Blocker-SELEX: a structure-guided strategy for developing inhibitory aptamers disrupting undruggable transcription factor interactions.

Despite the well-established significance of transcription factors (TFs) in pathogenesis, their utilization as pharmacological targets has been limited by the inherent challenges in modulating their protein interactions. The lack of defined small-molecule binding pockets and the nuclear localization of TFs do not favor the use of traditional tools. Aptamers possess large molecular weights, expansive blocking surfaces and efficient cellular internalization, making them compelling tools for modulating TF interactions. Here, we report a structure-guided design strategy called Blocker-SELEX to develop inhibitory aptamers (iAptamers) that selectively block TF interactions. Our approach leads to the discovery of iAptamers that cooperatively disrupt SCAF4/SCAF8-RNAP2 interactions, dysregulating RNAP2-dependent gene expression, which impairs cell proliferation. This approach is further applied to develop iAptamers blocking WDR5-MYC interactions. Overall, our study highlights the potential of iAptamers in disrupting pathogenic TF interactions, implicating their potential utility in studying the biological functions of TF interactions and in nucleic acids drug discovery.

DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Design, synthesis and evaluation of thieno[3,2-d]pyrimidine derivatives as novel potent CDK7 inhibitors.

The targeting of cyclin-dependent kinase 7 (CDK7) has become a highly desirable therapeutic approach in the field of oncology due to its dual role in regulating essential biological processes, encompassing cell cycle progression and transcriptional control. We have previously identified a highly selective thieno[3,2-d]pyrimidine-based CDK7 inhibitor with demonstrated efficacy and safety in animal model. In this study, we sought to optimize the thieno[3,2-d]pyrimidine core to discover a novel series of CDK7 inhibitors with improved potency and pharmacokinetic (PK) properties. Through extensive structure-activity relationship (SAR) studies, compound 20 has emerged as the lead candidate due to its potent inhibitory activity against CDK7 and remarkable efficacy on MDA-MB-453 cells, a representative triple negative breast cancer (TNBC) cell line. Furthermore, 20 has demonstrated favorable oral bioavailability and exhibited highly desirable pharmacokinetic (PK) properties, making it a promising lead candidate for further structural optimization.

Discovery of novel macrocyclic derivatives as potent and selective cyclin-dependent kinase 2 inhibitors.

Cyclin-dependent kinase 2 (CDK2) is a member of CDK family of kinases (CDKs) that regulate the cell cycle. Its inopportune or over-activation leads to uncontrolled cell cycle progression and drives numerous types of cancers, especially ovarian, uterine, gastric cancer, as well as those associated with amplified CCNE1 gene. However, developing selective lead compound as CDK2 inhibitors remains challenging owing to similarities in the ATP pockets among different CDKs. Herein, we described the optimization of compound 1, a novel macrocyclic inhibitor targeting CDK2/5/7/9, aiming to discover more selective and metabolically stable lead compound as CDK2 inhibitor. Molecular dynamic (MD) simulations were performed for compound 1 and 9 to gain insights into the improved selectivity against CDK5. Further optimization efforts led to compound 22, exhibiting excellent CDK2 inhibitory activity, good selectivity over other CDKs and potent cellular effects. Based on these characterizations, we propose that compound 22 holds great promise as a potential lead candidate for drug development.

Design and Synthesis of Novel Macrocyclic Derivatives as Potent and Selective Cyclin-Dependent Kinase 7 Inhibitors.

The duality of function (cell cycle regulation and gene transcription) of cyclin-dependent kinase 7 (CDK7) makes it an attractive oncology target and the discovery of CDK7 inhibitors has been a long-term pursuit by academia and pharmaceutical companies. However, achieving selective leading compounds is still difficult owing to the similarities among the ATP binding pocket. Herein, we detail the design and synthesis of a series of macrocyclic derivatives with pyrazolo[1,5-a]-1,3,5-triazine core structure as potent and selective CDK7 inhibitors. The diverse manners of macrocyclization led to distinguished selectivity profiles of the CDK family. Molecular dynamics (MD) simulation explained the binding difference between 15- and 16-membered macrocyclic compounds. Further optimization generated compound 37 exhibiting good CDK7 inhibitory activity and high selectivity over other CDKs. This work clearly demonstrated macrocyclization is a versatile method to finely tune the selectivity profile of small molecules and MD simulation can be a valuable tool in prioritizing designs of the macrocycle.

Live-cell RNA imaging using the CRISPR-dCas13 system with modified sgRNAs appended with fluorescent RNA aptamers.

The development of RNA imaging strategies in live cells is essential to improve our understanding of their role in various cellular functions. We report an efficient RNA imaging method based on the CRISPR-dPspCas13b system with fluorescent RNA aptamers in sgRNA (CasFAS) in live cells. Using modified sgRNA attached to fluorescent RNA aptamers that showed reduced background fluorescence, this approach provides a simple, sensitive way to image and track endogenous RNA with high accuracy and efficiency. In addition, color switching can be easily achieved by changing the fluorogenic dye analogues in living cells through user-friendly washing and restaining operations. CasFAS is compatible with orthogonal fluorescent aptamers, such as Broccoli and Pepper, enabling multiple colors RNA labeling or intracellular RNA-RNA interaction imaging. Finally, the visualization of severe fever with thrombocytopenia syndrome virus (SFTSV) was achieved by CasFAS, which may facilitate further studies on this virus.