The use of novel selectivity metrics in kinase research

Cancer selective cell death induction by a bivalent CK2 inhibitor targeting the ATP site and the allosteric αD pocket

Although the involvement of protein kinase CK2 in cancer is well-documented, there is a need for selective CK2 inhibitors suitable for investigating CK2 specific roles in cancer-related biological pathways and further exploring its therapeutic potential. Here, we report the discovery of AB668, an outstanding selective inhibitor that binds CK2 through a bivalent mode, interacting both at the ATP site and an allosteric αD pocket unique to CK2. Using caspase activation assay, live-cell imaging, and transcriptomic analysis, we have compared the effects of this bivalent inhibitor to representative ATP-competitive inhibitors, CX-4945, and SGC-CK2-1. Our results show that in contrast to CX-4945 or SGC-CK2-1, AB668, by targeting the CK2 αD pocket, has a distinct mechanism of action regarding its anti-cancer activity, inducing apoptotic cell death in several cancer cell lines and stimulating distinct biological pathways in renal cell carcinoma.

Discovery of IRAK4 Inhibitors (Zabedosertib) and

Interleukin-1 receptor-associated kinase 4 (IRAK4) plays a critical role in innate inflammatory processes. Here, we describe the discovery of two clinical candidate IRAK4 inhibitors, BAY1834845 (zabedosertib) and BAY1830839, starting from a high-throughput screening hit derived from Bayer's compound library. By exploiting binding site features distinct to IRAK4 using an in-house docking model, liabilities of the original hit could surprisingly be overcome to confer both candidates with a unique combination of good potency and selectivity. Favorable DMPK profiles and activity in animal inflammation models led to the selection of these two compounds for clinical development in patients.

Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

CMGC Kinases in Health and Cancer

CMGC kinases, encompassing cyclin-dependent kinases (CDKs), mitogen-activated protein kinases (MAPKs), glycogen synthase kinases (GSKs), and CDC-like kinases (CLKs), play pivotal roles in cellular signaling pathways, including cell cycle regulation, proliferation, differentiation, apoptosis, and gene expression regulation. The dysregulation and aberrant activation of these kinases have been implicated in cancer development and progression, making them attractive therapeutic targets. In recent years, kinase inhibitors targeting CMGC kinases, such as CDK4/6 inhibitors and BRAF/MEK inhibitors, have demonstrated clinical success in treating specific cancer types. However, challenges remain, including resistance to kinase inhibitors, off-target effects, and the need for better patient stratification. This review provides a comprehensive overview of the importance of CMGC kinases in cancer biology, their involvement in cellular signaling pathways, protein-protein interactions, and the current state of kinase inhibitors targeting these kinases. Furthermore, we discuss the challenges and future perspectives in targeting CMGC kinases for cancer therapy, including potential strategies to overcome resistance, the development of more selective inhibitors, and novel therapeutic approaches, such as targeting protein-protein interactions, exploiting synthetic lethality, and the evolution of omics in the study of the human kinome. As our understanding of the molecular mechanisms and protein-protein interactions involving CMGC kinases expands, so too will the opportunities for the development of more selective and effective therapeutic strategies for cancer treatment.

Modulation of tau tubulin kinases (TTBK1 and TTBK2) impacts ciliogenesis

Tau tubulin kinase 1 and 2 (TTBK1/2) are highly homologous kinases that are expressed and mediate disease-relevant pathways predominantly in the brain. Distinct roles for TTBK1 and TTBK2 have been delineated. While efforts have been devoted to characterizing the impact of TTBK1 inhibition in diseases like Alzheimer's disease and amyotrophic lateral sclerosis, TTBK2 inhibition has been less explored. TTBK2 serves a critical function during cilia assembly. Given the biological importance of these kinases, we designed a targeted library from which we identified several chemical tools that engage TTBK1 and TTBK2 in cells and inhibit their downstream signaling. Indolyl pyrimidinamine 10 significantly reduced the expression of primary cilia on the surface of human induced pluripotent stem cells (iPSCs). Furthermore, analog 10 phenocopies TTBK2 knockout in iPSCs, confirming a role for TTBK2 in ciliogenesis.

