基于人工智能和计算生物学的合成生物学元件设计

doi:10.12211/2096-8280.2023-004

合成生物学 ›› 2023, Vol. 4 ›› Issue (3): 422-443.DOI: 10.12211/2096-8280.2023-004

基于人工智能和计算生物学的合成生物学元件设计

王晟¹, 王泽琛¹^,², 陈威华¹, 陈珂¹, 彭向达¹, 欧发芬¹, 郑良振¹^,³, 孙瑨原¹^,⁴, 沈涛¹, 赵国屏³

^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

收稿日期:2023-01-11 修回日期:2023-04-03 出版日期:2023-06-30 发布日期:2023-07-05
通讯作者: 王晟
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
基金资助:
中国科学院国际大科学计划培育专项(153D31KYSB20170121)

Design of synthetic biology components based on artificial intelligence and computational biology

Sheng WANG¹, Zechen WANG¹^,², Weihua CHEN¹, Ke CHEN¹, Xiangda PENG¹, Fafen OU¹, Liangzhen ZHENG¹^,³, Jinyuan SUN¹^,⁴, Tao SHEN¹, Guoping ZHAO³

^1.Shanghai Zelixir Biotech Company Ltd. ，Shanghai 200030，China
^2.Shandong University，Jinan 250100，Shandong，China
^3.Shenzhen Institute of Advanced Technology，Chinese Academey of Sciences，Shenzhen 518055，Guangdong，China
^4.Institute of Microbiology，Chinese Academey of Sciences，Beijing 100101，China

Received:2023-01-11 Revised:2023-04-03 Online:2023-06-30 Published:2023-07-05
Contact: Sheng WANG

摘要/Abstract

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。

关键词: 人工智能, 合成生物学, 计算生物学, 蛋白质设计, 生物序列

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 applications. 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.

Key words: artificial intelligence, synthetic biology, computational biology, protein design, biological sequences

中图分类号:

Q812

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.

