Synthetic Biology Journal ›› 2023, Vol. 4 ›› Issue (3): 422-443.DOI: 10.12211/2096-8280.2023-004
• Invited Review • Previous Articles Next Articles
Sheng WANG1, Zechen WANG1,2, Weihua CHEN1, Ke CHEN1, Xiangda PENG1, Fafen OU1, Liangzhen ZHENG1,3, Jinyuan SUN1,4, Tao SHEN1, Guoping ZHAO3
Received:
2023-01-11
Revised:
2023-04-03
Online:
2023-07-05
Published:
2023-06-30
Contact:
Sheng WANG
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
通讯作者:
王晟
作者简介:
基金资助:
CLC Number:
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.
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.
Add to citation manager EndNote|Ris|BibTeX
URL: https://synbioj.cip.com.cn/EN/10.12211/2096-8280.2023-004
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
ConSurf | https://consurf.tau.ac.il/consurf_index.php | 基于序列搜索算法,分析保守性 | [ |
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 | [ |
Table 1 Online servers based on computational biology tools
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
ConSurf | https://consurf.tau.ac.il/consurf_index.php | 基于序列搜索算法,分析保守性 | [ |
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 | [ |
Fig. 2 Design of the catalytic components based on computational biology(a) Kemp elimination reaction mechanism. (b) Retro-Aldo reaction mechanism. (c) Mechanism of branching acid translocase catalysis. (d) Schematic diagram of two different near-attack state conformations of limonene epoxide hydrolysis with pro-RR on the left and pro-SS on the right, which were modified from reference [17] with permission. (e) Schematic diagram of the energetically unfavorable unsaturated hydrogen bond donor with the structure of IsPETase (PDB ID: 5XJH), where a water molecule and W159 already occupy the hydrogen bond that can be formed by the carbonyl group of H237 (yellow dashed line in the figure), and the side chain hydroxyl group of T183 is much further away and difficult to form a hydrogen bond. (f) Schematic diagram of the grouped greedy stacking strategy
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 | [ |
UniProt | https://www.uniprot.org/ | 蛋白质序列数据库 | [ |
AlphaFold DB | https://alphafold.ebi.ac.uk/ | AlphaFold预测的蛋白质结构数据库 | [ |
CATH | https://www.cathdb.info/ | 蛋白质结构域分类数据库 | [ |
InterPro | https://www.ebi.ac.uk/interpro | 蛋白质家族分类数据库 | [ |
BRENDA | https://www.brenda-enzymes.org/ | 综合性酶学数据库 | [ |
VariBench | http://structure.bmc.lu.se/VariBench/index.php | 突变体实验测定数据库 | [ |
Meltome | http://meltomeatlas.proteomics.wzw.tum.de:5003/ | 蛋白质熔融温度数据库 | [ |
FireProt-DB | https://loschmidt.chemi.muni.cz/fireprotdb/ | 蛋白质点突变稳定性数据库 | [ |
SABIO-RK | http://sabio.h-its.org/ | 酶动力学性质数据库 | [ |
Table 2 Databases available for machine learning model training
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 | [ |
UniProt | https://www.uniprot.org/ | 蛋白质序列数据库 | [ |
AlphaFold DB | https://alphafold.ebi.ac.uk/ | AlphaFold预测的蛋白质结构数据库 | [ |
CATH | https://www.cathdb.info/ | 蛋白质结构域分类数据库 | [ |
InterPro | https://www.ebi.ac.uk/interpro | 蛋白质家族分类数据库 | [ |
BRENDA | https://www.brenda-enzymes.org/ | 综合性酶学数据库 | [ |
VariBench | http://structure.bmc.lu.se/VariBench/index.php | 突变体实验测定数据库 | [ |
Meltome | http://meltomeatlas.proteomics.wzw.tum.de:5003/ | 蛋白质熔融温度数据库 | [ |
FireProt-DB | https://loschmidt.chemi.muni.cz/fireprotdb/ | 蛋白质点突变稳定性数据库 | [ |
SABIO-RK | http://sabio.h-its.org/ | 酶动力学性质数据库 | [ |
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 | [ |
dbSUPER | 包含小鼠和人类超级增强子信息 | http://asntech.org/dbsuper/ | 2016 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
REDfly | 包含实验验证的果蝇的CRM信息 | http://redfly.ccr.buffalo.edu/ | 2019 | [ |
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 | [ |
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 | [ |
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 | [ |
Table 3 Databases for cis-regulatory elements
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 | [ |
dbSUPER | 包含小鼠和人类超级增强子信息 | http://asntech.org/dbsuper/ | 2016 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
REDfly | 包含实验验证的果蝇的CRM信息 | http://redfly.ccr.buffalo.edu/ | 2019 | [ |
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 | [ |
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 | [ |
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 | [ |
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 | [ |
HOCOMOCO | 包含人类和小鼠转录因子结合位点 | https://hocomoco11.autosome.org/ | 2016 | [ |
