A postmodern moral tale: the ethics of research relationships

Application of a porous zirconium-based MOF nanoplate as an affinity ECL platform for the detection of protein kinase activity and inhibitor screening

Abnormal kinase expression affects phosphorylation in the human body, which is associated with numerous diseases, including cancer, diabetes mellitus, and Alzheimer's disease. In this study, we synthesized a highly stable, two-dimensional, luminescence-functionalized metal-organic framework with remarkable electrochemiluminescence (ECL) by immobilizing 9,10-Di(p-carboxyphenyl) anthracene (dca) on a zirconium cluster (dca-Zr₁₂) via a strong coordination bond between -COO⁻ and Zr⁴⁺. This novel and simple platform relies on the highly specific identification of phosphate molecules by the ultra-thin dca-Zr₁₂ nanoplate through carboxylate-Zr⁴⁺-phosphate chemistry. The ferrocene-labeled peptide substrate (Fc-S-Peptide) was phosphorylated in the presence of protein kinase A (PKA) and adenosine 5'-triphosphate (ATP), and the resulting phosphopeptide could subsequently be precisely captured by the zirconium sites of the dca-Zr12-modified electrode and, eventually, quench the ECL and gain a signal-off state. This rapid and simple detection strategy was successfully employed to measure PKA activity, with a detection limit as low as 0.35 mU mL-1. Based on the results, it exhibited high selectivity and can be applied for screening PKA inhibitors. The technique was subsequently applied to detect protein kinase activity in drug-stimulated MCF-7 cell lysates, demonstrating its potential for kinase-related investigations. Further, this platform could identify the activity of other kinase types with universal applicability.

Phosphoproteomic analysis of X-ray-irradiated planarians provides novel insights into the DNA damage response

Phosphorylation plays a crucial role in the cellular response to radiation and cancer therapies, yet phosphoproteomics studies in planarians remain underexplored despite the organism's remarkable regenerative capacities. This study utilized advanced ion mobility mass spectrometry for 4D-label-free quantitative proteomics to identify phosphorylation sites associated with irradiation in planarians. A total of 33,284 phosphorylation sites from 15,505 phosphorylated peptides and 4710 unique phosphoproteins were identified. In the sub-lethal dose irradiation group, 1695 phosphoproteins with 3483 phosphorylation sites exhibited significant changes, while exposure to lethal doses of radiation led to significant changes in 2308 phosphoproteins with 6112 phosphorylation sites, including many kinases, transcription factors, and cytoskeletal proteins. Functional enrichment analysis revealed that the altered phosphoproteins were primarily involved in transcription, RNA biosynthesis, mRNA processing regulation, and spliceosomal complex assembly. Functional validation of five differentially phosphorylated proteins revealed that their depletion impaired stem cell regeneration after irradiation by disrupting DNA repair, suggesting that these proteins are critical to planarian biology and their radiation response. By identifying the phosphorylation state and specific sites of planarian proteins, our study lays the foundation for further research on protein phosphorylation in the radiation-induced DNA damage response. In addition, our findings provide preliminary insights into the role of calnexin, a protein involved in interacting with newly synthesized N-linked glycoproteins, in planarians.

STK31 drives tumor immune evasion through STAT3-IL-6 mediated CD8+ T cell exhaustion

Dysregulations in protein kinases significantly contribute to the initiation, progression, and drug resistance in non-small cell lung cancer (NSCLC). Identification of novel oncogenic drivers within the human kinome is crucial for targeted therapy. In this study, we conducted a comprehensive analysis of the TCGA database and literature, pinpointing 16 candidate genes in lung cancer exhibiting frequent dysregulation and limited research. Our functional analysis revealed Serine/threonine kinase 31 (STK31) as a key player in driving tumor growth, in immune-competent mice, with minimal impact in nude mice. Further investigations unveiled upregulation of STK31 led to CD8+ T cell exhaustion. Mechanistically, STK31 induced CD8+ T cell exhaustion through the signal transducer and activator of transcription 3 (STAT3) - interleukin 6 (IL-6) signaling pathway. Direct interaction between STK31 and STAT3 activated the transcription of downstream oncogenic targets, such as IL-6, facilitating immune escape. Moreover, STK31 exhibited elevated expression levels in lung cancer tissues compared to adjacent tissues and displayed a significant correlation with poor prognosis in lung cancer patients. This study defines a critical role of STK31 in promoting immune escape through STAT3 activation, positioning it as a promising therapeutic target for lung cancer.

Harnessing pre-trained models for accurate prediction of protein-ligand binding affinity

BACKGROUND

The binding between proteins and ligands plays a crucial role in the field of drug discovery. However, this area currently faces numerous challenges. On one hand, existing methods are constrained by the limited availability of labeled data, often performing inadequately when addressing complex protein-ligand interactions. On the other hand, many models struggle to effectively capture the flexible variations and relative spatial relationships between proteins and ligands. These issues not only significantly hinder the advancement of protein-ligand binding research but also adversely affect the accuracy and efficiency of drug discovery. Therefore, in response to these challenges, our study aims to enhance predictive capabilities through innovative approaches, providing more reliable support for drug discovery efforts.

METHODS

This study leverages a pre-trained model with spatial awareness to enhance the prediction of protein-ligand binding affinity. By perturbing the structures of small molecules in a manner consistent with physical constraints and employing self-supervised tasks, we improve the representation of small molecule structures, allowing for better adaptation to affinity predictions. Meanwhile, our approach enables the identification of potential binding sites on proteins.

RESULTS

Our model demonstrates a significantly higher correlation coefficient in binding affinity predictions. Extensive evaluation on the PDBBind v2019 refined set, CASF, and Merck FEP benchmarks confirms the model's robustness and strong generalization across diverse datasets. Additionally, the model achieves over 95% in classification ROC for binding site identification, underscoring its high accuracy in pinpointing protein-ligand interaction regions.

CONCLUSION

This research presents a novel approach that not only enhances the accuracy of binding affinity predictions but also facilitates the identification of binding sites, showcasing the potential of pre-trained models in computational drug design. Data and code are available at https://github.com/MIALAB-RUC/SableBind .

Next-Generation Protein Sequencing and individual ion mass spectrometry enable complementary analysis of interleukin-6

The vast complexity of the proteome currently overwhelms any single analytical technology in capturing the full spectrum of proteoform diversity. In this study, we evaluated the complementarity of two cutting-edge proteomic technologies-single-molecule protein sequencing and individual ion mass spectrometry-for analyzing recombinant human IL-6 (rhIL-6) at the amino acid, peptide, and intact proteoform levels. For single-molecule protein sequencing, we employ the recently released Platinum® instrument. Next-Generation Protein Sequencing™ (NGPS™) on Platinum utilizes cycles of N-terminal amino acid recognizer binding and aminopeptidase cleavage to enable parallelized sequencing of single peptide molecules. We found that NGPS produces single amino acid coverage of multiple key regions of IL-6, including two peptides within helices A and C which harbor residues that reportedly impact IL-6 function. For top-down proteoform evaluation, we use individual ion mass spectrometry (I2MS), a highly parallelized orbitrap-based charge detection MS platform. Single ion detection of gas-phase fragmentation products (I2MS2) gives significant sequence coverage in key regions in IL-6, including two regions within helices B and D that are involved in IL-6 signaling. Together, these complementary technologies deliver a combined 52% sequence coverage, offering a more complete view of IL-6 structural and functional diversity than either technology alone. This study highlights the synergy of complementary protein detection methods to more comprehensively cover protein segments relevant to biological interactions.

