Gini Coefficients as a Single Value Metric to Define Chemical Probe Selectivity

Discovery of novel dihydropteridone derivatives as orally bioavailable PLK1 inhibitors with reduced hERG inhibitory activity for acute myeloid leukemia treatment

Polo like kinase 1 (PLK1) is a serine/threonine kinase that plays an important role in multiple phases of the cell cycle, inhibiting its activity has been considered an effective treatment for acute myeloid leukemia (AML). Here, we reported a series of highly potent PLK1 inhibitors. Among them, compound WD6 was identified as the most promising PLK1 inhibitor, with an IC50 value of 0.27 nM and greatly reduced hERG affinity, with 12.78 % inhibition at 10 μM. Compound WD6 displayed significant anti-proliferative activities against MV4-11 (IC50 = 23.3 nM), excellent pharmacokinetic properties (t1/2 = 7.59 h, AUC0-t = 29300 ng h mL-1 and F = 35.1 %), good PPB and low risk of drug-drug interactions. In vivo, oral administration of compound WD6 at a dose of 20 mg/kg effectively suppressed the tumor growth in the MV4-11 xenograft mouse model. Further research indicated that WD6 exhibited excellent kinase selectivity, arresting MV4-11 cells at G2 phase, inducing apoptosis in a dose-dependent manner and down-regulating the transcription of the proliferation-related oncogene c-MYC. These results showed that compound WD6 has the potential to be a promising drug candidate for treating AML.

Technologies for Targeted RNA Degradation and Induced RNA Decay

The vast majority of the human genome codes for RNA, but RNA-targeting therapeutics account for a small fraction of approved drugs. As such, there is great incentive to improve old and develop new approaches to RNA targeting. For many RNA targeting modalities, just binding is not sufficient to exert a therapeutic effect; thus, targeted RNA degradation and induced decay emerged as powerful approaches with a pronounced biological effect. This review covers the origins and advanced use cases of targeted RNA degrader technologies grouped by the nature of the targeting modality as well as by the mode of degradation. It covers both well-established methods and clinically successful platforms such as RNA interference, as well as emerging approaches such as recruitment of RNA quality control machinery, CRISPR, and direct targeted RNA degradation. We also share our thoughts on the biggest hurdles in this field, as well as possible ways to overcome them.

Characterisation of APOBEC3B-Mediated RNA editing in breast cancer cells reveals regulatory roles of NEAT1 and MALAT1 lncRNAs

RNA editing is a crucial post-transcriptional process that influences gene expression and increases the diversity of the proteome as a result of amino acid substitution. Recently, the APOBEC3 family has emerged as a significant player in this mechanism, with APOBEC3A (A3A) having prominent roles in base editing during immune and stress responses. APOBEC3B (A3B), another family member, has gained attention for its potential role in generating genomic DNA mutations in breast cancer. In this study, we coupled an inducible expression cell model with a novel methodology for identifying differential variants in RNA (DVRs) to map A3B-mediated RNA editing sites in a breast cancer cell model. Our findings indicate that A3B engages in selective RNA editing including targeting NEAT1 and MALAT1 long non-coding RNAs that are often highly expressed in tumour cells. Notably, the binding of these RNAs sequesters A3B and suppresses global A3B activity against RNA and DNA. Release of A3B from NEAT1/MALAT1 resulted in increased A3B activity at the expense of A3A activity suggesting a regulatory feedback loop between the two family members. This research substantially advances our understanding of A3B's role in RNA editing, its mechanistic underpinnings, and its potential relevance in the pathogenesis of breast cancer.

ProCogGraph: a graph-based mapping of cognate ligand domain interactions

Motivation

Mappings of domain-cognate ligand interactions can enhance our understanding of the core concepts of evolution and be used to aid docking and protein design. Since the last available cognate-ligand domain database was released, the PDB has grown significantly and new tools are available for measuring similarity and determining contacts.

