美国华盛顿大学David Baker报道了利用形状互补伪环结合和传感不同的小分子。相关研究成果发表在2024年7月19日出版的《科学》。

研究人员描述了一种设计用于下游传感的高亲和力小分子结合蛋白的方法。使用深度学习生成的具有围绕中心结合袋重复结构单元的伪循环，其形状因重复单元的几何形状和数量而异。研究人员将感兴趣的小分子对接成这些假环中形状最互补的，设计高结合亲和力的相互作用表面，并通过实验筛选来鉴定具有最高亲和力的设计。

研究人员获得了四种不同分子的结合剂，包括极性和柔性甲氨蝶呤和甲状腺素。利用模块化重复结构和中心结合袋，研究人员通过将设计拆分为在配体添加后重新组装的域，构建了化学诱导的二聚化系统和低噪声纳米孔传感器。

附：英文原文

Title: Binding and sensing diverse small molecules using shape-complementary pseudocycles

Author: Linna An, Meerit Said, Long Tran, Sagardip Majumder, Inna Goreshnik, Gyu Rie Lee, David Juergens, Justas Dauparas, Ivan Anishchenko, Brian Coventry, Asim K. Bera, Alex Kang, Paul M. Levine, Valentina Alvarez, Arvind Pillai, Christoffer Norn, David Feldman, Dmitri Zorine, Derrick R. Hicks, Xinting Li, Mariana Garcia Sanchez, Dionne K. Vafeados, Patrick J. Salveson, Anastassia A. Vorobieva, David Baker

Issue&Volume: 2024-07-19

Abstract: We describe an approach for designing high-affinity small molecule–binding proteins poised for downstream sensing. We use deep learning–generated pseudocycles with repeating structural units surrounding central binding pockets with widely varying shapes that depend on the geometry and number of the repeat units. We dock small molecules of interest into the most shape complementary of these pseudocycles, design the interaction surfaces for high binding affinity, and experimentally screen to identify designs with the highest affinity. We obtain binders to four diverse molecules, including the polar and flexible methotrexate and thyroxine. Taking advantage of the modular repeat structure and central binding pockets, we construct chemically induced dimerization systems and low-noise nanopore sensors by splitting designs into domains that reassemble upon ligand addition.

DOI: adn3780

Source: https://www.science.org/doi/10.1126/science.adn3780