Site-Specific Competitive Kinase Inhibitor Target Profiling Using Phosphonate Affinity Tags

Protein kinases are prime targets for drug development due to their involvement in various cancers. However, selective inhibition of kinases, while avoiding off-target effects remains a significant challenge for the development of protein kinase inhibitors. Activity-based protein profiling (ABPP), in combination with pan-kinase activity-based probes (ABPs) and mass spectrometry-based proteomics, enables the identification of kinase drug targets. Here, we extend existing ABPP strategies for kinase profiling with a site-specific analysis, allowing for protein kinase inhibitor target engagement profiling with amino acid specificity. The site-specific approach involves highly efficient enrichment of ABP-labeled peptides, resulting in a less complex peptide matrix, straightforward data analysis, and the screening of over ∼100 kinase active sites in a single LC-MS analysis. The complementary use of both trypsin and pepsin in parallel to generate the ABP-labeled peptides considerably expanded the coverage of kinases and pinpoint the exact binding sites. Using the site-specific strategy to examine the on- and off-targets of the Ephrin receptor (Eph) B4 inhibitor NVP-BHG712 showed binding to EphA2 with an IC50 of 17 nM and EphB4 with an IC50 of 20 nM. Next to the known targets, EphA2 and EphB4, NVP-BHG712 bound to the discoidin domain-containing receptor 1 with an IC50 of 2.1 nM, suggesting that a discoidin domain-containing receptor 1-targeting regio-isomer of NVP-BHG712 was used. The promiscuity of XO44 toward ATP-binding pockets on nonkinase proteins facilitated the screening of additional off-target sites, revealing inosine-5'-monophosphate dehydrogenase 2 as a putative off-target. Expanding the search to other amino acids revealed that XO44, in addition to 745 lysines, also covalently linked 715 tyrosines, which significantly expands the competitive ABPP search space and highlights the added value of the site-specific method. Therefore, the presented approach, which can be fully automated with liquid handling platforms, provides a straightforward, valuable new approach for competitive site-specific kinase inhibitor target profiling.

A computational tool to infer enzyme activity using post-translational modification profiling data

Enzymes play a pivotal role in orchestrating complex cellular responses to external stimuli and environmental changes through signal transduction pathways. Despite their crucial roles, measuring enzyme activities is typically indirect and performed on a smaller scale, unlike protein abundance measured by high-throughput proteomics. Moreover, it is challenging to derive the activity of enzymes from proteome-wide post-translational modification (PTM) profiling data. To address this challenge, we introduce enzyme activity inference with structural equation modeling under the JUMP umbrella (JUMPsem), a novel computational tool designed to infer enzyme activity using PTM profiling data. We demonstrate that the JUMPsem program enables estimating kinase activities using phosphoproteome data, ubiquitin E3 ligase activities from the ubiquitinome, and histone acetyltransferase (HAT) activities based on the acetylome. In addition, JUMPsem is capable of establishing novel enzyme-substrate relationships through searching motif sequences. JUMPsem outperforms widely used kinase activity tools, such as IKAP and KSEA, in terms of the number of kinases and the computational speed. The JUMPsem program is scalable and publicly available as an open-source R package and user-friendly web-based R/Shiny app. Collectively, JUMPsem provides an improved tool for inferring protein enzyme activities, potentially facilitating targeted drug development.

Atlas of the Bacterial Serine-Threonine Kinases expands the functional diversity of the kinome

Bacterial serine-threonine protein kinases (STKs) regulate diverse cellular processes associated with cell growth, virulence, and pathogenicity. They are evolutionarily related to the druggable eukaryotic STKs. However, an incomplete knowledge of how bacterial STKs differ from their eukaryotic counterparts and how they have diverged to regulate diverse bacterial signaling functions presents a bottleneck in targeting them for drug discovery efforts. Here, we classified over 300,000 bacterial STK sequences from the NCBI RefSeq non-redundant and UniProt protein databases into 35 canonical and seven non-canonical (pseudokinase) families based on the patterns of evolutionary constraints in the conserved catalytic domain and flanking regulatory domains. Through statistical comparisons, we identified distinguishing features of bacterial STKs, including a distinctive arginine residue in a regulatory helix (C-Helix) that dynamically couples ATP and substrate binding lobes of the kinase domain. Biochemical and peptide-library screens demonstrated that constrained residues contribute to substrate specificity and kinase activation in the Mycobacterium tuberculosis kinase PknB. Collectively, these findings open new avenues for investigating bacterial STK functions in cellular signaling and for the development of selective bacterial STK inhibitors.

Drug-target binding affinity prediction based on power graph and word2vec

BACKGROUND

Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.

RESULTS

Compared with other methods, regression-based drug target affinity is more representative of the binding ability. Accurate prediction of drug target affinity can effectively reduce the time and cost of drug retargeting and new drug development. In this paper, a drug target affinity prediction model (WPGraphDTA) based on power graph and word2vec is proposed.

CONCLUSIONS

In this model, the drug molecular features in the power graph module are extracted by a graph neural network, and then the protein features are obtained by the Word2vec method. After feature fusion, they are input into the three full connection layers to obtain the drug target affinity prediction value. We conducted experiments on the Davis and Kiba datasets, and the experimental results showed that WPGraphDTA exhibited good prediction performance.

Design, Synthesis, and Biochemical Evaluation of Novel MLK3 Inhibitors: A Target Hopping Example

The human kinome has tremendous medical potential. In the past decade, mixed-lineage protein kinase 3 (MLK3) has emerged as an interesting and druggable target in oncogenic signaling. The important role of MLK3 has been demonstrated in several types of cancer. In a target hopping example we started with the focal adhesion kinase (FAK) inhibitor PF-431396 (10), which shows off-target activity toward MLK3. We were able to develop highly active compounds in the single digit nanomolar range for MLK3. Furthermore, we achieved a dramatic shift in selectivity from FAK to MLK3. Here we present a new chemical class of MLK3 inhibitors, including our lead compound 37 with an outstanding IC50 value of <1 nM in a biochemical MLK3 assay while simultaneously exhibiting kinome-wide selectivity.

Identification of Critical Phosphorylation Sites Enhancing Kinase Activity With a Bimodal Fusion Framework

Phosphorylation is an indispensable regulatory mechanism in cells, with specific sites on kinases that can significantly enhance their activity. Although several such critical phosphorylation sites (phos-sites) have been experimentally identified, many more remain to be explored. To date, no computational method exists to systematically identify these critical phos-sites on kinases. In this study, we introduce PhoSiteformer, a transformer-inspired foundational model designed to generate embeddings of phos-sites using phosphorylation mass spectrometry data. Recognizing the complementary insights offered by protein sequence data and phosphorylation mass spectrometry data, we developed a classification model, CSPred, which employs a bimodal fusion strategy. CSPred combines embeddings from PhoSiteformer with those from the protein language model ProtT5. Our approach successfully identified 77 critical phos-sites on 58 human kinases. Two of these sites, T517 on PKG1 and T735 on PRKD3, have been experimentally verified. This study presents the first systematic and computational approach to identify critical phos-sites that enhance kinase activity.

Design, Synthesis and Molecular Modeling of Pyrazolo[1,5-a]pyrimidine Derivatives as Dual Inhibitors of CDK2 and TRKA Kinases with Antiproliferative Activity

BACKGROUND

The increasing prevalence of drug resistance in cancer therapy underscores the urgent need for novel therapeutic approaches. Dual enzyme inhibitors, targeting critical kinases such as CDK2 and TRKA, represent a promising strategy. The goal of this investigation was to design, synthesize, and evaluate a set of pyrazolo[1,5-a]pyrimidine derivatives for their dual inhibition potential toward CDK2 and TRKA kinases, along with their potential antiproliferative against cancer cell lines.

