Several common biological properties between cancer cells and embryonic stem (ES) cells suggest the possibility that some genes expressed in ES cells might play important roles in cancer cell growth. The transcription factor ZFP57 is expressed in self-renewing ES cells and its expression level decreases during ES cell differentiation.This study showed that ZFP57 is involved in the anchorage-independent growth of human fibrosarcoma HT1080 cells in soft agar. ZFP57 overexpression enhanced, while knockdown suppressed, HT1080 tumor formation in nude mice. Furthermore, ZFP57 regulates the expression of IGF2, which plays a critical role in ZFP57-induced anchorage-independent growth. ZFP57 also promotes anchorage-independent growth in ES cells and immortal fibroblasts. Finally, immunohistochemical analysis revealed that ZFP57 is overexpressed in human cancer clinical specimens. Taken together, these results suggest that the ES-specific transcription factor ZFP57 is a novel oncogene.

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Bacterial photoactivated adenylyl cyclase (bPAC) has been widely used in signal transduction research. However, due to its low two-photon absorption, bPAC cannot be efficiently activated by two-photon (2P) excitation. Taking advantage of the high two-photon absorption of monomeric teal fluorescent protein 1 (mTFP1), we herein developed 2P-activatable bPAC (2pab-PAC), a fusion protein consisting of bPAC and mTFP1. In 2pabPAC, the energy absorbed by mTFP1 excites bPAC by Furster resonance energy transfer (FRET) at ca. 43% efficiency. The light-induced increase in cAMP was monitored by a redshifted FRET biosensor for PKA. In 3D MDCK cells and mouse liver, PKA was activated at single-cell resolution under a 2P microscope. We found that PKA activation in a single hepatocyte caused PKA activation in neighboring cells, indicating the propagation of PKA activation. Thus, 2pabPAC will provide a versatile platform for controlling the cAMP signaling pathway and investigating cell-to-cell communication in vivo.

There has been considerable progress in the development of computational methods for designing protein-protein interactions, but engineering high-affinity binders without extensive screening and maturation remains challenging. Here, we test a protein design pipeline that uses iterative rounds of deep learning (DL)-based structure prediction (AlphaFold2) and sequence optimization (ProteinMPNN) to design autoinhibitory domains (AiDs) for a PD-L1 antagonist. Inspired by recent advances in therapeutic design, we sought to create autoinhibited (or masked) forms of the antagonist that can be conditionally activated by proteases. Twenty-three de novo designed AiDs, varying in length and topology, were fused to the antagonist with a protease sensitive linker, and binding to PD-L1 was tested with and without protease treatment. Nine of the fusion proteins demonstrated conditional binding to PD-L1 and the top performing AiDs were selected for further characterization as single domain proteins. Without any experimental affinity maturation, four of the AiDs bind to the PD-L1 antagonist with equilibrium dissociation constants (KDs) below 150 nM, with the lowest KD equal to 0.9 nM. Our study demonstrates that DL-based protein modeling can be used to rapidly generate high affinity protein binders.