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

图/表 9

参考文献 153

1	LV X Q, HUESO-GIL A, BI X Y, et al. New synthetic biology tools for metabolic control[J]. Current Opinion in Biotechnology, 2022, 76: 102724.
2	FAULON J L, FAURE L. In silico, in vitro, and in vivo machine learning in synthetic biology and metabolic engineering[J]. Current Opinion in Chemical Biology, 2021, 65: 85-92.
3	LAWSON C E, MARTÍ J M, RADIVOJEVIC T, et al. Machine learning for metabolic engineering: a review[J]. Metabolic Engineering, 2021, 63: 34-60.
4	MADHAVAN A, ARUN K B, BINOD P, et al. Design of novel enzyme biocatalysts for industrial bioprocess: harnessing the power of protein engineering, high throughput screening and synthetic biology[J]. Bioresource Technology, 2021, 325: 124617.
5	DE JONGH R P H, VAN DIJK A D J, JULSING M K, et al. Designing eukaryotic gene expression regulation using machine learning[J]. Trends in Biotechnology, 2020, 38(2): 191-201.
6	ALFORD R F, LEAVER-FAY A, JELIAZKOV J R, et al. The Rosetta all-atom energy function for macromolecular modeling and design[J]. Journal of Chemical Theory and Computation, 2017, 13(6): 3031-3048.
7	MUSIL M, STOURAC J, BENDL J, et al. FireProt: web server for automated design of thermostable proteins[J]. Nucleic Acids Research, 2017, 45(W1): W393-W399.
8	GOLDENZWEIG A, GOLDSMITH M, HILL S E, et al. Automated structure- and sequence-based design of proteins for high bacterial expression and stability[J]. Molecular Cell, 2016, 63(2): 337-346.
9	KHERSONSKY O, LIPSH R, AVIZEMER Z, et al. Automated design of efficient and functionally diverse enzyme repertoires[J]. Molecular Cell, 2018, 72(1): 178-186.e5.
10	XIONG P, HU X H, HUANG B, et al. Increasing the efficiency and accuracy of the ABACUS protein sequence design method[J]. Bioinformatics, 2020, 36(1): 136-144.
11	SCHWEDE T, KOPP J, GUEX N, et al. SWISS-MODEL: an automated protein homology-modeling server[J]. Nucleic Acids Research, 2003, 31(13): 3381-3385.
12	ASHKENAZY H, EREZ E, MARTZ E, et al. ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids[J]. Nucleic Acids Research, 2010, 38(): W529-W533.
13	MORETTI R, LYSKOV S, DAS R, et al. Web-accessible molecular modeling with Rosetta: the Rosetta Online Server that Includes Everyone (ROSIE)[J]. Protein Science, 2018, 27(1): 259-268.
14	RÖTHLISBERGER D, KHERSONSKY O, WOLLACOTT A M, et al. Kemp elimination catalysts by computational enzyme design[J]. Nature, 2008, 453(7192): 190-195.
15	JIANG L, ALTHOFF E A, CLEMENTE F R, et al. De novo computational design of Retro-Aldol enzymes[J]. Science, 2008, 319(5868): 1387-1391.
16	RUSS W P, FIGLIUZZI M, STOCKER C, et al. An evolution-based model for designing chorismate mutase enzymes[J]. Science, 2020, 369(6502): 440-445.
17	WIJMA H J, FLOOR R J, BJELIC S, et al. Enantioselective enzymes by computational design and in silico screening[J]. Angewandte Chemie International Edtion, 2015, 54(12): 3726-3730.
18	LI R F, WIJMA H J, SONG L, et al. Computational redesign of enzymes for regio- and enantioselective hydroamination[J]. Nature Chemical Biology, 2018, 14(7): 664-670.
19	CUI Y L, WANG Y H, TIAN W Y, et al. Development of a versatile and efficient C-N lyase platform for asymmetric hydroamination via computational enzyme redesign[J]. Nature Catalysis, 2021, 4(5): 364-373.
20	MENG Q L, CAPRA N, PALACIO C M, et al. Robust ω-transaminases by computational stabilization of the subunit interface[J]. ACS Catalysis, 2020, 10(5): 2915-2928.
21	BEDNAR D, BEERENS K, SEBESTOVA E, et al. FireProt: energy- and evolution-based computational design of thermostable multiple-point mutants[J]. PLoS Computational Biology, 2015, 11(11): e1004556.
22	WIJMA H J, FLOOR R J, JEKEL P A, et al. Computationally designed libraries for rapid enzyme stabilization[J]. Protein Engineering, Design and Selection, 2014, 27(2): 49-58.
23	DELGADO J, RADUSKY L G, CIANFERONI D, et al. FoldX 5.0: working with RNA, small molecules and a new graphical interface[J]. Bioinformatics, 2019, 35(20): 4168-4169.
24	WU B, WIJMA H J, SONG L, et al. Versatile peptide C-terminal functionalization via a computationally engineered peptide amidase[J]. ACS Catalysis, 2016, 6(8): 5405-5414.
25	CUI Y L, CHEN Y C, LIU X Y, et al. Computational redesign of a PETase for plastic biodegradation under ambient condition by the GRAPE strategy[J]. ACS Catalysis, 2021, 11(3): 1340-1350.
26	UNIPROT CONSORTIUM THE. UniProt: a worldwide hub of protein knowledge[J]. Nucleic Acids Research 2019, 47(D1), D506-D515.
27	VARADI M, ANYANGO S, DESHPANDE M, et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models[J]. Nucleic Acids Research, 2022, 50(D1): D439-D444.
28	ORENGO C A, MICHIE A D, JONES S, et al. CATH—a hierarchic classification of protein domain structures[J]. Structure, 1997, 5(8): 1093-1108.
29	BLUM M, CHANG H Y, CHUGURANSKY S, et al. The InterPro protein families and domains database: 20 years on[J]. Nucleic Acids Research, 2021, 49(D1): D344-D354.
30	CHANG A, JESKE L, ULBRICH S, et al. BRENDA, the ELIXIR core data resource in 2021: new developments and updates[J]. Nucleic Acids Research, 2021, 49(D1): D498-D508.
31	SARKAR A, YANG Y, VIHINEN M. Variation benchmark datasets: update, criteria, quality and applications[J]. Database, 2020, 2020: baz117.
32	JARZAB A, KURZAWA N, HOPF T, et al. Meltome atlas—thermal proteome stability across the tree of life[J]. Nature Methods, 2020, 17(5): 495-503.
33	STOURAC J, DUBRAVA J, MUSIL M, et al. FireProt^DB: dataBase of manually curated protein stability data[J]. Nucleic Acids Research, 2021, 49(D1): D319-D324.
34	WITTIG U, REY M, WEIDEMANN A, et al. SABIO-RK: an updated resource for manually curated biochemical reaction kinetics[J]. Nucleic Acids Research, 2018, 46(D1): D656-D660.
35	REPECKA D, JAUNISKIS V, KARPUS L, et al. Expanding functional protein sequence spaces using generative adversarial networks[J]. Nature Machine Intelligence, 2021, 3(4): 324-333.
36	WANG J, LISANZA S, JUERGENS D, et al. Scaffolding protein functional sites using deep learning[J]. Science, 2022, 377(6604): 387-394.
37	ANISHCHENKO I, PELLOCK S J, CHIDYAUSIKU T M, et al. De novo protein design by deep network hallucination[J]. Nature, 2021, 600(7889): 547-552.
38	DAUPARAS J, ANISHCHENKO I, BENNETT N, et al. Robust deep learning-based protein sequence design using ProteinMPNN[J]. Science, 2022, 378(6615): 49-56.
39	SUGIKI S, NIIDE T, TOYA Y, et al. Logistic regression-guided identification of cofactor specificity-contributing residues in enzyme with sequence datasets partitioned by catalytic properties[J]. ACS Synthetic Biology, 2022, 11(12): 3973-3985.
40	HU R Y, FU L H, CHEN Y C, et al. Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments[J]. Briefings in Bioinformatics, 2023, 24(1): bbac570.
