Interaction Of Kinase Inhibitors Research Articles

Selectivity and Ranking of Tight-Binding JAK-STAT Inhibitors Using Markovian Milestoning with Voronoi Tessellations.

Janus kinases (JAK), a group of proteins in the nonreceptor tyrosine kinase (NRTKs) family, play a crucial role in growth, survival, and angiogenesis. They are activated by cytokines through the Janus kinase-signal transducer and activator of a transcription (JAK-STAT) signaling pathway. JAK-STAT signaling pathways have significant roles in the regulation of cell division, apoptosis, and immunity. Identification of the V617F mutation in the Janus homology 2 (JH2) domain of JAK2 leading to myeloproliferative disorders has stimulated great interest in the drug discovery community to develop JAK2-specific inhibitors. However, such inhibitors should be selective toward JAK2 over other JAKs and display an extended residence time. Recently, novel JAK2/STAT5 axis inhibitors (N-(1H-pyrazol-3-yl)pyrimidin-2-amino derivatives) have displayed extended residence times (hours or longer) on target and adequate selectivity excluding JAK3. To facilitate a deeper understanding of the kinase-inhibitor interactions and advance the development of such inhibitors, we utilize a multiscale Markovian milestoning with Voronoi tessellations (MMVT) approach within the Simulation-Enabled Estimation of Kinetic Rates v.2 (SEEKR2) program to rank order these inhibitors based on their kinetic properties and further explain the selectivity of JAK2 inhibitors over JAK3. Our approach investigates the kinetic and thermodynamic properties of JAK-inhibitor complexes in a user-friendly, fast, efficient, and accurate manner compared to other brute force and hybrid-enhanced sampling approaches.

Mapping the conformational energy landscape of Abl kinase using ClyA nanopore tweezers

Protein kinases play central roles in cellular regulation by catalyzing the phosphorylation of target proteins. Kinases have inherent structural flexibility allowing them to switch between active and inactive states. Quantitative characterization of kinase conformational dynamics is challenging. Here, we use nanopore tweezers to assess the conformational dynamics of Abl kinase domain, which is shown to interconvert between two major conformational states where one conformation comprises three sub-states. Analysis of kinase-substrate and kinase-inhibitor interactions uncovers the functional roles of relevant states and enables the elucidation of the mechanism underlying the catalytic deficiency of an inactive Abl mutant G321V. Furthermore, we obtain the energy landscape of Abl kinase by quantifying the population and transition rates of the conformational states. These results extend the view on the dynamic nature of Abl kinase and suggest nanopore tweezers can be used as an efficient tool for other members of the human kinome.

A novel graph convolutional neural network for predicting interaction sites on protein kinase inhibitors in phosphorylation

Protein kinase-inhibitor interactions are key to the phosphorylation of proteins involved in cell proliferation, differentiation, and apoptosis, which shows the importance of binding mechanism research and kinase inhibitor design. In this study, a novel machine learning module (i.e., the WL Box) was designed and assembled to the Prediction of Interaction Sites of Protein Kinase Inhibitors (PISPKI) model, which is a graph convolutional neural network (GCN) to predict the interaction sites of protein kinase inhibitors. The WL Box is a novel module based on the well-known Weisfeiler-Lehman algorithm, which assembles multiple switch weights to effectively compute graph features. The PISPKI model was evaluated by testing with shuffled datasets and ablation analysis using 11 kinase classes. The accuracy of the PISPKI model with the shuffled datasets varied from 83 to 86%, demonstrating superior performance compared to two baseline models. The effectiveness of the model was confirmed by testing with shuffled datasets. Furthermore, the performance of each component of the model was analyzed via the ablation study, which demonstrated that the WL Box module was critical. The code is available at https://github.com/feiqiwang/PISPKI.

Kinase inhibitor data set for systematic analysis of representative kinases across the human kinome.

A large set of multi-kinase inhibitors with high-confidence activity data was assembled and used to generate network representations revealing kinase relationships based upon shared inhibitors [1]. Compounds and activity annotations were originally selected from public repositories and organized in an in-house database from which the data set was extracted and curated. The new data set comprises more than 36,000 inhibitors with multiple activity annotations for a total of 420 human kinases (providing 81% coverage of the human kinome), representing a total of ∼127,000 kinase-inhibitor interactions. Use of the data is not limited to the network application reported in [1]. It can also be used, for example, for different types of compound promiscuity analysis or machine learning (such a multi-task modeling). In addition, the data set provides a large resource for complementing kinase drug discovery projects with external compound information.

Synthesis, characterization and anti-inflammatory activity of N-(4- phenyl-1, 3-thiazol-2-yl)-N'-phenylureas.

A series of N-(4-phenyl-1, 3-thiazol-2-yl)-N'- phenylureas (5a-z) was synthesized from 2-amino-4-substituted phenylthiazoles and phenylisocyanates. The newly synthesized compounds were characterized by IR, (1)H NMR and Mass spectral data. All the twenty six N-(4-phenyl-1, 3-thiazol-2-yl)-N'-phenylurea derivatives were screened for antiinflammatory activity by following carrageenan induced rat paw edema method. Among the compounds screened, N-[4- (4-methoxy phenyl)-1, 3-thiazol-2-yl)-N'-phenylurea and N-[4-(4-methoxy phenyl-1, 3-thiazol-2-yl)-N'-(4-bromophenyl) urea were found to be more potent. The molecular docking interaction of aforementioned urea compounds revealed the traditional type II p38 kinase inhibitor's interactions in the DFG out active site.

