Hippo: targeting the Hippo pathway for regenerative medicine and cancer treatment

The Hippo signalling pathway is an emerging growth control and tumour suppressor pathway that regulates cell proliferation and stem cell functions. Defects in Hippo signalling and hyperactivation of its downstream effectors Yes-associated protein (YAP) and transcriptional co-activator with PDZ-binding motif (TAZ) contribute to the development of cancer, which suggests that pharmacological inhibition of YAP and TAZ activity may be an effective anticancer strategy. Conversely, YAP and TAZ can also have beneficial roles in stimulating tissue repair and regeneration following injury, so their activation may be therapeutically useful in these contexts. A complex network of intracellular and extracellular signalling pathways that modulate YAP and TAZ activities have recently been identified. Here, we review the regulation of the Hippo signalling pathway, its functions in normal homeostasis and disease, and recent progress in the identification of small-molecule pathway modulators.