Core Viewpoint - The rapid development of genomics presents unprecedented opportunities and challenges for characterizing protein functions, highlighting the limitations of traditional biochemical methods and the need for high-precision, quantitative approaches [3][4]. Group 1: Research Development - A machine learning model named AlphaCD was developed to accurately characterize 21,335 cytidine deaminases (CD), demonstrating high precision in predicting catalytic efficiency, off-target activity, target site window, and catalytic motifs [4][6]. - The research team experimentally characterized 1,100 APOBEC-like family cytidine deaminases fused with nCas9 in HEK293T cells, generating the largest dataset of experimental functional data for a single protein family to date [6][8]. Group 2: Model Performance - AlphaCD achieved high precision in predicting catalytic efficiency (0.92), off-target activity (0.84), target site window (0.73), and catalytic motifs (0.78) [6]. - The model was validated by subsampling 28 cytidine deaminases from the Uniprot database, with prediction accuracies of 0.84, 0.87, 0.75, and 0.73 for the respective features [6][8]. Group 3: Practical Application - The research team optimized the off-target site of one cytidine deaminase (A0A2R2Z4E4) using alanine scanning mutagenesis, creating a variant (A0A2R2Z4E4 E100A) that serves as a highly accurate and efficient cytidine base editor [8]. - This case exemplifies the application value of AlphaCD in high-precision, high-throughput protein function characterization and provides a paradigm for accelerating functional analysis of other proteins [8].

Core Viewpoint - The article discusses advancements in base editing technology, specifically focusing on the development of high-precision cytosine base editors (CBE) to enhance the accuracy of genetic modifications, which is crucial for clinical applications [3][7]. Group 1: Base Editing Technology - Base editors are created by fusing cytosine deaminase or adenine deaminase with a CRISPR protein that has lost nuclease activity, allowing for specific base conversions in the genome [2]. - Current base editors modify all cytosines or adenines within the editing window, which limits their precision [3]. Group 2: Research Development - A research team from the University of Chicago published a study in Nature Biotechnology, focusing on evolving nucleic-acid-recognition hotspots in deaminase to develop high-precision CBEs [3][6]. - The study involved the directed evolution of the tRNA-specific adenine deaminase (TadA) from E. coli to address the issue of non-specific editing in existing base editors [4][5]. Group 3: Results and Applications - The research team developed 16 variants of TadA that cover all possible -1 and +1 contexts for target cytosine editing, providing customizable deaminases for base editing [5]. - These variants were applied to correct disease-related T:A to C:G conversions with an accuracy improvement of 81.5% compared to traditional base editors [6]. - The study also simulated two cancer-driving mutations, KRAS G12D and TP53 R248Q, demonstrating the practical applications of the developed high-precision CBEs [6].

Core Viewpoint - The article discusses the potential of base editing technology as a new strategy for treating trinucleotide repeat (TNR) diseases, specifically Huntington's disease and Friedreich's ataxia, by interrupting pathogenic repeat sequences [3][8]. Group 1: TNR Diseases Overview - TNR diseases are caused by the expansion of trinucleotide repeat sequences in the genome, leading to neurodegenerative disorders, with no approved treatments currently available [2][6]. - CAG repeat expansions in the HTT gene are linked to Huntington's disease, while GAA repeat expansions in the FXN gene cause Friedreich's ataxia [2][6]. - The severity and progression of TNR diseases are primarily determined by the length of the repeat sequences at birth, with longer repeats correlating to worse prognosis [7]. Group 2: Base Editing Technology - Base editing is a precise genome editing technique developed by Professor Liu Ruqian, allowing targeted single-base changes in DNA within living cells [8]. - The technology utilizes cytosine base editors (CBE) and adenine base editors (ABE) to introduce interruptions in CAG and GAA repeat sequences, respectively, mimicking naturally occurring stable alleles [8][12]. - The recent study demonstrated that base editing can effectively reduce somatic repeat expansions in patient-derived cells and mouse models of Huntington's disease and Friedreich's ataxia [3][13]. Group 3: Research Findings - The research team successfully delivered optimized base editors using AAV9 to treat Huntington's disease Q111 mice and YG8s hereditary ataxia mice, achieving significant reductions in TNR expansions in the central nervous system [12][13]. - Introducing interruptions in pathogenic TNR sequences may alleviate key neurological features of TNR diseases, providing a promising avenue for future therapies [13].