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Phosphorylation of ubiquitin and the ubiquitin-like domain of Parkin, mediated by the kinase PINK1, is essential for the liberation of the E3 ligase from its autoinhibited state. In this issue of Structure, Lenka et al. 1 provide the structural basis for the specificity and stronger Parkin activation by phospho-NEDD8 compared to phospho-ubiquitin., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

PINK1 and Parkin mutations lead to the early onset of Parkinson's disease. PINK1-mediated phosphorylation of ubiquitin (Ub), ubiquitin-like protein (NEDD8), and ubiquitin-like (Ubl) domain of Parkin activate autoinhibited Parkin E3 ligase. The mechanism of various phospho-Ubls' specificity and conformational changes leading to Parkin activation remain elusive. Herein, we show that compared to Ub, NEDD8 is a more robust binder and activator of Parkin. Structures and biophysical/biochemical data reveal specific recognition and underlying mechanisms of pUb/pNEDD8 and pUbl domain binding to the RING1 and RING0 domains, respectively. Also, pUb/pNEDD8 binding in the RING1 pocket promotes allosteric conformational changes in Parkin's catalytic domain (RING2), leading to Parkin activation. Furthermore, Parkinson's disease mutation K211N in the RING0 domain was believed to perturb Parkin activation due to loss of pUb binding. However, our data reveal allosteric conformational changes due to N211 that lock RING2 with RING0 to inhibit Parkin activity without disrupting pNEDD8/pUb binding., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Artificial control of intracellular protein dynamics with high precision provides deep insight into complicated biomolecular networks. Optogenetics and caged compound-based chemically induced dimerization (CID) systems are emerging as tools for spatiotemporally regulating intracellular protein dynamics. However, both technologies face several challenges for accurate control such as the duration of activation, deactivation rate and repetition cycles. Herein, we report a photochromic CID system that uses the photoisomerization of a ligand so that both association and dissociation are controlled by light, enabling quick, repetitive and quantitative regulation of the target protein localization upon illumination with violet and green light. We also demonstrate the usability of the photochromic CID system as a potential tool to finely manipulate intracellular protein dynamics during multicolor fluorescence imaging to study diverse cellular processes. We use this system to manipulate PTEN-induced kinase 1 (PINK1)-Parkin-mediated mitophagy, showing that PINK1 recruitment to the mitochondria can promote Parkin recruitment to proceed with mitophagy., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Histidine kinases (HKs) are a central part of bacterial environmental-sensing two-component systems. They provide their hosts with the ability to respond to a wide range of physical and chemical signals. HKs are multidomain proteins consisting of at least a sensor domain, dimerization and phosphorylation domain (DHp), and a catalytic domain. They work as homodimers and the existence of two different autophosphorylation mechanisms (cis and trans) has been proposed as relevant for pathway specificity. Although several HKs have been intensively studied, a precise sequence-to-structure explanation of why and how either cis or trans phosphorylation occurs is still unavailable nor is there any evolutionary analysis on the subject. In this work, we show that AlphaFold can accurately determine whether an HK dimerizes in a cis or trans structure. By modeling multiple HKs we show that both cis- and trans-acting HKs are common in nature and the switch between mechanisms has happened multiple times in the evolutionary history of the family. We then use AlphaFold modeling to explore the molecular determinants of the phosphorylation mechanism. We conclude that it is the difference in lengths of the helices surrounding the DHp loop that determines the mechanism. We also show that very small changes in these helices can cause a mechanism switch. Despite this, previous evidence shows that for a particular HK the phosphorylation mechanism is conserved. This suggests that the phosphorylation mechanism participates in system specificity and mechanism switching provides these systems with a way to diverge., (© 2024 Wiley Periodicals LLC.)

