Structural bases for Na+-Cl- cotransporter inhibition by thiazide diuretic drugs and activation by kinases.

The Na+-Cl- cotransporter (NCC) drives salt reabsorption in the kidney and plays a decisive role in balancing electrolytes and blood pressure. Thiazide and thiazide-like diuretics inhibit NCC-mediated renal salt retention and have been cornerstones for treating hypertension and edema since the 1950s. Here we determine NCC co-structures individually complexed with the thiazide drug hydrochlorothiazide, and two thiazide-like drugs chlorthalidone and indapamide, revealing that they fit into an orthosteric site and occlude the NCC ion translocation pathway. Aberrant NCC activation by the WNKs-SPAK kinase cascade underlies Familial Hyperkalemic Hypertension, but it remains unknown whether/how phosphorylation transforms the NCC structure to accelerate ion translocation. We show that an intracellular amino-terminal motif of NCC, once phosphorylated, associates with the carboxyl-terminal domain, and together, they interact with the transmembrane domain. These interactions suggest a phosphorylation-dependent allosteric network that directly influences NCC ion translocation.

Impact of protein and small molecule interactions on kinase conformations.

Protein kinases act as central molecular switches in the control of cellular functions. Alterations in the regulation and function of protein kinases may provoke diseases including cancer. In this study we investigate the conformational states of such disease-associated kinases using the high sensitivity of the kinase conformation (KinCon) reporter system. We first track BRAF kinase activity conformational changes upon melanoma drug binding. Second, we also use the KinCon reporter technology to examine the impact of regulatory protein interactions on LKB1 kinase tumor suppressor functions. Third, we explore the conformational dynamics of RIP kinases in response to TNF pathway activation and small molecule interactions. Finally, we show that CDK4/6 interactions with regulatory proteins alter conformations which remain unaffected in the presence of clinically applied inhibitors. Apart from its predictive value, the KinCon technology helps to identify cellular factors that impact drug efficacies. The understanding of the structural dynamics of full-length protein kinases when interacting with small molecule inhibitors or regulatory proteins is crucial for designing more effective therapeutic strategies.

Allosteric activation of the co-receptor BAK1 by the EFR receptor kinase initiates immune signaling.

Transmembrane signaling by plant receptor kinases (RKs) has long been thought to involve reciprocal trans-phosphorylation of their intracellular kinase domains. The fact that many of these are pseudokinase domains, however, suggests that additional mechanisms must govern RK signaling activation. Non-catalytic signaling mechanisms of protein kinase domains have been described in metazoans, but information is scarce for plants. Recently, a non-catalytic function was reported for the leucine-rich repeat (LRR)-RK subfamily XIIa member EFR (elongation factor Tu receptor) and phosphorylation-dependent conformational changes were proposed to regulate signaling of RKs with non-RD kinase domains. Here, using EFR as a model, we describe a non-catalytic activation mechanism for LRR-RKs with non-RD kinase domains. EFR is an active kinase, but a kinase-dead variant retains the ability to enhance catalytic activity of its co-receptor kinase BAK1/SERK3 (brassinosteroid insensitive 1-associated kinase 1/somatic embryogenesis receptor kinase 3). Applying hydrogen-deuterium exchange mass spectrometry (HDX-MS) analysis and designing homology-based intragenic suppressor mutations, we provide evidence that the EFR kinase domain must adopt its active conformation in order to activate BAK1 allosterically, likely by supporting αC-helix positioning in BAK1. Our results suggest a conformational toggle model for signaling, in which BAK1 first phosphorylates EFR in the activation loop to stabilize its active conformation, allowing EFR in turn to allosterically activate BAK1.

Allosteric regulation of kinase activity.

The articles in this special issue highlight how modern cellular, biochemical, biophysical and computational techniques are allowing deeper and more detailed studies of allosteric kinase regulation.

A comprehensive exploration of the druggable conformational space of protein kinases using AI-predicted structures.

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs that are related to the catalytic activity of the kinase. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the active or inactive kinase conformation(s) they bind. Modern AI-based structural modeling methods have the potential to expand upon the limited availability of experimentally determined kinase structures in inactive states. Here, we first explored the conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) and ESMFold, two prominent AI-based protein structure prediction methods. Our investigation of AF2's ability to explore the conformational diversity of the kinome at various multiple sequence alignment (MSA) depths showed a bias within the predicted structures of kinases in DFG-in conformations, particularly those controlled by the DFG motif, based on their overabundance in the PDB. We demonstrate that predicting kinase structures using AF2 at lower MSA depths explored these alternative conformations more extensively, including identifying previously unobserved conformations for 398 kinases. Ligand enrichment analyses for 23 kinases showed that, on average, docked models distinguished between active molecules and decoys better than random (average AUC (avgAUC) of 64.58), but select models perform well (e.g., avgAUCs for PTK2 and JAK2 were 79.28 and 80.16, respectively). Further analysis explained the ligand enrichment discrepancy between low- and high-performing kinase models as binding site occlusions that would preclude docking. The overall results of our analyses suggested that, although AF2 explored previously uncharted regions of the kinase conformational space and select models exhibited enrichment scores suitable for rational drug discovery, rigorous refinement of AF2 models is likely still necessary for drug discovery campaigns.