Selectivity of Protein Interactions Stimulated by Terahertz Signals

It has been established that Terahertz (THz) band signals can interact with biomolecules through resonant modes. Specifically, of interest here, protein activation. Our research goal is to show how directing the mechanical signaling inside protein molecules using THz signals can control changes in their structure and activate associated biochemical and biomechanical events. To establish that, we formulate a selectivity metric that quantifies the system performance and captures the capability of the nanoantenna to induce a conformational change in the desired protein molecule/population. The metric provides a score between -1 and 1 that indicates the degree of control we have over the system to achieve targeted protein interactions. To develop the selectivity measure, we first use the Langevin stochastic equation driven by an external force to model the protein behavior. We then determine the probability of protein folding by computing the steady-state energy of the driven protein and then generalize our model to account for protein populations. Our numerical analysis results indicate that a maximum selectivity score is attained when only the targeted population experiences a folding behavior due to the impinging THz signal. From the achieved selectivity values, we conclude that the system response not only depends on the resonant frequency but also on the system controlling parameters namely, the nanoantenna force, the damping constant, and the abundance of each protein population. Based on the selectivity metric, the nanoantenna must be tuned to a frequency that is not necessarily the resonant frequency of the protein. The presented work sheds light on the potential associated with the electromagnetic-based control of protein networks, which could lead to a plethora of applications in the medical field ranging from bio-sensing to targeted therapy.

Death by a Thousand Cuts â€" Combining Kinase Inhibitors for Selective Target Inhibition and Rational Polypharmacology

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.

An Introduction to Chemogenomics

Chemogenomics is an innovative approach in chemical biology that synergizes combinatorial chemistry and genomic and proteomic biology to systematically study the response of a biological system to a set of compounds, which can aid the identification and validation of biological targets as well as biologically active small-molecule agents responsible for a phenotypic outcome. Central to this strategy is a collection of chemically diverse compounds, a so-called chemogenomics library. Selection and annotation of vastly available chemogenomic compound candidates for an inclusion in such set present a challenge, but optimal compound selection is critical for success of chemogenomics. The library can be used in a wide variety of research applications from biological mechanism deconvolution to drug discovery. However, phenotypic screening methods are typically required to be high-throughput and equipped with a systematic analysis of complex biological-chemical interactions. This chapter provides a general outline to the chemogenomics approach, including concept and critical steps in all stages of this innovative chemical biology strategy.

Selective Inhibition of Bruton's Tyrosine Kinase by a Designed Covalent Ligand Leads to Potent Therapeutic Efficacy in Blood Cancers Relative to Clinically Used Inhibitors

Bruton's tyrosine kinase (BTK) is a member of the TEC-family kinases and crucial for the proliferation and differentiation of B-cells. We evaluated the therapeutic potential of a covalent inhibitor (JS25) with nanomolar potency against BTK and with a more desirable selectivity and inhibitory profile compared to the FDA-approved BTK inhibitors ibrutinib and acalabrutinib. Structural prediction of the BTK/JS25 complex revealed sequestration of Tyr551 that leads to BTK's inactivation. JS25 also inhibited the proliferation of myeloid and lymphoid B-cell cancer cell lines. Its therapeutic potential was further tested against ibrutinib in preclinical models of B-cell cancers. JS25 treatment induced a more pronounced cell death in a murine xenograft model of Burkitt's lymphoma, causing a 30-40% reduction of the subcutaneous tumor and an overall reduction in the percentage of metastasis and secondary tumor formation. In a patient model of diffuse large B-cell lymphoma, the drug response of JS25 was higher than that of ibrutinib, leading to a 64% "on-target" efficacy. Finally, in zebrafish patient-derived xenografts of chronic lymphocytic leukemia, JS25 was faster and more effective in decreasing tumor burden, producing superior therapeutic effects compared to ibrutinib. We expect JS25 to become therapeutically relevant as a BTK inhibitor and to find applications in the treatment of hematological cancers and other pathologies with unmet clinical treatment.

