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Accurate identification of adenosine triphosphate (ATP) binding sites is crucial for understanding cellular functions and advancing drug discovery, particularly in targeting kinases for cancer treatment. Existing methods face significant challenges due to their reliance on time-consuming precomputed features and the heavily imbalanced nature of binding site data without further investigations on their utility in drug discovery. To address these limitations, we introduced Multiview-ATPBind and ResiBoost. Multiview-ATPBind is an end-to-end deep learning model that integrates one-dimensional (1D) sequence and three-dimensional (3D) structural information for rapid and precise residue-level pocket-ligand interaction predictions. Additionally, ResiBoost is a novel residue-level boosting algorithm designed to mitigate data imbalance by enhancing the prediction of rare positive binding residues. Our approach outperforms state-of-the-art models on benchmark data sets, showing significant improvements in balanced metrics with both experimental and AI-predicted structures. Furthermore, our model seamlessly transfers to predicting binding sites and enhancing docking simulations for kinase inhibitors, including imatinib and dasatinib, underscoring the potential of our method in drug discovery applications.

Developing selective kinase inhibitors remains a formidable challenge in drug discovery because of the highly conserved structural information on adenosine triphosphate (ATP) binding sites across the kinase family. Tailoring docking protocols to identify promising kinase inhibitor candidates for optimization has long been a substantial obstacle to drug discovery. Therefore, we introduced "Kinase-Bench," a pioneering benchmark suite designed for an advanced virtual screen, to improve the selectivity and efficacy of kinase inhibitors. Our comprehensive data set includes 6875 selective ligands and 422,799 decoys for 75 kinases, using extensive bioactivity and structural data from the ChEMBL database and decoys generated by the Directory of Useful Decoys-Enhanced version. Our benchmarking sets and retrospective case studies were designed to provide useful guidance in discovering selective kinase inhibitors. We employed a Glide High-Throughput Virtual Screen and Standard Precision complemented by three scoring functions and customized protein-ligand interaction filters that target specific kinase residue interactions. These innovations were successfully implemented in our virtual screen efforts targeting JAK1 inhibitors, achieving selectivity against its family member, TYK2. Consequently, we identified novel potential hits: Compound 2 (JAK1 IC 50 : 980.5 nM, TYK2 IC 50 : 4.5 μM) and the approved pan-AKT inhibitor Capivasertib (JAK1 IC 50 : 275.9 nM, TYK2 IC 50 : 10.9 μM). Using the Kinase-Bench protocol, both compounds demonstrated substantial JAK1 selectivity, making them strong candidates for further investigation. Our pharmaceutical results underscore the utility of tailored virtual screen protocols in identifying selective kinase inhibitors with substantial implications for rational drug design. Kinase-Bench offers a robust toolset for selective kinase drug discovery with the potential to effectively guide future therapeutic strategies effectively.

Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, resulting in risks for toxicity and general off-target effects. Assessing the binding of an inhibitor for the entire kinome is experimentally possible but expensive. A reliable and interpretable computational prediction of kinase selectivity would greatly benefit the inhibitor discovery and optimization process. Here, we use machine learning on docked poses to address this need. To this end, we aggregated all known inhibitor-kinase affinities and generated the complete accompanying 3D interactome by docking all inhibitors to the respective high-quality X-ray structures. We then used this resource to train a neural network as a kinase-specific scoring function, which achieved an overall performance ( R 2 ) of 0.63-0.74 on unseen inhibitors across the kinome. The entire pipeline from molecule to 3D-based affinity prediction has been fully automated and wrapped in a freely available package. This has a graphical user interface that is tightly integrated with PyMOL to allow immediate adoption in the medicinal chemistry practice.

In designing covalent kinase inhibitors (CKIs), the inclusion of electrophiles as attacking warheads demands careful choreography, ensuring not only their presence on the scaffold moiety but also their precise interaction with nucleophiles in the binding sites. Given the limited number of known electrophiles, exploring adjacent chemical space to broaden the palette of available electrophiles capable of covalent inhibition is desirable. Here, we systematically analyze the characteristics of warheads and the corresponding adjacent fragments for use in CKI design. We first collect all the released cysteine-targeted CKIs from multiple databases and create one CKI data set containing 16,961 kinase-inhibitor data points from 12,381 unique CKIs covering 146 kinases with accessible cysteines in their binding pockets. Then, we analyze this data set, focusing on the extended warheads (i.e., warheads + adjacent fragments)─including 30 common warheads and 1344 unique adjacent fragments. In so doing, we provide structural insights and delineate chemical properties and patterns in these extended warheads. Notably, we highlight the popular patterns observed within reversible CKIs for the popular warheads cyanoacrylamide and aldehyde. This study provides medicinal chemists with novel insights into extended warheads and a comprehensive source of adjacent fragments, thus guiding the design, synthesis, and optimization of CKIs.

In recent years, machine learning has transformed many aspects of the drug discovery process, including small molecule design, for which the prediction of bioactivity is an integral part. Leveraging structural information about the interactions between a small molecule and its protein target has great potential for downstream machine learning scoring approaches but is fundamentally limited by the accuracy with which protein-ligand complex structures can be predicted in a reliable and automated fashion. With the goal of finding practical approaches to generating useful kinase-inhibitor complex geometries for downstream machine learning scoring approaches, we present a kinase-centric docking benchmark assessing the performance of different classes of docking and pose selection strategies to assess how well experimentally observed binding modes are recapitulated in a realistic cross-docking scenario. The assembled benchmark data set focuses on the well-studied protein kinase family and comprises a subset of 589 protein structures cocrystallized with 423 ATP-competitive ligands. We find that the docking methods biased by the cocrystallized ligand, utilizing shape overlap with or without maximum common substructure matching, are more successful in recovering binding poses than standard physics-based docking alone. Also, docking into multiple structures significantly increases the chance of generating a low root-mean-square deviation (RMSD) docking pose. Docking utilizing an approach that combines all three methods (Posit) into structures with the most similar cocrystallized ligands according to the maximum common substructure (MCS) proved to be the most efficient way to reproduce binding poses, achieving a success rate of 70.4% across all included systems. The studied docking and pose selection strategies, which utilize the OpenEye Toolkits, were implemented into pipelines of the KinoML framework, allowing automated and reliable protein-ligand complex generation for future downstream machine learning tasks. Although focused on protein kinases, we believe that the general findings can also be transferred to other protein families.