Revised classification of kinases based on bioactivity data: the importance of data density and choice of visualization

Kinases are a major class of drug targets and are involved in a variety of diseases such as diabetes, cancer and inflammation. Still, understanding kinase inhibitor selectivity and promiscuity remains a major challenge. In order to improve upon the current situation, we analyzed a dataset comprising 157 compounds, tested at concentrations of 1 μM and 10 μM against a panel of 225 human protein kinases. Our bioactivity-based classification of kinases shows similarities with the Sugen sequence-based classification [1], where particularly kinases from the TK, CDK, CLK and AGC groups cluster together. However, 57% of all kinase pairs inhibited by 6 known inhibitors consist of kinases which lie far apart from each other in the Sugen tree (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). For 80% of all analyzed kinases, those classified as neighbors according to the bioactivity-based classification also show high similarity in shared active compounds. However, among the remaining ~20%, distant kinases did not necessarily show low SAR similarity, and neighboring kinases did not necessarily show high SAR similarity; i.e., the placement in the tree was misleading. We identified two reasons for this: firstly, 'misplaced' kinases exhibit inconsistent SAR, and secondly, these kinases had only a few shared activities with other kinases, making both the computation of their bioactivity-based distance and their place in the tree less accurate. In a follow-up analysis, we resolved both problems by visualizing inconsistent SAR more accurately using MDS plots, rather than phylogenetic trees, and by excluding kinases with 16 or fewer shared activities. Only 7 kinases (4% of the kinases analyzed) did not show a clear relationship between kinase bioactivity profile similarity and shared active compounds. Hence, this analysis improves on previous studies, where the influence of data density on kinase similarity was not considered, and leads to a more reliable placement of kinases into the kinome tree. Overall, our analysis suggests that bioactivity-based classification of kinases is indeed more useful than sequence-based classification for predicting kinase-inhibitor interactions. However, care needs to be taken with respect to data density (i.e., kinases with too few data points need to be omitted) and visualization of the data (i.e., phylogenetic trees imply a neighborhood relationship that is not consistently observed in every case).

KIDFamMap: a database of kinase-inhibitor-disease family maps for kinase inhibitor selectivity and binding mechanisms

Kinases play central roles in signaling pathways and are promising therapeutic targets for many diseases. Designing selective kinase inhibitors is an emergent and challenging task, because kinases share an evolutionary conserved ATP-binding site. KIDFamMap (http://gemdock.life.nctu.edu.tw/KIDFamMap/) is the first database to explore kinase-inhibitor families (KIFs) and kinase-inhibitor-disease (KID) relationships for kinase inhibitor selectivity and mechanisms. This database includes 1208 KIFs, 962 KIDs, 55 603 kinase-inhibitor interactions (KIIs), 35 788 kinase inhibitors, 399 human protein kinases, 339 diseases and 638 disease allelic variants. Here, a KIF can be defined as follows: (i) the kinases in the KIF with significant sequence similarity, (ii) the inhibitors in the KIF with significant topology similarity and (iii) the KIIs in the KIF with significant interaction similarity. The KIIs within a KIF are often conserved on some consensus KIDFamMap anchors, which represent conserved interactions between the kinase subsites and consensus moieties of their inhibitors. Our experimental results reveal that the members of a KIF often possess similar inhibition profiles. The KIDFamMap anchors can reflect kinase conformations types, kinase functions and kinase inhibitor selectivity. We believe that KIDFamMap provides biological insights into kinase inhibitor selectivity and binding mechanisms.

In Silico Design of Protein Kinase Inhibitors: Successes and Failures

Protein kinases are among the most exploited targets in modern drug discovery due to key roles these enzymes play in human diseases including cancer. The in silico approach, an important part of rational design of protein kinase inhibitors, is founded on vast information about 3D structures of these enzymes. This review summarizes general structural features of the kinase inhibitors and the studies applied toward a large scale chemical database for virtual screening. Analyzed are the ways of validating the modern docking tools and their combinations with different scoring functions. In particular, we discuss the kinase flexibility as a reason for failures of the docking procedure. Finally, evidence is provided for the main patterns of kinase-inhibitor interactions and creation of the hinge-region-directed 2D filters.

Structural basis of inhibitor selectivity in MAP kinases.

The mitogen-activated protein (MAP) kinases are important signaling molecules that participate in diverse cellular events and are potential targets for intervention in inflammation, cancer, and other diseases. The MAP kinase p38 is responsive to environmental stresses and is involved in the production of cytokines during inflammation. In contrast, the activation of the MAP kinase ERK2 (extracellular-signal-regulated kinase 2) leads to cellular differentiation or proliferation. The anti-inflammatory agent pyridinylimidazole and its analogs (SB [SmithKline Beecham] compounds) are highly potent and selective inhibitors of p38, but not of the closely-related ERK2, or other serine/threonine kinases. Although these compounds are known to bind to the ATP-binding site, the origin of the inhibitory specificity toward p38 is not clear. We report the structural basis for the exceptional selectivity of these SB compounds for p38 over ERK2, as determined by comparative crystallography. In addition, structural data on the origin of olomoucine (a better inhibitor of ERK2) selectivity are presented. The crystal structures of four SB compounds in complex with p38 and of one SB compound and olomoucine in complex with ERK2 are presented here. The SB inhibitors bind in an extended pocket in the active site and are complementary to the open domain structure of the low-activity form of p38. The relatively closed domain structure of ERK2 is able to accommodate the smaller olomoucine. The unique kinase-inhibitor interactions observed in these complexes originate from amino-acid replacements in the active site and replacements distant from the active site that affect the size of the domain interface. This structural information should facilitate the design of better MAP-kinase inhibitors for the treatment of inflammation and other diseases.