METHODS

A set of pyrazolo[1,5-a]pyrimidine derivatives (6a-t, 11a-g, and 12) was synthesized and subjected to in vitro enzymatic assays to determine their inhibitory activity against CDK2 and TRKA kinases. Selected compounds were further assessed for antiproliferative effects across the set of 60 cell lines from the NCI, representing various human cancer types. Additionally, simulations of molecular docking were conducted to explore the modes of binding for the whole active compounds and compare them with known inhibitors.

RESULTS

Compounds 6t and 6s exhibited potent dual inhibitory activity, showing an IC50 = 0.09 µM and 0.23 µM against CDK2, and 0.45 µM against TRKA, respectively. These results were comparable to reference inhibitors ribociclib (CDK2, IC50 = 0.07 µM) and larotrectinib (TRKA, IC50 = 0.07 µM). Among the studied derivatives, compound 6n displayed a notable broad-spectrum anticancer activity, achieving a mean growth inhibition (GI%) of 43.9% across 56 cell lines. Molecular docking simulations revealed that the synthesized compounds adopt modes of binding similar to those of the lead inhibitors. Conclusions: In this study, prepared pyrazolo[1,5-a]pyrimidine derivatives demonstrated significant potential as dual CDK2/TRKA inhibitors, and showed potent anticancer activity toward diverse cancer cell lines. These findings highlight their potential as key compounds for the design of novel anticancer therapeutics.

Evaluating anticancer activity of emodin by enhancing antioxidant activities and affecting PKC/ADAMTS4 pathway in thioacetamide-induced hepatocellular carcinoma in rats

Emodin is a naturally occurring anthraquinone derivative with a wide range of pharmacological activities, including neuroprotective and anti-inflammatory activities. We aim to assess the anticancer activity of emodin against hepatocellular carcinoma (HCC) in rat models using the proliferation, invasion, and angiogenesis biomarkers. After induction of HCC, assessment of the liver impairment and the histopathology of liver sections were investigated. Hepatic expression of both mRNA and protein of the oxidative stress biomarkers, HO-1, Nrf2; the mitogenic activation biomarkers, ERK5, PKCδ; the tissue destruction biomarker, ADAMTS4; the tissue homeostasis biomarker, aggregan; the cellular fibrinolytic biomarker, MMP3; and of the cellular angiogenesis biomarker, VEGF were measured. Emodin increased the survival percentage and reduced the number of hepatic nodules compared to the HCC group. Besides, emodin reduced the elevated expression of both mRNA and proteins of all PKC, ERK5, ADAMTS4, MMP3, and VEGF compared with the HCC group. On the other hand, emodin increased the expression of mRNA and proteins of Nrf2, HO-1, and aggrecan compared with the HCC group. Therefore, emodin is a promising anticancer agent against HCC preventing the cancer prognosis and infiltration. It works through many mechanisms of action, such as blocking oxidative stress, proliferation, invasion, and angiogenesis.

Protein kinase A and local signaling in cancer

Protein kinase A (PKA) is a basophilic kinase implicated in the modulation of many cell-signaling and physiological processes. PKA also contributes to cancer-relevant events such as growth factor action, cell cycle control, cell migration and tumor metabolism. Germline and somatic mutations in PKA, gene amplifications, and chromosome rearrangements that encode kinase fusions, are linked to a growing number of malignant neoplasms. Mislocalization of PKA by exclusion from A-Kinase Anchoring Protein (AKAP) signaling islands further underlies cancer progression. This article highlights the influence of AKAP signaling and local kinase action in selected hallmarks of cancer. We also feature the utility of kinase inhibitor drugs as frontline and future anti-cancer therapies.

Expression and clinical significance of U2AF homology motif kinase 1 in oral squamous cell carcinoma

OBJECTIVE

U2AF homology motif kinase 1 (UHMK1) is a newly discovered molecule that may have multiple functions. Recent studies have revealed that UHMK1 had aberrant expression in many tumors and was associated with tumor progression. However, UHMK1 was rarely reported in oral squamous cell carcinoma (OSCC).

STUDY DESIGN

In this study, Western blot, quantitative real-time polymerase chain reaction (PCR), and immunohistochemistry were used to detect the expression of UHMK1 in OSCC and peritumoral non-neoplastic tissues. Then, its relationship with clinicopathologic parameters was analyzed. The Kaplan-Meier method and Cox regression model were used to analyze the effects of UHMK1 expression on the prognosis and survival of OSCC patients.

RESULTS

Our results showed that UHMK1 had higher expression in OSCC tissues compared with in peritumoral non-neoplastic tissues, and its high expression was associated with high TNM stage and lymph node metastasis. High UHMK1 expression was related to short overall and disease-free survival times. Moreover, UHMK1 expression was identified as an independent prognostic factor that influences overall and disease-free survival of OSCC patients.

CONCLUSIONS

High expression of UHMK1 is associated with the poor prognosis of patients, and it can be used as a potential prognostic molecule for OSCC.

Dynamic Interplay of Loop Motions Governs the Molecular Level Regulatory Dynamics in Spleen Tyrosine Kinase: Insights from Molecular Dynamics Simulations

The spleen tyrosine kinase (Syk) is a key regulator in immune cell signaling and is linked to various mechanisms in cancer and neurodegenerative diseases. Although most computational research on Syk focuses on novel drug design, the molecular-level regulatory dynamics remain unexplored. In this study, we utilized 5 × 1 μs all-atom molecular dynamics simulations of the Syk kinase domain, examining it in combinations of activation segment phosphorylated/unphosphorylated (at Tyr525, Tyr526) and the "DFG"-Asp protonated/deprotonated (at Asp512) states to investigate conformational variations and regulatory dynamics of various loops and motifs within the kinase domain. Our findings revealed that the formation and disruption of several electrostatic interactions among residues within and near the activation segment likely influenced its dynamics. The protein structure network analysis indicated that the N-terminal and C-terminal anchors were stabilized by connections with the nearby stable helical regions. The P-loop showed conformational variation characterized by movements toward and away from the conserved "HRD"-motif. Additionally, there was a significant correlation between the movement of the β3-αC loop and the P-loop, which controls the dimensions of the adenine-binding cavity of the C-spine region. Overall, understanding these significant motions of the Syk kinase domain enhances our knowledge of its functional regulatory mechanism and can guide future research.

Role of Tyrosine Phosphorylation in PTP-PEST

We study the influence of tyrosine phosphorylation on PTP-PEST, a cytosolic protein tyrosine phosphatase. Utilizing a combination of experimental data and computational modeling, specific tyrosine sites, notably, Y64 and Y88, are identified for potential phosphorylation. Phosphorylation at these sites affects loop dynamics near the catalytic site, altering interactions among key residues and modifying the size of the binding pocket. This, in turn, impacts substrate binding, as indicated by changes in the binding energy. Our findings provide insights into the structural and functional consequences of tyrosine phosphorylation on PTP-PEST, enhancing our understanding of its effects on substrate binding and catalytic conformation.

