以色列魏兹曼研究所
研究人员介绍了一种原子学和机器学习策略，用于酶的组合装配和设计（CADENZ），以设计相互结合的片段，产生具有稳定催化结构的多样化、低能量结构。研究人员将CADENZ应用于内切木聚糖酶，并使用基于活性的蛋白质分析来恢复数千种结构多样的酶。功能性设计表现出高活性位点预组织和活性位点外更稳定和紧凑的包装。研究人员将这些经验运用到CADENZ中，使命中率提高了10倍，恢复的酶超过了1万个。这种设计-测试-学习的循环原则上可以应用于任何模块化的蛋白质家族，并产生巨大的多样性和关于蛋白质设计原则的通用经验。
据悉，结构多样的酶的设计受到长程相互作用的制约，而这种相互作用是准确折叠所必需的。
附：英文原文
Title: Combinatorial assembly and design of enzymes
Author: R. Lipsh-Sokolik, O. Khersonsky, S. P. Schrder, C. de Boer, S.-Y. Hoch, G. J. Davies, H. S. Overkleeft, S. J. Fleishman
Issue&Volume: 2023-01-13
Abstract: The design of structurally diverse enzymes is constrained by long-range interactions that are necessary for accurate folding. We introduce an atomistic and machine learning strategy for the combinatorial assembly and design of enzymes (CADENZ) to design fragments that combine with one another to generate diverse, low-energy structures with stable catalytic constellations. We applied CADENZ to endoxylanases and used activity-based protein profiling to recover thousands of structurally diverse enzymes. Functional designs exhibit high active-site preorganization and more stable and compact packing outside the active site. Implementing these lessons into CADENZ led to a 10-fold improved hit rate and more than 10,000 recovered enzymes. This design-test-learn loop can be applied, in principle, to any modular protein family, yielding huge diversity and general lessons on protein design principles.
DOI: ade9434
Source: https://www.science.org/doi/10.1126/science.ade9434