Crystal structure of the CDK11 kinase domain bound to the small-molecule inhibitor OTS964

Mechanistic Roles of Transcriptional Cyclin-Dependent Kinases in Oncogenesis: Implications for Cancer Therapy

Cyclin-dependent kinases (CDKs) are pivotal in regulating cell cycle progression and transcription, making them crucial targets in cancer research. The two types of CDKs that regulate different biological activities are transcription-associated CDKs (e.g., CDK7, 8, 9, 12, and 13) and cell cycle-associated CDKs (e.g., CDK1, 2, 4, and 6). One characteristic of cancer is the dysregulation of CDK activity, which results in unchecked cell division and tumor expansion. Targeting transcriptional CDKs, which control RNA polymerase II activity and gene expression essential for cancer cell survival, has shown promise as a therapeutic approach in recent research. While research into selective inhibitors for transcriptional CDKs is ongoing, inhibitors that target CDK4/6, such as palbociclib and ribociclib, have demonstrated encouraging outcomes in treating breast cancer. CDK7, CDK8, and CDK9 are desirable targets for therapy since they have shown oncogenic roles in a variety of cancer types, such as colorectal, ovarian, and breast malignancies. Even with significant advancements, creating selective inhibitors with negligible off-target effects is still difficult. This review highlights the need for more research to optimize therapeutic strategies and improve patient outcomes by giving a thorough overview of the non-transcriptional roles of CDKs in cancer biology, their therapeutic potential, and the difficulties in targeting these kinases for cancer treatment.

Inferring Drug-Gene Relationships in Cancer Using Literature-Augmented Large Language Models

SIGNIFICANCE

This study presents a novel approach that integrates LLMs with real-time biomedical literature to uncover drug-gene relationships, transforming how cancer researchers identify therapeutic targets, repurpose drugs, and interpret complex molecular interactions. GeneRxGPT, our user-friendly tool, enables researchers to leverage this approach without requiring computational expertise.

Targeting cyclin-dependent kinase 11: a computational approach for natural anti-cancer compound discovery

Cancer, a leading global cause of death, presents considerable treatment challenges due to resistance to conventional therapies like chemotherapy and radiotherapy. Cyclin-dependent kinase 11 (CDK11), which plays a pivotal role in cell cycle regulation and transcription, is overexpressed in various cancers and is linked to poor prognosis. This study focused on identifying potential inhibitors of CDK11 using computational drug discovery methods. Techniques such as pharmacophore modeling, virtual screening, molecular docking, ADMET predictions, molecular dynamics simulations, and binding free energy analysis were applied to screen a large natural product database. Three pharmacophore models were validated, leading to the identification of several promising compounds with stronger binding affinities than the reference inhibitor. ADMET profiling indicated favorable drug-like properties, while molecular dynamics simulations confirmed the stability and favorable interactions of top candidates with CDK11. Binding free energy calculations further revealed that UNPD29888 exhibited the strongest binding affinity. In conclusion, the identified compound shows potential as a CDK11 inhibitor based on computational predictions, suggesting their future application in cancer treatment by targeting CDK11. These computational findings encourage further experimental validation as anti-cancer agents.

CDK11, a splicing-associated kinase regulating gene expression

The ability of a cell to properly express its genes depends on optimal transcription and splicing. RNA polymerase II (RNAPII) transcribes protein-coding genes and produces pre-mRNAs, which undergo, largely co-transcriptionally, intron excision by the spliceosome complex. Spliceosome activation is a major control step, leading to a catalytically active complex. Recent work has showed that cyclin-dependent kinase (CDK)11 regulates spliceosome activation via the phosphorylation of SF3B1, a core spliceosome component. Thus, CDK11 arises as a major coordinator of gene expression in metazoans due to its role in the rate-limiting step of pre-mRNA splicing. This review outlines the evolution of CDK11 and SF3B1 and their emerging roles in splicing regulation. It also discusses how CDK11 and its inhibition affect transcription and cell cycle progression.

CDK11 requires a critical activator SAP30BP to regulate pre-mRNA splicing

CDK11 is an emerging druggable target for cancer therapy due to its prevalent roles in phosphorylating critical transcription and splicing factors and in facilitating cell cycle progression in cancer cells. Like other cyclin-dependent kinases, CDK11 requires its cognate cyclin, cyclin L1 or cyclin L2, for activation. However, little is known about how CDK11 activities might be modulated by other regulators. In this study, we show that CDK11 forms a tight complex with cyclins L1/L2 and SAP30BP, the latter of which is a poorly characterized factor. Acute degradation of SAP30BP mirrors that of CDK11 in causing widespread and strong defects in pre-mRNA splicing. Furthermore, we demonstrate that SAP30BP facilitates CDK11 kinase activities in vitro and in vivo, through ensuring the stabilities and the assembly of cyclins L1/L2 with CDK11. Together, these findings uncover SAP30BP as a critical CDK11 activator that regulates global pre-mRNA splicing.

Transcriptomic and genomic identification of spliceosomal genes from Euglena gracilis

Diverse splicing types in nuclear and chloroplast genes of protist Euglena gracilis have been recognized for decades. However, the splicing machinery responsible for processing nuclear precursor messenger RNA introns, including trans-splicing of the 5' terminal outron and spliced leader (SL) RNA, remains elusive. Here, we identify 166 spliceosomal protein genes and two snRNA genes from E. gracilis by performing bioinformatics analysis from a combination of next-generation and full-length transcriptomic RNA sequencing (RNAseq) data as well as draft genomic data. With the spliceosomal proteins we identified in hand, the insensitivity of E. gracilis to some splicing modulators is revealed at the sequence level. The prevalence of SL RNA-mediated trans-splicing is estimated to be more than 70% from our full-length RNAseq data. Finally, the splicing proteomes between E. gracilis and its three evolutionary cousins within the same Excavata group are compared. In conclusion, our study characterizes the spliceosomal components in E. gracilis and provides the molecular basis for further exploration of underlying splicing mechanisms.