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 | [ |
TRRUST | 人类TF-靶标相互作用数据库 | https://www.grnpedia.org/trrust/ | 2015 | [ |
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 | [ |
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 | [ |
hTFtarget | 包含人类转录因子及其靶体 | http://bioinfo.life.hust.edu.cn/hTFtarget#!/ | 2020 | [ |
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 | [ |
JASPAR 2022 | 包含真核生物转录因子结合位点 | https://jaspar.genereg.net/ | 2022 | [ |
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 | [ |
Table 4 Databases for transcription factor
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 | [ |
HOCOMOCO | 包含人类和小鼠转录因子结合位点 | https://hocomoco11.autosome.org/ | 2016 | [ |
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 | [ |
TRRUST | 人类TF-靶标相互作用数据库 | https://www.grnpedia.org/trrust/ | 2015 | [ |
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 | [ |
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 | [ |
hTFtarget | 包含人类转录因子及其靶体 | http://bioinfo.life.hust.edu.cn/hTFtarget#!/ | 2020 | [ |
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 | [ |
JASPAR 2022 | 包含真核生物转录因子结合位点 | https://jaspar.genereg.net/ | 2022 | [ |
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 | [ |
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 |
Table 5 Comparison of the computation methods
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 |
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. FireProtDB: 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. |
[1] | Zhidian DIAO, Xixian WANG, Qing SUN, Jian XU, Bo MA. Advances and applications of single-cell Raman spectroscopy testing and sorting equipment [J]. Synthetic Biology Journal, 2023, 4(5): 1020-1035. |
[2] | Hui LU, Fangli ZHANG, Lei HUANG. Establishment of iBioFoundry for synthetic biology applications [J]. Synthetic Biology Journal, 2023, 4(5): 877-891. |
[3] | Zhonghu BAI, He REN, Jianqi NIE, Yang SUN. The recent progresses and applications of in-parallel fermentation technology [J]. Synthetic Biology Journal, 2023, 4(5): 904-915. |
[4] | Yujie WU, Xinxin LIU, Jianhui LIU, Kaiguang Yang, Zhigang SUI, Lihua ZHANG, Yukui ZHANG. Research progress of strain screening and quantitative analysis of key molecules based on high-throughput liquid chromatography and mass spectrometry [J]. Synthetic Biology Journal, 2023, 4(5): 1000-1019. |
[5] | Zhehui HU, Juan XU, Guangkai BIAN. Application of automated high-throughput technology in natural product biosynthesis [J]. Synthetic Biology Journal, 2023, 4(5): 932-946. |
[6] | Huan LIU, Qiu CUI. Advances and applications of ambient ionization mass spectrometry in screening of microbial strains [J]. Synthetic Biology Journal, 2023, 4(5): 980-999. |
[7] | Yannan WANG, Yuhui SUN. Base editing technology and its application in microbial synthetic biology [J]. Synthetic Biology Journal, 2023, 4(4): 720-737. |
[8] | Wanqiu LIU, Xiangyang JI, Huiling XU, Yicong LU, Jian LI. Cell-free protein synthesis system enables rapid and efficient biosynthesis of restriction endonucleases [J]. Synthetic Biology Journal, 2023, 4(4): 840-851. |
[9] | Meili SUN, Kaifeng WANG, Ran LU, Xiaojun JI. Rewiring and application of Yarrowia lipolytica chassis cell [J]. Synthetic Biology Journal, 2023, 4(4): 779-807. |
[10] | Zhi SUN, Ning YANG, Chunbo LOU, Chao TANG, Xiaojing YANG. Rational design for functional topology and its applications in synthetic biology [J]. Synthetic Biology Journal, 2023, 4(3): 444-463. |
[11] | Qilong LAI, Shuai YAO, Yuguo ZHA, Hong BAI, Kang NING. Microbiome-based biosynthetic gene cluster data mining techniques and application potentials [J]. Synthetic Biology Journal, 2023, 4(3): 611-627. |
[12] | Zhihang CHEN, Menglin JI, Yifei QI. Research progress of artificial intelligence in desiging protein structures [J]. Synthetic Biology Journal, 2023, 4(3): 464-487. |
[13] | Qiaozhen MENG, Fei GUO. Applications of foldability in intelligent enzyme engineering and design: take AlphaFold2 for example [J]. Synthetic Biology Journal, 2023, 4(3): 571-589. |
[14] | Liqi KANG, Pan TAN, Liang HONG. Enzyme engineering in the age of artificial intelligence [J]. Synthetic Biology Journal, 2023, 4(3): 524-534. |
[15] | Hailong LV, Jian WANG, Hao LV, Jin WANG, Yong XU, Dayong GU. Synthetic biology for next-generation genetic diagnostics [J]. Synthetic Biology Journal, 2023, 4(2): 318-332. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||