Gebauer, N. | bioRxiv Science

bioRxiv2025

The MYCN/Aurora-A complex is a cyclin activating kinase for CDK12

Deregulated MYCN is a driver of aggressive pediatric and adult neuroendocrine tumors, but critical oncogenic processes downstream of MYCN remain poorly defined. In neuroblastoma, MYCN interacts with and activates the Aurora-A kinase. Here we show that Aurora-A is a CDK-activating kinase for CDK12 by phosphorylating T893 in the T-loop, thereby enhancing its kinase activity. Aurora-A-dependent activation of CDK12 controls phosphorylation of T4 of RNA polymerase and recruits transcription termination complexes, thereby preventing transcription-replication conflicts. Enhanced crosslinking and immunoprecipitation sequencing reveals that Aurora-A associates with splice sites on nascent RNA. RNA-bound Aurora-A is catalytically inactive. MYCN competes with RNA for binding to Aurora-A and displaces Aurora-A from RNA in cells, promoting its CDK12 kinase activity. Combining Aurora-A and CDK12 inhibition potently suppresses the growth of MYCN-amplified neuroblastoma cells and patient-derived xenografts. Our data demonstrate that an Aurora-A/CDK12-dependent transcription termination pathway is a critical and targetable dependency of MYCN-driven tumors.

cancer biology↗

bioRxiv2021

3D-Scaffold: Deep Learning Framework to Generate 3D Coordinates of Drug-like Molecules with Desired Scaffolds.

The prerequisite of therapeutic drug design is to identify novel molecules with desired biophysical and biochemical properties. Deep generative models have demonstrated their ability to find such molecules by exploring a huge chemical space efficiently. An effective way to obtain molecules with desired target properties is the preservation of critical scaffolds in the generation process. To this end, we propose a domain aware generative framework called 3D-Scaffold that takes 3D coordinates of the desired scaffold as an input and generates 3D coordinates of novel therapeutic candidates as an output while always preserving the desired scaffolds in generated structures. We show that our framework generates predominantly valid, unique, novel, and experimentally synthesizable molecules that have drug-like properties similar to the molecules in the training set. Using domain specific datasets, we generate covalent and non-covalent antiviral inhibitors. To measure the success of our framework in generating therapeutic candidates, generated structures were subjected to high throughput virtual screening via docking simulations, which shows favorable interaction against SARS-CoV-2 main protease and non-structural protein endoribonuclease (NSP15) targets. Most importantly, our model performs well with relatively small volumes of training data and generalizes to new scaffolds, making it applicable to other domains.

bioinformatics↗