The continuous emergence of resistance against most frontline antimalarial drugs has led to countless deaths in malaria-endemic countries, counting 619,000 deaths in 2021, with mutation in drug targets being the sole cause. As mutation is correlated frequently with fitness cost, the likelihood of mutation emergence in multiple targets at a time is extremely low. Hence, multitargeting compounds may seem promising to address drug resistance issues with additional benefits like increased efficacy, improved safety profile, and the requirement of fewer pills compared to traditional single and combinational drugs. In this study, we attempted to use the High Throughput Virtual Screening approach to predict multitarget inhibitors against six chemically validated Plasmodium falciparum (Pf) kinases (PfPKG, PfMAP2, PfCDPK4, PfTMK, PfPK5, PfPI4K), resulting in 21 multitargeting hits. The molecular dynamic simulation of the top six complexes (Myricetin-MAP2, Quercetin-CDPK4, Myricetin-TMK, Quercetin-PKG, Salidroside-PK5, and Salidroside-PI4K) showed stable interactions. Moreover, hierarchical clustering reveals the structural divergence of the compounds from the existing antimalarials, indicating less chance of cross-resistance. Additionally, the top three hits were validated through parasite growth inhibition assays, with quercetin and myricetin exhibiting an IC 50 value of 1.84 and 3.93 µM, respectively., Competing Interests: Declarations. Conflict of interest: The authors declare that there are no conflicts of interest., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Protein kinase inhibitors designed to compete with ATP as a primary mode of action turn out to have considerable effects that go beyond their interference of nucleotide binding. New research shows how kinase activation and sometimes noncatalytic functions of protein kinases can be controlled by allosteric properties of kinase inhibitors, communicating perturbations from the active site to distal regulatory regions., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The protozoan protein kinase calcium-dependent protein kinase 1 (CDPK1) has emerged as a potential therapeutic target for the treatment of cryptosporidiosis. A focused screen of known kinase inhibitors identified a pyridopyrimidinone as a new chemotype of Cryptosporidium parvum (Cp) CDPK1 inhibitors. Structural comparison of CpCDPK1 to two representative human kinases, RIPK2 and Src, revealed differences in the positioning of the αC-helix that was used in the design of a potent pyridopyrimidinone-based CpCDPK1 inhibitor 7 (a.k.a. UH15-16, IC 50 = 10 nM), which blocked the growth of three C. parvum strains (EC 50 = 12-40 nM) as well as C. hominis (EC 50 = 85 nM) in HCT-8 host cells. Pharmacokinetic and tissue distribution analyses indicated that 7 had low systemic exposure after oral administration, but high gastrointestinal concentration, as well as good Caco-2 cell permeability. Finally, 7 demonstrated partial efficacy in an IL-12 knock-out mouse model of acute cryptosporidiosis., Competing Interests: Declaration of Competing Interest WCVV is an owner/officer of ParaTheraTech Inc, a company which is seeking to bring bumped kinase (e.g. CDPK1) inhibitors to the animal health market. All other authors declare no interests., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Structure-based virtual screening (SBVS) is a crucial computational approach in drug discovery, but its performance is sensitive to structural variations. Kinases, which are major drug targets, exemplify this challenge due to active site conformational changes caused by different inhibitor types. Most experimentally determined kinase structures have the DFGin state, potentially biasing SBVS towards type I inhibitors and limiting the discovery of diverse scaffolds. We introduce a multi-state modeling (MSM) protocol for AlphaFold2 (AF2) kinase structures using state-specific templates to address these challenges. Our comprehensive benchmarks evaluate predicted model qualities, binding pose prediction accuracy, and hit compound identification through ensemble SBVS. Results demonstrate that MSM models exhibit comparable or improved structural accuracy compared to standard AF2 models, enhancing pose prediction accuracy and effectively capturing kinase-ligand interactions. In virtual screening experiments, our MSM approach consistently outperforms standard AF2 and AF3 modeling, particularly in identifying diverse hit compounds. This study highlights the potential of MSM in broadening kinase inhibitor discovery by facilitating the identification of chemically diverse inhibitors, offering a promising solution to the structural bias problem in kinase-targeted drug discovery., (© 2024. The Author(s).)

Characterizing the kinome selectivity profiles of kinase inhibitors is essential in the early stages of novel small-molecule drug discovery. This characterization is critical for interpreting potential adverse events caused by off-target polypharmacology effects and provides unique pharmacological insights for drug repurposing development of existing kinase inhibitor drugs. However, experimental profiling of whole kinome selectivity is still time-consuming and resource-demanding. Here, we report a deep learning classification model using an in-house built data set of inhibitors against 191 well-representative kinases constructed based on a novel strategy by systematically cleaning and integrating six public data sets. This model, a multitask deep neural network, predicts the kinome selectivity profiles of compounds with novel structures. The model demonstrates excellent predictive performance, with auROC, prc-AUC, Accuracy, and Binary_cross_entropy of 0.95, 0.92, 0.90, and 0.37, respectively. It also performs well in a priori testing for inhibitors targeting different categories of proteins from internal compound collections, significantly improving over similar models on data sets from practical application scenarios. Integrated to subsequent machine learning-enhanced virtual screening workflow, novel CDK2 kinase inhibitors with potent kinase inhibitory activity and excellent kinome selectivity profiles are successfully identified. Additionally, we developed a free online web server, KinomePro-DL, to predict the kinome selectivity profiles and kinome-wide polypharmacology effects of small molecules (available on kinomepro-dl.pharmablock.com). Uniquely, our model allows users to quickly fine-tune it with their own training data sets, enhancing both prediction accuracy and robustness.

In drug discovery, human protein kinases (PKs) represent one of the major target classes due to their central role in cellular signaling, implication in various diseases as a consequence of deregulated signaling, and notable druggability. Individual PKs and their disease biology have been explored to different degrees, giving rise to heterogeneous functional knowledge and disease associations across the human kinome. The U.S. National Institutes of Health previously designated 162 understudied ("dark") human PKs and lipid kinases due to the lack of functional annotations and high-quality molecular probes for functional investigations. Given the large volumes of available PK inhibitors (PKIs) and activity data, we have systematically analyzed the distribution of PKIs and associated data at different confidence levels across the human kinome and distinguished between chemically explored, underexplored, and unexplored PKs. The analysis provides a medicinal chemistry-centric view of PK exploration and further extends prior assessment of the dark kinome.

Given their central role in signal transduction, protein kinases (PKs) were first implicated in cancer development, caused by aberrant intracellular signaling events. Since then, PKs have become major targets in different therapeutic areas. The preferred approach to therapeutic intervention of PK-dependent diseases is the use of small molecules to inhibit their catalytic phosphate group transfer activity. PK inhibitors (PKIs) are among the most intensely pursued drug candidates, with currently 80 approved compounds and several hundred in clinical trials. Following the elucidation of the human kinome and development of robust PK expression systems and high-throughput assays, large volumes of PK/PKI data have been produced in industrial and academic environments, more so than for many other pharmaceutical targets. In addition, hundreds of X-ray structures of PKs and their complexes with PKIs have been reported. Substantial amounts of PK/PKI data have been made publicly available in part as a result of open science initiatives. PK drug discovery is further supported through the incorporation of data science approaches, including the development of various specialized databases and online resources. Compound and activity data wealth compared to other targets has also made PKs a focal point for the application of artificial intelligence (AI) in pharmaceutical research. Herein, we discuss the interplay of open and data science in PK drug discovery and review exemplary studies that have substantially contributed to its development, including kinome profiling or the analysis of PKI promiscuity versus selectivity. We also take a close look at how AI approaches are beginning to impact PK drug discovery in light of their increasing data orientation.