41	SHROFF R, COLE A W, DIAZ D J, et al. Discovery of novel gain-of-function mutations guided by structure-based deep learning[J]. ACS Synthetic Biology, 2020, 9(11): 2927-2935.
42	LU H Y, DIAZ D J, CZARNECKI N J, et al. Machine learning-aided engineering of hydrolases for PET depolymerization[J]. Nature, 2022, 604(7907): 662-667.
43	BARBER-ZUCKER S, MINDEL V, GARCIA-RUIZ E, et al. Stable and functionally diverse versatile peroxidases designed directly from sequences[J]. Journal of the American Chemical Society, 2022, 144(8): 3564-3571.
44	DOERR S, MAJEWSKI M, PÉREZ A, et al. TorchMD: a deep learning framework for molecular simulations[J]. Journal of Chemical Theory and Computation, 2021, 17(4): 2355-2363.
45	GREEN P J, PINES O, INOUYE M. The role of antisense RNA in gene regulation[J]. Annual Review of Biochemistry, 1986, 55: 569-597.
46	PAPENFORT K, VANDERPOOL C K. Target activation by regulatory RNAs in bacteria[J]. FEMS Microbiology Reviews, 2015, 39(3): 362-378.
47	PANG B X, VAN WEERD J H, HAMOEN F L, et al. Identification of non-coding silencer elements and their regulation of gene expression[J]. Nature Reviews Molecular Cell Biology, 2022: 1-13.
48	DREOS R, AMBROSINI G, PÉRIER R C, et al. The eukaryotic promoter database: expansion of EPDnew and new promoter analysis tools[J]. Nucleic Acids Research, 2015, 43(Database issue): D92-D96.
49	KHAN A, ZHANG X G. dbSUPER: a database of super-enhancers in mouse and human genome[J]. Nucleic Acids Research, 2016, 44(D1): D164-D171.
50	WEI Y J, ZHANG S M, SHANG S P, et al. SEA: a super-enhancer archive[J]. Nucleic Acids Research, 2016, 44(D1): D172-D179.
51	ZHANG G X, SHI J, ZHU S W, et al. DiseaseEnhancer: a resource of human disease-associated enhancer catalog[J]. Nucleic Acids Research, 2018, 46(D1): D78-D84.
52	WANG Z, ZHANG Q W, ZHANG W, et al. HEDD: human enhancer disease database[J]. Nucleic Acids Research, 2018, 46(D1): D113-D120.
53	JIANG Y, QIAN F C, BAI X F, et al. SEdb: a comprehensive human super-enhancer database[J]. Nucleic Acids Research, 2019, 47(D1): D235-D243.
54	CHOW C N, LEE T Y, HUNG Y C, et al. PlantPAN3.0: a new and updated resource for reconstructing transcriptional regulatory networks from ChIP-seq experiments in plants[J]. Nucleic Acids Research, 2019, 47(D1): D1155-D1163.
55	RIVERA J, KERÄNEN S V E, GALLO S M, et al. REDfly: the transcriptional regulatory element database for Drosophila[J]. Nucleic Acids Research, 2019, 47(D1): D828-D834.
56	GAO T S, HE B, LIU S, et al. EnhancerAtlas: a resource for enhancer annotation and analysis in 105 human cell/tissue types[J]. Bioinformatics, 2016, 32(23): 3543-3551.
57	GONZALEZ J N, ZWEIG A S, SPEIR M L, et al. The UCSC genome browser database: 2021 update[J]. Nucleic Acids Research, 2021, 49(D1): D1046-D1057.
58	ZENG W W, CHEN S Q, CUI X J, et al. SilencerDB: a comprehensive database of silencers[J]. Nucleic Acids Research, 2021, 49(D1): D221-D228.
59	UMAROV R, LI Y, ARAKAWA T, et al. ReFeaFi: genome-wide prediction of regulatory elements driving transcription initiation[J]. PLoS Computational Biology, 2021, 17(9): e1009376.
60	ZRIMEC J, BÖRLIN C S, BURIC F, et al. Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure[J]. Nature Communications, 2020, 11: 6141.
61	ZRIMEC J, FU X Z, MUHAMMAD A S, et al. Controlling gene expression with deep generative design of regulatory DNA[J]. Nature Communications, 2022, 13: 5099.
62	MENG H L, MA Y F, MAI G Q, et al. Construction of precise support vector machine based models for predicting promoter strength[J]. Quantitative Biology, 2017, 5(1): 90-98.
63	CAZIER A P, BLAZECK J. Advances in promoter engineering: novel applications and predefined transcriptional control[J]. Biotechnology Journal, 2021, 16(10): e2100239.
64	OUBOUNYT M, LOUADI Z, TAYARA H, et al. DeePromoter: robust promoter predictor using deep learning[J]. Frontiers in Genetics, 2019, 10: 286.
65	WANG Y, WANG H C, WEI L, et al. Synthetic promoter design in Escherichia coli based on a deep generative network[J]. Nucleic Acids Research, 2020, 48(12): 6403-6412.
66	MENON S, PIRAMANAYAKAM S, AGARWAL G. Computational identification of promoter regions in prokaryotes and eukaryotes[J]. EPRA International Journal of Agriculture and Rural Economic Research, 2021, 9(7): 21-28.
67	CATARINO R R, STARK A. Assessing sufficiency and necessity of enhancer activities for gene expression and the mechanisms of transcription activation[J]. Genes & Development, 2018, 32(3/4): 202-223.
68	ANDERSSON R, SANDELIN A. Determinants of enhancer and promoter activities of regulatory elements[J]. Nature Reviews Genetics, 2020, 21(2): 71-87.
69	ANDERSSON R, REFSING ANDERSEN P, VALEN E, et al. Nuclear stability and transcriptional directionality separate functionally distinct RNA species[J]. Nature Communications, 2014, 5: 5336.
70	DIAO Y R, FANG R X, LI B, et al. A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells[J]. Nature Methods, 2017, 14(6): 629-635.
71	KIM T K, HEMBERG M, GRAY J M, et al. Widespread transcription at neuronal activity-regulated enhancers[J]. Nature, 2010, 465(7295): 182-187.
72	FULCO C P, NASSER J, JONES T R, et al. Activity-by-contact model of enhancer-promoter regulation from thousands of CRISPR perturbations[J]. Nature Genetics, 2019, 51(12): 1664-1669.
73	MAURANO M T, HUMBERT R, RYNES E, et al. Systematic localization of common disease-associated variation in regulatory DNA[J]. Science, 2012, 337(6099): 1190-1195.
74	KHANAL J, TAYARA H, CHONG K T. Identifying enhancers and their strength by the integration of word embedding and convolution neural network[J]. IEEE Access, 2020, 8: 58369-58376.
75	MIN X P, YE C M, LIU X R, et al. Predicting enhancer-promoter interactions by deep learning and matching heuristic[J]. Briefings in Bioinformatics, 2021, 22(4): bbaa254.
76	DE ALMEIDA B P, REITER F, PAGANI M, et al. DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers[J]. Nature Genetics, 2022, 54(5): 613-624.
77	FENG C Q, ZHANG Z Y, ZHU X J, et al. iTerm-PseKNC: a sequence-based tool for predicting bacterial transcriptional terminators[J]. Bioinformatics, 2019, 35(9): 1469-1477.
78	YEVSHIN I, SHARIPOV R, VALEEV T, et al. GTRD: a database of transcription factor binding sites identified by ChIP-seq experiments[J]. Nucleic Acids Research, 2017, 45(D1): D61-D67.
79	KULAKOVSKIY I V, VORONTSOV I E, YEVSHIN I S, et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis[J]. Nucleic Acids Research, 2018, 46(D1): D252-D259.
80	KULAKOVSKIY I V, VORONTSOV I E, YEVSHIN I S, et al. HOCOMOCO: expansion and enhancement of the collection of transcription factor binding sites models[J]. Nucleic Acids Research, 2016, 44(D1): D116-D125.
81	WANG G H, LUO X M, WANG J N, et al. MeDReaders: a database for transcription factors that bind to methylated DNA[J]. Nucleic Acids Research, 2018, 46(D1): D146-D151.