Protease activated receptor-1 regulates mixed lineage kinase-3 to drive triple-negative breast cancer tumorigenesis

Triple-negative breast cancer (TNBC) is difficult to treat breast cancer subtype due to lack or insignificant expressions of targetable estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Therefore, finding a targetable protein or signaling pathway in TNBC would impact patient care. Here, we report that a member of the Mixed Lineage Kinase (MLK) family, MLK3, is an effector of G-protein-coupled protease-activated receptors 1 (PAR1) and targeting MLK3 by a small-molecule inhibitor prevented PAR1-mediated TNBC tumorigenesis. In silico and immunohistochemistry analysis of human breast tumors showed overexpression of PAR1 and MLK3 in TNBC tumors. Treating α-thrombin and PAR1 agonist increased MLK3 and JNK activities and induced cell migration in TNBC cells. The PAR1 positive/high (PAR1+/hi) population of TNBC cells showed aggressive tumor phenotype with increased MLK3 signaling. Moreover, combined inhibition of the PAR1 and MLK3 mitigated the TNBC tumor burden in preclinical TNBC models. Our data suggests that activation of the PAR1-MLK3 axis promotes TNBC tumorigenesis. Therefore, combinatorial therapy targeting MLK3 and PAR1 could effectively reduce TNBC tumor burden.

True Mitotic Count Prediction in Gastrointestinal Stromal Tumors: Bayesian Network Model and PROMETheus (Preoperative Mitosis Estimator Tool) Application Development

BACKGROUND

Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy.

OBJECTIVE

The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs.

METHODS

Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed.

RESULTS

Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model's accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model.

CONCLUSIONS

The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.

The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform

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.

Special Issue: "Molecular Dynamics Simulations and Structural Analysis of Protein Domains"

The 3D protein structure is the basis for all their biological functions [...].

Identification of high-affinity pyridoxal kinase inhibitors targeting cancer therapy: an integrated docking and molecular dynamics simulation approach

Pyridoxal kinase (PDXK) is a vitamin B6-dependent transferase enzyme encoded by the PDXK gene, crucial for leukemic cell proliferation. Disruption of its activity causes altered metabolism and reduced levels of nucleotides and polyamines. PDXK and pyridoxal 5'-phosphate (PLP) are overexpressed in various carcinomas, making them promising targets for drug design against cancer. Targeting PDXK may hold promise as a therapeutic approach for cancer treatment. This study focused on discovering potential inhibitors that could selectively interrupt the binding of pyridoxal phosphate (PLP) to pyridoxal kinase (PDXK). A commercially available library of 7,28,747 natural and druglike compounds was virtually screened using a molecular docking approach to target the substrate binding pocket of PDXK. Six promising inhibitors were identified, and all-atom molecular dynamics simulations were conducted on the PDXK-ligand complexes for 100 ns to assess their binding conformational stability. The simulation results indicated that the binding of ZINC095099376, ZINC01612996, ZINC049841390, ZINC095098959, ZINC01482077, and ZINC03830976 induced a slight structural change and stabilized the PDXK structure. This analysis provided valuable information about the critical residues involved in the PDXK-PLP complex formation and can be utilized in designing specific and effective PDXK inhibitors. According to this study, these compounds could be developed as anticancer agents targeting PDXK as a potential candidate for further study.Communicated by Ramaswamy H. Sarma.

An experimental and computational investigation of the cyclopentene-containing peptide-derived compounds: focus on pseudo-cyclic motifs via intramolecular interactions

Conformational flexibility is one of the main disadvantages of peptide-based compounds. We focus on their molecular 'chameleonicity' related to forming pseudo-cyclic motifs via modulation of weak intramolecular interactions. It is an appealing strategy for controlling equilibrium between the polar open and the nonpolar closed conformations. Within this context, we report here the crystal structure of the (R)-(2-tert-butoxycarbonyl)amino-1-oxo-3-phenyl)propyl)-1-cyclopentene (1), synthesis of which in high yield was achieved by a facile multi-step protocol. Our Cambridge Structural Database (CSD) overview for the peptide-based crystals revealed the exclusivity of this compound from the viewpoint of the unusual pseudo-bicyclic system via C-H…O and C-O…π interactions, in which cyclopentene shields the amide bond. Notably, cyclopentene as a bioisostere of proline is an appealing scaffold in medicinal chemistry. An extensive combined experimental and computational study provided more profound insight into the supramolecular landscape of 1 with respect to similar derivatives deposited in the CSD, including the tendency of cyclopentene for the generation of pseudo-cyclic motifs through weak H-bonding and π-based intramolecular interactions. These weak interactions have been examined by either the quantum theory of 'atoms-in-molecules' (QTAIM) or complex Hirshfeld surface methodology, including enrichment ratios, molecular electrostatic potential surfaces and energy frameworks. In all analysed crystals, all types of H-bonded motifs involving cyclopentene are formed at all levels of supramolecular architecture. A library of cyclopentene-based H-bonding synthons is provided. A molecular docking study depicted vital interactions of cyclopentene with key amino acid residues inside the active sites of two prominent protein kinases, uncovering the therapeutic potential of 1 against breast cancer. To a large extent, dispersion forces have significance in stabilizing the supramolecular structure of both ligand and bio-complex ligand-protein. Finally, the satisfactory in silico bio-pharmacokinetic profile of 1 related to drug-likeness and blood-brain barrier permeation was also revealed.

Identification of Potent CHK2 Inhibitors‐Modulators for Therapeutic Application in Cancer: A Machine Learning Integrated Fragment‐Based Drug Design Approach

The CHK2 protein regulates the cell division cycle and responds to DNA damage. Additionally, it facilitates the repair of DNA damage and maintains the integrity of its biological processes. Dysregulation of the CHK2 protein is associated with a predisposition to harmful diseases. The current research protocol was designed to identify novel hit molecules as CHK2 inhibitors and disrupt the normal biological function of the CHK2 protein via a fragment‐based drug discovery approach. The protocol involved generating fragments using the MacFrag tool, followed by a chemical similarity search utilizing RDKit to identify fragment molecules analogous to previously established CHK2 inhibitor scaffolds. The bioactive molecules were constructed using the Fragmenstein tool, followed by molecular docking simulations to investigate their binding affinity. In addition, pharmacokinetic properties were analyzed, and a molecular dynamics simulation study was conducted to assess the stability of selected compounds with CHK2 protein. Finally, five novel compounds were identified as excellent CHK2 inhibitors through the FBDD and show good binding interactions at active sites of CHK2 with beneficial ADMET properties. This research work presents novel CHK2 inhibitor molecules that have the potential to be utilized in drug discovery, serving as key leads for future advancements in healthcare industries and sectors.

Cellular and molecular biology of posttranslational modifications in cardiovascular disease

Cardiovascular disease (CVD) has now become the leading cause of death worldwide, and its high morbidity and mortality rates pose a great threat to society. Although numerous studies have reported the pathophysiology of CVD, the exact pathogenesis of all types of CVD is not fully understood. Therefore, much more research is still needed to explore the pathogenesis of CVD. With the development of proteomics, many studies have successfully identified the role of posttranslational modifications in the pathogenesis of CVD, including key processes such as apoptosis, cell metabolism, and oxidative stress. In this review, we summarize the progress in the understanding of posttranslational modifications in cardiovascular diseases, including novel protein posttranslational modifications such as succinylation and nitrosylation. Furthermore, we summarize the currently identified histone deacetylase (HDAC) inhibitors used to treat CVD, providing new perspectives on CVD treatment modalities. We critically analyze the roles of posttranslational modifications in the pathogenesis of CVD-related diseases and explore future research directions related to posttranslational modifications in cardiovascular diseases.

