作者: Schmid-Burgk, Jonathan L ; Cigler, Marko ; Suchyta, Dakota J ; Gillingham, Dennis ; Fischer, Eric S ; Thomä, Nicolas H ; Słabicki, Mikołaj ; Petzold, Georg ; Häussinger, Daniel ; Kempf, Georg ; Zou, Charles ; Mayor-Ruiz, Cristina ; Ebert, Benjamin L ; Jentzsch, Marius ; Coomar, Seemon ; Winter, Georg E ; Kozicka, Zuzanna ; Donovan, Katherine A ; Di Genua, Cristina ; Focht, Vivian

AbstractMolecular glue degraders are an effective therapeutic modality, but their design principles are not well understood. Recently, several unexpectedly diverse compounds were reported to deplete cyclin K by linking CDK12–cyclin K to the DDB1–CUL4–RBX1 E3 ligase. Here, to investigate how chemically dissimilar small molecules trigger cyclin K degradation, we evaluated 91 candidate degraders in structural, biophysical and cellular studies and reveal all compounds acquire glue activity via simultaneous CDK12 binding and engagement of DDB1 interfacial residues, in particular Arg928. While we identify multiple published kinase inhibitors as cryptic degraders, we also show that these glues do not require pronounced inhibitory properties for activity and that the relative degree of CDK12 inhibition versus cyclin K degradation is tuneable. We further demonstrate cyclin K degraders have transcriptional signatures distinct from CDK12 inhibitors, thereby offering unique therapeutic opportunities. The systematic structure–activity relationship analysis presented herein provides a conceptual framework for rational molecular glue design.

2024-01-01·International Journal of Biological Sciences

Tubule-specific cyclin-dependent kinase 12 knockdown potentiates kidney injury through transcriptional elongation defects

Article

作者: Ni, Wei-Jie ; Li, Zhong-Tang ; Lv, Lin-Li ; Zhang, Yi-Lin ; Tang, Tao-Tao ; Yin, Qing ; Wang, Bin ; Jiang, Liang-Yun-Zi ; Zhou, Xuan ; Wang, Yao ; Zhang, Ai-Qing ; Zhao, Ya-Jie ; Jiang, Wei ; Wen, Yi ; Cao, Jing-Yuan ; Gan, Wei-Hua ; Li, Zuo-Lin ; Liu, Bi-Cheng

Direct tubular injury caused by several medications, especially chemotherapeutic drugs, is a common cause of AKI. Inhibition or loss of cyclin-dependent kinase 12 (CDK12) triggers a transcriptional elongation defect that results in deficiencies in DNA damage repair, producing genomic instability in a variety of cancers. Notably, 10-25% of individuals developed AKI after treatment with a CDK12 inhibitor, and the potential mechanism is not well understood. Here, we found that CDK12 was downregulated in the renal tubular epithelial cells in both patients with AKI and murine AKI models. Moreover, tubular cell-specific knockdown of CDK12 in mice enhanced cisplatin-induced AKI through promotion of genome instability, apoptosis, and proliferative inhibition, whereas CDK12 overexpression protected against AKI. Using the single molecule real-time (SMRT) platform on the kidneys of CDK12^RTEC+/- mice, we found that CDK12 knockdown targeted Fgf1 and Cast through transcriptional elongation defects, thereby enhancing genome instability and apoptosis. Overall, these data demonstrated that CDK12 knockdown could potentiate the development of AKI by altering the transcriptional elongation defect of the Fgf1 and Cast genes, and more attention should be given to patients treated with CDK12 inhibitors to prevent AKI.

2023-03-01·European Journal of Medicinal Chemistry

Development, validation, and evaluation of a deep learning model to screen cyclin-dependent kinase 12 inhibitors in cancers

Article

作者: Tan, Shuoyan ; Wen, Tingyu ; Xie, Guotong ; Yi, Zongbi ; Li, Lixi ; Yao, Xiaojun ; Qian, Haili ; Liu, Shuning ; Liu, Huanxiang ; Gao, Peng ; Li, Pengyong ; Lu, Ruiqiang ; Wang, Jun ; Ma, Fei

Deep learning-based in silico alternatives have been demonstrated to be of significant importance in the acceleration of the drug discovery process and enhancement of success rates. Cyclin-dependent kinase 12 (CDK12) is a transcription-related cyclin-dependent kinase that may act as a biomarker and therapeutic target for cancers. However, currently, there is no high selective CDK12 inhibitor in clinical development and the identification of new specific CDK12 inhibitors has become increasingly challenging due to their similarity with CDK13. In this study, we developed a virtual screening workflow that combines deep learning with virtual screening tools and can be applied rapidly to millions of molecules. We designed a Transformer architecture Drug-Target Interaction (DTI) model with dual-branched self-supervised pre-trained molecular graph models and protein sequence models. Our predictive model produced satisfactory predictions for various targets, including CDK12, with several novel hits. We screened a large compound library consisting of 4.5 million drug-like molecules and recommended a list of potential CDK12 inhibitors for further experimental testing. In kinase assay, compared to the positive CDK12 inhibitor THZ531, the compounds CICAMPA-01, 02, 03 displayed more effective inhibition of CDK12, up to three times as much as THZ531. The compounds CICAMPA-03, 05, 04, 07 showed less inhibition of CDK13 compare to THZ531. In vitro, the IC50 of CICAMPA-01, 04, 05, 06, 09 was less than 3 μM in the HER2 positive CDK12 amplification breast cancer cell line BT-474. Overall, this study provides a highly efficient and end-to-end deep learning protocol, in conjunction with molecular docking, for discovering CDK12 inhibitors in cancers. Additionally, we disclose five novel CDK12 inhibitors. These results may accelerate the discovery of novel chemical-class drugs for cancer treatment.

100 项与 CDK12 Target candidate (Insilico Medicine) 相关的药物交易

登录后查看更多信息