合成生物学 ›› 2024, Vol. 5 ›› Issue (3): 507-526.DOI: 10.12211/2096-8280.2023-098
雷茹, 陶慧, 刘天罡
收稿日期:
2023-12-01
修回日期:
2024-02-22
出版日期:
2024-06-30
发布日期:
2024-07-12
通讯作者:
陶慧,刘天罡
作者简介:
基金资助:
Ru LEI, Hui TAO, Tiangang LIU
Received:
2023-12-01
Revised:
2024-02-22
Online:
2024-06-30
Published:
2024-07-12
Contact:
Hui TAO, Tiangang LIU
摘要:
萜类天然产物广泛分布于动物(包括海洋无脊椎动物)、植物、微生物中,具有复杂的化学结构和丰富的生物活性。人们通过从植物和微生物中直接分离提取的方式获得了大量萜类天然产物,然而随着越来越多化合物被发现,使用基于自然筛选的传统挖掘方式很难获得新的萜类天然产物。随着基因组测序技术和合成生物学使能技术的不断发展,我们进入了基因组挖掘驱动天然产物发现的时代,萜类天然产物的挖掘也进入了“井喷式”发现新阶段。针对基因组挖掘在微生物萜类天然产物发现方面的应用,本文综述了近年来使用的主要研究策略和最新研究进展,介绍了多种高效微生物底盘、基因组深度挖掘策略、人工智能与自动化平台等驱动的萜类化合物挖掘的最新研究进展,讨论了基因组挖掘萜类天然产物面临的挑战,展望了未来萜类化合物创新发现的发展趋势。通过在多种微生物中强化前体供应途径,人们打造了多个萜类化合物合成底盘,突破了异源合成萜类天然产物时“产量低”和“产物难获取”的瓶颈;针对萜类天然产物生物合成基因簇或萜类合酶进行深度挖掘,可以有效地解决“重复发现”和“集中度低”的难题;随着人工智能和自动化技术在合成生物学领域的发展和应用,萜类化合物的发现也进入了高通量智能发现时期,显著地改善了“研究通量低”的现状,高效获得了大量新结构萜类天然产物。在未来,更多萜类化合物将开发成药物、进入工业化生产应用,更多萜类“暗物质”会走进我们视野。
中图分类号:
雷茹, 陶慧, 刘天罡. 基因组深度挖掘驱动微生物萜类化合物高效发现[J]. 合成生物学, 2024, 5(3): 507-526.
Ru LEI, Hui TAO, Tiangang LIU. Deep genome mining boosts the discovery of microbial terpenoids[J]. Synthetic Biology Journal, 2024, 5(3): 507-526.
主要微生物底盘 | 优点 | 缺点 |
---|---|---|
Escherichia coli | 遗传操作简单 培养周期短 具有内源MEP途径 | 不具有跨膜结构域,可能导致功能蛋白的错误折叠、失去活性甚至降解 不适合表达真菌和植物来源的萜类合成基因(簇) |
Saccharomyces cerevisiae | 遗传操作相对简易 重组效率高效 具有完整的细胞器与内膜系统 具有内源MVA途径 | 存在密码子偏好性 无法正确识别和剪切内含子 不适合表达真菌来源的萜类合成基因(簇) |
Aspergillus oryzae | 具有强大的蛋白分泌特性 功能酶后修饰能力强大 可以正确地识别和剪切内含子 具有内源MVA途径 适合表达真菌或植物来源的萜类合成基因(簇) | 遗传操作复杂 培养周期长 |
Streptomyces albus | 功能酶后修饰能力强大 具有内源MEP途径 适合表达细菌来源的萜类合成基因(簇) | 遗传操作复杂 培养周期长 |
表1 用于萜类化合物挖掘的主要微生物底盘
Table 1 Major microbial chassis for terpenoid mining
主要微生物底盘 | 优点 | 缺点 |
---|---|---|
Escherichia coli | 遗传操作简单 培养周期短 具有内源MEP途径 | 不具有跨膜结构域,可能导致功能蛋白的错误折叠、失去活性甚至降解 不适合表达真菌和植物来源的萜类合成基因(簇) |
Saccharomyces cerevisiae | 遗传操作相对简易 重组效率高效 具有完整的细胞器与内膜系统 具有内源MVA途径 | 存在密码子偏好性 无法正确识别和剪切内含子 不适合表达真菌来源的萜类合成基因(簇) |
Aspergillus oryzae | 具有强大的蛋白分泌特性 功能酶后修饰能力强大 可以正确地识别和剪切内含子 具有内源MVA途径 适合表达真菌或植物来源的萜类合成基因(簇) | 遗传操作复杂 培养周期长 |
Streptomyces albus | 功能酶后修饰能力强大 具有内源MEP途径 适合表达细菌来源的萜类合成基因(簇) | 遗传操作复杂 培养周期长 |
数据库或网络工具 | 简介 | 网址 | 参考文献 |
---|---|---|---|
BGC数据库 | |||
antiSMASH Database | 细菌、古细菌和真菌等基因组中可检测的高质量BGC集合 | https://antismash-db.secondarymetabolites.org/ | [ |
BiG-FAM | 微生物生物合成基因簇家族(GCF)模型集合 | https://bigfam.bioinformatics.nl/home | [ |
IMG-ABC | 微生物BGC的集成基因组图谱 | https://img.jgi.doe.gov/cgi-bin/abc-public/main.cgi | [ |
MIBiG | 已知生物合成基因簇及其分子产物的信息集合 | https://mibig.secondarymetabolites.org/ | [ |
BGC网络识别工具 | |||
antiSMASH | 用于自动识别和分析微生物次级代谢产物基因簇 | https://antismash.secondarymetabolites.org/ | [ |
ARTS | 细菌基因组靶向挖掘,筛选潜在新型抗生素靶点 | https://arts.ziemertlab.com/ | [ |
