Data sets of human and mouse protein kinase inhibitors with curated activity data including covalent inhibitors
Ontology highlight
ABSTRACT:
SUBMITTER: Xerxa E
PROVIDER: S-EPMC10518807 | biostudies-literature | 2023 Aug
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:The free available eutherian genomic sequence data sets advanced scientific field of genomics. Of note, future revisions of gene data sets were expected, due to incompleteness of public eutherian genomic sequence assemblies and potential genomic sequence errors. The eutherian comparative genomic analysis protocol was proposed as guidance in protection against potential genomic sequence errors in public eutherian genomic sequences. The protocol was applicable in updates of 7 major eutherian gene data sets, including 812 complete coding sequences deposited in European Nucleotide Archive as curated third party data gene data sets.
| S-EPMC4707174 | biostudies-literature
Project description:In drug discovery, protein kinase inhibitors (PKIs) are intensely investigated as drug candidates in different therapeutic areas. While ATP site-directed, non-covalent PKIs have long been a focal point in protein kinase (PK) drug discovery, in recent years, there has been increasing interest in allosteric PKIs (APKIs), which are expected to have high kinase selectivity. In addition, as compounds acting by covalent mechanisms experience a renaissance in drug discovery, there is also increasing interest in covalent PKIs (CPKIs). There are various reasons for this increasing interest such as the anticipated high potency, prolonged residence times compared to non-competitive PKIs, and other favorable pharmacokinetic properties. Due to the popularity of PKIs for therapeutic intervention, large numbers of PKIs and large volumes of activity data have accumulated in the public domain, providing a basis for large-scale computational analysis. We have systematically searched for CPKIs containing different reactive groups (warheads) and investigated their potency and promiscuity (multi-PK activity) on the basis of carefully curated activity data. For seven different warheads, sufficiently large numbers of CPKIs were available for detailed follow-up analysis. For only three warheads, the median potency of corresponding CPKIs was significantly higher than of non-covalent PKIs. However, for CKPIs with five of seven warheads, there was a significant increase in the median potency of at least 100-fold compared to PKI analogues without warheads. However, in the analysis of multi-PK activity, there was no general increase in the promiscuity of CPKIs compared to non-covalent PKIs. In addition, we have identified 29 new APKIs in X-ray structures of PK-PKI complexes. Among structurally characterized APKIs, 13 covalent APKIs in complexes with five PKs are currently available, enabling structure-based investigation of PK inhibition by covalent-allosteric mechanisms.
| S-EPMC10420927 | biostudies-literature
Project description:The number of protein kinase inhibitors (PKIs) approved worldwide continues to grow steadily, with 39 drugs approved in the period between 2001 and January 2018. PKIs on the market have been the subject of many reviews, and structure-property relationships specific to this class of drugs have been inferred. However, the large number of PKIs under development is often overlooked. In this paper, we present PKIDB (Protein Kinase Inhibitor Database), a monthly-updated database gathering approved PKIs as well as PKIs currently in clinical trials. The database compiles currently 180 inhibitors ranging from phase 0 to 4 clinical trials along with annotations extracted from seven public resources. The distribution and property ranges of standard physicochemical properties are presented. They can be used as filters to better prioritize compound selection for future screening campaigns. Interestingly, more than one-third of the kinase inhibitors violate at least one Lipinski's rule. A Principal Component Analysis (PCA) reveals that Type-II inhibitors are mapped to a distinct chemical space as compared to orally administrated drugs as well as to other types of kinase inhibitors. Using a Principal Moment of Inertia (PMI) analysis, we show that PKIs under development tend to explore new shape territories as compared to approved PKIs. In order to facilitate the analysis of the protein space, the kinome tree has been annotated with all protein kinases being targeted by PKIs. Finally, we analyzed the pipeline of the pharmaceutical companies having PKIs on the market or still under development. We hope that this work will assist researchers in the kinase field in identifying and designing the next generation of kinase inhibitors for still untargeted kinases. The PKIDB database is freely accessible from a website at http://www.icoa.fr/pkidb and can be easily browsed through a user-friendly spreadsheet-like interface.
| S-EPMC6017449 | biostudies-literature
Project description:BackgroundMany biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here.ResultsIn this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval.ConclusionsThis method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.
| S-EPMC5879614 | biostudies-literature
Project description:Reversible covalent kinase inhibitors (RCKIs) are a class of novel kinase inhibitors attracting increasing attention because they simultaneously show the selectivity of covalent kinase inhibitors yet avoid permanent protein-modification-induced adverse effects. Over the last decade, RCKIs have been reported to target different kinases, including Atypical group of kinases. Currently, three RCKIs are undergoing clinical trials. Here, advances in RCKIs are reviewed to systematically summarize the characteristics of electrophilic groups, chemical scaffolds, nucleophilic residues, and binding modes. In so doing, we integrate key insights into privileged electrophiles, the distribution of nucleophiles, and hence effective design strategies for the development of RCKIs. Finally, we provide a further perspective on future design strategies for RCKIs, including those that target proteins other than kinases.
