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.

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.

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.

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.

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.

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.

Pathogenic mutation hotspots in protein kinase domain structure.

Control of eukaryotic cellular function is heavily reliant on the phosphorylation of proteins at specific amino acid residues, such as serine, threonine, tyrosine, and histidine. Protein kinases that are responsible for this process comprise one of the largest families of evolutionarily related proteins. Dysregulation of protein kinase signaling pathways is a frequent cause of a large variety of human diseases including cancer, autoimmune, neurodegenerative, and cardiovascular disorders. In this study, we mapped all pathogenic mutations in 497 human protein kinase domains from the ClinVar database to the reference structure of Aurora kinase A (AURKA) and grouped them by the relevance to the disease type. Our study revealed that the majority of mutation hotspots associated with cancer are situated within the catalytic and activation loops of the kinase domain, whereas non-cancer-related hotspots tend to be located outside of these regions. Additionally, we identified a hotspot at residue R371 of the AURKA structure that has the highest number of exclusively non-cancer-related pathogenic mutations (21) and has not been previously discussed.

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).

Molecular Simulations of Conformational Transitions within the Insulin Receptor Kinase Reveal Consensus Features in a Multistep Activation Pathway.

Modulating the transitions between active and inactive conformations of protein kinases is the primary means of regulating their catalytic activity, achieved by phosphorylation of the activation loop (A-loop). To elucidate the mechanism of this conformational activation, we applied the string method to determine the conformational transition path of insulin receptor kinase between the active and inactive conformations and the corresponding free-energy profiles with and without A-loop phosphorylation. The conformational change was found to proceed in three sequential steps: first, the flipping of the DFG motif of the active site; second, rotation of the A-loop; finally, the inward movement of the αC helix. The main energetic bottleneck corresponds to the conformational change in the A-loop, while changes in the DFG motif and αC helix occur before and after A-loop conformational change, respectively. In accordance with this, two intermediate states are identified, the first state just after the DFG flipping and the second state after the A-loop rotation. These intermediates exhibit structural features characteristic of the corresponding inactive and active conformations of other protein kinases. To understand the impact of A-loop phosphorylation on kinase conformation, the free energies of A-loop phosphorylation were determined at several states along the conformational transition path using the free-energy perturbation simulations. The calculated free energies reveal that while the unphosphorylated kinase interconverts between the inactive and active conformations, A-loop phosphorylation restricts access to the inactive conformation, thereby increasing the active conformation population. Overall, this study suggests a consensus mechanism of conformational activation between different protein kinases.

GPS 6.0: an updated server for prediction of kinase-specific phosphorylation sites in proteins.

Protein phosphorylation, catalyzed by protein kinases (PKs), is one of the most important post-translational modifications (PTMs), and involved in regulating almost all of biological processes. Here, we report an updated server, Group-based Prediction System (GPS) 6.0, for prediction of PK-specific phosphorylation sites (p-sites) in eukaryotes. First, we pre-trained a general model using penalized logistic regression (PLR), deep neural network (DNN), and Light Gradient Boosting Machine (LightGMB) on 490 762 non-redundant p-sites in 71 407 proteins. Then, transfer learning was conducted to obtain 577 PK-specific predictors at the group, family and single PK levels, using a well-curated data set of 30 043 known site-specific kinase-substrate relations in 7041 proteins. Together with the evolutionary information, GPS 6.0 could hierarchically predict PK-specific p-sites for 44046 PKs in 185 species. Besides the basic statistics, we also offered the knowledge from 22 public resources to annotate the prediction results, including the experimental evidence, physical interactions, sequence logos, and p-sites in sequences and 3D structures. The GPS 6.0 server is freely available at https://gps.biocuckoo.cn. We believe that GPS 6.0 could be a highly useful service for further analysis of phosphorylation.

Single-Molecule Monitoring of Membrane Association of the Necroptosis Executioner MLKL with Discernible Anchoring and Insertion Dynamics.

The dynamics of membrane proteins that are well-folded in water and become functional after self-insertion into cell membranes is not well understood. Herein we report on single-molecule monitoring of membrane association dynamics of the necroptosis executioner MLKL. We observed that, upon landing, the N-terminal region (NTR) of MLKL anchors onto the surface with an oblique angle and then is immersed in the membrane. The anchoring end does not insert into the membrane, but the opposite end does. The protein is not static, switching slowly between water-exposed and membrane-embedded conformations. The results suggest a mechanism for the activation and function of MLKL in which exposure of H4 is critical for MLKL to adsorb on the membrane, and the brace helix H6 regulates MLKL rather than inhibits it. Our findings provide deeper insights into membrane association and function regulation of MLKL and would have impacts on biotechnological applications.

Norovirus MLKL-like protein initiates cell death to induce viral egress.

Non-enveloped viruses require cell lysis to release new virions from infected cells, suggesting that these viruses require mechanisms to induce cell death. Noroviruses are one such group of viruses, but there is no known mechanism that causes norovirus infection-triggered cell death and lysis1-3. Here we identify a molecular mechanism of norovirus-induced cell death. We found that the norovirus-encoded NTPase NS3 contains an N-terminal four-helix bundle domain homologous to the membrane-disruption domain of the pseudokinase mixed lineage kinase domain-like (MLKL). NS3 has a mitochondrial localization signal and thus induces cell death by targeting mitochondria. Full-length NS3 and an N-terminal fragment of the protein bound the mitochondrial membrane lipid cardiolipin, permeabilized the mitochondrial membrane and induced mitochondrial dysfunction. Both the N-terminal region and the mitochondrial localization motif of NS3 were essential for cell death, viral egress from cells and viral replication in mice. These findings suggest that noroviruses have acquired a host MLKL-like pore-forming domain to facilitate viral egress by inducing mitochondrial dysfunction.

