Novel Insights into Enzymatic Thermostability: The "Short Board" Theory and Zero-Shot Hamiltonian Model

文献类型： 外文期刊

第一作者： Liao, Min

作者： Liao, Min;Liu, Xiaoqing;Xu, Guoshun;Bai, Yingguo;Luo, Huiying;Yao, Bin;Tu, Tao;Feng, Shihao;Li, Sicong;Wang, Haobo

作者机构：

关键词： coevolution; "short board" theory; thermostabilization; zero-shot hamiltonian (ZSH) model

期刊名称：ADVANCED SCIENCE （ 影响因子：14.1； 五年影响因子：15.6 ）

ISSN：

年卷期： 2024 年 11 卷 45 期

页码：

收录情况： SCI

摘要： Understanding the mechanism underlying thermostabilization in naturally stable enzymes and enhancing the thermostability of unstable enzymes are crucial aspects in enzyme engineering. Despite the development of various engineering methods, there remains substantial scope for improvement. In this study, a novel concept termed as the "short board" theory is proposed, which conceptualizes proteins as barrels with each component representing a jagged board. Notably, optimizing modifications to the shortest board yields optimal enhancements in terms of thermostability performance. To validate this theory, alpha-amylase, an industrial bulk enzyme with multiple domains, is employed as a model enzyme. The existence of "short boards" and their impact on thermostability modification are demonstrated at the domain, residue, and atomic levels through experimental confirmation using domain substitution. Furthermore, a novel thermostable design and prediction model called Zero-Shot Hamiltonian (ZSH) is established and evaluated on alpha-amylase. This coevolutionary approach based on thermostability and deep learning exhibits remarkable success exclusively when applied to enzymes with fixed short boards. The integration of the "short board" theory with the ZSH model presents an innovative tool for enhancing enzymatic thermostability. This study presents the "short board" theory to explain enzyme thermostability, drawing an analogy between proteins and barrels with uneven wood lengths. It proposes that enhancing the shortest board can lead to stability improvements. Validated using of alpha-amylase, it employs a novel Zero-Shot Hamiltonian model that integrates coevolution and deep learning techniques to advance enzymatic engineering. image

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