The Selective WEE1 Inhibitor Azenosertib Shows Synergistic Antitumor Activity with KRASG12C Inhibitors in Preclinical Models.

KRAS is a potent oncogenic driver which results in downstream hyperactivation of MAPK signaling, while simultaneously increasing replication stress (RS) and accumulation of DNA damage. KRASG12C mutations are common and targetable alterations. Therapeutic inhibition of KRASG12C and eventual resistance to these inhibitors are also known to drive RS and DNA damage through adaptive mechanisms that maintain addiction to high MAPK signaling.

Cyclin E1/CDK2 activation defines a key vulnerability to WEE1 kinase inhibition in gynecological cancers.

Upregulation of Cyclin E1 and subsequent activation of CDK2 accelerates cell cycle progression from G1 to S phase and is a common oncogenic driver in gynecological malignancies. WEE1 kinase counteracts the effects of Cyclin E1/CDK2 activation by regulating multiple cell cycle checkpoints. Here we characterized the relationship between Cyclin E1/CDK2 activation and sensitivity to the selective WEE1 inhibitor azenosertib.

Structural and functional effects of phosphopriming and scaffolding in the kinase GSK-3β.

Glycogen synthase kinase 3β (GSK-3β) targets specific signaling pathways in response to distinct upstream signals. We used structural and functional studies to dissect how an upstream phosphorylation step primes the Wnt signaling component β-catenin for phosphorylation by GSK-3β and how scaffolding interactions contribute to this reaction. Our crystal structure of GSK-3β bound to a phosphoprimed β-catenin peptide confirmed the expected binding mode of the phosphoprimed residue adjacent to the catalytic site.

Improving Protein Expression, Stability, and Function with ProteinMPNN.

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function.