The stem cell-supporting small molecule UM171 triggers Cul3-KBTBD4-mediated degradation of ELM2 domain-harboring proteins.

To chemically modulate the ubiquitin-proteasome system for the degradation of specific target proteins is currently emerging as an alternative therapeutic modality. Earlier, we discovered such properties of the stem cell-supporting small molecule UM171 and identified that members of the CoREST complex (RCOR1 and LSD1) are targeted for degradation. UM171 supports the in vitro propagation of hematopoietic stem cells by transiently perturbing the differentiation-promoting effects of CoREST. Here, we employed global proteomics to map the UM171-targeted proteome and identified the additional target proteins, namely RCOR3, RREB1, ZNF217, and MIER2. Further, we discovered that critical elements recognized by Cul3KBTBD4 ligase in the presence of UM171 are located within the EGL-27 and MTA1 homology 2 (ELM2) domain of the substrate proteins. Subsequent experiments identified conserved amino acid sites in the N-terminus of the ELM2 domain that are essential for UM171-mediated degradation. Overall, our findings provide a detailed account on the ELM2 degrome targeted by UM171 and identify critical sites required for UM171-mediated degradation of specific substrates. Given the target profile, our results are highly relevant in a clinical context and point towards new therapeutic applications for UM171.

Protein kinases: Role of their dysregulation in carcinogenesis, identification and inhibition.

Protein kinases belong to the phosphor-transferases superfamily of enzymes, which "activate" enzymes via phosphorylation. The kinome of an organism is the total set of genes in the genome, which encode for all the protein kinases. Certain mutations in the kinome have been linked to dysregulation of protein kinases, which in turn can lead to several diseases and disorders including cancer. In this review, we have briefly discussed the role of protein kinases in various biochemical processes by categorizing cancer associated phenotypes and giving their protein kinase examples. Various techniques have also been discussed, which are being used to analyze the structure of protein kinases, and associate their roles in the oncogenesis. We have also discussed protein kinase inhibitors and United States Federal Drug Administration (USFDA) approved drugs, which target protein kinases and can serve as a counter to protein kinase dysregulation and mitigate the effects of oncogenesis. Overall, this review briefs about the importance of protein kinases, their roles in oncogenesis on dysregulation and how their inhibition via various drugs can be used to mitigate their effects.

Natural Metabolite Ursolic Acid as an Inhibitor of Dormancy Regulator DosR of Mycobacterium tuberculosis: Evidence from Molecular Docking, Molecular Dynamics Simulation and Free Energy Analysis.

BACKGROUND: DosR is a transcriptional regulator of Mycobacterium tuberculosis (MTB), governing the expression of a set of nearly 50 genes that is often referred to as 'dormancy regulon'. The inhibition of DosR expression by an appropriate inhibitor may be a crucial step against MTB. OBJECTIVE: We targeted the DosR with natural metabolites, ursolic acid (UA) and carvacrol (CV), using in silico approaches. METHODS: The molecular docking, molecular dynamics (MD) simulation for 200 ns, calculation of binding energies by MM-GBSA method, and ADMET calculation were performed to evaluate the inhibitory potential of natural metabolites ursolic acid (UA) and carvacrol (CV) against DosR of MTB. RESULTS: Our study demonstrated that UA displayed significant compatibility with DosR during the 200 ns timeframe of MD simulation. The thermodynamic binding energies by MM-GBSA also suggested UA conformational stability within the binding pocket. The SwissADME, pkCSM, and OSIRIS DataWarrior showed a drug-likeness profile of UA, where Lipinski profile was satisfied with one violation (MogP > 4.15) with no toxicities, no mutagenicity, no reproductive effect, and no irritant nature. CONCLUSION: The present study suggests that UA has the potency to inhibit the DosR expression and warrants further investigation on harnessing its clinical potential.

Construction of a phos-tag-directed self-assembled fluorescent magnetobiosensor for the simultaneous detection of multiple protein kinases.

Protein kinases play important roles in regulating various cellular processes and may function as potential diagnostic and therapeutic targets for various diseases including cancers. Herein, we construct a phos-tag-directed self-assembled fluorescent magnetobiosensor to simultaneously detect multiple protein kinases with good selectivity and high sensitivity. In the presence of protein kinases (i.e., PKA and Akt1), their substrate peptides (i.e., a FITC-labeled substrate peptide and a Cy5-labeled substrate peptide) are phosphorylated, and are then specifically recognized and captured by a biotinylated phos-tag to generate biotinylated substrate peptides for the assembly of magnetic bead (MB)-peptides-FITC/Cy5 nanostructures. After magnetic separation, the phosphorylated substrate peptides are disassembled from the MB-peptides-FITC/Cy5 nanostructures using deionized water at 80 °C, releasing FITC and Cy5 molecules. The released FITC and Cy5 molecules are detected by steady-state fluorescence measurements, with FITC indicating PKA and Cy5 indicating Akt1. This magnetobiosensor only involves one phos-tag without the requirement of radiolabeling, antibody screening, carboxypeptidase Y (CPY) cleavage, and cumbersome chemical/enzyme reactions. The introduction of magnetic separation can effectively eliminate the interference from complex real samples, generating an extremely low background signal. Moreover, this magnetobiosensor can accurately measure cellular protein kinase activities and screen inhibitors, with great potential for kinase-related biomedical research and therapeutic applications.