Role of the CDKL1-SOX11 signaling axis in acute kidney injury

The biology of the cyclin-dependent kinase-like (CDKL) kinase family remains enigmatic. Contrary to their nomenclature, CDKLs do not rely on cyclins for activation and are not involved in cell cycle regulation. Instead, they share structural similarities with mitogen-activated protein kinases and glycogen synthase kinase-3, although their specific functions and associated signaling pathways are still unknown. Previous studies have shown that the activation of CDKL5 kinase contributes to the development of acute kidney injury (AKI) by suppressing the protective SOX9-dependent transcriptional program in tubular epithelial cells. In the current study, we measured the functional activity of all five CDKL kinases and discovered that, in addition to CDKL5, CDKL1 is also activated in tubular epithelial cells during AKI. To explore the role of CDKL1, we generated a germline knockout mouse that exhibited no abnormalities under normal conditions. Notably, when these mice were challenged with bilateral ischemia-reperfusion and rhabdomyolysis, they were found to be protected from AKI. Further mechanistic investigations revealed that CDKL1 phosphorylates and destabilizes SOX11, contributing to tubular dysfunction. In summary, this study has unveiled a previously unknown CDKL1-SOX11 axis that drives tubular dysfunction during AKI.NEW & NOTEWORTHY Identifying and targeting pathogenic protein kinases holds potential for drug discovery in treating acute kidney injury. Our study, using novel germline knockout mice, revealed that Cdkl1 kinase deficiency does not affect mouse viability but provides protection against acute kidney injury. This underscores the importance of Cdkl1 kinase in kidney injury and supports the development of targeted small-molecule inhibitors as potential therapeutics.

Proteomic approaches for protein kinase substrate identification in Apicomplexa

Apicomplexa is a phylum of protist parasites, notable for causing life-threatening diseases including malaria, toxoplasmosis, cryptosporidiosis, and babesiosis. Apicomplexan pathogenesis is generally a function of lytic replication, dissemination, persistence, host cell modification, and immune subversion. Decades of research have revealed essential roles for apicomplexan protein kinases in establishing infections and promoting pathogenesis. Protein kinases modify their substrates by phosphorylating serine, threonine, tyrosine, or other residues, resulting in rapid functional changes in the target protein. Post-translational modification by phosphorylation can activate or inhibit a substrate, alter its localization, or promote interactions with other proteins or ligands. Deciphering direct kinase substrates is crucial to understand mechanisms of kinase signaling, yet can be challenging due to the transient nature of kinase phosphorylation and potential for downstream indirect phosphorylation events. However, with recent advances in proteomic approaches, our understanding of kinase function in Apicomplexa has improved dramatically. Here, we discuss methods that have been used to identify kinase substrates in apicomplexan parasites, classifying them into three main categories: i) kinase interactome, ii) indirect phosphoproteomics and iii) direct labeling. We briefly discuss each approach, including their advantages and limitations, and highlight representative examples from the Apicomplexa literature. Finally, we conclude each main category by introducing prospective approaches from other fields that would benefit kinase substrate identification in Apicomplexa.

In silico identification of putative druggable pockets in PRL3, a significant oncology target

Protein tyrosine phosphatases (PTP) have emerged as targets in diseases characterized by aberrant phosphorylations such as cancers. The activity of the phosphatase of regenerating liver 3, PRL3, has been linked to several oncogenic and metastatic pathways, particularly in breast, ovarian, colorectal, and blood cancers. Development of small molecules that directly target PRL3, however, has been challenging. This is partly due to the lack of structural information on how PRL3 interacts with its inhibitors. Here, computational methods are used to bridge this gap by evaluating the druggability of PRL3. In particular, web-based pocket prediction tools, DoGSite3 and FTMap, were used to identify binding pockets using structures of PRL3 currently available in the Protein Data Bank. Druggability assessment by molecular dynamics simulations with probes was also performed to validate these results and to predict the strength of binding in the identified pockets. While several druggable pockets were identified, those in the closed conformation show more promise given their volume and depth. These two pockets flank the active site loops and roughly correspond to pockets predicted by molecular docking in previous papers. Notably, druggability simulations predict the possibility of low nanomolar affinity inhibitors in these sites implying the potential to identify highly potent small molecule inhibitors for PRL3. Putative pockets identified here can be leveraged for high-throughput virtual screening to further accelerate the drug discovery against PRL3 and development of PRL3-directed therapeutics.

The prospect of substrate-based kinase inhibitors to improve target selectivity and overcome drug resistance

Human kinases are recognized as one of the most important drug targets associated with cancer. There are >80 FDA-approved kinase inhibitors to date, most of which work by inhibiting ATP binding to the kinase. However, the frequent development of single-point mutations within the kinase domain has made overcoming drug resistance a major challenge in drug discovery today. Targeting the substrate site of kinases can offer a more selective and resistance-resilient solution compared to ATP inhibition but has traditionally been challenging. However, emerging technologies for the discovery of drug leads using recombinant display and stabilization of lead compounds have increased interest in targeting the substrate site of kinases. This review discusses recent advances in the substrate-based inhibition of protein kinases and the potential of such approaches for overcoming the emergence of resistance.

Discovery of Two Highly Selective Structurally Orthogonal Chemical Probes for Activin Receptor-like Kinases 1 and 2

Activin receptor-like kinases 1-7 (ALK1-7) regulate a complex network of SMAD-independent as well as SMAD-dependent signaling pathways. One of the widely used inhibitors for functional investigations of these processes, in particular for bone morphogenetic protein (BMP) signaling, is LDN-193189. However, LDN-193189 has insufficient kinome-wide selectivity complicating its use in cellular target validation assays. Herein, we report the identification and comprehensive characterization of two chemically distinct highly selective inhibitors of ALK1 and ALK2, M4K2234 and MU1700, along with their negative controls. We show that both MU1700 and M4K2234 efficiently block the BMP pathway via selective in cellulo inhibition of ALK1/2 kinases and exhibit favorable in vivo profiles in mice. MU1700 is highly brain penetrant and shows remarkably high accumulation in the brain. These high-quality orthogonal chemical probes offer the selectivity required to become widely used tools for in vitro and in vivo investigation of BMP signaling.

Identifying potential Alzheimer's disease therapeutics through GSK-3β inhibition: A molecular docking and dynamics approach

Emerging as a promising drug target for Alzheimer's disease (AD) therapy, glycogen synthase kinase 3β (GSK-3β) has garnered attention. This study sought to rigorously scrutinize a compendium of natural compounds retrieved from the ZINC database through pharmacodynamic experiments, employing a 1 H-indazole-3-carboxamide (INDZ) scaffold, to identify compounds capable of inhibiting the GSK-3β protein. Utilizing a multi-step approach, the study involved pharmacophore analysis, followed by molecular docking to select five promising ligands for further investigation. Subsequently, ESMACS simulations were employed to assess the stability of the ligand-protein interactions. Evaluation of the binding modes and free energy of the ligands revealed that five compounds (2a-6a) exhibited crucial interactions with the active site residues. Furthermore, various methodologies, including hydrogen bond and clustering analyses, were utilized to ascertain their inhibitory potential and elucidate the factors contributing to ligand binding in the protein's active site. The findings from MMPBSA/GBSA analysis indicated that these five selected small molecules closely approached the IC50 value of the reference ligand (OH8), yielding energy values of -34.85, -32.58, -31.71, and -30.39 kcal/mol, respectively. Additionally, an assessment of the interactions using hydrogen bond and dynamic analyses delineated the effective binding of the ligands with the binding pockets in the protein. Through computational analysis, we obtained valuable insights into the molecular mechanisms of GSK-3β, aiding in the development of more potent inhibitors.

