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EnzyACT:一种新的深度学习方法,用于预测单个和多个突变对酶活动的影响

Gen Li¹, Ning Zhang², Xiaowen Dai²

¹Production and R&D Center I of LSS, GenScript (Shanghai) Biotech Co.,Ltd., Shanghai 200131, China.

Journal of chemical information and modeling

|July 22, 2024

概括

酶工程面临着活性和稳定性之间的权衡. 一个新的深度学习工具,EnzyACT,准确预测突变对酶活性的影响,帮助酶设计.

科学领域:

生物技术是生物技术.
计算生物学计算生物学
生物化学生物化学

背景情况:

酶工程旨在通过突变为特定应用量身定制酶.
一个关键的挑战是活动稳定性权衡,其中改善一个往往会损害另一个.
预测突变对酶稳定性的影响已经确立,但预测活性变化仍然很困难.

研究的目的:

开发一种快速准确的方法来预测突变诱导的酶活性变化.
帮助酶设计,并加深对酶活性-稳定性权衡的理解.
为了解远程突变在酶活性调节中的作用提供见解.

主要方法:

介绍了EnzyACT,一种新的深度学习方法.
用蛋白质嵌入进行活动预测的融合图谱技术.
在单点和多点突变的精选数据集上训练模型.

主要成果:

EnzyACT在多个独立的基准数据集中展示了统一的性能.
该模型有效地预测单个和多个突变的活性变化.
分析提供了关于远程突变如何影响酶活性的见解.

结论:

EnzyACT为预测酶活性变化提供了有价值的工具,促进了酶工程.
该方法有助于理解并可能克服活动稳定性权衡.
这项工作有助于改进酶设计策略和确定关键的催化残留物.

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Catalytically Perfect Enzymes

Catalytically Perfect Enzymes

The theory of catalytically perfect enzymes was first proposed by W.J. Albery and J. R. Knowles in 1976. These enzymes catalyze biochemical reactions at high-speed. Their catalytic efficiency values range from 108-109 M-1s-1. These enzymes are also called 'diffusion-controlled' as the only rate-limiting step in the catalysis is that of the substrate diffusion into the active site. Examples include triose phosphate isomerase, fumarase, and superoxide dismutase.

Most enzymes...