KUALA: a machine learning-driven framework for kinase inhibitors repositioning

The family of protein kinases comprises more than 500 genes involved in numerous functions. Hence, their physiological dysfunction has paved the way toward drug discovery for cancer, cardiovascular, and inflammatory diseases. As a matter of fact, Kinase binding sites high similarity has a double role. On the one hand it is a critical issue for selectivity, on the other hand, according to poly-pharmacology, a synergistic controlled effect on more than one target could be of great pharmacological interest. Another important aspect of binding similarity is the possibility of exploit it for repositioning of drugs on targets of the same family. In this study, we propose our approach called Kinase drUgs mAchine Learning frAmework (KUALA) to automatically identify kinase active ligands by using specific sets of molecular descriptors and provide a multi-target priority score and a repurposing threshold to suggest the best repurposable and non-repurposable molecules. The comprehensive list of all kinase-ligand pairs and their scores can be found at https://github.com/molinfrimed/multi-kinases .

Target-specific compound selectivity for multi-target drug discovery and repurposing

Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound’s potency against the target of interest (absolute potency), and 2) the compound’s potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.

Cell-Active, Reversible, and Irreversible Covalent Inhibitors That Selectively Target the Catalytic Lysine of BCR-ABL Kinase

Despite recent interests in developing lysine-targeting covalent inhibitors, no general approach is available to create such compounds. We report herein a general approach to develop cell-active covalent inhibitors of protein kinases by targeting the conserved catalytic lysine residue using key SuFEx and salicylaldehyde-based imine chemistries. We validated the strategy by successfully developing (irreversible and reversible) covalent inhibitors against BCR-ABL kinase. Our lead compounds showed high levels of selectivity in biochemical assays, exhibited nanomolar potency against endogenous ABL kinase in cellular assays, and were active against most drug-resistant ABL mutations. Among them, the salicylaldehyde-containing A5 is the first-ever reversible covalent ABL inhibitor that possessed time-dependent ABL inhibition with prolonged residence time and few cellular off-targets in K562 cells. Bioinformatics further suggested the generality of our strategy against the human kinome.

Evaluation of a Selective Chemical Probe Validates That CK2 Mediates Neuroinflammation in a Human Induced Pluripotent Stem Cell-Derived Microglial Model

Novel treatments for neurodegenerative disorders are in high demand. It is imperative that new protein targets be identified to address this need. Characterization and validation of nascent targets can be accomplished very effectively using highly specific and potent chemical probes. Human induced pluripotent stem cells (hiPSCs) provide a relevant platform for testing new compounds in disease relevant cell types. However, many recent studies utilizing this platform have focused on neuronal cells. In this study, we used hiPSC-derived microglia-like cells (MGLs) to perform side-by-side testing of a selective chemical probe, SGC-CK2-1, compared with an advanced clinical candidate, CX-4945, both targeting casein kinase 2 (CK2), one of the first kinases shown to be dysregulated in Alzheimer's disease (AD). CK2 can mediate neuroinflammation in AD, however, its role in microglia, the innate immune cells of the central nervous system (CNS), has not been defined. We analyzed available RNA-seq data to determine the microglial expression of kinases inhibited by SGC-CK2-1 and CX-4945 with a reported role in mediating inflammation in glial cells. As proof-of-concept for using hiPSC-MGLs as a potential screening platform, we used both wild-type (WT) MGLs and MGLs harboring a mutation in presenilin-1 (PSEN1), which is causative for early-onset, familial AD (FAD). We stimulated these MGLs with pro-inflammatory lipopolysaccharides (LPS) derived from E. coli and observed strong inhibition of the expression and secretion of proinflammatory cytokines by simultaneous treatment with SGC-CK2-1. A direct comparison shows that SGC-CK2-1 was more effective at suppression of proinflammatory cytokines than CX-4945. Together, these results validate a selective chemical probe, SGC-CK2-1, in human microglia as a tool to reduce neuroinflammation.

