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基于遗传编程的方法工程化 MRI 报告基因。

A Genetic Programming Approach to Engineering MRI Reporter Genes.

机构信息

Department of Biomedical Engineering, Michigan State University, East Lansing, Michigan 48823, United States.

Department of Computer Science & Engineering, Michigan State University, East Lansing, Michigan 48823, United States.

出版信息

ACS Synth Biol. 2023 Apr 21;12(4):1154-1163. doi: 10.1021/acssynbio.2c00648. Epub 2023 Mar 22.

Abstract

Here we develop a mechanism of protein optimization using a computational approach known as "genetic programming". We developed an algorithm called Protein Optimization Engineering Tool (POET). Starting from a small library of literature values, the use of this tool allowed us to develop proteins that produce four times more MRI contrast than what was previously state-of-the-art. Interestingly, many of the peptides produced using POET were dramatically different with respect to their sequence and chemical environment than existing CEST producing peptides, and challenge prior understandings of how those peptides function. While existing algorithms for protein engineering rely on divergent evolution, POET relies on convergent evolution and consequently allows discovery of peptides with completely different sequences that perform the same function with as good or even better efficiency. Thus, this novel approach can be expanded beyond developing imaging agents and can be used widely in protein engineering.

摘要

在这里,我们开发了一种使用称为“遗传编程”的计算方法进行蛋白质优化的机制。我们开发了一种名为蛋白质优化工程工具(POET)的算法。从一个小型文献值库开始,使用此工具可以帮助我们开发出比以前最先进的技术多产生四倍 MRI 对比的蛋白质。有趣的是,使用 POET 生成的许多肽在序列和化学环境方面与现有的 CEST 产生的肽有很大的不同,这对如何理解这些肽的功能提出了挑战。虽然现有的蛋白质工程算法依赖于分歧进化,但 POET 依赖于趋同进化,因此可以发现具有完全不同序列的肽,它们以相同的效率或甚至更好的效率发挥相同的功能。因此,这种新方法不仅可以用于开发成像剂,还可以广泛应用于蛋白质工程。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d106/10128068/07cc206fb5e4/sb2c00648_0001.jpg

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