Cryptosporidiosis, a prevalent gastrointestinal illness worldwide, is caused by the protozoan parasite Cryptosporidium parvum. Calcium-dependent protein kinase 1 (CpCDPK1), crucial for the parasite's life cycle, serves as a promising drug target due to its role in regulating invasion and egress from host cells. While potent Pyrazolopyrimidine analogs have been identified as candidate hit molecules, they exhibit limitations in inhibiting Cryptosporidium growth in cell culture, prompting exploration of alternative scaffolds. Leveraging the most potent compound, RM-1-95, co-crystallized with CpCDPK1, an E-pharmacophore model was generated and validated alongside a deep learning model trained on known CpCDPK1 compounds. These models facilitated screening Enamine's 2 million HTS compound library for novel CpCDPK1 inhibitors. Subsequent hierarchical docking prioritized hits, with final selections subjected to Quantum polarized docking for accurate ranking. Results from docking studies and MD simulations highlighted similarities in interactions between the cocrystallized ligand RM-1-95 and identified hit molecules, indicating comparable inhibitory potential against CpCDPK1. Furthermore, assessing metabolic stability through Cytochrome 450 site of metabolism prediction offered crucial insights for drug design, optimization, and regulatory approval processes., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Solvent-front mutations have emerged as a common mechanism leading to acquired resistance to kinase inhibitors, representing a major challenge in the clinic. Several new-generation kinase inhibitors targeting solvent-front mutations have either been approved or advanced to clinical trials. However, there remains a need to discover effective, new-generation inhibitors. In this Perspective, we systematically summarize the general types of solvent-front mutations across the kinome and describe the development of inhibitors targeting some key solvent-front mutations. Additionally, we highlight the challenges and opportunities for the next generation of kinase inhibitors directed toward overcoming solvent-front mutations.

Remarkable progress has been made in the development of cysteine-targeted covalent inhibitors. In kinase drug discovery, covalent inhibitors capable of targeting other nucleophilic residues (i.e. lysine, or K) have emerged in recent years. Besides a highly conserved catalytic lysine, almost all human protein kinases possess an equally conserved glutamate/aspartate (e.g. E/D) that forms a K-E/D salt bridge within the enzyme's active site. Electrophilic ynamides were previously used as effective peptide coupling reagents and to develop E/D-targeting covalent protein inhibitors/probes. In the present study, we report the first ynamide-based small-molecule inhibitors capable of inducing intramolecular cross-linking of various protein kinases, leading to subsequent irreversible inhibition of kinase activity. Our strategy took advantage of the close distance between the highly conserved catalytic K and E/D residues in a targeted kinase, thus providing a conceptually general approach to achieve irreversible kinase inhibition with high specificity and desirable cellular potency. Finally, this ynamide-facilitated, ligand-induced mechanism leading to intramolecular kinase cross-linking and inhibition was unequivocally established by using recombinant ABL kinase as a representative., (© 2024 Wiley-VCH GmbH.)

Kinase-targeted inhibitors hold promise for new therapeutic options, with multi-target inhibitors offering the potential for broader efficacy while minimizing polypharmacology risks. However, comprehensive experimental profiling of kinome-wide activity is expensive, and existing computational approaches often lack scalability or accuracy for understudied kinases. We introduce KinomeMETA, an artificial intelligence (AI)-powered web platform that significantly expands the predictive range with scalability for predicting the polypharmacological effects of small molecules across the kinome. By leveraging a novel meta-learning algorithm, KinomeMETA efficiently utilizes sparse activity data, enabling rapid generalization to new kinase tasks even with limited information. This significantly expands the repertoire of accurately predictable kinases to 661 wild-type and clinically-relevant mutant kinases, far exceeding existing methods. Additionally, KinomeMETA empowers users to customize models with their proprietary data for specific research needs. Case studies demonstrate its ability to discover new active compounds by quickly adapting to small dataset. Overall, KinomeMETA offers enhanced kinome virtual profiling capabilities and is positioned as a powerful tool for developing new kinase inhibitors and advancing kinase research. The KinomeMETA server is freely accessible without registration at https://kinomemeta.alphama.com.cn/., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The continued growth of data from biological screening and medicinal chemistry provides opportunities for data-driven experimental design and decision making in early-phase drug discovery. Approaches adopted from data science help to integrate internal and public domain data and extract knowledge from historical in-house data. Protein kinase (PK) drug discovery is an exemplary area where large amounts of data are accumulating, providing a valuable knowledge base for discovery projects. Herein, the evolution of PK drug discovery and development of small molecular PK inhibitors (PKIs) is reviewed, highlighting milestone developments in the field and discussing exemplary studies providing a basis for increasing data orientation of PK discovery efforts., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Protein kinases are essential regulators of cell function and represent one of the largest and most diverse protein families. They are particularly influential in signal transduction and coordinating complex processes like the cell cycle. Out of the 518 human protein kinases identified, 478 are part of a single superfamily sharing catalytic domains that are related in sequence. The dysregulation of protein kinases due to certain mutations has been associated with various diseases, including cancer. Although most of the protein kinase inhibitors identified as type I or type II primarily target the ATP-binding pockets of kinases, the structural and sequential resemblances among these pockets pose a significant challenge for selective inhibition. Therefore, targeting allosteric pockets that are beside highly conserved ATP pockets has emerged as a promising strategy to prevail current limitations, such as poor selectivity and drug resistance. In this article, we compared the binding pockets of various protein kinases for which allosteric (type III) inhibitors have already been developed. Additionally, understanding the structure and shape of existing ligands could aid in identifying key interaction sites within the allosteric pockets of kinases. This comprehensive review aims to facilitate the design of more effective and selective allosteric inhibitors.

Protein kinases are key actors of signaling networks and important drug targets. They cycle between active and inactive conformations, distinguished by a few elements within the catalytic domain. One is the activation loop, whose conserved DFG motif can occupy DFG-in, DFG-out, and some rarer conformations. Annotation and classification of the structural kinome are important, as different conformations can be targeted by different inhibitors and activators. Valuable resources exist; however, large-scale applications will benefit from increased automation and interpretability of structural annotation. Interpretable machine learning models are described for this purpose, based on ensembles of decision trees. To train them, a set of catalytic domain sequences and structures was collected, somewhat larger and more diverse than existing resources. The structures were clustered based on the DFG conformation and manually annotated. They were then used as training input. Two main models were constructed, which distinguished active/inactive and in/out/other DFG conformations. They considered initially 1692 structural variables, spanning the whole catalytic domain, then identified ("learned") a small subset that sufficed for accurate classification. The first model correctly labeled all but 3 of 3289 structures as active or inactive, while the second assigned the correct DFG label to all but 17 of 8826 structures. The most potent classifying variables were all related to well-known structural elements in or near the activation loop and their ranking gives insights into the conformational preferences. The models were used to automatically annotate 3850 kinase structures predicted recently with the Alphafold2 tool, showing that Alphafold2 reproduced the active/inactive but not the DFG-in proportions seen in the Protein Data Bank. We expect the models will be useful for understanding and engineering kinases., (© 2024 The Protein Society.)