82	HAN H, CHO J W, LEE S, et al. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions[J]. Nucleic Acids Research, 2018, 46(D1): D380-D386.
83	HAN H, SHIM H, SHIN D, et al. TRRUST: a reference database of human transcriptional regulatory interactions[J]. Scientific Reports, 2015, 5: 11432.
84	HU H, MIAO Y R, JIA L H, et al. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors[J]. Nucleic Acids Research, 2019, 47(D1): D33-D38.
85	MULUGETA T D, NOME T, TO T H, et al. SalMotifDB: a tool for analyzing putative transcription factor binding sites in salmonid genomes[J]. BMC Genomics, 2019, 20(1): 694.
86	ZHANG Q, LIU W, ZHANG H M, et al. hTFtarget: a comprehensive database for regulations of human transcription factors and their targets[J]. Genomics, Proteomics & Bioinformatics, 2020, 18(2): 120-128.
87	FENG C C, SONG C, LIU Y J, et al. KnockTF: a comprehensive human gene expression profile database with knockdown/knockout of transcription factors[J]. Nucleic Acids Research, 2020, 48(D1): D93-D100.
88	CASTRO-MONDRAGON J A, RIUDAVETS-PUIG R, RAULUSEVICIUTE I, et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles[J]. Nucleic Acids Research, 2022, 50(D1): D165-D173.
89	NI P Y, SU Z C. PCRMS: a database of predicted cis-regulatory modules and constituent transcription factor binding sites in genomes[J]. Database, 2022, 2022: baac024.
90	FU L Y, ZHANG L H, DOLLINGER E, et al. Predicting transcription factor binding in single cells through deep learning[J]. Science Advances, 2020, 6(51): eaba9031.
91	KIM G B, GAO Y, PALSSON B O, et al. DeepTFactor: a deep learning-based tool for the prediction of transcription factors[J]. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118(2): e2021171118.
92	ZHANG Q H, WANG S G, CHEN Z H, et al. Locating transcription factor binding sites by fully convolutional neural network[J]. Briefings in Bioinformatics, 2021, 22(5): bbaa435.
93	ZHENG A, LAMKIN M, ZHAO H Q, et al. Deep neural networks identify sequence context features predictive of transcription factor binding[J]. Nature Machine Intelligence, 2021, 3(2): 172-180.
94	NAKASHIMA N, TAMURA T, GOOD L. Paired termini stabilize antisense RNAs and enhance conditional gene silencing in Escherichia coli [J]. Nucleic Acids Research, 2006, 34(20): e138.
95	NA D, YOO S M, CHUNG H, et al. Metabolic engineering of Escherichia coli using synthetic small regulatory RNAs[J]. Nature Biotechnology, 2013, 31(2): 170-174.
96	ZHANG R H, ZHANG Y, WANG J, et al. Development of antisense RNA-mediated quantifiable inhibition for metabolic regulation[J]. Metabolic Engineering Communications, 2021, 12: e00168.
97	GREEN A A, SILVER P A, COLLINS J J, et al. Toehold switches: de-novo-designed regulators of gene expression[J]. Cell, 2014, 159(4): 925-939.
98	CHAPPELL J, WESTBROOK A, VEROSLOFF M, et al. Computational design of small transcription activating RNAs for versatile and dynamic gene regulation[J]. Nature Communications, 2017, 8: 1051.
99	CHAPPELL J, TAKAHASHI M K, LUCKS J B. Creating small transcription activating RNAs[J]. Nature Chemical Biology, 2015, 11(3): 214-220.
100	JANG S H, JANG S Y, YANG J N, et al. RNA-based dynamic genetic controllers: development strategies and applications[J]. Current Opinion in Biotechnology, 2018, 53: 1-11.
101	ZADEH J N, STEENBERG C D, BOIS J S, et al. NUPACK: analysis and design of nucleic acid systems[J]. Journal of Computational Chemistry, 2011, 32(1): 170-173.
102	GARDNER T S, CANTOR C R, COLLINS J J. Construction of a genetic toggle switch in Escherichia coli [J]. Nature, 2000, 403(6767): 339-342.
103	CHEN T, CHENG G Y, AHMED S, et al. New methodologies in screening of antibiotic residues in animal-derived foods: Biosensors[J]. Talanta, 2017, 175: 435-442.
104	SONGA E A, OKONKWO J O. Recent approaches to improving selectivity and sensitivity of enzyme-based biosensors for organophosphorus pesticides: a review[J]. Talanta, 2016, 155: 289-304.
105	PUIU M, BALA C. Peptide-based biosensors: from self-assembled interfaces to molecular probes in electrochemical assays[J]. Bioelectrochemistry, 2018, 120: 66-75.
106	SHARMA S, BYRNE H, O'KENNEDY R J. Antibodies and antibody-derived analytical biosensors[J]. Essays in Biochemistry, 2016, 60(1): 9-18.
107	WANG R E, ZHANG Y, CAI J, et al. Aptamer-based fluorescent biosensors[J]. Current Medicinal Chemistry, 2011, 18(27): 4175-4184.
108	BLIND M, BLANK M. Aptamer selection technology and recent advances[J]. Molecular Therapy Nucleic Acids, 2015, 4(1): e223.
109	HONG P T K, JANG C H. Sensitive and label-free liquid crystal-based optical sensor for the detection of malathion[J]. Analytical Biochemistry, 2020, 593: 113589.
110	KIM H S, AN Z F, JANG C H. Label-free optical detection of thrombin using a liquid crystal-based aptasensor[J]. Microchemical Journal, 2018, 141: 71-79.
111	O'NEILL M, KELLY S M. Liquid crystals for charge transport, luminescence, and photonics[J]. Advanced Materials, 2003, 15(14): 1135-1146.
112	BYKHOVSKI A, ZHANG W D, JENSEN J, et al. Analysis of electronic structure, binding, and vibrations in biotin-streptavidin complexes based on density functional theory and molecular mechanics[J]. The Journal of Physical Chemistry B, 2013, 117(1): 25-37.
113	PAULLA K K, FARAJIAN A A. Concentration effects of carbon oxides on sensing by graphene nanoribbons: ab initio modeling[J]. The Journal of Physical Chemistry C, 2013, 117(24): 12815-12825.
114	KUMAR N, SEMINARIO J M. Design of nanosensors for fissile materials in nuclear waste water[J]. The Journal of Physical Chemistry C, 2013, 117(45): 24033-24041.
115	NEZHADALI A, MOJARRA M. Computational study and multivariate optimization of hydrochlorothiazide analysis using molecularly imprinted polymer electrochemical sensor based on carbon nanotube/polypyrrole film[J]. Sensors and Actuators B: Chemical, 2014, 190: 829-837.
116	LIU Q Y, ZUO F, ZHAO Z G, et al. Molecular dynamics investigations of an indicator displacement assay mechanism in a liquid crystal sensor[J]. Physical Chemistry Chemical Physics: PCCP, 2017, 19(35): 23924-23933.
117	CAUSIN P, SACCO R, VERRI M. A multiscale approach in the computational modeling of the biophysical environment in artificial cartilage tissue regeneration[J]. Biomechanics and Modeling in Mechanobiology, 2013, 12(4): 763-780.
118	ZHANG W J, DU Y Q, CRANFORD S W, et al. Biosensor design through molecular dynamics simulation[J]. World Academy of Science, Engineering and Technology, International Journal of Biomedical and Biological Engineering 2016, 10(1): 10-14.
119	KHOSHBIN Z, HOUSAINDOKHT M R, IZADYAR M, et al. Theoretical design and experimental study of new aptamers with the improved target-affinity: new insights into the Pb²⁺-specific aptamers as a case study[J]. Journal of Molecular Liquids, 2019, 289: 111159.
