Zhai Silong, Liu Tiantao, Lin Shaolong, Li Dan, Liu Huanxiang, Yao Xiaojun, Hou Tingjun
Faculty of Applied Science, Macao Polytechnic University, 999078, Macao; College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China.
Faculty of Applied Science, Macao Polytechnic University, 999078, Macao.
Drug Discov Today. 2025 Feb;30(2):104300. doi: 10.1016/j.drudis.2025.104300. Epub 2025 Jan 20.
Protein-protein interactions (PPIs) are fundamental to a variety of biological processes, but targeting them with small molecules is challenging because of their large and complex interaction interfaces. However, peptides have emerged as highly promising modulators of PPIs, because they can bind to protein surfaces with high affinity and specificity. Nonetheless, computational peptide design remains difficult, hindered by the intrinsic flexibility of peptides and the substantial computational resources required. Recent advances in artificial intelligence (AI) are paving new paths for peptide-based drug design. In this review, we explore the advanced deep generative models for designing target-specific peptide binders, highlight key challenges, and offer insights into the future direction of this rapidly evolving field.
蛋白质-蛋白质相互作用(PPIs)是多种生物过程的基础,但由于其相互作用界面庞大且复杂,用小分子靶向它们具有挑战性。然而,肽已成为极具前景的PPIs调节剂,因为它们可以高亲和力和特异性结合到蛋白质表面。尽管如此,由于肽的固有灵活性和所需的大量计算资源,计算肽设计仍然困难。人工智能(AI)的最新进展为基于肽的药物设计开辟了新途径。在这篇综述中,我们探索用于设计靶向特异性肽结合剂的先进深度生成模型,突出关键挑战,并对这个快速发展领域的未来方向提供见解。