Protein kinases (PKs) are involved in many intracellular signal transduction pathways through phosphorylation cascades and have become intensely investigated pharmaceutical targets over the past two decades. Inhibition of PKs using small-molecular inhibitors is a premier strategy for the treatment of diseases in different therapeutic areas that are caused by uncontrolled PK-mediated phosphorylation and aberrant signaling. Most PK inhibitors (PKIs) are directed against the ATP cofactor binding site that is largely conserved across the human kinome comprising 518 wild-type PKs (and many mutant forms). Hence, these PKIs often have varying degrees of multi-PK activity (promiscuity) that is also influenced by factors such as single-site mutations in the cofactor binding region, compound binding kinetics, and residence times. The promiscuity of PKIs is often-but not always-critically important for therapeutic efficacy through polypharmacology. Various in vitro and in vivo studies have also indicated that PKIs have the potential of interacting with additional targets other than PKs, and different secondary cellular targets of individual PKIs have been identified on a case-by-case basis. Given the strong interest in PKs as drug targets, a wealth of PKIs from medicinal chemistry and their activity data from many assays and biological screens have become publicly available over the years. On the basis of these data, for the first time, we conducted a systematic search for non-PK targets of PKIs across the human kinome. Starting from a pool of more than 155,000 curated human PKIs, our large-scale analysis confirmed secondary targets from diverse protein classes for 447 PKIs on the basis of high-confidence activity data. These PKIs were active against 390 human PKs, covering all kinase groups of the kinome and 210 non-PK targets, which included other popular pharmaceutical targets as well as currently unclassified proteins. The target distribution and promiscuity of the 447 PKIs were determined, and different interaction profiles with PK and non-PK targets were identified. As a part of our study, the collection of PKIs with activity against non-PK targets and the associated information are made freely available.

Background: Pyrazole-scaffold protein kinase inhibitors (PKIs) have emerged as promising therapeutic agents for the treatment of various diseases, such as cancer, inflammatory disorders, and neurological diseases. This review article provides an overview of the pharmacological properties of pyrazole-scaffold PKIs, including their mechanism of action, selectivity, potency, and toxicity. The article also summarizes the recent developments in the design and synthesis of pyrazole-scaffold PKIs, highlighting the structural features and modifications that contribute to their pharmacological activity. In addition, the article discusses the preclinical and clinical studies of pyrazole-scaffold PKIs, including their efficacy, safety, and pharmacokinetic properties., Methods: A comprehensive search has been conducted on several online patent databases, including the United States Patent and Trademark Office (USPTO), the European Patent Office (EPO), and the World Intellectual Property Organization (WIPO). The search was conducted using pyrazole as the keyword. The search was limited to patents filed between 2015 and 2022. Patents were included if they involved articles in the fields of protein kinase inhibitors, and included literature on some pyrazoles and their pharmacological activities., Results: Data were extracted from each included patent on the following variables: patent title, patent number, inventors, assignee, filing date, publication date, patent type, and field of invention. Data were extracted from each patent using a standardized form to ensure consistency and accuracy., Conclusion: The design and pharmacological evaluation of organic compounds containing pyrazole structure as biologically active substances have been done, and the key structures from the pharmacological data obtained as protein kinase inhibitors have been addressed in detail. The review concludes with a discussion on the current challenges and future directions for the development of pyrazole-scaffold PKIs as therapeutic agents. Overall, this review article provides a comprehensive summary of the pharmacological properties of pyrazole-scaffold PKIs, which will be of interest to researchers and clinicians in the field of drug discovery and development., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Allosteric mechanisms are pervasive in nature, but human-designed allosteric perturbagens are rare. The history of KRAS G12C inhibitor development suggests that covalent chemistry may be a key to expanding the armamentarium of allosteric inhibitors. In that effort, irreversible targeting of a cysteine converted a non-deal allosteric binding pocket and low affinity ligands into a tractable drugging strategy. Here we examine the feasibility of expanding this approach to other allosteric pockets of RAS and kinase family members, given that both protein families are regulators of vital cellular processes that are often dysregulated in cancer and other human diseases. Moreover, these heavily studied families are the subject of numerous drug development campaigns that have resulted, sometimes serendipitously, in the discovery of allosteric inhibitors. We consequently conducted a comprehensive search for cysteines, a commonly targeted amino acid for covalent drugs, using AlphaFold-generated structures of those families. This new analysis presents potential opportunities for allosteric targeting of validated and understudied drug targets, with an emphasis on cancer therapy., Competing Interests: Competing Interests Statement Kenneth Westover is a member of the Scientific Advisory Board for Vibliome Therapeutics and on the Advosory Panel for Reactive Biosciences. The Westover lab receives or has received research funding from Astellas and Revolution Medicines. None of these relationships are in conflict with the content of this manuscript., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Protein kinases are amongst the most focused enzymes in the current century to design, synthesize and formulate drugs that ought to be effective in the treatment of various disordered and diseased states involving either overexpression or deficiency situations. The ATP pocket on the kinases is the active binding site for most of the kinase inhibitors. However, the kinase mutations prevent the binding of kinase inhibitors to the ATP pocket. The enzyme becomes inactive even in the mutated state when the switch pocket site on the enzyme is occupied by switch pocket inhibitors. This review comprises detailed information regarding various classical protein kinases and switch pocket kinase inhibitors with their mechanism of action so that new molecules can be designed to encounter mutations in the kinase enzyme., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Protein kinases are key components in cellular signaling pathways as they carry out the phosphorylation of proteins, primarily on Ser, Thr, and Tyr residues. The catalytic activity of protein kinases is regulated, and they can be thought of as molecular switches that are controlled through protein-protein interactions and post-translational modifications. Protein kinases exhibit diverse structural mechanisms of regulation and have been fascinating subjects for structural biologists from the first crystal structure of a protein kinase over 30 years ago, to recent insights into kinase assemblies enabled by the breakthroughs in cryo-EM. Protein kinases are high-priority targets for drug discovery in oncology and other disease settings, and kinase inhibitors have transformed the outcomes of specific groups of patients. Most kinase inhibitors are ATP competitive, deriving potency by occupying the deep hydrophobic pocket at the heart of the kinase domain. Selectivity of inhibitors depends on exploiting differences between the amino acids that line the ATP site and exploring the surrounding pockets that are present in inactive states of the kinase. More recently, allosteric pockets outside the ATP site are being targeted to achieve high selectivity and to overcome resistance to current therapeutics. Here, we review the key regulatory features of the protein kinase family, describe the different types of kinase inhibitors, and highlight examples where the understanding of kinase regulatory mechanisms has gone hand in hand with the development of inhibitors., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

A series of latonduine and indoloquinoline derivatives HL 1 - HL 8 and their copper(II) complexes ( 1-8 ) were synthesized and comprehensively characterized. The structures of five compounds ( HL 6 , [CuCl(L 1 )(DMF)]·DMF , [CuCl(L 2 )(CH 3 OH)] , [CuCl(L 3 )]·0.5H 2 O , and [CuCl 2 (H 2 L 5 )]Cl·2DMF ) were elucidated by single crystal X-ray diffraction. The copper(II) complexes revealed low micro- to sub-micromolar IC 50 values with promising selectivity toward human colon adenocarcinoma multidrug-resistant Colo320 cancer cells as compared to the doxorubicin-sensitive Colo205 cell line. The lead compounds HL 4 and 4 as well as HL 8 and 8 induced apoptosis efficiently in Colo320 cells. In addition, the copper(II) complexes had higher affinity to DNA than their metal-free ligands. HL 8 showed selective inhibition for the PIM-1 enzyme, while 8 revealed strong inhibition of five other enzymes, i.e., SGK-1, PKA, CaMK-1, GSK3β, and MSK1, from a panel of 50 kinases. Furthermore, molecular modeling of the ligands and complexes showed a good fit to the binding pockets of these targets.

