Druggable Transient Pockets in Protein Kinases

Mirk/Dyrk1B Kinase Inhibitors in Targeted Cancer Therapy

During the last years, there has been an increased effort in the discovery of selective and potent kinase inhibitors for targeted cancer therapy. Kinase inhibitors exhibit less toxicity compared to conventional chemotherapy, and several have entered the market. Mirk/Dyrk1B kinase is a promising pharmacological target in cancer since it is overexpressed in many tumors, and its overexpression is correlated with patients' poor prognosis. Mirk/Dyrk1B acts as a negative cell cycle regulator, maintaining the survival of quiescent cancer cells and conferring their resistance to chemotherapies. Many studies have demonstrated the valuable therapeutic effect of Mirk/Dyrk1B inhibitors in cancer cell lines, mouse xenografts, and patient-derived 3D-organoids, providing a perspective for entering clinical trials. Since the majority of Mirk/Dyrk1B inhibitors target the highly conserved ATP-binding site, they exhibit off-target effects with other kinases, especially with the highly similar Dyrk1A. In this review, apart from summarizing the data establishing Dyrk1B as a therapeutic target in cancer, we highlight the most potent Mirk/Dyrk1B inhibitors recently reported. We also discuss the limitations and perspectives for the structure-based design of Mirk/Dyrk1B potent and highly selective inhibitors based on the accumulated structural data of Dyrk1A and the recent crystal structure of Dyrk1B with AZ191 inhibitor.

DnaK promotes autophosphorylation of DYRK1A and its family kinases in Escherichia coli-based cell-free protein expression

Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is one of the drug target kinases involved in neurological disorders. DYRK1A phosphorylates substrate proteins related to disease progression in an intermolecular manner. Meanwhile, DYRK1A intramolecularly phosphorylates its own residues on key segments during folding process, which is required for its activation and stabilization. To reproduce the autophosphorylation in vitro, DYRK1A was expressed in Escherichia coli-based cell-free protein synthesis system. Although this system was useful for investigating autophosphorylation of serine residue at position 97 (Ser97) in DYRK1A, only a small fraction of the synthesized protein was successfully autophosphorylated. In this study, we found that the addition of DnaK, a bacterial HSP70 chaperone, to cell-free expression of DYRK1A promoted its Ser97 autophosphorylation. Structure prediction with AlphaFold2 indicates that Ser97 forms a hydrogen bond within an α-helix structure, indicating a possibility that DnaK unfolds the α-helix and maintains the structure around Ser97 in a conformation susceptible to phosphorylation. In addition, DnaK promoted phosphorylation of DYRK1B and HIPK2, but not DYRK2 and DYRK4, suggesting a sequence selectivity in the action of DnaK. This study provides a facile method for promoting autophosphorylation of DYRK family kinases in cell-free protein expression.

Artificial intelligence methods in kinase target profiling: Advances and challenges

Kinases have a crucial role in regulating almost the full range of cellular processes, making them essential targets for therapeutic interventions against various diseases. Accurate kinase-profiling prediction is vital for addressing the selectivity/specificity challenges in kinase drug discovery, which is closely related to lead optimization, drug repurposing, and the understanding of potential drug side effects. In this review, we provide an overview of the latest advancements in machine learning (ML)-based and deep learning (DL)-based quantitative structure-activity relationship (QSAR) models for kinase profiling. We highlight current trends in this rapidly evolving field and discuss the existing challenges and future directions regarding experimental data set construction and model architecture design. Our aim is to offer practical insights and guidance for the development and utilization of these approaches.

Affimer-mediated locking of p21-activated kinase 5 in an intermediate activation state results in kinase inhibition

Kinases are important therapeutic targets, and their inhibitors are classified according to their mechanism of action, which range from blocking ATP binding to covalent inhibition. Here, a mechanism of inhibition is highlighted by capturing p21-activated kinase 5 (PAK5) in an intermediate state of activation using an Affimer reagent that binds in the P+1 pocket. PAK5 was identified from a non-hypothesis-driven high-content imaging RNAi screen in urothelial cancer cells. Silencing of PAK5 resulted in reduced cell number, G1/S arrest, and enlargement of cells, suggesting it to be important in urothelial cancer cell line survival and proliferation. Affimer reagents were isolated to identify mechanisms of inhibition. The Affimer PAK5-Af17 recapitulated the phenotype seen with siRNA. Co-crystallization revealed that PAK5-Af17 bound in the P+1 pocket of PAK5, locking the kinase into a partial activation state. This mechanism of inhibition indicates that another class of kinase inhibitors is possible.