Results

We present ProCogGraph, a graph database of cognate-ligand domain mappings in PDB structures. Building upon the work of the predecessor database, PROCOGNATE, we use data-driven approaches to develop thresholds and interaction modes. We explore new aspects of domain-cognate ligand interactions, including the chemical similarity of bound cognate ligands and how domain combinations influence cognate ligand binding. Finally, we use the graph to add specificity to partial EC IDs, showing that ProCogGraph can complete partial annotations systematically through assigned cognate ligands.

Availability and implementation

The ProCogGraph pipeline, database and flat files are available at https://github.com/bashton-lab/ProCogGraph and https://doi.org/10.5281/zenodo.13165851.

Competitive Microarray Screening Reveals Functional Ligands for the DHX15 RNA G-Quadruplex

RNAs are increasingly considered valuable therapeutic targets, and the development of methods to identify and validate both RNA targets and ligands is more important than ever. Here, we utilized a bioinformatic approach to identify a hairpin-containing RNA G-quadruplex (rG4) in the 5' untranslated region (5' UTR) of DHX15 mRNA. By using a novel competitive small molecule microarray (SMM) approach, we identified a compound that specifically binds to the DHX15 rG4 (K D = 12.6 ± 1.0 μM). This rG4 directly impacts translation of a DHX15 reporter mRNA in vitro, and binding of our compound (F1) to the structure inhibits translation up to 57% (IC50 = 22.9 ± 3.8 μM). This methodology allowed us to identify and target the mRNA of a cancer-relevant helicase with no known inhibitors. Our target identification method and the novelty of our screening approach make our work informative for future development of novel small molecule cancer therapeutics for RNA targets.

Alnustone promotes megakaryocyte differentiation and platelet production via the interleukin-17A/interleukin-17A receptor/Src/RAC1/MEK/ERK signaling pathway

OBJECTIVES

Thrombocytopenia is a disease in which the number of platelets in the peripheral blood decreases. It can be caused by multiple genetic factors, and numerous challenges are associated with its treatment. In this study, the effects of alnustone on megakaryocytes and platelets were investigated, with the aim of developing a new therapeutic approach for thrombocytopenia.

METHODS

Random forest algorithm was used to establish a drug screening model, and alnustone was identified as a natural active compound that could promote megakaryocyte differentiation. The effect of alnustone on megakaryocyte activity was determined using cell counting kit-8. The effect of alnustone on megakaryocyte differentiation was determined using flow cytometry, Giemsa staining, and phalloidin staining. A mouse model of thrombocytopenia was established by exposing mice to X-rays at 4 Gy and was used to test the bioactivity of alnustone in vivo. The effect of alnustone on platelet production was determined using zebrafish. Network pharmacology was used to predict targets and signaling pathways. Western blotting and immunofluorescence staining determined the expression levels of proteins.

RESULTS

Alnustone promoted the differentiation and maturation of megakaryocytes in vitro and restored platelet production in thrombocytopenic mice and zebrafish. Network pharmacology and western blotting showed that alnustone promoted the expression of interleukin-17A and enhanced its interaction with its receptor, and thereby regulated downstream MEK/ERK signaling and promoted megakaryocyte differentiation.

CONCLUSIONS

Alnustone can promote megakaryocyte differentiation and platelet production via the interleukin-17A/interleukin-17A receptor/Src/RAC1/MEK/ERK signaling pathway and thus provides a new therapeutic strategy for the treatment of thrombocytopenia.

Large-scale analysis of small molecule-RNA interactions using multiplexed RNA structure libraries

The large-scale analysis of small-molecule binding to diverse RNA structures is key to understanding the required interaction properties and selectivity for developing RNA-binding molecules toward RNA-targeted therapies. Here, we report a new system for performing the large-scale analysis of small molecule-RNA interactions using a multiplexed pull-down assay with RNA structure libraries. The system profiled the RNA-binding landscapes of G-clamp and thiazole orange derivatives, which recognizes an unpaired guanine base and are good probes for fluorescent indicator displacement (FID) assays, respectively. We discuss the binding preferences of these molecules based on their large-scale affinity profiles. In addition, we selected combinations of fluorescent indicators and different ranks of RNA based on the information and screened for RNA-binding molecules using FID. RNAs with high- and intermediate-rank RNA provided reliable results. Our system provides fundamental information about small molecule-RNA interactions and facilitates the discovery of novel RNA-binding molecules.