Allosteric kinase inhibitors are thought to have high selectivity and are prime candidates for kinase drug discovery. In addition, the exploration of allosteric mechanisms represents an attractive topic for basic research and drug design. Although the identification and characterization of allosteric kinase inhibitors is still far from being routine, X-ray structures of kinase complexes have been determined for a significant number of such inhibitors. On the basis of structural data, allosteric inhibitors can be confirmed. We report a comprehensive survey of allosteric kinase inhibitors and activators from publicly available X-ray structures, map their binding sites, and determine their distribution over binding pockets in kinases. In addition, we discuss structural features of these compounds and identify active structural analogues and high-confidence target annotations, indicating additional activities for a subset of allosteric inhibitors. This contribution aims to provide a detailed structure-based view of allosteric kinase inhibition.

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 ., (© 2022. The Author(s).)

The active form of kinases is shared across different family members, as are several commonly observed inactive forms. We previously performed a clustering of the conformation of the activation loop of all protein kinase structures in the Protein Data Bank (PDB) into eight classes based on the dihedral angles that place the Phe side chain of the DFG motif at the N-terminus of the activation loop. Our clusters are strongly associated with the placement of the activation loop, the C-helix, and other structural elements of kinases. We present Kincore, a web resource providing access to our conformational assignments for kinase structures in the PDB. While other available databases provide conformational states or drug type but not both, KinCore includes the conformational state and the inhibitor type (Type 1, 1.5, 2, 3, allosteric) for each kinase chain. The user can query and browse the database using these attributes or determine the conformational labels of a kinase structure using the web server or a standalone program. The database and labeled structure files can be downloaded from the server. Kincore will help in understanding the conformational dynamics of these proteins and guide development of inhibitors targeting specific states. Kincore is available at http://dunbrack.fccc.edu/kincore., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 68 FDA-approved therapeutic agents that target about two dozen different protein kinases and six of these drugs were approved in 2021. Of the approved drugs, twelve target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), thirteen block nonreceptor protein-tyrosine kinases, and 39 target receptor protein-tyrosine kinases. The data indicate that 58 of these drugs are prescribed for the treatment of neoplasms (49 against solid tumors including breast, lung, and colon, five against nonsolid tumors such as leukemias, and four against both solid and nonsolid tumors: acalabrutinib, ibrutinib, imatinib, and midostaurin). Three drugs (baricitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases including rheumatoid arthritis. Of the 68 approved drugs, eighteen are used in the treatment of multiple diseases. The following six drugs received FDA approval in 2021 for the treatment of these specified diseases: belumosudil (graft vs. host disease), infigratinib (cholangiocarcinomas), mobocertinib and tepotinib (specific forms of non-small cell lung cancer), tivozanib (renal cell carcinoma), and trilaciclib (to decrease chemotherapy-induced myelosuppression). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and the newly approved trilaciclib. This review summarizes the physicochemical properties of all 68 FDA-approved small molecule protein kinase inhibitors including lipophilic efficiency and ligand efficiency., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Eukaryotic protein kinases contain an Asp-Phe-Gly (DFG) motif, the conformation of which is involved in controlling the catalytic activity, at the N-terminus of the activation segment. The motif can be switched between active-state (DFG-in) and inactive-state (DFG-out) conformations: however, the mechanism of conformational change is poorly understood, partly because there are few reports of the DFG-out conformation. Here, a novel crystal structure of nonphosphorylated human mitogen-activated protein kinase kinase 1 (MEK1; amino acids 38-381) complexed with ATP-γS is reported in which MEK1 adopts the DFG-out conformation. The crystal structure revealed that the structural elements (the αC helix and HRD motif) surrounding the active site are involved in the formation/stabilization of the DFG-out conformation. The ATP-γS molecule was bound to the canonical ATP-binding site in a different binding mode that has never been found in previously determined crystal structures of MEK1. This novel ATP-γS binding mode provides a starting point for the design of high-affinity inhibitors of nonphosphorylated inactive MEK1 that adopts the DFG-out conformation.

A novel class of potential protein kinase inhibitors 7 - 16 was synthesized in high yields using various substituted purines. The most promising compounds, 7 and 10, exhibited inhibitory activity against seven cancer cell lines. The IC 50 values for compounds 7 and 10 were 2.27 and 2.53 μM for K562 cells, 1.42 and 1.52 μM for HL-60 cells, and 4.56 and 24.77 μM for OKP-GS cells, respectively. In addition, compounds 7 and 10 dose-dependently induced the apoptosis and cell cycle arrest at G2/M phase, preventing the cell division of OKP-GS cells. Compounds 7 , 9, and 10 showed 36-45% inhibitory activity against PDGFRα and PDGFRβ at the concentration of 1 μM. Molecular modeling experiments showed that obtained compounds could bind to PDGFRα as either type 1 (compound 7 , ATP-competitive) or type 2 (compound 10 , allosteric) inhibitors, depending on the substituent in the amide part of the molecule.

In the last few years, small molecules endowed with different heterocyclic scaffolds have been developed as kinase inhibitors. Some of them are being tested at preclinical or clinical levels for the potential treatment of neuroblastoma (NB). This disease is the most common extracranial solid tumor in childhood and is responsible for 10% to 15% of pediatric cancer deaths. Despite the availability of some treatments, including the use of very toxic cytotoxic chemotherapeutic agents, high-risk (HR)-NB patients still have a poor prognosis and a survival rate below 50%. For these reasons, new pharmacological options are urgently needed. This review focuses on synthetic heterocyclic compounds published in the last five years, which showed at least some activity on this severe disease and act as kinase inhibitors. The specific mechanism of action, selectivity, and biological activity of these drug candidates are described, when established. Moreover, the most remarkable clinical trials are reported. Importantly, kinase inhibitors approved for other diseases have shown to be active and endowed with lower toxicity compared to conventional cytotoxic agents. The data collected in this article can be particularly useful for the researchers working in this area.

Cholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry-based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients. SIGNIFICANCE: Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies., (©2021 The Authors; Published by the American Association for Cancer Research.)