Background: Protein kinases are a diverse superfamily of proteins common to organisms across the tree of life that are typically involved in signal transduction, allowing organisms to sense and respond to biotic or abiotic environmental factors. They have important roles in organismal physiology, including development, reproduction, acclimation to environmental stress, while their dysregulation can lead to disease, including several forms of cancer. Identifying the complement of protein kinases (the kinome) of any organism is useful for understanding its physiological capabilities, limitations and adaptations to environmental stress. The increasing availability of genomes makes it now possible to examine and compare the kinomes across a broad diversity of organisms. Here we present a pipeline respecting the FAIR principles (findable, accessible, interoperable and reusable) that facilitates the search and identification of protein kinases from a predicted proteome, and classifies them according to group of serine/threonine/tyrosine protein kinases present in eukaryotes., Results: KiNext is a Nextflow pipeline that regroups a number of existing bioinformatic tools to search for and classify the protein kinases of an organism in a reproducible manner, starting from a set of amino acid sequences. Conventional eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs) are identified by using Hidden Markov Models (HMMs) generated from the catalytic domains of kinases. Furthermore, KiNext categorizes ePKs into the eight kinase groups by employing dedicated Hidden Markov Models (HMMs) tailored for each group. The performance of the KiNext pipeline was validated against previously identified kinomes obtained with other tools that were already published for two marine species, the Pacific oyster Crassostrea gigas and the unicellular green alga Ostreoccocus tauri. KiNext outperformed previous results by finding previously unidentified kinases and by attributing a large proportion of previously unclassified kinases to a group in both species. These results demonstrate improvements in kinase identification and classification, all while providing traceability and reproducibility of results in a FAIR pipeline. The default HMM models provided with KiNext are most suitable for eukaryotes, but the pipeline can be easily modified to include HMM models for other taxa of interest., Conclusion: The KiNext pipeline enables efficient and reproducible identification of kinomes based on predicted amino acid sequences (i.e. proteomes). KiNext was designed to be easy to use, automated, portable and scalable., (© 2024. The Author(s).)

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).)

Protein kinases are critical components of a myriad biological processes and strongly associated with various diseases. While kinase research has been a point of focus in biomedical research for several decades, a large portion of the kinome is still considered understudied or "dark," because prior research is targeted towards a subset of kinases with well-established roles in cellular processes. We present an empirical and in-silico hybrid workflow to extend the functional knowledge of understudied kinases. Utilizing multiplex peptide activity arrays and robust in-silico analyses, we extended the functional knowledge of five dark tyrosine kinases (AATK, EPHA6, INSRR, LTK, TNK1) and explored their roles in schizophrenia, Alzheimer's dementia (AD), and major depressive disorder (MDD). Using this hybrid approach, we identified 195 novel kinase-substrate interactions with variable degrees of affinity and linked extended functional networks for these kinases to biological processes that are impaired in psychiatric and neurological disorders. Biochemical assays and mass spectrometry were used to confirm a putative substrate of EPHA6, an understudied dark tyrosine kinase. We examined the EPHA6 network and knowledgebase in schizophrenia using reporter peptides identified and validated from the multi-plex array with high affinity for phosphorylation by EPHA6. Identification and confirmation of putative substrates for understudied kinases provides a wealth of actionable information for the development of new drug treatments as well as exploration of the pathophysiology of disease states using signaling network approaches., (© 2024. The Author(s).)

Two-component signal transduction systems (TCSs) are regulatory systems widely distributed in eubacteria, archaea, and a few eukaryotic organisms, but not in mammalian cells. A typical TCS consists of a histidine kinase and a response regulator protein. Functional and mechanistic studies on different TCSs have greatly advanced the understanding of cellular phosphotransfer signal transduction mechanisms. In this concept paper, we focus on the His-Asp phosphotransfer mechanism, the ATP synthesis function, antimicrobial drug design, cellular biosensors design, and protein allostery mechanisms based on recent TCS investigations to inspire new applications and future research perspectives., (© 2024 Wiley-VCH GmbH.)

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.)

