David Baker最新论文：AI从头设计大环肽，高亲和力靶向目标蛋白

Core Viewpoint - The article discusses the development of a new framework, RFpeptides, for the de novo design of high-affinity macrocyclic peptides targeting proteins, utilizing advancements in deep learning and artificial intelligence [2][3][10]. Group 1: Background and Challenges - Traditional methods for peptide drug development rely on natural product discovery or high-throughput screening of random peptides, which are resource-intensive and limited in scope [6][8]. - The challenges in natural product discovery include difficulties in synthesis, poor stability, and low tolerance to mutations [6]. - High-throughput screening methods, while powerful, are time-consuming and costly, covering only a small fraction of the chemical diversity available in macrocyclic compounds [6][9]. Group 2: Innovations in Design Methodology - The RFpeptides framework allows for precise de novo design of macrocyclic peptides with high affinity for target proteins, addressing the limitations of previous methods [3][12]. - The research team expanded existing structural prediction networks and protein backbone generation frameworks to incorporate cyclic relative position encoding, enhancing the design process [12]. Group 3: Experimental Results - The team tested up to 20 designed macrocyclic peptides against four different proteins (MCL1, MDM2, GABARAP, and RbtA), achieving medium to high affinity binders for all targets [13]. - Notably, a high-affinity binder for RbtA was designed with a dissociation constant (K_d) of less than 10 nM based solely on predicted target structure [13]. - Structural analysis of the designed macrocyclic peptide complexes with MCL1, GABARAP, and RbtA showed high agreement with computational models, with Cα RMSD values less than 1.5 Å [14]. Group 4: Implications and Future Applications - The RFpeptides framework provides a systematic approach for the rapid custom design of macrocyclic peptides for diagnostic and therapeutic applications, indicating significant potential in the pharmaceutical industry [16].