Antiapoptotic and proapoptotic BCL-2 protein family members regulate mitochondrial apoptotic pathway. Small molecule inhibitors of antiapoptotic BCL-2 proteins including BCL-2-specific inhibitor ABT-199 (Venetoclax) are in clinical development. However, the efficiency of ABT-199 as a single agent in solid tumors is limited. We performed a high-throughput RNAi kinome screen targeting 691 kinases to identify potentially targetable kinases to enhance ABT-199 response in breast cancer cells. Our studies identified Wee1 as the primary target kinase to overcome resistance to ABT-199. Depletion of Wee1 by siRNA-mediated knockdown or inhibition of Wee1 by the small molecule Wee1 inhibitor AZD1775 sensitized SKBR3, MDA-MB-468, T47D and CAMA-1 breast cancer cells to ABT-199 along with decreased MCL1. BH3-only proteins PUMA and BIM functionally contribute to apoptosis signaling following co-targeting BCL-2 and Wee1. Suppression of Wee1 function increased mitochondrial cell death priming. Furthermore, we found that Wee1 inhibition altered MCL1 phosphorylation and protein stability, which led to HUWE1-mediated MCL1 degradation. Our findings suggest that Wee1 inhibition can overcome resistance to ABT-199 and provide a rationale for further translational investigation of BCL-2 inhibitor/Wee1 inhibitor combination in breast cancer., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Leishmaniasis is a public health disease that requires the development of more effective treatments and the identification of novel molecular targets. Since blocking the PI3K/AKT pathway has been successfully studied as an effective anticancer strategy for decades, we examined whether the same approach would also be feasible in Leishmania due to their high amount and diverse set of annotated proteins. Here, we used a best reciprocal hits protocol to identify potential protein kinase homologues in an annotated human PI3K/AKT pathway. We calculated their ligandibility based on available bioactivity data of the reported homologues and modelled their 3D structures to estimate the druggability of their binding pockets. The models were used to run a virtual screening method with molecular docking. We found and studied five protein kinases in five different Leishmania species, which are AKT, CDK, AMPK, mTOR and GSK3 homologues from the studied pathways. The compounds found for different enzymes and species were analysed and suggested as starting point scaffolds for the design of inhibitors. We studied the kinases' participation in protein-protein interaction networks, and the potential deleterious effects, if inhibited, were supported with the literature. In the case of Leishmania GSK3, an inhibitor of its human counterpart, prioritized by our method, was validated in vitro to test its anti- Leishmania activity and indirectly infer the presence of the enzyme in the parasite. The analysis contributes to improving the knowledge about the presence of similar signalling pathways in Leishmania , as well as the discovery of compounds acting against any of these kinases as potential molecular targets in the parasite.

Treatments currently used to prevent congenital toxoplasmosis are non-specific of Toxoplasma gondii and have grievous side effects. To develop a more specific and less toxic drug, we have designed SP230, an imidazo[1,2- b ]pyridazine salt targeting the Toxoplasma gondii calcium-dependent protein kinase 1 ( Tg CDPK1) and active against acute toxoplasmosis in mice. Efficiency of SP230 to inhibit foetal transmission of the parasite was evaluated in a mouse model of congenital toxoplasmosis. Swiss mice were infected at mid-pregnancy with tachyzoites or cysts of the ME49 strain of T. gondii by intraperitoneal and oral route, respectively, and treated with SP230 at 50 mg/kg for 5 days by the same routes. Parasite burden in organs of dams and in foetuses was measured by quantitative PCR. Intraperitoneal administration of SP230 drastically reduced the number of parasites (more than 97% of reduction) in the brain and lungs of dams, and led to a reduction of 66% of parasite burden in foetuses. Oral administration of SP230 was particularly efficient with 97% of reduction of parasite burdens in foetuses. SP230 did not impact number and weight of offspring in our conditions. This inhibitor of Tg CDPK1 is a promising candidate for the development of alternative therapeutics to treat infected pregnant women.

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome.

Over one billion people are currently infected with a parasitic nematode. Symptoms can include anemia, malnutrition, developmental delay, and in severe cases, death. Resistance is emerging to the anthelmintics currently used to treat nematode infection, prompting the need to develop new anthelmintics. Towards this end, we identified a set of kinases that may be targeted in a nematode-selective manner. We first screened 2040 inhibitors of vertebrate kinases for those that impair the model nematode Caenorhabditis elegans. By determining whether the terminal phenotype induced by each kinase inhibitor matched that of the predicted target mutant in C. elegans, we identified 17 druggable nematode kinase targets. Of these, we found that nematode EGFR, MEK1, and PLK1 kinases have diverged from vertebrates within their drug-binding pocket. For each of these targets, we identified small molecule scaffolds that may be further modified to develop nematode-selective inhibitors. Nematode EGFR, MEK1, and PLK1 therefore represent key targets for the development of new anthelmintic medicines.

Multifunctional entities have recently been attractive for the development of anticancer chemotherapeutic drugs. However, such entities with concurrent CK2 along with cancer stem cell (CSC) inhibitory activities are rare in a single small molecule. Herein, a series of 5-(3-chlorophenylamino)benzo[ c ][2,6]naphthyridine derivatives were synthesized using a known CK2 inhibitor, silmitasertib ( CX-4945 ), as the lead compound. Among the resulting compounds, 1c exhibited stronger CK2 inhibitory activity with higher Clk2/CK2 selectivity than CX-4945 . Significantly, 1c could modulate the Akt1(ser129)-GSK-3β(ser9)-Wnt/β-catenin signaling pathway and inhibit the expression of the stemness marker ALDH1A1, CSC surface antigens, and stem genes, showing potent CSC inhibitory activity. Moreover, 1c also displayed superior pharmacokinetics and antitumor activity compared with CX-4945 sodium salt, without obvious toxicity. The favorable antiproliferative and antitumor activity of 1c , its high inhibitory selectivity for CK2, and its potent inhibition of cancer cell stemness make this molecule a candidate for the treatment of cancer.

Protein kinases are a large class of enzymes with numerous biological roles and many have been implicated in a vast array of diseases, including cancer and the novel coronavirus infection COVID-19. Thus, the development of chemical probes to selectively target each kinase is of great interest. Inhibition of protein kinases with ATP-competitive inhibitors has historically been the most widely used method. However, due to the highly conserved structures of ATP-sites, the identification of truly selective chemical probes is challenging. In this review, we use the Ser/Thr kinase CK2 as an example to highlight the historical challenges in effective and selective chemical probe development, alongside recent advances in the field and alternative strategies aiming to overcome these problems. The methods utilised for CK2 can be applied to an array of protein kinases to aid in the discovery of chemical probes to further understand each kinase's biology, with wide-reaching implications for drug development.

