Core Viewpoint - The article discusses advancements in prime editing (PE) technology, particularly focusing on the development of MLH1 small binders (MLH1-SB) using AI tools to enhance editing efficiency in genome editing applications [2][4]. Group 1: Prime Editing Technology - Prime editing is a novel genome editing technique that allows for precise modifications, including base substitutions and small insertions or deletions [2]. - The efficiency of prime editing is often limited by the mismatch repair (MMR) pathway, which can hinder the integration of desired edits at target sites [6][7]. Group 2: AI-Driven Innovations - The research utilized the AI protein design tool RFdiffusion to create MLH1 small binders that inhibit MMR activity, thereby improving prime editing efficiency [3][9]. - AlphaFold3 was employed to efficiently screen the designed proteins, leading to the identification of an optimal MLH1-SB composed of only 82 amino acids, which integrates well with existing PE architectures [10][11]. Group 3: Efficiency Improvements - The newly developed PE-SB platforms, such as PEmax-SB, PE6-SB, and PE7-SB, demonstrated significant improvements in editing efficiency, with PE7-SB2 showing an increase of approximately 18.8 times compared to PEmax and 2.5 times compared to PE7 in human cells [11]. - In vivo studies indicated that PE7-SB2's efficiency was about 3.4 times greater than that of PE7 in mouse models [11]. Group 4: Implications for Gene Therapy - The compact size of the MLH1-SB allows for easier integration and delivery in gene therapy applications, which is crucial for effective in vivo gene editing [11]. - The advancements in AI-driven protein design are expected to facilitate the development of efficient gene editing therapies, potentially transforming the landscape of genetic medicine [15].

Core Viewpoint - Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) represent about 60% of the human proteome and are crucial for various cellular functions and disease progression. Recent advancements in artificial intelligence (AI) have enabled the design of specific binding agents for these previously considered "undruggable" targets, unlocking new therapeutic possibilities [1][2][20]. Group 1: Importance of IDPs and IDRs - IDPs and IDRs play significant roles in cellular signaling, stress responses, and disease progression, making them valuable targets for clinical diagnostics and drug development [2][8]. - Traditional drug design struggles with IDPs due to their lack of stable structure, which complicates the development of targeted therapies [6][7]. Group 2: AI Breakthroughs in Drug Design - The research led by David Baker's team utilized generative AI to design proteins that can accurately bind to IDPs and IDRs, achieving atomic-level precision [2][11]. - The AI model, RFdiffusion, allows for dynamic matching without pre-setting structures, enabling the generation of binding proteins that can adapt to the flexible nature of IDPs [11][12]. Group 3: Experimental Results and Applications - The studies published in Nature and Science demonstrated the successful design of binding proteins for various IDPs, with binding affinities ranging from 3 to 100 nanomolar [15][18]. - These binding proteins have shown potential in therapeutic applications, such as inhibiting amyloid fiber formation related to type 2 diabetes and disrupting stress granule formation in neurodegenerative diseases [16][18]. Group 4: Future Implications - The new design strategies developed could lead to innovative treatment methods and diagnostic tools for diseases associated with IDPs and IDRs, marking a significant advancement in precision medicine [20][24]. - The complementary strategies of RFdiffusion and logos provide a robust framework for targeting both structured and unstructured protein regions, enhancing the versatility of drug design [21][22].

Core Viewpoint - The article discusses the breakthrough in targeting intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) using artificial intelligence (AI), specifically through the work of Professor David Baker and his team, which has made previously "undruggable" targets accessible for drug development [3][5][20]. Group 1: Research Breakthroughs - The research led by Professor David Baker utilizes generative AI to design proteins that can precisely bind to IDPs and IDRs, achieving atomic-level accuracy [3][5]. - The studies employed two complementary design strategies based on amino acid sequences, eliminating the need for structural information, thus enhancing the universality of drug discovery [7][22]. - The first study published in Science demonstrated the design of binding proteins for 43 diverse disordered protein targets, achieving tight binding for 39 of them, with affinities ranging from 100 picomolar to 100 nanomolar [14][20]. Group 2: Applications and Implications - The designed binding proteins show potential applications in various fields, including cancer treatment, disease diagnostics, and intervention in neurodegenerative diseases [14][20]. - Specific examples include a binding protein targeting enkephalin that successfully blocked pain signal transduction in human cells [14][21]. - The second study, available on bioRxiv, reported the design of binding proteins for various IDPs and IDRs, with affinities also in the range of 3-100 nanomolar [17][20]. Group 3: Methodology and Tools - The research utilized a protein design strategy called "logos," which created a library of binding pockets to recognize amino acid side chains, allowing for the assembly of binding proteins [9][11]. - The RFdiffusion model was employed to generate novel proteins that do not exist in nature, demonstrating its effectiveness in various therapeutic contexts [5][22]. - The strategies developed in these studies are now available online for researchers to use freely, promoting further exploration in the field [23][24].