BiG-SCAPE | 用于构建BGC的序列相似性网络并将其分组到GCF中 | https://git.wageningenur.nl/medema-group/BiG-SCAPE/wikis/home | [ |
BiG-SLiCE | 聚集大量BGC,通过序列相似性网络构建GCF模型 | https://github.com/medema-group/bigslice | [ |
ClusterFinder | 预测基因组中的BGC | https://github.com/petercim/ClusterFinder | [ |
DeepBGC | 使用深度学习的方法预测细菌和真菌基因组中的BGC | https://github.com/Merck/deepbgc | [ |
e-DeepBGC | 基于深度学习方法,引入了Pfam信息,预测细菌基因组中BGC | — | [ |
RL-BGC | 基于Pfam蛋白质家族结构域和功能注释的强化学习方法,精准获得真菌候选BGC | https://github.com/bioinfoUQAM/RL-bgc-components | [ |
PRISM4 | 基于微生物基因组序列,识别BGC并生成结构预测的组合文库 | https://prism.adapsyn.com/ | [ |
SMURF | 用于真菌基因组BGC和途径预测分析 | http://smurf.jcvi.org/index.php | [ |
TeroKit | 萜类化合物的化学空间、生物活性和生物合成途径的在线检索和分析 | http://terokit.qmclab.com/ | [ |
酶功能预测工具 | |||
CLEAN | 启用对比学习的酶注释,能够对未表征的酶实现功能预测 | https://clean.platform.moleculemaker.org/configuration | [ |
ECRECer | 基于深度学习实现酶催化功能预测 | https://ecrecer.biodesign.ac.cn/ | [ |
PfamScan | 根据Pfam HMM数据库进行酶功能预测分析 | https://www.ebi.ac.uk/Tools/pfa/pfamscan/ | [ |
表2 萜类BGC挖掘的网络工具和数据库[104-106]
Table 2 Tools and databases for mining terpene BGCs [104-106]
数据库或网络工具 | 简介 | 网址 | 参考文献 |
---|---|---|---|
BGC数据库 | |||
antiSMASH Database | 细菌、古细菌和真菌等基因组中可检测的高质量BGC集合 | https://antismash-db.secondarymetabolites.org/ | [ |
BiG-FAM | 微生物生物合成基因簇家族(GCF)模型集合 | https://bigfam.bioinformatics.nl/home | [ |
IMG-ABC | 微生物BGC的集成基因组图谱 | https://img.jgi.doe.gov/cgi-bin/abc-public/main.cgi | [ |
MIBiG | 已知生物合成基因簇及其分子产物的信息集合 | https://mibig.secondarymetabolites.org/ | [ |
BGC网络识别工具 | |||
antiSMASH | 用于自动识别和分析微生物次级代谢产物基因簇 | https://antismash.secondarymetabolites.org/ | [ |
ARTS | 细菌基因组靶向挖掘,筛选潜在新型抗生素靶点 | https://arts.ziemertlab.com/ | [ |
BiG-SCAPE | 用于构建BGC的序列相似性网络并将其分组到GCF中 | https://git.wageningenur.nl/medema-group/BiG-SCAPE/wikis/home | [ |
BiG-SLiCE | 聚集大量BGC,通过序列相似性网络构建GCF模型 | https://github.com/medema-group/bigslice | [ |
ClusterFinder | 预测基因组中的BGC | https://github.com/petercim/ClusterFinder | [ |
DeepBGC | 使用深度学习的方法预测细菌和真菌基因组中的BGC | https://github.com/Merck/deepbgc | [ |
e-DeepBGC | 基于深度学习方法,引入了Pfam信息,预测细菌基因组中BGC | — | [ |
RL-BGC | 基于Pfam蛋白质家族结构域和功能注释的强化学习方法,精准获得真菌候选BGC | https://github.com/bioinfoUQAM/RL-bgc-components | [ |
PRISM4 | 基于微生物基因组序列,识别BGC并生成结构预测的组合文库 | https://prism.adapsyn.com/ | [ |
SMURF | 用于真菌基因组BGC和途径预测分析 | http://smurf.jcvi.org/index.php | [ |
TeroKit | 萜类化合物的化学空间、生物活性和生物合成途径的在线检索和分析 | http://terokit.qmclab.com/ | [ |
酶功能预测工具 | |||
CLEAN | 启用对比学习的酶注释,能够对未表征的酶实现功能预测 | https://clean.platform.moleculemaker.org/configuration | [ |
ECRECer | 基于深度学习实现酶催化功能预测 | https://ecrecer.biodesign.ac.cn/ | [ |
PfamScan | 根据Pfam HMM数据库进行酶功能预测分析 | https://www.ebi.ac.uk/Tools/pfa/pfamscan/ | [ |
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