CRISPR technologies for genome, epigenome and transcriptome editing.

Our ability to edit genomes lags behind our capacity to sequence them, but the growing understanding of CRISPR biology and its application to genome, epigenome and transcriptome engineering is narrowing this gap. In this Review, we discuss recent developments of various CRISPR-based systems that can transiently or permanently modify the genome and the transcriptome. The discovery of further CRISPR enzymes and systems through functional metagenomics has meaningfully broadened the applicability of CRISPR-based editing. Engineered Cas variants offer diverse capabilities such as base editing, prime editing, gene insertion and gene regulation, thereby providing a panoply of tools for the scientific community. We highlight the strengths and weaknesses of current CRISPR tools, considering their efficiency, precision, specificity, reliance on cellular DNA repair mechanisms and their applications in both fundamental biology and therapeutics. Finally, we discuss ongoing clinical trials that illustrate the potential impact of CRISPR systems on human health.

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.