Kinematic analysis of kinases and their oncogenic mutations - Kinases and their mutation kinematic analysis.

Protein kinases are crucial cellular enzymes that facilitate the transfer of phosphates from adenosine triphosphate (ATP) to their substrates, thereby regulating numerous cellular activities. Dysfunctional kinase activity often leads to oncogenic conditions. Chosen by using structural similarity to 5UG9, we selected 79 crystal structures from the PDB and based on the position of the phenylalanine side chain in the DFG motif, we classified these 79 crystal structures into 5 group clusters. Our approach applies our kinematic flexibility analysis (KFA) to explore the flexibility of kinases in various activity states and examine the impact of the activation loop on kinase structure. KFA enables the rapid decomposition of macromolecules into different flexibility regions, allowing comprehensive analysis of conformational structures. The results reveal that the activation loop of kinases acts as a "lock" that stabilizes the active conformation of kinases by rigidifying the adjacent α-helices. Furthermore, we investigate specific kinase mutations, such as the L858R mutation commonly associated with non-small cell lung cancer, which induces increased flexibility in active-state kinases. In addition, through analyzing the hydrogen bond pattern, we examine the substructure of kinases in different states. Notably, active-state kinases exhibit a higher occurrence of α-helices compared to inactive-state kinases. This study contributes to the understanding of biomolecular conformation at a level relevant to drug development.

Guided Docking as a Data Generation Approach Facilitates Structure-Based Machine Learning on Kinases.

Drug discovery pipelines nowadays rely on machine learning models to explore and evaluate large chemical spaces. While including 3D structural information is considered beneficial, structural models are hindered by the availability of protein-ligand complex structures. Exemplified for kinase drug discovery, we address this issue by generating kinase-ligand complex data using template docking for the kinase compound subset of available ChEMBL assay data. To evaluate the benefit of the created complex data, we use it to train a structure-based E(3)-invariant graph neural network. Our evaluation shows that binding affinities can be predicted with significantly higher precision by models that take synthetic binding poses into account compared to ligand- or drug-target interaction models alone.

Gaining Insights into Key Structural Hotspots within the Allosteric Binding Pockets of Protein Kinases.

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.

Acetylation and Phosphorylation Regulate the Role of Pyruvate Kinase as a Glycolytic Enzyme or a Protein Kinase in Lamb.

Protein post-translational modifications (PTMs) play an essential role in meat quality development. However, the effect of specific PTM sites on meat proteins has not been investigated yet. The characteristics of pyruvate kinase M (PKM) were found to exhibit a close correlation with final meat quality, and thus, serine 99 (S99) and lysine 137 (K137) in PKM were mutated to study their effect on PKM function. The structural and functional properties of five lamb PKM variants, including wild-type PKM (wtPKM), PKM_S99D (S99 phosphorylation), PKM_S99A (PKM S99 dephosphorylation), PKM_K137Q (PKM K137 acetylation), and PKM_K137R (PKM K137 deacetylation), were evaluated. The results showed that the secondary structure, tertiary structure, and polymer formation were affected among different PKM variants. In addition, the glycolytic activity of PKM_K137Q was decreased because of its weakened binding with phosphoenolpyruvate. In the PKM_K137R variant, the actin phosphorylation level exhibited a decrease, suggesting a low kinase activity of PKM_K137R. The results of molecular simulation showed a 42% reduction in the interface area between PKM_K137R and actin, in contrast to wtPKM and actin. These findings are significant for revealing the mechanism of how PTMs regulate PKM function and provide a theoretical foundation for the development of precise meat quality preservation technology.

Exploring the conformational landscapes of protein kinases: perspectives from FRET and DEER.

Conformational changes of catalytically-important structural elements are a key feature of the regulation mechanisms of protein kinases and are important for dictating inhibitor binding modes and affinities. The lack of widely applicable methods for tracking kinase conformational changes in solution has hindered our understanding of kinase regulation and our ability to design conformationally selective inhibitors. Here we provide an overview of two recently developed methods that detect conformational changes of the regulatory activation loop and αC-helix of kinases and that yield complementary information about allosteric mechanisms. An intramolecular Förster resonance energy transfer-based approach provides a scalable platform for detecting and classifying structural changes in high-throughput, as well as quantifying ligand binding cooperativity, shedding light on the energetics governing allostery. The pulsed electron paramagnetic resonance technique double electron-electron resonance provides lower throughput but higher resolution information on structural changes that allows for unambiguous assignment of conformational states and quantification of population shifts. Together, these methods are shedding new light on kinase regulation and drug interactions and providing new routes for the identification of novel kinase inhibitors and allosteric modulators.

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships.