Protein proximity networks and functional evaluation of the casein kinase 1 gamma family reveal unique roles for CK1γ3 in WNT signaling

Aberrant activation or suppression of WNT/β-catenin signaling contributes to cancer initiation and progression, neurodegeneration, and bone disease. However, despite great need and more than 40 years of research, targeted therapies for the WNT pathway have yet to be fully realized. Kinases are considered exceptionally druggable and occupy key nodes within the WNT signaling network, but several pathway-relevant kinases remain understudied and "dark." Here, we studied the function of the casein kinase 1γ (CSNK1γ) subfamily of human kinases and their roles in WNT signaling. miniTurbo-based proximity biotinylation and mass spectrometry analysis of CSNK1γ1, CSNK1γ2, and CSNK1γ3 revealed numerous components of the β-catenin-dependent and β-catenin-independent WNT pathways. In gain-of-function experiments, we found that CSNK1γ3 but not CSNK1γ1 or CSNK1γ2 activated β-catenin-dependent WNT signaling, with minimal effect on other signaling pathways. We also show that within the family, CSNK1γ3 expression uniquely induced low-density lipoprotein receptor-related protein 6 phosphorylation, which mediates downstream WNT signaling transduction. Conversely, siRNA-mediated silencing of CSNK1γ3 alone had no impact on WNT signaling, though cosilencing of all three family members decreased WNT pathway activity. Finally, we characterized two moderately selective and potent small-molecule inhibitors of the CSNK1γ family. We show that these inhibitors and a CSNK1γ3 kinase-dead mutant suppressed but did not eliminate WNT-driven low-density lipoprotein receptor-related protein 6 phosphorylation and β-catenin stabilization. Our data suggest that while CSNK1γ3 expression uniquely drives pathway activity, potential functional redundancy within the family necessitates loss of all three family members to suppress the WNT signaling pathway.

Contribution of Meliaceous plants in furnishing lead compounds for antiplasmodial and insecticidal drug development

ETHNOPHARMACOLOGICAL RELEVANCE

Malaria remains one of the greatest threats to human life especially in the tropical and sub-tropical regions where it claims hundreds of thousands of lives of young children every year. Meliaceae represent a large family of trees and shrubs, which are widely used in African traditional medicine for the treatment of several ailments including fever due to malaria. The in vitro and in vivo antiplasmodial as well as insecticidal investigations of their extracts or isolated compounds have led to promising results but to the best of our knowledge, no specific review on the traditional uses, phytochemistry of the antiplasmodial, insecticidal and cytotoxic lead compounds and extracts of Meliaceae plants has been compiled.

AIMS

To review the literature up to 2021 on the Meliaceae family with antiplasmodial, insecticidal and cytotoxic activity.

MATERIALS AND METHODS

A number of online libraries including PubMed, Scifinder, Google Scholar and Web of Science were used in searching for information on antiplasmodial metabolites from Meliaceous plants. The keywords Meliaceae, malaria, Plasmodium, Anopheles and antiplasmodial were used to monitor and refine our search without language restriction.

RESULTS

The phytochemical investigations of genera of the family Meliaceae led to the isolation and characterization of a wide range of structural diversity of compounds, 124 of which have been evaluated for their antiplasmodial potency against 11 chloroquine-sensitive and chloroquine-resistant Plasmodium falciparum strains. A total of 45 compounds were reported with promising insecticidal potentials against two efficient vector species, Anopheles stephensi Liston and A. gambiae Giles. Limonoids were the most abundant (51.6%) reported compounds and they exhibited the most promising antiplasmodial activity such as gedunin (3) which demonstrated an activity equal to quinine or azadirachtin (1) displaying promising larvicidal, pupicidal and adulticidal effects on different larval instars of A. stephensi with almost 100% larval mortality at 1 ppm concentration.