Protein kinases are key enzymes in many signal transduction pathways, and play a crucial role in cellular proliferation, differentiation, and various cell regulatory processes. However, aberrant function of kinases has been associated with cancers and many other diseases. Consequently, competitive inhibition of the ATP binding site of protein kinases has emerged as an effective means of curing these diseases. Over the past three decades, thousands of protein kinase inhibitors (PKIs) with varying molecular frames have been developed. Large-scale data mining of the Protein Data Bank resulted in a database of 2139 non-redundant high-resolution X-ray crystal structures of PKIs bound to protein kinases. This provided us with a unique opportunity to study molecular determinants for the molecular recognition of PKIs. A chemoinformatic analysis of 2139 PKIs resulted in findings that PKIs are "flat" molecules with high aromatic ring counts and low fractions of sp 3 carbon. All but one PKI possessed one or more aromatic rings. More importantly, it was found that the average weighted hydrogen bond count is inversely proportional to the number of aromatic rings. Based on this linear relationship, we put forward the exchange rule of hydrogen bonding interactions and non-bonded π-interactions . Specifically, a loss of binding affinity caused by a decrease in hydrogen bonding interactions is compensated by a gain in binding affinity acquired by an increase in aromatic ring-originated non-bonded interactions (i.e., π-π stacking interactions, CH-π interactions, cation-π interactions, etc.), and vice versa. The very existence of this inverse relationship strongly suggests that both hydrogen bonding and aromatic ring-originated non-bonded interactions are responsible for the molecular recognition of PKIs. As an illustration, two representative PKI-kinase complexes were employed to examine the relative importance of different modes of non-bonded interactions for the molecular recognition of PKIs. For this purpose, two FDA-approved PKI drugs, ibrutinib and lenvatinib, were chosen. The binding pockets of both PKIs were thoroughly examined to identify all non-bonded intermolecular interactions. Subsequently, the strengths of interaction energies between ibrutinib and its interacting residues in tyrosine kinase BTK were quantified by means of the double hybrid DFT method B2PLYP. The resulting energetics for the binding of ibrutinib in tyrosine kinase BTK showed that CH-π interactions and π-π stacking interactions between aromatic rings of the drug and hydrophobic residues in its binding pocket dominate the binding interactions. Thus, this work establishes that, in addition to hydrogen bonding, aromatic rings function as important molecular determinants for the molecular recognition of PKIs. In conclusion, our findings support the following pharmacophore model for ATP-competitive kinase inhibitors: a small molecule features a scaffold of one or more aromatic rings which is linked with one or more hydrophilic functional groups. The former has the structural role of acting as a scaffold and the functional role of participating in aromatic ring-originated non-bonded interactions with multiple hydrophobic regions in the ATP binding pocket of kinases. The latter ensure water solubility and form hydrogen bonds with the hinge region and other hydrophilic residues of the ATP binding pocket.

Because dysregulation of protein kinases owing to mutations or overexpression plays causal roles in human diseases, this family of enzymes has become one of the most important drug targets of the 21st century. Of the 62 protein kinases inhibitors that are approved by the FDA, seven of them form irreversible covalent adducts with their target enzymes. The clinical success of ibrutinib, an inhibitor of Bruton tyrosine kinase, in the treatment of mantle cell lymphomas following its approval in 2013 helped to overcome a general bias against the development of irreversible drug inhibitors. The other approved covalent drugs include acalabrutinib and zanubrutinib, which also inhibit Bruton tyrosine kinase. Furthermore afatinib, dacomitinib, and osimertinib, inhibitors of members of the epidermal growth factor receptor family (ErbB1/2/3/4), are used in the treatment of non-small cell lung cancers. Neratinib is an inhibitor of ErbB2 and is used in the treatment of ErbB2/HER2-positive breast cancer. The seven drugs considered in this review have a common mechanism of action; this process involves the addition of a protein cysteine thiolate anion (protein‒S: - ) to an acrylamide derivative (CH 2 =CHC(=O)N(H)R) where R represents the pharmacophore. Such reactions are commonly referred to as Michael additions and each reaction results in the formation of a covalent bond between carbon and sulfur; the final product is a thioether. This process consists of two discrete steps; the first step involves the reversible association of the drug with its target enzyme so that a weakly electrophilic functionality, a warhead, is bound near an appropriately positioned nucleophilic cysteine. In the second step, a reaction occurs between the warhead and the target enzyme cysteine to form a covalently modified and inactive protein. For this process to work, the warhead must be appropriately juxtaposed in relationship to the cysteinyl thiolate so that the covalent addition can occur. Covalent inhibitors have emerged from the ranks of drugs to be avoided to become an emerging paradigm. Much of this recent success can be attributed to the clinical efficacy of ibrutinib as well as the other antagonists covered in this review. Moreover, the covalent inhibitor methodology is swiftly gaining acceptance as a valuable component of the medicinal chemist's toolbox and is primed to make a significant impact on the development of enzyme antagonists and receptor modulators., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Introduction: High fidelity between non-small cell lung cancer (NSCLC) primary tumours and patient-derived tumour xenografts (PDTXs) is of paramount relevance to spur their application. Extensive proteomic and kinomic analysis of these preclinical models are missing and may inform about their functional status, in terms of phosphopeptides and hyperactive signalling pathways., Methods: We investigated tumour xenografts derived from patients with NSCLC to identify hyperactive signalling pathways. Fresh tumour fragments from 81 NSCLC surgical samples were implanted in Nod/Scid/Gamma mice, and engrafted tumours were compared with primary specimens by morphology, immunohistochemistry, gene mutation analyses, and kinase activity profiling. Four different tyrosine and serine/threonine kinase inhibitors were tested against primary tumour and PDTX lysates using the PamGene peptide microarray platform., Results: The engraftment rate was 33%, with successful engraftment being more associated with poor clinical outcomes. Genomic profiles led to the recognition of hotspot mutations, some of which were initially undetected in donor samples. Kinomic analyses showed that characteristics of primary tumours were retained in PDTXs, and tyrosine kinase inhibitors (TKIs) responses of individual PDTX lines were either expected, based on the genetic status, or alternatively defined suitable targets unpredictable by single-genome fingerprints., Conclusions: Collectively, PDTXs mostly resembled their parental NSCLC. Combining genomic and kinomic analyses of tumour xenografts derived from patients with NSCLC, we identified patients' specific targetable pathways, confirming PDTXs as a preclinical tool for biomarker identification and therapeutic algorithm'' improvement., Competing Interests: Conflict of interest statement The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: A van den Berg, Rob Ruijtenbeek, Rik de Wijn and Riet Hilhorst are/were employees of PamGene International BV. All other authors have nothing to declare., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Protein kinases are receiving wide research interest, from drug perspective, due to their important roles in human body. Available kinase-inhibitor data, including crystallized structures, revealed many details about the mechanism of inhibition and binding modes. The understanding and analysis of these binding modes are expected to support the discovery of kinase-targeting drugs. The huge amounts of data made it possible to utilize computational techniques, including machine learning, to help in the discovery of kinase-targeting drugs. Machine learning gave reasonable predictions when applied to differentiate between the binding modes of kinase inhibitors, promoting a wider application in that domain. In this study, we applied machine learning supported by feature selection techniques to classify kinase inhibitors according to their binding modes. We represented inhibitors as a large number of molecular descriptors, as features, and systematically reduced these features in a multi-step manner while trying to attain high classification accuracy. Our predictive models could satisfy both goals by achieving high accuracy while utilizing at most 5% of the modeling features. The models could differentiate between binding mode types with MCC values between 0.67 and 0.92, and balanced accuracy values between 0.78 and 0.97 for independent test sets.