Kinase inhibitors: 20 years of success and many new challenges and recent trends in their patents

INTRODUCTION

Protein kinases (PKs) play key roles in cellular signaling and regulation cascades and therefore are listed among the most investigated enzymes with the intent to develop drugs that are able to modulate their catalytic features. Specifically, PKs are involved in chronic diseases of large impact in the society such as cancers and neurodegeneration. Since the approval of Fasudil for the management of cerebral vasospasm, frantic efforts are currently ongoing for the development of selective PK-modulating agents.

AREAS COVERED

A selection of the most relevant patents in the European Patent Office for biomedical innovation and/or industrial development covering the years 2020-2023 on PK modulators either of the antibody and small-molecule type is reported. In addition to the examined patents, we also reported the contributions claiming the use of antibody-targeted PKs for lab bench identification kits.

EXPERT OPINION

The field of PK modulators for biomedical purposes is particularly crowded with contributions, making it rich in valuable information for the development of potential drugs. An emerging frontier is represented by PK activators that aims to complement the use of PK inhibitors with the final intent of finely adjusting any PK-related disruption responsible for triggering any disease.

Multiplexed Profiling of CDK Kinase Activities in Tumor Biopsies with Fluorescent Peptide Biosensors

Detection of disease biomarkers constitutes a major challenge for the development of personalized and predictive diagnostics as well as companion assays. Protein kinases (PKs) involved in the coordination of cell cycle progression and proliferation that are hyperactivated in human cancers constitute attractive pharmacological targets and relevant biomarkers. Although it is relatively straightforward to assess the relative abundance of PKs in a biological sample, there is not always a direct correlation with enzymatic activity, which is regulated by several posttranslational mechanisms. Studies of relative abundance therefore convey limited information, and the lack of selective, sensitive, and standardized tools together with the inherent complexity of biological samples makes it difficult to quantify PK activities in physio-pathological tissues. To address this challenge, we have developed a toolbox of fluorescent biosensors that report on CDK activities in a sensitive, selective, dose-dependent, and quantitative fashion, which we have implemented to profile CDK activity signatures in cancer cell lines and biopsies from human tumors. In this study, we report on a standardized and calibrated biosensing approach to quantify CDK1,2,4, and 6 activities simultaneously through a combination of four different biosensors in a panel of 40 lung adenocarcinoma and 40 follicular lymphoma samples. CDK activity profiling highlighted two major patterns which were further correlated with age, sex of patients, tumor size, grade, and genetic and immunohistochemical features of the biopsies. Multiplex CDKACT biosensing technology provides new and complementary information relative to current genetic and immunohistochemical characterization of tumor biopsies, which will be useful for diagnostic purposes, potentially guiding therapeutic decision. These fluorescent peptide biosensors offer promise for personalized diagnostics based on kinase activity profiling.

Functional analysis of the Aspergillus fumigatus kinome identifies a druggable DYRK kinase that regulates septal plugging

More than 10 million people suffer from lung diseases caused by the pathogenic fungus Aspergillus fumigatus. Azole antifungals represent first-line therapeutics for most of these infections but resistance is rising, therefore the identification of antifungal targets whose inhibition synergises with the azoles could improve therapeutic outcomes. Here, we generate a library of 111 genetically barcoded null mutants of Aspergillus fumigatus in genes encoding protein kinases, and show that loss of function of kinase YakA results in hypersensitivity to the azoles and reduced pathogenicity. YakA is an orthologue of Candida albicans Yak1, a TOR signalling pathway kinase involved in modulation of stress responsive transcriptional regulators. We show that YakA has been repurposed in A. fumigatus to regulate blocking of the septal pore upon exposure to stress. Loss of YakA function reduces the ability of A. fumigatus to penetrate solid media and to grow in mouse lung tissue. We also show that 1-ethoxycarbonyl-beta-carboline (1-ECBC), a compound previously shown to inhibit C. albicans Yak1, prevents stress-mediated septal spore blocking and synergises with the azoles to inhibit A. fumigatus growth.

The mechanism of covalent inhibition of LAR phosphatase by illudalic acid

Leukocyte antigen-related (LAR) phosphatase is a receptor-type protein tyrosine phosphatase involved in cellular signaling and associated with human disease including cancer and metabolic disorders. Selective inhibition of LAR phosphatase activity by well characterized and well validated small molecules would provide key insights into the roles of LAR phosphatase in health and disease, but identifying selective inhibitors of LAR phosphatase activity has been challenging. Recently, we described potent and selective inhibition of LAR phosphatase activity by the fungal natural product illudalic acid. Here we provide a detailed biochemical characterization of the adduct formed between LAR phosphatase and illudalic acid. A mass spectrometric analysis indicates that two cysteine residues are covalently labeled by illudalic acid and a related analog. Mutational analysis supports the hypothesis that inhibition of LAR phosphatase activity is due primarily to the adduct with the catalytic cysteine residue. A computational study suggests potential interactions between the illudalic acid moiety and the enzyme active site. Taken together, these data offer novel insights into the mechanism of inhibition of LAR phosphatase activity by illudalic acid.

Targeting oncogenic kinases: Insights on FDA approved tyrosine kinase inhibitors

Protein kinases play pivotal roles in various biological functions, influencing cell differentiation, promoting survival, and regulating the cell cycle. The disruption of protein kinase activity is intricately linked to pathways in tumor development. This manuscript explores the transformative impact of protein kinase inhibitors on cancer therapy, particularly their efficacy in cases driven by targeted mutations. Focusing on key tyrosine kinase inhibitors (TKIs) like Bcr-Abl, Epidermal Growth Factor Receptor (EGFR), and Vascular Endothelial Growth Factor Receptor (VEGFR), it targets critical kinase families in cancer progression. Clinical trial details of these TKIs offer insights into their therapeutic potentials. Learning from FDA-approved kinase inhibitors, the review dissects trends in kinase drug development since imatinib's paradigm-shifting approval in 2001. TKIs have evolved into pivotal drugs, extending beyond oncology. Ongoing clinical trials explore novel kinase targets, revealing the vast potential within the human kinome. The manuscript provides a detailed analysis of advancements until 2022, discussing the roles of specific oncogenic protein kinases in cancer development and carcinogenesis. Our exploration on PubMed for relevant and significant TKIs undergoing pre-FDA approval phase III clinical trials enriches the discussion with valuable findings. While kinase inhibitors exhibit lower toxicity than traditional chemotherapy in cancer treatment, challenges like resistance and side effects emphasize the necessity of understanding resistance mechanisms, prompting the development of novel inhibitors like osimertinib targeting specific mutant proteins. The review advocates thorough research on effective combination therapies, highlighting the future development of more selective RTKIs to optimize patient-specific cancer treatment and reduce adverse events.

Predicting the functional state of protein kinases using interpretable graph neural networks from sequence and structural data

Protein kinases are central to cellular activities and are actively pursued as drug targets for several conditions including cancer and autoimmune diseases. Despite the availability of a large structural database for kinases, methodologies to elucidate the structure-function relationship of these proteins (without manual intervention) are lacking. Such techniques are essential in structural biology and to accelerate drug discovery efforts. Here, we implement an interpretable graph neural network (GNN) framework for classifying the functionally active and inactive states of a large set of protein kinases by only using their tertiary structure and amino acid sequence. We show that the GNN models can classify kinase structures with high accuracy (>97%). We implement the Gradient-weighted Class Activation Mapping for graphs (Graph Grad-CAM) to automatically identify structurally important residues and residue-residue contacts of the kinases without any a priori input. We show that the motifs identified through the Graph Grad-CAM methodology are functionally critical, consistent with the existing kinase literature. Notably, the highly conserved DFG and HRD motifs of the well-known hydrophobic spine are identified by the interpretable framework in addition to some of the lesser known motifs. Further, using Grad-CAM maps as the vector embedding of the protein structures, we identify the subtle differences in the crystal structures among different sub-classes of kinases in the Protein Data Bank (PDB). Frameworks such as the one implemented here, for high-throughput identification of protein structure-function relationships are essential in designing targeted small molecules therapies as well as in engineering new proteins for novel applications.