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions.

Kinase Inhibition via Small Molecule-Induced Intramolecular Protein Cross-Linking.

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.

KinomeMETA: a web platform for kinome-wide polypharmacology profiling with meta-learning.

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/.

Oxygen affinities of DosT and DosS sensor kinases with implications for hypoxia adaptation in Mycobacterium tuberculosis.

DosT and DosS are heme-based kinases involved in sensing and signaling O2 tension in the microenvironment of Mycobacterium tuberculosis (Mtb). Under conditions of low O2, they activate >50 dormancy-related genes and play a pivotal role in the induction of dormancy and associated drug resistance during tuberculosis infection. In this work, we reexamine the O2 binding affinities of DosT and DosS to show that their equilibrium dissociation constants are 3.3±1.0 µM and 0.46±0.08 µM respectively, which are six to eight-fold stronger than what has been widely referred to in literature. Furthermore, stopped-flow kinetic studies reveal association and dissociation rate constants of 0.84 µM-1 s-1 and 2.8 s-1, respectively for DosT, and 7.2 µM-1 s-1 and 3.3 s-1, respectively for DosS. Remarkably, these tighter O2 binding constants correlate with distinct stages of hypoxia-induced non-replicating persistence in the Wayne model of Mtb. This knowledge opens doors to deconvoluting the intricate interplay between hypoxia adaptation stages and the signal transduction capabilities of these important heme-based O2 sensors.

Data-oriented protein kinase drug discovery.

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.

DeepKa Web Server: High-Throughput Protein pKa Prediction.

DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Comprehensive Data-Driven Assessment of Non-Kinase Targets of Inhibitors of the Human Kinome.

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.

Classifying protein kinase conformations with machine learning.

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.

Nanointegrative Glycoengineering-Activated Necroptosis of Triple Negative Breast Cancer Stem Cells Enables Self-Amplifiable Immunotherapy for Systemic Tumor Rejection.

Triple-negative breast cancer stem cells (TCSCs) are considered as the origin of recurrence and relapse. It is difficult to kill not only for its resistance, but also the lacking of targetable molecules on membrane. Here, it is confirmed that ST6 ß-galactoside alpha-2,6-sialyltransferase 1 (ST6Gal-1) is highly expressed in TCSCs that may be the key enzyme involved in glycoengineering via sialic acid (SA) metabolism. SA co-localizes with a microdomain on cell membrane termed as lipid rafts that enrich CSCs marker and necroptosis proteins mixed lineage kinase domain-like protein (MLKL), suggesting that TCSCs may be sensitive to necroptosis. Thus, the triacetylated N-azidoacetyl-d-mannosamine (Ac3ManNAz) is synthesized as the glycoengineering substrate and applied to introduce artificial azido receptors, dibenzocyclooctyne (DBCO)-modified liposome is used to deliver Compound 6i (C6), a receptor-interacting serine/threonine protein kinase 1(RIPL1)-RIP3K-mixed lineage kinase domain-like protein(MLKL) activator, to induce necroptosis. The pro-necroptosis effect is aggravated by nitric oxide (NO), which is released from NO-depot of cholesterol-NO integrated in DBCO-PEG-liposome@NO/C6 (DLip@NO/C6). Together with the immunogenicity of necroptosis that releases high mobility group box 1(HMGB1) of damage-associated molecular patterns, TCSCs are significantly killed in vitro and in vivo. The results suggest a promising strategy to improve the therapeutic effect on the non-targetable TCSCs with high expression of ST6Gal-1 via combination of glycoengineering and necroptosis induction.

Kinase-catalyzed Biotinylation with Inactivated Lysates for Discovery of Substrates (K-BILDS).

Protein phosphorylation is catalyzed by kinases to regulate a large variety of cellular activities, including growth and signal transduction. Methods to identify kinase substrates are crucial to fully understand phosphorylation-mediated cellular events and disease states. Here, we report a set of protocols to identify substrates of a target kinase using Kinase-catalyzed Biotinylation with Inactivated Lysates for Discovery of Substrates (K-BILDS). As described in these protocols, K-BILDS involves inactivation of endogenous kinases in lysates, followed by addition of an active exogenous kinase and the γ-phosphate-modified ATP analog ATP-biotin for kinase-catalyzed biotinylation of cellular substrates. Avidin enrichment isolates biotinylated substrates of the active kinase, which can be monitored by western blot. Substrates of the target kinase can also be discovered using mass spectrometry analysis. Key advantages of K-BILDS include compatibility with any lysate, tissue homogenate, or complex mixture of biological relevance and any active kinase of interest. K-BILDS is a versatile method for studying or discovering substrates of a kinase of interest to characterize biological pathways thoroughly. © 2023 Wiley Periodicals LLC. Basic Protocol 1: FSBA treatment of lysates to inactivate kinases Basic Protocol 2: Kinase-catalyzed Biotinylation with Inactivated Lysates for Discovery of Substrates (K-BILDS).