Pseudokinases are pseudoenzyme variants of the protein kinase superfamily that primarily signal through non-catalytic mechanisms. The aberrant expression of pseudokinases correlates with the pathogenesis of many human diseases. However, pseudokinases remain relatively untapped as therapeutic targets due to difficulties associated with regulating their biological functions. Many protein kinase- and few pseudokinase-specific inhibitors have been reported to influence the non-catalytic functions of active kinases, giving the hope that pseudokinases can also be exploited for therapeutic purposes. This chapter presents the structural characteristics of selected pseudokinases, their known roles in human diseases, and the progress made toward developing pseudokinase-centric therapies., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Neglected tropical diseases (NTDs) are a group of twenty-one diseases classified by the World Health Organization that prevail in regions with tropical and subtropical climate and affect more than one billion people. There is an urgent need to develop new and safer drugs for these diseases. Protein kinases are a potential class of targets for developing new drugs against NTDs, since they play crucial role in many biological processes, such as signaling pathways, regulating cellular communication, division, metabolism and death. Bioinformatics is a field that aims to organize large amounts of biological data as well as develop and use tools for understanding and analyze them in order to produce meaningful information in a biological manner. In combination with chemogenomics, which analyzes chemical-biological interactions to screen ligands against selected targets families, these approaches can be used to stablish a rational strategy for prioritizing new drug targets for NTDs. Here, we describe how bioinformatics and chemogenomics tools can help to identify protein kinases and their potential inhibitors for the development of new drugs for NTDs. We present a review of bioinformatics tools and techniques that can be used to define an organisms kinome for drug prioritization, drug and target repurposing, multi-quinase inhibition approachs and selectivity profiling. We also present some successful examples of the application of such approaches in recent case studies., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Protein kinases use ATP to phosphorylate other proteins. Phosphorylation (p) universally orchestrates a fine-tuned network modulating a multitude of biological processes. Moreover, the start of networks, ATP-binding site, has been recognized dual roles to impact protein kinases function: (i) orthosteric inhibition, via being blocked to interference ATP occupying and (ii) allosteric regulation, via being altered first to induce further conformational changes. The above two terminologies are widely used in drug design, which has acquired quite a significant progress in the protein kinases field over the past 2 decades. Most small molecular inhibitors directly compete with ATP to implement orthosteric inhibition, still exhibiting irreplaceable and promising therapeutic effects. Additionally, numerous inhibitors can paradoxically lead protein kinases to hyperphosphorylation, even activation, indicative of the allosteric regulation role of the ATP-binding site. Here, we review the quintessential examples that apply for the dual roles in diverse ways. Our work provides an insight into the molecular mechanisms under the dual roles and will be promisingly instructive for future drug development., Competing Interests: Declaration of competing interest The authors declare that they do not have any conflict of interest related to this manuscript., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Despite recent advances in the treatment of autoimmune and inflammatory diseases, unmet medical needs in some areas still exist. One of the main therapeutic approaches to alleviate dysregulated inflammation has been to target the activity of kinases that regulate production of inflammatory mediators. Small-molecule kinase inhibitors have the potential for broad efficacy, convenience and tissue penetrance, and thus often offer important advantages over biologics. However, designing kinase inhibitors with target selectivity and minimal off-target effects can be challenging. Nevertheless, immense progress has been made in advancing kinase inhibitors with desirable drug-like properties into the clinic, including inhibitors of JAKs, IRAK4, RIPKs, BTK, SYK and TPL2. This Review will address the latest discoveries around kinase inhibitors with an emphasis on clinically validated autoimmunity and inflammatory pathways.

Nanopore enzymology is a powerful single-molecule technique for the label-free study of enzymes using engineered protein nanopore sensors. The technique has been applied to protein kinases, where it has enabled the full repertoire of kinase function to be observed, including: kinetics of substrate binding and dissociation, product binding and dissociation, nucleotide binding, and reversible phosphorylation. Further, minor modifications enable the screening of type I kinase inhibitors and the determination of inhibition constants in a facile and label-free manner. Here, we describe the design and production of suitably engineered protein nanopores and their use for the determination of key mechanistic parameters of kinases. We also provide procedures for the determination of inhibition constants of protein kinase inhibitors.

Seventeen compounds, rather selective, direct or indirect inhibitors and activators of PKA, PKG, and PKC, were analysed for effects on vascular Ca V 1.2 channel current (I Ca1.2 ) by using the patch-clamp technique in single rat tail artery myocytes. The aim was to investigate how PKs regulate I Ca1.2 and disclose any unexpected modulation of Ca V 1.2 channel function by these agents. The cAMP analogues 8-Br-cAMP and 6-Bnz-cAMP partially reduced I Ca1.2 in dialysed cells, while weakly increasing it under the perforated configuration. The β-adrenoceptor agonist isoproterenol and the adenylate cyclase activator forskolin concentration-dependently increased I Ca1.2 ; this effect was reversed by PKA inhibitors H-89 and KT5720, but not by PKI 6-22. The cGMP analogue 8-Br-cGMP, similarly to the NO-donor SNP, moderately reduced I Ca1.2 , this effect being reversed to a slight stimulation under the perforated configuration. Among PKG inhibitors, Rp-8-Br-PET-cGMPS decreased current amplitude in a concentration-dependent manner while Rp-8-Br-cGMPS was ineffective. The non-specific phosphodiesterase inhibitor IBMX increased I Ca1.2 , while H-89, KT5720, and PKI 6-22 antagonized this effect. The PKC activator PMA, but not the diacylglycerol analogue OAG, stimulated I Ca1.2 in a concentration-dependent manner; conversely, the PKCα inhibitor Gö6976 markedly reduced basal I Ca1.2 and, similarly to the PKCδ (rottlerin) and PKCε translocation inhibitors antagonised PMA-induced current stimulation. The ensemble of findings indicates that the stimulation of cAMP/PKA, in spite of the paradoxical effect of both 8-Br-cAMP and 6-Bnz-cAMP, or PKC pathways enhanced, while that of cGMP/PKG weakly inhibited I Ca1.2 in rat tail artery myocytes. Since Rp-8-Br-PET-cGMPS and Gö6976 appeared to block directly Ca V 1.2 channel, their docking to the channel protein was investigated. Both compounds appeared to bind the α 1C subunit in a region involved in Ca V 1.2 channel inactivation, forming an interaction network comparable to that of Ca V 1.2 channel blockers. Therefore, caution should accompany the use of these agents as pharmacological tools to elucidate the mechanism of action of drugs on vascular preparations., (Copyright © 2020 Elsevier Inc. All rights reserved.)