Bemelmans Martijn P, Cournia Zoe, Damm-Ganamet Kelly L, Gervasio Francesco L, Pande Vineet
Computer-Aided Drug Design, In Silico Discovery, Therapeutics Discovery, Johnson & Johnson Innovative Medicine, Turnhoutseweg 30, 2340 Beerse, Belgium; School of Pharmaceutical Sciences, University of Geneva, Rue Michel Servet 1, Geneva, 1206, Switzerland.
Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephesiou, Athens 11527, Greece.
Curr Opin Struct Biol. 2025 Feb;90:102975. doi: 10.1016/j.sbi.2024.102975. Epub 2025 Jan 7.
A number of promising therapeutic target proteins have been considered "undruggable" due to the lack of well-defined ligandable pockets. Substantial research in protein dynamics has elucidated the existence of "cryptic" pockets that only exist transiently and become favorable for binding in the presence of a ligand. These pockets provide an avenue to target challenging proteins, inspiring the development of multiple computational methods. This review highlights established cryptic pocket modeling approaches like mixed solvent molecular dynamics and recent applications of enhanced sampling and AI-based methods in therapeutically relevant proteins.
由于缺乏明确的可配体口袋，许多有前景的治疗靶点蛋白被认为是“不可成药的”。蛋白质动力学的大量研究阐明了“隐秘”口袋的存在，这些口袋仅短暂存在，并在配体存在时变得有利于结合。这些口袋为靶向具有挑战性的蛋白质提供了一条途径，激发了多种计算方法的发展。本文综述重点介绍了已确立的隐秘口袋建模方法，如混合溶剂分子动力学，以及增强采样和基于人工智能的方法在治疗相关蛋白质中的最新应用。