Genistin: A Novel Estrogen Analogue Targeting ERβ to Alleviate Thrombocytopenia

Thrombocytopenia, a prevalent hematologic challenge, correlates directly with the mortality of numerous ailments. Current therapeutic avenues for thrombocytopenia are not without limitations. Here, we identify genistin, an estrogen analogue, as a promising candidate for thrombocytopenia intervention, discovered through AI-driven compound library screening. While estrogen's involvement in diverse biological processes is recognized, its role in thrombopoiesis remains underexplored. Our findings elucidate genistin's ability to enhance megakaryocyte differentiation, thereby augmenting platelet formation and production. In vivo assessments further underscore genistin's remedial potential against radiation-induced thrombocytopenia. Mechanistically, genistin's efficacy is attributed to its direct interaction with estrogen receptor β (ERβ), with subsequent activation of both ERK1/2 and the Akt signaling pathways membrane ERβ. Collectively, our study positions genistin as a prospective therapeutic strategy for thrombocytopenia, shedding light on novel interplays between platelet production and ERβ.

Contemporary Progress and Opportunities in RNA-Targeted Drug Discovery

The surprising discovery that RNAs are the predominant gene products to emerge from the human genome catalyzed a renaissance in RNA biology. It is now well-understood that RNAs act as more than just a messenger and comprise a large and diverse family of ribonucleic acids of differing sizes, structures, and functions. RNAs play expansive roles in the cell, contributing to the regulation and fine-tuning of nearly all aspects of gene expression and genome architecture. In line with the significance of these functions, we have witnessed an explosion in discoveries connecting RNAs with a variety of human diseases. Consequently, the targeting of RNAs, and more broadly RNA biology, has emerged as an untapped area of drug discovery, making the search for RNA-targeted therapeutics of great interest. In this Microperspective, I highlight contemporary learnings in the field and present my views on how to catapult us toward the systematic discovery of RNA-targeted medicines.

Machine Learning Informs RNA-Binding Chemical Space

Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.

An Introduction to Chemogenomics

Chemogenomics is an innovative approach in chemical biology that synergizes combinatorial chemistry and genomic and proteomic biology to systematically study the response of a biological system to a set of compounds, which can aid the identification and validation of biological targets as well as biologically active small-molecule agents responsible for a phenotypic outcome. Central to this strategy is a collection of chemically diverse compounds, a so-called chemogenomics library. Selection and annotation of vastly available chemogenomic compound candidates for an inclusion in such set present a challenge, but optimal compound selection is critical for success of chemogenomics. The library can be used in a wide variety of research applications from biological mechanism deconvolution to drug discovery. However, phenotypic screening methods are typically required to be high-throughput and equipped with a systematic analysis of complex biological-chemical interactions. This chapter provides a general outline to the chemogenomics approach, including concept and critical steps in all stages of this innovative chemical biology strategy.

Targeting RNA structures with small molecules

RNA adopts 3D structures that confer varied functional roles in human biology and dysfunction in disease. Approaches to therapeutically target RNA structures with small molecules are being actively pursued, aided by key advances in the field including the development of computational tools that predict evolutionarily conserved RNA structures, as well as strategies that expand mode of action and facilitate interactions with cellular machinery. Existing RNA-targeted small molecules use a range of mechanisms including directing splicing - by acting as molecular glues with cellular proteins (such as branaplam and the FDA-approved risdiplam), inhibition of translation of undruggable proteins and deactivation of functional structures in noncoding RNAs. Here, we describe strategies to identify, validate and optimize small molecules that target the functional transcriptome, laying out a roadmap to advance these agents into the next decade.

