诺奖得主David Baker最新论文：AI设计蛋白新突破，精准设计蛋白结合剂，克服“不可成药”靶点

Core Insights - The article highlights a significant breakthrough in protein design using conditional RFdiffusion to create high-affinity binding proteins for hydrophilic targets, led by Nobel laureate David Baker [4][7]. Design Strategy - The design strategy involves generating extended beta-sheet structures that geometrically match the edges of the target protein's beta strands through conditional RFdiffusion [5]. - Specially designed hydrogen bond groups are created to complement the polar groups on the target protein [6]. Experimental Validation - This technology overcomes traditional limitations in computational protein design, significantly expanding the range of target proteins for designed binding agents, particularly addressing challenges related to hydrophilic interactions. This advancement holds substantial value for drug development and protein function research [7]. - The designed protein binding agents exhibit high specificity and affinity, achieving picomolar to nanomolar levels of binding affinity for important protein targets such as KIT and PDGFRα [9]. Training and Courses - A series of online courses are offered, including AI protein design, antimicrobial peptide design, and computer-aided drug design, aimed at equipping participants with cutting-edge knowledge and practical skills in protein design [8]. - Various promotional offers are available for course registrations, including discounts for early sign-ups and bundled course registrations [8]. Future Trends - The article emphasizes the importance of AI protein design as a key technology to watch in 2026, with a growing demand for training and resources in this field, as evidenced by the high attendance and positive feedback from previous training sessions [7].