| S-EPMC11796325 | biostudies-literature
Project description:Phosphatidylinositol 5-phosphate 4-kinases (PI5P4Ks) are important molecular players in a variety of diseases, such as cancer. Currently available PI5P4K inhibitors are reversible small molecules, which may lack selectivity and sufficient cellular on-target activity. In this study, we present a new class of covalent pan-PI5P4K inhibitors with potent biochemical and cellular activity. Our designs are based on THZ-P1-2, a covalent PI5P4K inhibitor previously developed in our lab. Here, we report further structure-guided optimization and structure-activity relationship (SAR) study of this scaffold, resulting in compound 30, which retained biochemical and cellular potency, while demonstrating a significantly improved selectivity profile. Furthermore, we confirm that the inhibitors show efficient binding affinity in the context of HEK 293T cells using isothermal CETSA methods. Taken together, compound 30 represents a highly selective pan-PI5P4K covalent lead molecule.
| S-EPMC7074221 | biostudies-literature
Project description:The ability of G protein-coupled receptor (GPCR) kinases (GRKs) to regulate desensitization of GPCRs has made GRK2 and GRK5 attractive targets for treating heart failure and other diseases such as cancer. Although advances have been made toward developing inhibitors that are selective for GRK2, there have been far fewer reports of GRK5 selective compounds. Herein, we describe the development of GRK5 subfamily selective inhibitors, 5 and 16d that covalently interact with a nonconserved cysteine (Cys474) unique to this subfamily. Compounds 5 and 16d feature a highly amenable pyrrolopyrimidine scaffold that affords high nanomolar to low micromolar activity that can be easily modified with Michael acceptors with various reactivities and geometries. Our work thereby establishes a new pathway toward further development of subfamily selective GRK inhibitors and establishes Cys474 as a new and useful covalent handle in GRK5 drug discovery.
| S-EPMC6912873 | biostudies-literature
Project description:The ability of G protein-coupled receptor (GPCR) kinases (GRKs) to regulate the desensitization of GPCRs has made GRK2 and GRK5 attractive targets for treating diseases such as heart failure and cancer. Previously, our work showed that Cys474, a GRK5 subfamily-specific residue located on a flexible loop adjacent to the active site, can be used as a covalent handle to achieve selective inhibition of GRK5 over GRK2 subfamily members. However, the potency of the most selective inhibitors remained modest. Herein, we describe a successful campaign to adapt an indolinone scaffold with covalent warheads, resulting in a series of 2-haloacetyl-containing compounds that react quickly and exhibit three orders of magnitude selectivity for GRK5 over GRK2 and low nanomolar potency. They however retain a similar selectivity profile across the kinome as the core scaffold, which was based on Sunitinib.
| S-EPMC7909074 | biostudies-literature
Project description:Deep machine learning is expanding the conceptual framework and capacity of computational compound design, enabling new applications through generative modeling. We have explored the systematic design of covalent protein kinase inhibitors by learning from kinome-relevant chemical space, followed by focusing on an exemplary kinase of interest. Covalent inhibitors experience a renaissance in drug discovery, especially for targeting protein kinases. However, computational design of this class of inhibitors has thus far only been little investigated. To this end, we have devised a computational approach combining fragment-based design and deep generative modeling augmented by three-dimensional pharmacophore screening. This approach is thought to be particularly relevant for medicinal chemistry applications because it combines knowledge-based elements with deep learning and is chemically intuitive. As an exemplary application, we report for Bruton's tyrosine kinase (BTK), a major drug target for the treatment of inflammatory diseases and leukemia, the generation of novel candidate inhibitors with a specific chemically reactive group for covalent modification, requiring only little target-specific compound information to guide the design efforts. Newly generated compounds include known inhibitors and characteristic substructures and many novel candidates, thus lending credence to the computational approach, which is readily applicable to other targets.
| S-EPMC8778003 | biostudies-literature
Project description:The Janus kinases (JAKs) and their downstream effectors, signal transducer and activator of transcription proteins (STATs), form a critical immune cell signaling circuit, which is of fundamental importance in innate immunity, inflammation, and hematopoiesis, and dysregulation is frequently observed in immune disease and cancer. The high degree of structural conservation of the JAK ATP binding pockets has posed a considerable challenge to medicinal chemists seeking to develop highly selective inhibitors as pharmacological probes and as clinical drugs. Here we report the discovery and optimization of 2,4-substituted pyrimidines as covalent JAK3 inhibitors that exploit a unique cysteine (Cys909) residue in JAK3. Investigation of structure-activity relationship (SAR) utilizing biochemical and transformed Ba/F3 cellular assays resulted in identification of potent and selective inhibitors such as compounds 9 and 45. A 2.9 Å cocrystal structure of JAK3 in complex with 9 confirms the covalent interaction. Compound 9 exhibited decent pharmacokinetic properties and is suitable for use in vivo. These inhibitors provide a set of useful tools to pharmacologically interrogate JAK3-dependent biology.
| S-EPMC4777322 | biostudies-literature