CONCLUSION

Studies performed so far on Meliaceae plants have reported compounds with significant antiplasmodial and insecticidal activity, lending support to the use of species of this family in folk medicine, for the treatment of malaria. Moreover, results qualified several of these species as important sources of compounds for the development of eco-friendly pesticides to control mosquito vectors. However, more in vitro, in vivo and full ADMET studies are still required to provide additional data that could guide in developing novel drugs and insecticides.

Chemical Probes for Understudied Kinases: Challenges and Opportunities

Over 20 years after the approval of the first-in-class protein kinase inhibitor imatinib, the biological function of a significant fraction of the human kinome remains poorly understood while most research continues to be focused on few well-validated targets. Given the strong genetic evidence for involvement of many kinases in health and disease, the understudied fraction of the kinome holds a large and unexplored potential for future therapies. Specific chemical probes are indispensable tools to interrogate biology enabling proper preclinical validation of novel kinase targets. In this Perspective, we highlight recent case studies illustrating the development of high-quality chemical probes for less-studied kinases and their application in target validation. We spotlight emerging techniques and approaches employed in the generation of chemical probes for protein kinases and beyond and discuss the associated challenges and opportunities.

Development of a potent and selective chemical probe for the pleiotropic kinase CK2

Building on the pyrazolopyrimidine CK2 (casein kinase 2) inhibitor scaffold, we designed a small targeted library. Through comprehensive evaluation of inhibitor selectivity, we identified inhibitor 24 (SGC-CK2-1) as a highly potent and cell-active CK2 chemical probe with exclusive selectivity for both human CK2 isoforms. Remarkably, despite years of research pointing to CK2 as a key driver in cancer, our chemical probe did not elicit a broad antiproliferative phenotype in >90% of >140 cell lines when tested in dose-response. While many publications have reported CK2 functions, CK2 biology is complex and an available high-quality chemical tool such as SGC-CK2-1 will be indispensable in deciphering the relationships between CK2 function and phenotypes.

Is Structure-Based Drug Design Ready for Selectivity Optimization?

Alchemical free-energy calculations are now widely used to drive or maintain potency in small-molecule lead optimization with a roughly 1 kcal/mol accuracy. Despite this, the potential to use free-energy calculations to drive optimization of compound selectivity among two similar targets has been relatively unexplored in published studies. In the most optimistic scenario, the similarity of binding sites might lead to a fortuitous cancellation of errors and allow selectivity to be predicted more accurately than affinity. Here, we assess the accuracy with which selectivity can be predicted in the context of small-molecule kinase inhibitors, considering the very similar binding sites of human kinases CDK2 and CDK9 as well as another series of ligands attempting to achieve selectivity between the more distantly related kinases CDK2 and ERK2. Using a Bayesian analysis approach, we separate systematic from statistical errors and quantify the correlation in systematic errors between selectivity targets. We find that, in the CDK2/CDK9 case, a high correlation in systematic errors suggests that free-energy calculations can have significant impact in aiding chemists in achieving selectivity, while in more distantly related kinases (CDK2/ERK2), the correlation in systematic error suggests that fortuitous cancellation may even occur between systems that are not as closely related. In both cases, the correlation in systematic error suggests that longer simulations are beneficial to properly balance statistical error with systematic error to take full advantage of the increase in apparent free-energy calculation accuracy in selectivity prediction.

