Calponin 1 inhibits agonist-induced ERK activation and decreases calcium sensitization in vascular smooth muscle.

Smooth muscle cell (SMC) contraction and vascular tone are modulated by phosphorylation and multiple modifications of the thick filament, and thin filament regulation of SMC contraction has been reported to involve extracellular regulated kinase (ERK). Previous studies in ferrets suggest that the actin-binding protein, calponin 1 (CNN1), acts as a scaffold linking protein kinase C (PKC), Raf, MEK and ERK, promoting PKC-dependent ERK activation. To gain further insight into this function of CNN1 in ERK activation and the regulation of SMC contractility in mice, we generated a novel Calponin 1 knockout mouse (Cnn1 KO) by a single base substitution in an intronic CArG box that preferentially abolishes expression of CNN1 in vascular SMCs. Using this new Cnn1 KO mouse, we show that ablation of CNN1 has two effects, depending on the cytosolic free calcium level: (1) in the presence of elevated intracellular calcium caused by agonist stimulation, Cnn1 KO mice display a reduced amplitude of stress and stiffness but an increase in agonist-induced ERK activation; and (2) during intracellular calcium depletion, in the presence of an agonist, Cnn1 KO mice exhibit increased duration of SM tone maintenance. Together, these results suggest that CNN1 plays an important and complex modulatory role in SMC contractile tone amplitude and maintenance.

Rapid protein evolution by few-shot learning with a protein language model.

Directed evolution of proteins is critical for applications in basic biological research, therapeutics, diagnostics, and sustainability. However, directed evolution methods are labor intensive, cannot efficiently optimize over multiple protein properties, and are often trapped by local maxima. In silico-directed evolution methods incorporating protein language models (PLMs) have the potential to accelerate this engineering process, but current approaches fail to generalize across diverse protein families. We introduce EVOLVEpro, a few-shot active learning framework to rapidly improve protein activity using a combination of PLMs and protein activity predictors, achieving improved activity with as few as four rounds of evolution. EVOLVEpro substantially enhances the efficiency and effectiveness of in silico protein evolution, surpassing current state-of-the-art methods and yielding proteins with up to 100-fold improvement of desired properties. We showcase EVOLVEpro for five proteins across three applications: T7 RNA polymerase for RNA production, a miniature CRISPR nuclease, a prime editor, and an integrase for genome editing, and a monoclonal antibody for epitope binding. These results demonstrate the advantages of few-shot active learning with small amounts of experimental data over zero-shot predictions. EVOLVEpro paves the way for broader applications of AI-guided protein engineering in biology and medicine.

Programmable eukaryotic protein synthesis with RNA sensors by harnessing ADAR.

Programmable approaches to sense and respond to the presence of specific RNAs in biological systems have broad applications in research, diagnostics, and therapeutics. Here we engineer a programmable RNA-sensing technology, reprogrammable ADAR sensors (RADARS), which harnesses RNA editing by adenosine deaminases acting on RNA (ADAR) to gate translation of a cargo protein by the presence of endogenous RNA transcripts. Introduction of a stop codon in a guide upstream of the cargo makes translation contingent on binding of an endogenous transcript to the guide, leading to ADAR editing of the stop codon and allowing translational readthrough. Through systematic sensor engineering, we achieve 277 fold improvement in sensor activation and engineer RADARS with diverse cargo proteins, including luciferases, fluorescent proteins, recombinases, and caspases, enabling detection sensitivity on endogenous transcripts expressed at levels as low as 13 transcripts per million. We show that RADARS are functional as either expressed DNA or synthetic mRNA and with either exogenous or endogenous ADAR. We apply RADARS in multiple contexts, including tracking transcriptional states, RNA-sensing-induced cell death, cell-type identification, and control of synthetic mRNA translation.