120	ZHUANG S L, WANG H F, DING K K, et al.. Interactions of benzotriazole UV stabilizers with human serum albumin: atomic insights revealed by biosensors, spectroscopies and molecular dynamics simulations[J]. Chemosphere, 2016, 144: 1050-1059.
121	KOLLMAN P A, MASSOVA I, REYES C, et al. Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models[J]. Accounts of Chemical Research, 2000, 33(12): 889-897.
122	KHOSHBIN Z, HOUSAINDOKHT M R. Computer-aided aptamer design for sulfadimethoxine antibiotic: step by step mutation based on MD simulation approach[J]. Journal of Biomolecular Structure & Dynamics, 2021, 39(9): 3071-3079.
123	DO P C, LEE E H, LE L. Steered molecular dynamics simulation in rational drug design[J]. Journal of Chemical Information and Modeling, 2018, 58(8): 1473-1482.
124	THYPARAMBIL A A, ABRAMYAN T M, BAZIN I, et al. Site of tagging influences the ochratoxin recognition by peptide NFO4: a molecular dynamics study[J]. Journal of Chemical Information and Modeling, 2017, 57(8): 2035-2044.
125	THYPARAMBIL A A, BAZIN I, GUISEPPI-ELIE A. Evaluation of ochratoxin recognition by peptides using explicit solvent molecular dynamics[J]. Toxins, 2017, 9(5): 164.
126	SALMASO V, STURLESE M, CUZZOLIN A, et al. Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R grand challenge 2[J]. Journal of Computer-Aided Molecular Design, 2018, 32(1): 251-264.
127	LIU Q J, WANG H, LI H L, et al. Impedance sensing and molecular modeling of an olfactory biosensor based on chemosensory proteins of honeybee[J]. Biosensors & Bioelectronics, 2013, 40(1): 174-179.
128	ABOLHASAN R, MEHDIZADEH A, RASHIDI M R, et al. Application of hairpin DNA-based biosensors with various signal amplification strategies in clinical diagnosis[J]. Biosensors & Bioelectronics, 2019, 129: 164-174.
129	CROSS S, BARONI M, CAROSATI E, et al. FLAP: GRID molecular interaction fields in virtual screening. validation using the DUD data set[J]. Journal of Chemical Information and Modeling, 2010, 50(8): 1442-1450.
130	MA D L, CHAN D S H, LEE P, et al. Molecular modeling of drug-DNA interactions: virtual screening to structure-based design[J]. Biochimie, 2011, 93(8): 1252-1266.
131	PIZZONI D, MASCINI M, COMPAGNONE D, et al. Virtual screening peptide selection for a peptide based gas sensors array[C/OL]//Proceedings of the Second National Conference on Sensors, Rome, Italy, 19-21 February, 2014, 2015: 89-93 [2023-01-01]. .
132	MASCINI M, PIZZONI D, PEREZ G, et al. Tailoring gas sensor arrays via the design of short peptides sequences as binding elements[J]. Biosensors & Bioelectronics, 2017, 93: 161-169.
133	FRANCA E F, LEITE F L, CUNHA R A, et al. Designing an enzyme-based nanobiosensor using molecular modeling techniques[J]. Physical Chemistry Chemical Physics: PCCP, 2011, 13(19): 8894-8899.
134	HONG ENRIQUEZ R P, PAVAN S, BENEDETTI F, et al. Designing short peptides with high affinity for organic molecules: A combined docking, molecular dynamics, and Monte Carlo approach[J]. Journal of Chemical Theory and Computation, 2012, 8(3): 1121-1128.
135	SHCHERBININ D S, GNEDENKO O V, KHMELEVA S A, et al. Computer-aided design of aptamers for cytochrome P450[J]. Journal of Structural Biology, 2015, 191(2): 112-119.
136	PRANDI I G, RAMALHO T C, FRANÇA T C C. Esterase 2 as a fluorescent biosensor for the detection of organophosphorus compounds: docking and electronic insights from molecular dynamics[J]. Molecular Simulation, 2019, 45(17): 1432-1436.
137	SHAHBAAZ M, KANCHI S, SABELA M, et al. Structural basis of pesticide detection by enzymatic biosensing: a molecular docking and MD simulation study[J]. Journal of Biomolecular Structure & Dynamics, 2018, 36(6): 1402-1416.
138	CHAKRAVORTY D K, PARKER T M, GUERRA A J, et al. Energetics of zinc-mediated interactions in the allosteric pathways of metal sensor proteins[J]. Journal of the American Chemical Society, 2013, 135(1): 30-33.
139	GROENHOF G. Introduction to QM/MM simulations[J]. Methods in Molecular Biology, 2013, 924: 43-66.
140	PAPAMICHAEL E M, STAMATIS H, STERGIOU P Y, et al. Enzyme kinetics and modeling of enzymatic systems[M/OL]. Advances in Enzyme Technology, Amsterdam: Elsevier, 2019: 71-104[2023-01-01]. .
141	RYDE U. QM/MM calculations on proteins[J]. Methods in Enzymology, 2016, 577: 119-158.
142	WONG M W, XIE H F, KWA S T. Anion recognition by azophenol thiourea-based chromogenic sensors: a combined DFT and molecular dynamics investigation[J]. Journal of Molecular Modeling, 2013, 19(1): 205-213.
143	CHARCHAR P, CHRISTOFFERSON A J, TODOROVA N, et al. Understanding and designing the gold-bio interface: insights from simulations[J]. Small, 2016, 12(18): 2395-2418.
144	ZHU C, LI L S, YANG G, et al. High-efficiency selection of aptamers for bovine lactoferrin by capillary electrophoresis and its aptasensor application in milk powder[J]. Talanta, 2019, 205: 120088.
145	YARIZADEH K, BEHBAHANI M, MOHABATKAR H, et al. Computational analysis and optimization of carcinoembryonic antigen aptamers and experimental evaluation[J]. Journal of Biotechnology, 2019, 306: 1-8.
146	KHAVANI M, IZADYAR M, HOUSAINDOKHT M R. Theoretical design and experimental study on the gold nanoparticles based colorimetric aptasensors for detection of neomycin B[J]. Sensors and Actuators B: Chemical, 2019, 300: 126947.
147	MITCHLER M M, GARCIA J M, MONTERO N E, et al. Transcription factor-based biosensors: a molecular-guided approach for natural product engineering[J]. Current Opinion in Biotechnology, 2021, 69: 172-181.
148	HOSSAIN G S, SAINI M, MIYAKE R, et al. Genetic biosensor design for natural product biosynthesis in microorganisms[J]. Trends in Biotechnology, 2020, 38(7): 797-810.
149	LIANG W F, CUI L Y, CUI J Y, et al. Biosensor-assisted transcriptional regulator engineering for Methylobacterium extorquens AM1 to improve mevalonate synthesis by increasing the acetyl-CoA supply[J]. Metabolic Engineering, 2017, 39: 159-168.
150	KASEY C M, ZERRAD M, LI Y W, et al. Development of transcription factor-based designer macrolide biosensors for metabolic engineering and synthetic biology[J]. ACS Synthetic Biology, 2018, 7(1): 227-239.
151	DE PAEPE B, MAERTENS J, VANHOLME B, et al. Chimeric LysR-type transcriptional biosensors for customizing ligand specificity profiles toward flavonoids[J]. ACS Synthetic Biology, 2019, 8(2): 318-331.
152	LIANG M D, LI Z L, WANG W S, et al. A CRISPR-Cas12a-derived biosensing platform for the highly sensitive detection of diverse small molecules[J]. Nature Communications, 2019, 10: 3672.
153	MCCANN J J, PIKE D H, BROWN M C, et al. Computational design of a sensitive, selective phase-changing sensor protein for the VX nerve agent[J]. Science Advances, 2022, 8(27): eabh3421.

