Small Molecule Inhibitors of DYRK1A Identified by Computational and Experimental Approaches

Identification, biological evaluation, and crystallographic analysis of coumestrol as a novel dual-specificity tyrosine-phosphorylation-regulated kinase 1A inhibitor

Alzheimer's disease (AD) is an irreversible neurodegenerative disease, with tau pathology caused by abnormally activated dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) being one of the culprits. Coumestrol, a phytoestrogen and natural antioxidant found in various plants, has been reported to alleviate AD, but the underlying mechanism remains unclear. We confirmed coumestrol as a novel DYRK1A inhibitor through enzyme-based assays, X-ray crystallography, and cell line experiments. Coumestrol exhibited minimal cytotoxicity at concentrations up to 100 μM in cell types such as N2A and SH-SY5Y and reduced DYRK1A-induced phosphorylated tau protein levels by >50 % at 60 μM. In the tau protein phosphorylation and microtubule assembly assay, coumestrol at 30 μM reduced phosphorylated tau by >50 % and restored the microtubule assembly process. Coumestrol also significantly reduced amyloid-β (Aβ)-induced oxidative stress in microglia at 1 μM. In zebrafish larvae co-overexpressing DYRK1A and tau, coumestrol mitigated neuronal damage and protected motor function at 48 h-postfertilization. Our results suggest that coumestrol has potential therapeutic effects in AD by inhibiting DYRK1A, lowering p-Tau levels, restoring microtubule assembly, and protecting microglia cells from Aβ-induced cell death, providing new insights into the development of coumestrol as a potential AD treatment.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

TWN-FS method: A novel fragment screening method for drug discovery

Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.

TWN-RENCOD: A novel method for protein binding site comparison

Several diverse proteins possess similar binding sites. Protein binding site comparison provides valuable insights for the drug discovery and development. Binding site similarities are useful in understanding polypharmacology, identifying potential off-targets and repurposing of known drugs. Many binding site analysis and comparison methods are available today, however, these methods may not be adequate to explain variation in the activity of a drug or a small molecule against a number of similar proteins. Water molecules surrounding the protein surface contribute to structure and function of proteins. Water molecules form diverse types of hydrogen-bonded cyclic water-ring networks known as topological water networks (TWNs). Analysis of TWNs in binding site of proteins may improve understanding of the characteristics of binding sites. We propose TWN-based residue encoding (TWN-RENCOD), a novel binding site comparison method which compares the aqueous environment in binding sites of similar proteins. As compared to other existing methods, results obtained using our method correlated better with differences in wide range of activity of a known drug (Sunitinib) against nine different protein kinases (KIT, PDGFRA, VEGFR2, PHKG2, ITK, HPK1, MST3, PAK6 and CDK2).

The Omnipresence of DYRK1A in Human Diseases

The increasing population will challenge healthcare, particularly because the worldwide population has never been older. Therapeutic solutions to age-related disease will be increasingly critical. Kinases are key regulators of human health and represent promising therapeutic targets for novel drug candidates. The dual-specificity tyrosine-regulated kinase (DYRKs) family is of particular interest and, among them, DYRK1A has been implicated ubiquitously in varied human diseases. Herein, we focus on the characteristics of DYRK1A, its regulation and functional role in different human diseases, which leads us to an overview of future research on this protein of promising therapeutic potential.

The chromosome 21 kinase DYRK1A: emerging roles in cancer biology and potential as a therapeutic target

Dual-specificity tyrosine phosphorylation-regulated kinase 1 A (DYRK1A) is a serine/threonine kinase that belongs to the DYRK family of proteins, a subgroup of the evolutionarily conserved CMGC protein kinase superfamily. Due to its localization on chromosome 21, the biological significance of DYRK1A was initially characterized in the pathogenesis of Down syndrome (DS) and related neurodegenerative diseases. However, increasing evidence has demonstrated a prominent role in cancer through its ability to regulate biologic processes including cell cycle progression, DNA damage repair, transcription, ubiquitination, tyrosine kinase activity, and cancer stem cell maintenance. DYRK1A has been identified as both an oncogene and tumor suppressor in different models, underscoring the importance of cellular context in its function. Here, we review mechanistic contributions of DYRK1A to cancer biology and its role as a potential therapeutic target.

Identification of Pharmacophoric Fragments of DYRK1A Inhibitors Using Machine Learning Classification Models

Dual-specific tyrosine phosphorylation regulated kinase 1 (DYRK1A) has been regarded as a potential therapeutic target of neurodegenerative diseases, and considerable progress has been made in the discovery of DYRK1A inhibitors. Identification of pharmacophoric fragments provides valuable information for structure- and fragment-based design of potent and selective DYRK1A inhibitors. In this study, seven machine learning methods along with five molecular fingerprints were employed to develop qualitative classification models of DYRK1A inhibitors, which were evaluated by cross-validation, test set, and external validation set with four performance indicators of predictive classification accuracy (CA), the area under receiver operating characteristic (AUC), Matthews correlation coefficient (MCC), and balanced accuracy (BA). The PubChem fingerprint-support vector machine model (CA = 0.909, AUC = 0.933, MCC = 0.717, BA = 0.855) and PubChem fingerprint along with the artificial neural model (CA = 0.862, AUC = 0.911, MCC = 0.705, BA = 0.870) were considered as the optimal modes for training set and test set, respectively. A hybrid data balancing method SMOTETL, a combination of synthetic minority over-sampling technique (SMOTE) and Tomek link (TL) algorithms, was applied to explore the impact of balanced learning on the performance of models. Based on the frequency analysis and information gain, pharmacophoric fragments related to DYRK1A inhibition were also identified. All the results will provide theoretical supports and clues for the screening and design of novel DYRK1A inhibitors.

Neuroprotective effects of microRNA-211-5p on chronic stress-induced neuronal apoptosis and depression-like behaviours

Findings from recent studies have revealed that microRNAs (miRNAs) are related to numerous neurological disorders. However, whether miRNAs regulate neuronal anomalies involved in the pathogenesis of depression remain unclear. In the present study, we screened miRNA expression profiles in the CA1 hippocampus of a rat model of depression and found that a specific miRNA, microRNA-211-5p, was significantly down-regulated in depressed rats. When miR-211-5p was up-regulated in these rats, neuronal apoptosis within the CA1 area was suppressed, effects which were accompanied with an amelioration of depression-like behaviours in these rats. These neuroprotective effects of miR-211-5p in depressed rats appear to result through suppression of the Dyrk1A/ASK1/JNK signalling pathway within the CA1 area. In further support of this proposal are the findings that knock-down of miR-211-5p within the CA1 area of normal rats activated the Dyrk1A/ASK1/JNK pathway, resulting in the promotion of neuronal apoptosis and display of depression-like behaviours in these rats. Taken together, these results demonstrate that deficits in miR-211-5p contribute to neuronal apoptosis and thus depression-like behaviours in rats. Therefore, the miR-211-5p/Dyrk1A pathway may be critically involved in the pathogenesis of depression and serve as a potential therapeutic target for the treatment of depression.