Amodiaquine drug pressure selects nonsynonymous mutations in pantothenate kinase 1, diacylglycerol kinase, and phosphatidylinositol-4 kinase in Plasmodium berghei ANKA

Background

Lumefantrine (LM), piperaquine (PQ), and amodiaquine (AQ), the long-acting components of the artemisinin-based combination therapies (ACTs), are a cornerstone of malaria treatment in Africa. Studies have shown that PQ, AQ, and LM resistance may arise independently of predicted modes of action. Protein kinases have emerged as mediators of drug action and efficacy in malaria parasites; however, the link between top druggable Plasmodium kinases with LM, PQ, and AQ resistance remains unclear. Using LM, PQ, or AQ-resistant Plasmodium berghei parasites, we have evaluated the association of choline kinase (CK), pantothenate kinase 1 (PANK1), diacylglycerol kinase (DAGK), and phosphatidylinositol-4 kinase (PI4Kβ), and calcium-dependent protein kinase 1 (CDPK1) with LM, PQ, and AQ resistance in Plasmodium berghei ANKA.

Methods

We used in silico bioinformatics tools to identify ligand-binding motifs, active sites, and sequence conservation across the different parasites. We then used PCR and sequencing analysis to probe for single nucleotide polymorphisms (SNPs) within the predicted functional motifs in the CK, PANK1, DAGK, PI4Kβ, and CDPK1. Using qPCR analysis, we measured the mRNA amount of PANK1, DAGK, and PI4Kβ at trophozoites and schizonts stages.

Results

We reveal sequence conservation and unique ligand-binding motifs in the CK, PANK1, DAGK, PI4Kβ, and CDPK1 across malaria species. DAGK, PANK1, and PI4Kβ possessed nonsynonymous mutations; surprisingly, the mutations only occurred in the AQr parasites. PANK1 acquired Asn394His, while DAGK contained K270R and K292R mutations. PI4Kβ had Asp366Asn, Ser1367Arg, Tyr1394Asn and Asp1423Asn. We show downregulation of PANK1, DAGK, and PI4Kβ in the trophozoites but upregulation at the schizonts stages in the AQr parasites.

Conclusions

The selective acquisition of the mutations and the differential gene expression in AQ-resistant parasites may signify proteins under AQ pressure. The role of the mutations in the resistant parasites and their impact on drug responses require investigations using reverse genetics techniques in malaria parasites.

Insights into fourth generation selective inhibitors of (C797S) EGFR mutation combating non-small cell lung cancer resistance: a critical review

Lung cancer is the second most common cause of morbidity and mortality among cancer types worldwide, with non-small cell lung cancer (NSCLC) representing the majority of most cases. Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs) are among the most commonly used targeted therapy to treat NSCLC. Recent years have seen the evaluation of many synthetic EGFR TKIs, most of which showed therapeutic activity in pertinent models and were classified as first, second, and third-generation. The latest studies have concluded that their efficacy was also compromised by additional acquired mutations, including C797S. Because second- and third-generation EGFR TKIs are irreversible inhibitors, they are ineffective against C797S containing EGFR triple mutations (Del19/T790M/C797S and L858R/T790M/C797S). Therefore, there is an urgent unmet medical need to develop next-generation EGFR TKIs that selectively inhibit EGFR triple mutations via a non-irreversible mechanism. This review covers the fourth-generation EGFR-TKIs' most recent design with their essential binding interactions, the clinical difficulties, and the potential outcomes of treating patients with EGFR mutation C797S resistant to third-generation EGFR-TKIs was also discussed. Moreover, the utilization of various therapeutic strategies, including multi-targeting drugs and combination therapies, has also been reviewed.

Predicting allosteric pockets in protein biological assemblages

MOTIVATION

Allostery enables changes to the dynamic behavior of a protein at distant positions induced by binding. Here, we present APOP, a new allosteric pocket prediction method, which perturbs the pockets formed in the structure by stiffening pairwise interactions in the elastic network across the pocket, to emulate ligand binding. Ranking the pockets based on the shifts in the global mode frequencies, as well as their mean local hydrophobicities, leads to high prediction success when tested on a dataset of allosteric proteins, composed of both monomers and multimeric assemblages.

RESULTS

Out of the 104 test cases, APOP predicts known allosteric pockets for 92 within the top 3 rank out of multiple pockets available in the protein. In addition, we demonstrate that APOP can also find new alternative allosteric pockets in proteins. Particularly interesting findings are the discovery of previously overlooked large pockets located in the centers of many protein biological assemblages; binding of ligands at these sites would likely be particularly effective in changing the protein's global dynamics.

AVAILABILITY AND IMPLEMENTATION

APOP is freely available as an open-source code (https://github.com/Ambuj-UF/APOP) and as a web server at https://apop.bb.iastate.edu/.