名称	地址	用途与原理	参考文献
FireProt	https://loschmidt.chemi.muni.cz/fireprotweb/	通过结合进化的保守性和基于结构的能量计算，进行多点突变稳定性设计	[7]
GRAPE-WEB	https://nmdc.cn/grape-web/	结合基于物理能量和统计能量的蛋白质设计力场，进行单点突变稳定性设计，利用结构特征和实验结果使用机器学习算法组合突变分类	—
PROSS	https://pross.weizmann.ac.il/step/pross-terms/	基于Rosetta能量函数，设计可以提高稳定性的组合突变	[8]
Funclib	https://funclib.weizmann.ac.il/bin/steps	通过单点突变在进化中的概率和结构计算的能量筛选用于组合突变的候选，使用Rosetta能量函数评价组合突变的稳定性，设计突变组合突变改变底物谱、改善可溶性表达	[9]
ABACUS2	https://biocomp.ustc.edu.cn/servers/abacus-design.php	基于从蛋白质晶体结构中统计得到的能量函数，进行单点突变能量计算和序列骨架适配度打分	[10]
Swiss-Model	https://swissmodel.expasy.org/	基于序列搜索，以同源的晶体结构为模版，进行同源建模	[11]
ConSurf	https://consurf.tau.ac.il/consurf_index.php	基于序列搜索算法，分析保守性	[12]
ROSIE	https://rosie.graylab.jhu.edu/	基于Rosetta的结构relax，分子对接，突变预测等一系列任务	[13]