Protein kinases are dynamic enzymes that display complex regulatory mechanisms. Although they possess a structurally conserved catalytic domain, significant conformational dynamics are evident both within a single kinase and across different kinases in the kinome. Here, we highlight methods for exploring this conformational space and its dynamics using kinase domains from ABL1 (Abelson kinase), PKA (protein kinase A), AurA (Aurora A), and PYK2 (proline-rich tyrosine kinase 2) as examples. Such experimental approaches combined with AI-driven methods, such as AlphaFold, will yield discoveries about kinase regulation, the catalytic process, substrate specificity, the effect of disease-associated mutations, as well as new opportunities for structure-based drug design., 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 article., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Classification of protein families from their sequences is an enduring task in Proteomics and related studies. Numerous deep-learning models have been moulded to tackle this challenge, but due to the black-box character, they still fall short in reliability. Here, we present a novel explainability pipeline that explains the pivotal decisions of the deep learning model on the classification of the Eukaryotic kinome. Based on a comparative and experimental analysis of the most cutting-edge deep learning algorithms, the best deep learning model CNN-BLSTM was chosen to classify the eight eukaryotic kinase sequences to their corresponding families. As a substitution for the conventional class activation map-based interpretation of CNN-based models in the domain, we have cascaded the GRAD CAM and Integrated Gradient (IG) explainability modus operandi for improved and responsible results. To ensure the trustworthiness of the classifier, we have masked the kinase domain traces, identified from the explainability pipeline and observed a class-specific drop in F1-score from 0.96 to 0.76. In compliance with the Explainable AI paradigm, our results are promising and contribute to enhancing the trustworthiness of deep learning models for biological sequence-associated studies., 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. No funding applicable., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Necroptosis is a lytic form of programmed cell death implicated in inflammatory pathologies, leading to intense interest in the underlying mechanisms and therapeutic prospects. Here, we review our current structural understanding of how the terminal executioner of the pathway, the dead kinase, mixed lineage kinase domain-like (MLKL), is converted from a dormant to killer form by the upstream regulatory kinase, RIPK3. RIPK3-mediated phosphorylation of MLKL's pseudokinase domain toggles a molecular switch that induces dissociation from a cytoplasmic platform, assembly of MLKL oligomers, and trafficking to the plasma membrane, where activated MLKL accumulates and permeabilises the lipid bilayer to induce cell death. We highlight gaps in mechanistic knowledge of MLKL's activation, how mechanisms diverge between species, and the power of modelling in advancing structural insights., Competing Interests: Declaration of competing interest The authors contribute to a program developing necroptosis inhibitors in collaboration with Anaxis Pharma Pty Ltd. PEC and JMM have received research funding from Anaxis Pharma Pty Ltd., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Mutations in parkin and PINK1 cause early-onset Parkinson's disease (EOPD). The ubiquitin ligase parkin is recruited to damaged mitochondria and activated by PINK1, a kinase that phosphorylates ubiquitin and the ubiquitin-like domain of parkin. Activated phospho-parkin then ubiquitinates mitochondrial proteins to target the damaged organelle for degradation. Here, we present the mechanism of activation of a new class of small molecule allosteric modulators that enhance parkin activity. The compounds act as molecular glues to enhance the ability of phospho-ubiquitin (pUb) to activate parkin. Ubiquitination assays and isothermal titration calorimetry with the most active compound (BIO-2007817) identify the mechanism of action. We present the crystal structure of a closely related compound (BIO-1975900) bound to a complex of parkin and two pUb molecules. The compound binds next to pUb on RING0 and contacts both proteins. Hydrogen-deuterium exchange mass spectrometry (HDX-MS) experiments confirm that activation occurs through release of the catalytic Rcat domain. In organello and mitophagy assays demonstrate that BIO-2007817 partially rescues the activity of parkin EOPD mutants, R42P and V56E, offering a basis for the design of activators as therapeutics for Parkinson's disease., (© 2024. The Author(s).)

Two-component systems (TCSs) are diverse cell signaling pathways that play a significant role in coping with a wide range of environmental cues in both prokaryotic and eukaryotic organisms. These transduction circuitries are primarily governed by histidine kinases (HKs), which act as sensing proteins of a broad variety of stressors. To date, nineteen HK groups have been previously described in the fungal kingdom. However, the structure and distribution of these prominent sensing proteins were hitherto investigated in a limited number of fungal species. In this study, we took advantage of recent genomic resources in fungi to refine the fungal HK classification by deciphering the structural diversity and phylogenetic distribution of HKs across a large number of fungal clades. To this end, we browsed the genome of 91 species representative of different fungal clades, which yielded 726 predicted HK sequences. A domain organization analysis, coupled with a robust phylogenomic approach, led to an improved categorization of fungal HKs. While most of the compiled sequences were categorized into previously described fungal HK groups, some new groups were also defined. Overall, this study provides an improved overview of the structure, distribution, and evolution of HKs in the fungal kingdom., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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.

Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally., Competing Interests: XG, AA No competing interests declared, PT P.T. is a consultant to Schrodinger, Inc and is on their Scientific Advisory Board, (© 2024, Gu et al.)

Reversible lysine acetylation is an important post-translational modification (PTM). This process in cells is typically carried out enzymatically by lysine acetyltransferases and deacetylases. The catalytic lysine in the human kinome is highly conserved and ligandable. Small-molecule strategies that enable post-translational acetylation of the catalytic lysine on kinases in a target-selective manner therefore provide tremendous potential in kinase biology. Herein, we report the first small molecule-induced chemical strategy capable of global acetylation of the catalytic lysine on kinases from mammalian cells. By surveying various lysine-acetylating agents installed on a promiscuous kinase-binding scaffold, Ac4 was identified and shown to effectively acetylate the catalytic lysine of >100 different protein kinases from live Jurkat/K562 cells. In order to demonstrate that this strategy was capable of target-selective and reversible chemical acetylation of protein kinases, we further developed six acetylating compounds on the basis of VX-680 (a noncovalent inhibitor of AURKA). Among them, Ac13 / Ac14 , while displaying excellent in vitro potency and sustained cellular activity against AURKA, showed robust acetylation of its catalytic lysine (K162) in a target-selective manner, leading to irreversible inhibition of endogenous kinase activity. The reversibility of this chemical acetylation was confirmed on Ac14 -treated recombinant AURKA protein, followed by deacetylation with SIRT3 (a lysine deacetylase). Finally, the reversible Ac13 -induced acetylation of endogenous AURKA was demonstrated in SIRT3-transfected HCT116 cells. By disclosing the first cell-active acetylating compounds capable of both global and target-selective post-translational acetylation of the catalytic lysine on kinases, our strategy could provide a useful chemical tool in kinase biology and drug discovery.

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., (© 2024. The Author(s).)

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., Competing Interests: VK, SS, AF, FE, JF, SS, SS, RW, JT, VS, PM, TK No competing interests declared, PT, ES ES and PT are co-founders of KinCon biolabs; KinCon-reporters are subject of patents (WO2018060415A1), (© 2024, Kugler, Schwaighofer et al.)

DosT and DosS are heme-based kinases involved in sensing and signaling O 2 tension in the microenvironment of Mycobacterium tuberculosis (Mtb). Under conditions of low O 2 , 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 O 2 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 O 2 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 O 2 sensors., 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 Inc. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Herrington et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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., Competing Interests: HM, YT, ML, KB, CZ No competing interests declared, (© 2023, Mühlenbeck et al.)

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., Competing Interests: AA, VD No competing interests declared, (© 2024, Andreotti and Dötsch.)

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.)

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., (© 2024 The Author(s).)

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., (© 2024 The Authors. Molecular Informatics published by Wiley-VCH GmbH.)

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.

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.

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., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kroll et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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.

DeepKa is a deep-learning-based protein p K a predictor proposed in our previous work. In this study, a web server was developed that enables online protein p K a 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 p K a '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.

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.)

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 β-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 (Ac 3 ManNAz) 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., (© 2024 Wiley‐VCH GmbH.)

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.)

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., (© 2023 The Authors. Protein Science published by Wiley Periodicals LLC on behalf of The Protein Society.)

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)., (© 2023 Wiley Periodicals LLC.)

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.

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., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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.

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 Cul3 KBTBD4 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., Competing Interests: Conflict of interests The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: Authors declare no conflict of interest regarding the present work., (Thieme. All rights reserved.)

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 lysis 1-3 . 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., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

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., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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.