Accessing Transient Binding Pockets by Protein Engineering and Yeast Surface Display Screening

The binding pocket of some therapeutic targets can acquire multiple conformations that, to some extent, depend on the protein dynamics and the interaction with other molecules. The inability to reach the binding pocket can impose a substantial or even insurmountable barrier for the de novo identification or optimization of small-molecule ligands. Herein, we describe a protocol for the engineering of a target protein and a yeast display FACS sorting strategy to identify protein variants with a stable transient binding pocket with improved binding for a cryptic site-specific ligand. This strategy may facilitate drug discovery using the resulting protein variants with accessible binding pockets for ligand screening.

Binding pocket stabilization by high-throughput screening of yeast display libraries

Protein dynamics have a great influence on the binding pockets of some therapeutic targets. Flexible protein binding sites can result in transient binding pocket formation which might have a negative impact on drug screening efforts. Here, we describe a protein engineering strategy with FK506-binding protein 51 (FKBP51) as a model protein, which is a promising target for stress-related disorders. High-throughput screening of yeast display libraries of FKBP51 resulted in the identification of variants exhibiting higher affinity binding of conformation-specific FKBP51 selective inhibitors. The gene libraries of a random mutagenesis and site saturation mutagenesis of the FK1 domain of FKBP51 encoding sequence were used to create a yeast surface display library. Fluorescence-activated cell sorting for FKBP51 variants that bind conformation-specific fluorescently labeled ligands with high affinity allowed for the identification of 15 different protein variants with improved binding to either, or both FKBP51-specific ligands used in the screening, with improved affinities up to 34-fold compared to the wild type. These variants will pave the way to a better understanding of the conformational flexibility of the FKBP51 binding pocket and may enable the isolation of new selective ligands that preferably and selectively bind the active site of the protein in its open conformation state.

DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model

Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's R = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.

Switch Pocket Kinase: An Emerging Therapeutic Target for the Design of Anticancer Agents

Protein kinases are amongst the most focused enzymes in current century to design, synthesize and formulate drugs ought to be effective in the treatment of various disordered and diseased states involving either overexpression or deficiency situations. The ATP pocket on the kinases is the binding active site for most of the kinase inhibitors. However, the kinase mutations prevent the binding of kinase inhibitors to ATP pocket. The switch pocket site on this enzyme when occupied by switch pocket inhibitors, the enzyme become inactive even in the mutated state. This review comprises the detailed information on various classical protein kinases and switch pocket kinase inhibitors with their mechanism of action so that new molecules can be designed to encounter mutations in the kinase enzyme.

Identification of a novel anticancer mechanism of Paeoniae Radix extracts based on systematic transcriptome analysis

Paeoniae Radix (PR) has a great therapeutic value in many clinical applications; however, the presence of various bioactive compounds and its complicated effects on human health makes its precise mechanisms of action unclear. This study investigated the effects of PR at the molecular pathway level by profiling genome-wide gene expression changes following dose-dependent treatment of human lung cancer cells (A549) with PR water extract (WPR), PR ethanol extracts (EPR), as well as their individual components. We found that PR exerts anticancer effects in A549 cells by regulating numerous pathways. Specifically, EPR and two compounds, namely, hederagenin (HG) and oleanolic acid (OA), significantly downregulate the Aurora B pathway. Furthermore, we generated an integrated PR extracts-compounds-target genes network in the Aurora B pathway to understand their interactions. Our findings reinforce that inhibiting Aurora kinase activity is a therapeutic target for treating cancers, providing the potential for novel mechanisms of action for PR and its components against lung cancer.

What Features of Ligands Are Relevant to the Opening of Cryptic Pockets in Drug Targets?

Small-molecule drug design aims to identify inhibitors that can specifically bind to a functionally important region on the target, i.e., an active site of an enzyme. Identification of potential binding pockets is typically based on static three-dimensional structures. However, small molecules may induce and select a dynamic binding pocket that is not visible in the apo protein, which presents a well-recognized challenge for structure-based drug discovery. Here, we assessed whether it is possible to identify features in molecules, which we refer to as inducers, that can induce the opening of cryptic pockets. The volume change between apo and bound protein conformations was used as a metric to differentiate chemical features in inducers vs. non-inducers. Based on the dataset of holo–apo pairs, classification models were built to determine an optimum threshold. The model analysis suggested that inducers preferred to be more hydrophobic and aromatic. The impact of sulfur was ambiguous, while phosphorus and halogen atoms were overrepresented in inducers. The fragment analysis showed that small changes in the structures of molecules can strongly affect the potential to induce a cryptic pocket. This analysis and developed model can be used to design inducers that can potentially open cryptic pockets for undruggable proteins.