名称	地址	用途与原理	参考文献
FireProt	https://loschmidt.chemi.muni.cz/fireprotweb/	通过结合进化的保守性和基于结构的能量计算，进行多点突变稳定性设计	[7]
GRAPE-WEB	https://nmdc.cn/grape-web/	结合基于物理能量和统计能量的蛋白质设计力场，进行单点突变稳定性设计，利用结构特征和实验结果使用机器学习算法组合突变分类	—
PROSS	https://pross.weizmann.ac.il/step/pross-terms/	基于Rosetta能量函数，设计可以提高稳定性的组合突变	[8]
Funclib	https://funclib.weizmann.ac.il/bin/steps	通过单点突变在进化中的概率和结构计算的能量筛选用于组合突变的候选，使用Rosetta能量函数评价组合突变的稳定性，设计突变组合突变改变底物谱、改善可溶性表达	[9]
ABACUS2	https://biocomp.ustc.edu.cn/servers/abacus-design.php	基于从蛋白质晶体结构中统计得到的能量函数，进行单点突变能量计算和序列骨架适配度打分	[10]
Swiss-Model	https://swissmodel.expasy.org/	基于序列搜索，以同源的晶体结构为模版，进行同源建模	[11]
ConSurf	https://consurf.tau.ac.il/consurf_index.php	基于序列搜索算法，分析保守性	[12]
ROSIE	https://rosie.graylab.jhu.edu/	基于Rosetta的结构relax，分子对接，突变预测等一系列任务	[13]