A magnetic calcium phosphate for selective capture of multi-phosphopeptides

The specific enrichment of multi-phosphopeptides in the presence of non-phosphopeptides and mono-phosphopeptides was still a challenge for phosphoproteomics research. Most of these enrichment materials relied on Zn, Ti, Sn, and other rare precious metals as the bonding center to enrich multi-phosphopeptides while ignoring the use of common metal elements. The addition of rare metals increased the cost of the experiment, which was not conducive to their large-scale application in biomedical proteomics laboratories. In addition, multiple high-speed centrifugation steps also resulted in the loss of low-abundance multi-phosphopeptides in the treatment procedure of biological samples. This study proposed the use of calcium, a common element, as the central bonding agent for synthesizing magnetic calcium phosphate materials (designated as CaP-Fe3O4). These materials aim to capture multi-phosphopeptides and identifying phosphorylation sites. The current results demonstrate that CaP-Fe3O4 exhibited excellent selection specificity, high sensitivity, and stability in the enrichment of multi-phosphopeptides and the identification of phosphorylation sites. Additionally, the introduction of magnetic separation not only reduced the time required for multi-phosphopeptides enrichment but also prevented the loss of these peptides during high-speed centrifugation. These findings contribute to the widespread application and advancement of phosphoproteomics research.

Discovery of a Covalent Inhibitor Selectively Targeting the Autophosphorylation Site of c-Src Kinase

Nonreceptor tyrosine kinase c-Src plays a crucial role in cell signaling and contributes to tumor progression. However, the development of selective c-Src inhibitors turns out to be challenging. In our previous study, we performed posttranslational modification-inspired drug design (PTMI-DD) to provide a plausible way for designing selective kinase inhibitors. In this study, after identifying a unique pocket comprising a less conserved cysteine and an autophosphorylation site in c-Src as well as a promiscuous covalent inhibitor, chemical optimization was performed to obtain (R)-LW-Srci-8 with nearly 75-fold improved potency (IC50 = 35.83 ± 7.21 nM). Crystallographic studies revealed the critical C-F···C═O interactions that may contribute to tight binding. The kinact and Ki values validated the improved binding affinity and decreased warhead reactivity of (R)-LW-Srci-8 for c-Src. Notably, in vitro tyrosine kinase profiling and cellular activity-based protein profiling (ABPP) cooperatively indicated a specific inhibition of c-Src by (R)-LW-Srci-8. Intriguingly, (R)-LW-Srci-8 preferentially binds to inactive c-Src with unphosphorylated Y419 both in vitro and in cells, subsequently disrupting the autophosphorylation. Collectively, our study demonstrated the feasibility of developing selective kinase inhibitors by cotargeting a nucleophilic residue and a posttranslational modification site and providing a chemical probe for c-Src functional studies.

Variola Virus and Clade I Mpox Virus Differentially Modulate Cellular Responses Longitudinally in Monocytes During Infection

Variola virus (VARV), the etiological agent of smallpox, had enormous impacts on global health prior to its eradication. In the absence of global vaccination programs, mpox virus (MPXV) has become a growing public health threat that includes endemic and nonendemic regions across the globe. While human mpox resembles smallpox in clinical presentation, there are considerable knowledge gaps regarding conserved molecular pathogenesis between these 2 orthopoxviruses. Thus, we sought to compare MPXV and VARV infections in human monocytes through kinome analysis. We performed a longitudinal analysis of host cellular responses to VARV infection in human monocytes as well as a comparative analysis to clade I MPXV-mediated responses. While both viruses elicited strong activation of cell responses early during infection as compared to later time points, several key differences in cell signaling events were identified and validated. These observations will help in the design and development of panorthopoxvirus therapeutics.

Efficient and metal-free synthesis of 2-aroyl 7-azaindoles via thermally induced denitrogenative intramolecular annulation of 1,2,3,4-tetrazolopyridines

A facile and metal-free intramolecular denitrogenative annulation strategy for the preparation of novel 2-aroyl 7-azaindoles has been developed from 3-(tetrazolo[1,5-a]pyridin-8-yl)prop-2-en-1-one in the presence of the deep eutectic solvent Dowtherm A. The valuable features of the protocol include a short reaction time, absence of any metal catalyst, utilization of a eutectic solvent, easy product isolation, and very good yields of novel 2-aroyl 7-azaindoles.

Investigating the transcriptomic variances in two phases Ecytonucleospora hepatopenaei (EHP) in Litopenaeus vannamei

This study explores the transcriptomic differences in two distinct phases of Ecytonucleospora hepatopenaei (EHP) in Litopenaeus vannamei, a crucial aspect in shrimp health management. We employed high-throughput sequencing to categorize samples into two phases, 'Phase A' and 'Phase B', defined by the differential expression of PTP2 and TPS1 genes. Our analysis identified 2057 genes, with 78 exhibiting significant variances, including 62 upregulated and 16 downregulated genes. Enrichment analyses via GO and KEGG pathways highlighted these genes' roles in cellular metabolism, signal transduction, and immune responses. Notably, genes like IQGAP2, Rhob, Pim1, and PCM1 emerged as potentially crucial in EHP's infection process and lifecycle. We hypothesize that these genes may influence trehalose metabolism and glucose provision, impacting the biological activities within EHP during different phases. Interestingly, a lower transcript count in 'Phase A' EHP suggests a reduction in biological activities, likely preparing for host cell invasion. This research provides a foundational understanding of EHP infection mechanisms, offering vital insights for future studies and therapeutic interventions.

Harnessing memantine in Alzheimer's disease therapy through inhibition of microtubule affinity-regulating kinase: Mechanistic insights

Alzheimer's disease (AD) is one of the neurodegenerative disorder that primarily affects memory, thinking, and behavior, eventually leading to severe cognitive impairment. Therapeutic management of AD is urgently needed to improve the quality and lifestyle of patients. Tau phosphorylating kinases are considered attractive therapeutic targets. Microtubule affinity-regulating kinase 4 (MARK4) is directly linked with pathological phosphorylations of tau, highlighting its role in the therapeutic targeting of AD. The current manuscript shows the MARK4 inhibitory effect of Memantine (MEM), a drug used in treating AD. We have performed fluorescence based binding measurements, enzyme inhibition assay, docking and molecular dynamics (MD) simulations to understand the binding of of MARK4 and MEM and subsequent inhibition in the kinase activity. A 100 ns MD simulations provided a detailed analysis of MARK4-MEM complex and the role of potential critical residues in the binding. Finally, this study provides molecular insights into the therapeutic implication of MEM in AD therapeutics. We propose MEM effectively inhibits MARK4, it may be implicated in the development of targeted and efficient treatments for AD.

