Natural products are molecules that fulfil a range of important ecological functions. Many natural products have been exploited for pharmaceutical and agricultural applications. In contrast to many other specialised metabolites, the products of modular nonribosomal peptide synthetase (NRPS) and polyketide synthase (PKS) systems can often (partially) be predicted from the DNA sequence of the biosynthetic gene clusters. This is because the biosynthetic pathways of NRPS and PKS systems adhere to consistent rulesets. These universal biosynthetic rules can be leveraged to generate biosynthetic models of biosynthetic pathways. While these principles have been largely deciphered, software that leverages these rules to automatically generate visualisations of biosynthetic models has not yet been developed. To enable high-quality automated visualisations of natural product biosynthetic pathways, we developed RAIChU (Reaction Analysis through Illustrating Chemical Units), which produces depictions of biosynthetic transformations of PKS, NRPS, and hybrid PKS/NRPS systems from predicted or experimentally verified module architectures and domain substrate specificities. RAIChU also boasts a library of functions to perform and visualise reactions and pathways whose specifics (e.g., regioselectivity, stereoselectivity) are still difficult to predict, including terpenes, ribosomally synthesised and posttranslationally modified peptides and alkaloids. Additionally, RAIChU includes 34 prevalent tailoring reactions to enable the visualisation of biosynthetic pathways of fully maturated natural products. RAIChU can be integrated into Python pipelines, allowing users to upload and edit results from antiSMASH, a widely used BGC detection and annotation tool, or to build biosynthetic PKS/NRPS systems from scratch. RAIChU’s cluster drawing correctness (100%) and drawing readability (97.66%) were validated on 5000 randomly generated PKS/NRPS systems, and on the MIBiG database. The automated visualisation of these pathways accelerates the generation of biosynthetic models, facilitates the analysis of large (meta-) genomic datasets and reduces human error. RAIChU is available at https://github.com/BTheDragonMaster/RAIChU and https://pypi.org/project/raichu . Scientific contribution RAIChU is the first software package capable of automating high-quality visualisations of natural product biosynthetic pathways. By leveraging universal biosynthetic rules, RAIChU enables the depiction of complex biosynthetic transformations for PKS, NRPS, ribosomally synthesised and posttranslationally modified peptide (RiPP), terpene and alkaloid systems, enhancing predictive and analytical capabilities. This innovation not only streamlines the creation of biosynthetic models, making the analysis of large genomic datasets more efficient and accurate, but also bridges a crucial gap in predicting and visualising the complexities of natural product biosynthesis.

中文翻译：

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RAIChU：天然产物生物合成的可视化自动化


天然产物是具有一系列重要生态功能的分子。许多天然产物已被开发用于制药和农业应用。与许多其他专门的代谢物相比，模块化非核糖体肽合成酶 (NRPS) 和聚酮化合物合成酶 (PKS) 系统的产物通常可以（部分）从生物合成基因簇的 DNA 序列预测。这是因为 NRPS 和 PKS 系统的生物合成途径遵循一致的规则集。这些通用的生物合成规则可用于生成生物合成途径的生物合成模型。虽然这些原理已基本被破译，但利用这些规则自动生成生物合成模型可视化的软件尚未开发出来。为了实现天然产物生物合成途径的高质量自动可视化，我们开发了 RAIChU（通过说明化学单元进行反应分析），它根据预测或实验验证的模块架构生成 PKS、NRPS 和混合 PKS/NRPS 系统的生物合成转化描述，域底物特异性。 RAIChU 还拥有一个功能库，用于执行和可视化反应和途径，其具体特征（例如，区域选择性、立体选择性）仍然难以预测，包括萜烯、核糖体合成和翻译后修饰的肽和生物碱。此外，RAIChU 包括 34 种流行的定制反应，使完全成熟的天然产物的生物合成途径可视化。 RAIChU 可以集成到 Python 管道中，允许用户上传和编辑 antiSMASH（一种广泛使用的 BGC 检测和注释工具）的结果，或从头开始构建生物合成 PKS/NRPS 系统。 RAIChU 的聚类绘图正确性 (100%) 和绘图可读性 (97.66%) 在 5000 个随机生成的 PKS/NRPS 系统和 MIBiG 数据库上进行了验证。这些途径的自动可视化加速了生物合成模型的生成，促进了大型（元）基因组数据集的分析并减少了人为错误。 RAIChU 可在 https://github.com/BTheDragonMaster/RAIChU 和 https://pypi.org/project/raichu 获取。科学贡献 RAIChU 是第一个能够自动实现天然产物生物合成途径高质量可视化的软件包。通过利用通用生物合成规则，RAIChU 能够描述 PKS、NRPS、核糖体合成和翻译后修饰肽 (RiPP)、萜烯和生物碱系统的复杂生物合成转化，从而增强预测和分析能力。这项创新不仅简化了生物合成模型的创建，使大型基因组数据集的分析更加高效和准确，而且弥合了预测和可视化天然产物生物合成的复杂性方面的关键差距。