Target-specific compound selectivity for multi-target drug discovery and repurposing

Most drug molecules modulate multiple target proteins, leading either to therapeutic effects or unwanted side effects. Such target promiscuity partly contributes to high attrition rates and leads to wasted costs and time in the current drug discovery process, and makes the assessment of compound selectivity an important factor in drug development and repurposing efforts. Traditionally, selectivity of a compound is characterized in terms of its target activity profile (wide or narrow), which can be quantified using various statistical and information theoretic metrics. Even though the existing selectivity metrics are widely used for characterizing the overall selectivity of a compound, they fall short in quantifying how selective the compound is against a particular target protein (e.g., disease target of interest). We therefore extended the concept of compound selectivity towards target-specific selectivity, defined as the potency of a compound to bind to the particular protein in comparison to the other potential targets. We decompose the target-specific selectivity into two components: 1) the compound’s potency against the target of interest (absolute potency), and 2) the compound’s potency against the other targets (relative potency). The maximally selective compound-target pairs are then identified as a solution of a bi-objective optimization problem that simultaneously optimizes these two potency metrics. In computational experiments carried out using large-scale kinase inhibitor dataset, which represents a wide range of polypharmacological activities, we show how the optimization-based selectivity scoring offers a systematic approach to finding both potent and selective compounds against given kinase targets. Compared to the existing selectivity metrics, we show how the target-specific selectivity provides additional insights into the target selectivity and promiscuity of multi-targeting kinase inhibitors. Even though the selectivity score is shown to be relatively robust against both missing bioactivity values and the dataset size, we further developed a permutation-based procedure to calculate empirical p-values to assess the statistical significance of the observed selectivity of a compound-target pair in the given bioactivity dataset. We present several case studies that show how the target-specific selectivity can distinguish between highly selective and broadly-active kinase inhibitors, hence facilitating the discovery or repurposing of multi-targeting drugs.

Molecular Plasticity of Crystalline CK2α' Leads to KN2, a Bivalent Inhibitor of Protein Kinase CK2 with Extraordinary Selectivity

CK2α and CK2α' are paralogous catalytic subunits of CK2, which belongs to the eukaryotic protein kinases. CK2 promotes tumorigenesis and the spread of pathogenic viruses like SARS-CoV-2 and is thus an attractive drug target. Efforts to develop selective CK2 inhibitors binding offside the ATP site had disclosed the αD pocket in CK2α; its occupation requires large conformational adaptations of the helix αD. As shown here, the αD pocket is accessible also in CK2α', where the necessary structural plasticity can be triggered with suitable ligands even in the crystalline state. A CK2α' structure with an ATP site and an αD pocket ligand guided the design of the bivalent CK2 inhibitor KN2. It binds to CK2 with low nanomolar affinity, is cell-permeable, and suppresses the intracellular phosphorylation of typical CK2 substrates. Kinase profiling revealed a high selectivity of KN2 for CK2 and emphasizes the selectivity-promoting potential of the αD pocket.

Crystal Structure-Guided Design of Bisubstrate Inhibitors and Photoluminescent Probes for Protein Kinases of the PIM Family

We performed an X-ray crystallographic study of complexes of protein kinase PIM-1 with three inhibitors comprising an adenosine mimetic moiety, a linker, and a peptide-mimetic (d-Arg)₆ fragment. Guided by the structural models, simplified chemical structures with a reduced number of polar groups and chiral centers were designed. The developed inhibitors retained low-nanomolar potency and possessed remarkable selectivity toward the PIM kinases. The new inhibitors were derivatized with biotin or fluorescent dye Cy5 and then applied for the detection of PIM kinases in biochemical solutions and in complex biological samples. The sandwich assay utilizing a PIM-2-selective detection antibody featured a low limit of quantification (44 pg of active recombinant PIM-2). Fluorescent probes were efficiently taken up by U2OS cells and showed a high extent of co-localization with PIM-1 fused with a fluorescent protein. Overall, the developed inhibitors and derivatives represent versatile chemical tools for studying PIM function in cellular systems in normal and disease physiology.