PKIS deep dive yields a chemical starting point for dark kinases and a cell active BRSK2 inhibitor

The Published Kinase Inhibitor Set (PKIS) is a publicly-available chemogenomic library distributed to more than 300 laboratories by GlaxoSmithKline (GSK) between 2011 and 2015 and by SGC-UNC from 2015 to 2017. Screening this library of well-annotated, published kinase inhibitors has yielded a plethora of data in diverse therapeutic and scientific areas, funded applications, publications, and provided impactful pre-clinical results. GW296115 is a compound that was included in PKIS based on its promising selectivity following profiling against 260 human kinases. Herein we present more comprehensive profiling data for 403 wild type human kinases and follow-up enzymatic screening results for GW296115. This more thorough investigation of GW296115 has confirmed it as a potent inhibitor of kinases including BRSK1 and BRSK2 that were identified in the original panel of 260 kinases as well as surfaced other kinases that it potently inhibits. Based on these new kinome-wide screening results, we report that GW296115 is an inhibitor of several members of the Illuminating the Druggable Genome (IDG) list of understudied dark kinases. Specifically, our results establish GW296115 as a potent lead chemical tool that inhibits six IDG kinases with IC50 values less than 100 nM. Focused studies establish that GW296115 is cell active, and directly engages BRSK2. Further evaluation showed that GW296115 downregulates BRSK2-driven phosphorylation and downstream signaling. Therefore, we present GW296115 as a cell-active chemical tool that can be used to interrogate the poorly characterized function(s) of BRSK2.

How We Think about Targeting RNA with Small Molecules

RNA offers nearly unlimited potential as a target for small molecule chemical probes and lead medicines. Many RNAs fold into structures that can be selectively targeted with small molecules. This Perspective discusses molecular recognition of RNA by small molecules and highlights key enabling technologies and properties of bioactive interactions. Sequence-based design of ligands targeting RNA has established rules for affecting RNA targets and provided a potentially general platform for the discovery of bioactive small molecules. The RNA targets that contain preferred small molecule binding sites can be identified from sequence, allowing identification of off-targets and prediction of bioactive interactions by nature of ligand recognition of functional sites. Small molecule targeted degradation of RNA targets (ribonuclease-targeted chimeras, RIBOTACs) and direct cleavage by small molecules have also been developed. These growing technologies suggest that the time is right to provide small molecule chemical probes to target functionally relevant RNAs throughout the human transcriptome.

Gini Coefficients as a Single Value Metric to Define Chemical Probe Selectivity

Selectivity is a key requirement of high-quality chemical probes and lead medicines; however, methods to quantify and compare the selectivity of small molecules have not been standardized across the field. Herein, we discuss the origins and use of a comprehensive, single value term to quantify selectivity, the Gini coefficient. Case studies presented include compounds that target protein kinases, small molecules that bind RNA structures, and small molecule chimeras that bind to and degrade the target RNA. With an increasing number of transcriptome- and proteome-wide studies, we submit that reporting Gini coefficients as a quantitative descriptor of selectivity should be used broadly.

Quantitative Interrogation of the Human Kinome Perturbed by Two BRAF Inhibitors

Oncogenic BRAF mutations contribute to the development of a number of cancers, and small-molecule BRAF inhibitors have been approved by the Food and Drug Administration (FDA) for anticancer therapy. In this study, we employed two targeted quantitative proteomics approaches for monitoring separately the alterations in protein expression and ATP binding affinities of kinases in cultured human melanoma cells elicited by two FDA-approved small-molecule BRAF inhibitors, dabrafenib and vemurafenib. Our results showed that treatment with the two inhibitors led to markedly different reprograming of the human kinome. Furthermore, we confirmed that vemurafenib could compromise the ATP binding capacity of MAP2K5 in vitro and inhibit its kinase activity in cells. Together, our targeted quantitative proteomic methods revealed profound changes in expression levels of kinase proteins in cultured melanoma cells upon treatment with clinically used BRAF inhibitors and led to the discovery of novel putative target kinases for these inhibitors.