基于人工智能和计算生物学的合成生物学元件设计

王晟; 王泽琛; 陈威华; 陈珂; 彭向达; 欧发芬; 郑良振; 孙瑨原; 沈涛; 赵国屏

doi:10.12211/2096-8280.2023-004

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基于人工智能和计算生物学的合成生物学元件设计

特约评述 | 更新时间：2025-03-20

- 基于人工智能和计算生物学的合成生物学元件设计
- Design of synthetic biology components based on artificial intelligence and computational biology
- 合成生物学 2023年4卷第3期页码：422-443
- 作者机构：
  
  1.上海智峪生物科技有限公司，上海 200030
  2.山东大学，山东济南250100
  3.中国科学院深圳先进技术研究院，广东深圳 518055
  4.中国科学院微生物研究所，北京 100101
- 作者简介：
  
  [ "王晟（1983—），男，博士，上海智峪生物科技有限公司CEO，中国科学院深圳先进技术研究院客座研究员。研究方向为基于深度学习的蛋白质结构预测、基于人工智能的合成生物学。 E-mail：wangsheng@zelixir.com" ]
  [ "王泽琛（1997—），男，博士研究生。研究方向为基于深度学习的蛋白质-配体相互作用预测和虚拟筛选。 E-mail：wangzechen@mail.sdu.edu.cn" ]
  [ "陈威华（1982—），男，博士研究生。研究方向为合成生物学方向，基因合成与组装。 E-mail：chenweihua@zelixir.com" ]
- 基金信息：
  
  中国科学院国际大科学计划培育专项(153D31KYSB20170121)
- DOI：10.12211/2096-8280.2023-004
  中图分类号： Q812
- 收稿：2023-01-11，
  
  修回：2023-04-03，
  
  纸质出版：2023-06-30
- 稿件说明：
移动端阅览
王晟，王泽琛，陈威华，陈珂，彭向达，欧发芬，郑良振，孙瑨原，沈涛，赵国屏. 基于人工智能和计算生物学的合成生物学元件设计［J］. 合成生物学， 2023， 4（3）： 422-443

WANG Sheng， WANG Zechen， CHEN Weihua， CHEN Ke， PENG Xiangda， OU Fafen， ZHENG Liangzhen， SUN Jinyuan， SHEN Tao， ZHAO Guoping. Design of synthetic biology components based on artificial intelligence and computational biology［J］. Synthetic Biology Journal， 2023， 4（3）： 422-443
王晟，王泽琛，陈威华，陈珂，彭向达，欧发芬，郑良振，孙瑨原，沈涛，赵国屏. 基于人工智能和计算生物学的合成生物学元件设计［J］. 合成生物学， 2023， 4（3）： 422-443 DOI： 10.12211/2096-8280.2023-004.

WANG Sheng， WANG Zechen， CHEN Weihua， CHEN Ke， PENG Xiangda， OU Fafen， ZHENG Liangzhen， SUN Jinyuan， SHEN Tao， ZHAO Guoping. Design of synthetic biology components based on artificial intelligence and computational biology［J］. Synthetic Biology Journal， 2023， 4（3）： 422-443 DOI： 10.12211/2096-8280.2023-004.

摘要

合成生物学是按照一定的规律综合已有的信息设计和构建全新的生物元件、装置和系统，或者重新设计已有的天然生物系统。合成生物学的核心在于设计、改造、重建或制造生物元件、生物反应系统、代谢途径与过程，乃至创造具有生命活动能力的细胞和生物个体，为解决人类发展在环境、资源、能源等方面面临的若干重大挑战提供新技术方案。毫无疑问，从DNA重组到基因电路设计，合成生物学的发展为众多领域带来全新的解决方案，优良的催化与调控元件是设计高效、鲁棒的系统的基础。然而，合成生物学的元件通常是天然的生物大分子，其固有的复杂性限制了对其工程化改造，导致合成生物技术的潜力未能得到充分发掘。随着人工智能（artificial intelligence，AI）与计算生物学的兴起和发展，有望助力该技术更好地发挥其价值。本文主要介绍了基于AI与计算生物学的不同类型的元件设计，聚焦催化元件、调控元件、传感元件三类元件的设计和前沿进展以及生物元件改造在合成生物学研究领域中的应用方面的研究进展。

Abstract

The primary objective of synthetic biology is to conceptualize

engineer

and construct novel biological components

devices

and systems based on established principles and extant information or to reconfigure existing natural biological systems. The core concept of synthetic biology encompasses the design

modification

reconstruction

or fabrication of biological components

reaction systems

metabolic pathways and processes

and even the creation of cells and organisms with functions or living characteristics. This burgeoning field offers innovative technologies to address challenges with sustainable development in environment

resource

energy

and so on. Undeniably

synthetic biology has yielded significant progress in numerous fields

ranging from DNA recombination to gene circuit design

yet its full potential remains insufficiently explored

but the emergence and application of artificial intelligence (AI) definitely can facilitate the development of synthetic biology for more applications. From a synthetic biology perspective

essence for life is rooted in digitalization and designability. This article reviews current advances in computational biology

particularly AI for synthetic biology to be more efficient and effective

focusing on the development of biocatalysts

regulators

and sensors.

De novo

enzyme design has been successfully implemented by using Rosetta software

as AI exhibiting significant potential for generating innovative structures and protein sequences with diverse functions. Also

the reprogramming of natural enzymes for specific purposes is crucial for synthetic biology appl

ications. By employing various force fields and sampling techniques

promiscuity and thermal stability can be modified to accommodate specific requirements rather than those with natural hosts. AI can be integrated into the life-cycle of synthetic biology through an active learning paradigm

which enables alterations in enzyme specificity

and demonstrates potential for accurately and rapidly predicting mutation effects

surpassing force-field-based methods. The rapidly decreasing cost of sequencing has facilitated the characterization of

cis

-regulators

primarily DNA and RNA

with high-throughput. Concurrently

more trans-regulators have been identified in sequenced genomes. The expanding wealth in big data serves as a driving force for AI. AI models have successfully predicted the strength of promoters

ribosome binding sites (RBSs)

and enhancers

and generated artificial protomers and RBSs. Recent progress in RNA structure prediction is expected to aid the design of RNA elements. Sensors

vital for genetic circuits and other applications such as toxin detection

typically involve interactions among various molecules

including nucleic acids

proteins

small organic molecules

and metal ions. Consequently

sensor design necessitates the integration of diverse computational biology tools to balance accuracy and computational cost. As the pool of data keeps growing

we anticipate that AI will be increasingly applied to the design of more bio-parts.

关键词

Keywords

references

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