Kinome-Wide Virtual Screening by Multi-Task Deep Learning

Deep learning is a machine learning technique to model high-level abstractions in data by utilizing a graph composed of multiple processing layers that experience various linear and non-linear transformations. This technique has been shown to perform well for applications in drug discovery, utilizing structural features of small molecules to predict activity. Here, we report a large-scale study to predict the activity of small molecules across the human kinome-a major family of drug targets, particularly in anti-cancer agents. While small-molecule kinase inhibitors exhibit impressive clinical efficacy in several different diseases, resistance often arises through adaptive kinome reprogramming or subpopulation diversity. Polypharmacology and combination therapies offer potential therapeutic strategies for patients with resistant diseases. Their development would benefit from a more comprehensive and dense knowledge of small-molecule inhibition across the human kinome. Leveraging over 650,000 bioactivity annotations for more than 300,000 small molecules, we evaluated multiple machine learning methods to predict the small-molecule inhibition of 342 kinases across the human kinome. Our results demonstrated that multi-task deep neural networks outperformed classical single-task methods, offering the potential for conducting large-scale virtual screening, predicting activity profiles, and bridging the gaps in the available data.

Recent Advances in Structural Optimization of Quinazoline-Based Protein Kinase Inhibitors for Cancer Therapy (2021-Present)

Cancer is a complicated, multifaceted disease that can impact any organ in the body. Various chemotherapeutic agents have a low selectivity and are very toxic when used alone or in combination with others. Resistance is one of the most important hurdles that develop due to the use of many anticancer therapeutics. As a result, treating cancer requires a target-specific palliative care strategy. Remarkable scientific discoveries have shed light on several of the molecular mechanisms underlying cancer, resulting in the development of various targeted anticancer agents. One of the most important heterocyclic motifs is quinazoline, which has a wide range of biological uses and chemical reactivities. Newer, more sophisticated medications with quinazoline structures have been found in the last few years, and great strides have been made in creating effective protocols for building these pharmacologically active scaffolds. A new class of chemotherapeutic agents known as quinazoline-based derivatives possessing anticancer properties consists of several well-known compounds that block different protein kinases and other molecular targets. This review highlights recent updates (2021-2024) on various quinazoline-based derivatives acting against different protein kinases as anticancer chemotherapeutics. It also provides guidance for the design and synthesis of novel quinazoline analogues that could serve as lead compounds.

Properties of FDA-approved small molecule protein kinase inhibitors: A 2024 update

Owing to the dysregulation of protein kinase activity in many diseases including cancer, this enzyme family has become one of the most important drug targets in the 21st century. There are 80 FDA-approved therapeutic agents that target about two dozen different protein kinases and seven of these drugs were approved in 2023. Of the approved drugs, thirteen target protein-serine/threonine protein kinases, four are directed against dual specificity protein kinases (MEK1/2), twenty block nonreceptor protein-tyrosine kinases, and 43 inhibit receptor protein-tyrosine kinases. The data indicate that 69 of these drugs are prescribed for the treatment of neoplasms. Six drugs (abrocitinib, baricitinib, deucravacitinib, ritlecitinib, tofacitinib, upadacitinib) are used for the treatment of inflammatory diseases (atopic dermatitis, rheumatoid arthritis, psoriasis, alopecia areata, and ulcerative colitis). Of the 80 approved drugs, nearly two dozen are used in the treatment of multiple diseases. The following seven drugs received FDA approval in 2023: capivasertib (HER2-positive breast cancer), fruquintinib (metastatic colorectal cancer), momelotinib (myelofibrosis), pirtobrutinib (mantle cell lymphoma, chronic lymphocytic leukemia, small lymphocytic lymphoma), quizartinib (Flt3-mutant acute myelogenous leukemia), repotrectinib (ROS1-positive lung cancer), and ritlecitinib (alopecia areata). All of the FDA-approved drugs are orally effective with the exception of netarsudil, temsirolimus, and trilaciclib. This review summarizes the physicochemical properties of all 80 FDA-approved small molecule protein kinase inhibitors including the molecular weight, number of hydrogen bond donors/acceptors, polar surface area, potency, solubility, lipophilic efficiency, and ligand efficiency.

Exploring the therapeutic potential of focal adhesion kinase inhibition in overcoming chemoresistance in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is among the leading causes of cancer-related deaths worldwide. Focal adhesion kinase (FAK) is a nonreceptor tyrosine kinase often overexpressed in PDAC. FAK has been linked to cell migration, survival, proliferation, angiogenesis and adhesion. This review first highlights the chemoresistant nature of PDAC. Second, the role of FAK in PDAC cancer progression and resistance is carefully described. Additionally, it discusses recent developments of FAK inhibitors as valuable drugs in the treatment of PDAC, with a focus on diamine-substituted-2,4-pyrimidine-based compounds, which represent the most potent class of FAK inhibitors in clinical trials for the treatment of PDAC disease. To conclude, relevant computational studies performed on FAK inhibitors are reported to highlight the key structural features required for interaction with the protein, with the aim of optimizing this novel targeted therapy.

Recent advances of anti-angiogenic inhibitors targeting VEGF/VEGFR axis

Vascular endothelial growth factors (VEGF), Vascular endothelial growth factor receptors (VEGFR) and their downstream signaling pathways are promising targets in anti-angiogenic therapy. They constitute a crucial system to regulate physiological and pathological angiogenesis. In the last 20 years, many anti-angiogenic drugs have been developed based on VEGF/VEGFR system to treat diverse cancers and retinopathies, and new drugs with improved properties continue to emerge at a fast rate. They consist of different molecular structures and characteristics, which enable them to inhibit the interaction of VEGF/VEGFR, to inhibit the activity of VEGFR tyrosine kinase (TK), or to inhibit VEGFR downstream signaling. In this paper, we reviewed the development of marketed anti-angiogenic drugs involved in the VEGF/VEGFR axis, as well as some important drug candidates in clinical trials. We discuss their mode of action, their clinical benefits, and the current challenges that will need to be addressed by the next-generation of anti-angiogenic drugs. We focus on the molecular structures and characteristics of each drug, including those approved only in China.

A Long Way to Go: A Scenario for Clinical Trials of PI3K Inhibitors in Treating Cancer

BACKGROUND

Alterations in PI3K function are directly related to cancer, making PI3K inhibitors suitable options for anticancer therapies. Information on therapy using different types of PI3K inhibitors is available in literature, providing indications of trends in developing new therapies. Although some studies on PI3K inhibitors for cancer treatment provide clinical evidence, they do not allow a careful search for potential PI3K inhibitors conducted by development indicators. Here, we performed a foresight study of clinical trials involving PI3K inhibitors from the past 11 years using indicators of clinical evolution to identify technological trends and provide data for supporting recommendations for new study designs.

METHODS

A comprehensive foresight study was designed based on documents from clinical trials on PI3K inhibitors to perform a systematic and comparative analysis, in order to identify technological trends on new cancer therapies.

RESULTS

Our results demonstrate that total number of clinical trials has decreased over the years and, currently, there is a clear prevalence of studies using isoform-specific inhibitors in combined interventions. Clinical trials in Phases I and II were the most frequently found in the database, whereas Phase III trials correspond to 7% of studies. The measurement of clinical trials progression using indicators (drugs in Phase III profile, top-10 drugs, and top-10 combined drugs) demonstrated that the 3 new medicines BKM120, IBI-376, and PF-05212384 have a high potential to provide more efficient cancer treatment in combined interventions. These data also include the groups of targets for each drug, providing a useful and reliable source for design new combinations to overcome the resistance and the poor tolerability observed in some PI3K therapies.

CONCLUSIONS

The establishment of development indicators based on clinical trials for cancer treatment was useful to highlight the clinical investment in 3 new PI3K drugs and the advantages of combine therapy using FDA-approved drugs.