名称	地址	用途	参考文献
Protein Data Bank	https://www.rcsb.org/	蛋白质实验解析的结构数据库	[13]
UniProt	https://www.uniprot.org/	蛋白质序列数据库	[26]
AlphaFold DB	https://alphafold.ebi.ac.uk/	AlphaFold预测的蛋白质结构数据库	[27]
CATH	https://www.cathdb.info/	蛋白质结构域分类数据库	[28]
InterPro	https://www.ebi.ac.uk/interpro	蛋白质家族分类数据库	[29]
BRENDA	https://www.brenda-enzymes.org/	综合性酶学数据库	[30]
VariBench	http://structure.bmc.lu.se/VariBench/index.php	突变体实验测定数据库	[31]
Meltome	http://meltomeatlas.proteomics.wzw.tum.de:5003/	蛋白质熔融温度数据库	[32]
FireProt-DB	https://loschmidt.chemi.muni.cz/fireprotdb/	蛋白质点突变稳定性数据库	[33]
SABIO-RK	http://sabio.h-its.org/	酶动力学性质数据库	[34]

名称	地址	用途	参考文献
Protein Data Bank	https://www.rcsb.org/	蛋白质实验解析的结构数据库	[13]
UniProt	https://www.uniprot.org/	蛋白质序列数据库	[26]
AlphaFold DB	https://alphafold.ebi.ac.uk/	AlphaFold预测的蛋白质结构数据库	[27]
CATH	https://www.cathdb.info/	蛋白质结构域分类数据库	[28]
InterPro	https://www.ebi.ac.uk/interpro	蛋白质家族分类数据库	[29]
BRENDA	https://www.brenda-enzymes.org/	综合性酶学数据库	[30]
VariBench	http://structure.bmc.lu.se/VariBench/index.php	突变体实验测定数据库	[31]
Meltome	http://meltomeatlas.proteomics.wzw.tum.de:5003/	蛋白质熔融温度数据库	[32]
FireProt-DB	https://loschmidt.chemi.muni.cz/fireprotdb/	蛋白质点突变稳定性数据库	[33]
SABIO-RK	http://sabio.h-its.org/	酶动力学性质数据库	[34]

名称	描述	链接	年份	参考文献
EPDNew	真核启动子数据库	http://epd.vital-it.ch/	2015	[48]
dbSUPER	包含小鼠和人类超级增强子信息	http://asntech.org/dbsuper/	2016	[49]
SEA	包含人类、小鼠等多种生物的超级增强子信息	http://sea.edbc.org/	2016	[50]
DiseaseEnhancer	包含143种人类疾病中的847种疾病相关的增强子信息	http://biocc.hrbmu.edu.cn/DiseaseEnhancer/	2018	[51]
HEDD	人类增强子疾病数据库，包含约280万人类增强子的基因组信息	https://zdzlab.einsteinmed.edu/1/hedd.php	2018	[52]
SEdb	人类超级增强子数据库，注释了超级增强子在基因调控中的功能	http://www.licpathway.net/sedb/	2019	[53]
PlantPAN3.0	从78种植物中收集了17 230个转录因子，部分包含结合位点信息	http://plantpan.itps.ncku.edu.tw/	2019	[54]
REDfly	包含实验验证的果蝇的CRM信息	http://redfly.ccr.buffalo.edu/	2019	[55]
EnhancerAtlas2.0	包含586种组织/细胞中的13 494 603个增强子	http://www.enhanceratlas.org/indexv2.php	2016	[56]
UCSC Genome Browser database	提供了人类、小鼠和SARS-Cov-2的基因组数据	http://genome.ucsc.edu	2021	[57]
SilencerDB	包含33 060个试验确定的沉默子和5 045 547个机器学习算法预测的沉默子	http://health.tsinghua.edu.cn/silencerdb/	2021	[58]

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

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名称	描述	链接	年份	参考文献
GTRD	包含试验确定的人类和小鼠转录因子结合位点	http://gtrd.biouml.org/	2017	[78]
HOCOMOCO	包含人类和小鼠转录因子结合位点	https://hocomoco11.autosome.org/	2016	[79-80]
MeDReaders	包含人类和小鼠中731个转录因子与甲基化DNA序列结合的信息	http://medreader. org/	2018	[81]
TRRUST	人类TF-靶标相互作用数据库	https://www.grnpedia.org/trrust/	2015	[82-83]
AnimalTFDB 3.0	包含97个动物基因组中125 135个TF基因和80 060个转录辅助因子基因	http://bioinfo.life.hust.edu.cn/AnimalTFDB/#!/	2019	[84]
SalMotifDB	包含5个鲑鱼基因组中的转录因子及其顺式调控结合位点	https://salmobase.org/SalMotifDB/	2019	[85]
hTFtarget	包含人类转录因子及其靶体	http://bioinfo.life.hust.edu.cn/hTFtarget#!/	2020	[86]
KnockTF	包含人类组织/细胞中转录因子及其靶基因	http://www.licpathway.net/KnockTF/	2020	[87]
JASPAR 2022	包含真核生物转录因子结合位点	https://jaspar.genereg.net/	2022	[88]
PCRMS	提供了人类和小鼠基因组中CRM和转录因子结合位点的预测数据	https://cci-bioinfo.uncc.edu/	2022	[89]

计算方法	优点	缺点	在生物传感器领域的应用
QM方法	精确、可以计算化学反应或电荷转移	计算成本高、难以直接应用于生物大分子的计算	高精度的评估结合能；计算化学反应（结合QM/MM）；计算量子电导
MD方法	在一定范围内有着可靠的精度，计算效率比QM高数个数量级	当电子运动不可忽略时，精度不够可靠；面临优化序列空间的问题是计算效率依然不够高	对生物传感器的作用机制或分子的构象变化做机理分析；评估配体和受体之间的亲和力；在一定范围内做序列优化
分子对接和虚拟筛选	高的计算效率	精度低于MD方法	高效评估配体和受体的结合状态和结合模式；从打数据库或序列空间寻找潜在的配体或受体待优化对象

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