Proteinogenic and non-proteinogenic amino acids, fatty acids or glycans are some of the main building blocks of nonribsosomal peptides (NRPs) and as such may give insight into the origin, biosynthesis and bioactivities of their constitutive peptides. Hence, the structural representation of NRPs using monomers provides a biologically interesting skeleton of these secondary metabolites. Databases dedicated to NRPs such as Norine, already integrate monomer-based annotations in order to facilitate the development of structural analysis tools. In this paper, we present rBAN (retro-biosynthetic analysis of nonribosomal peptides), a new computational tool designed to predict the monomeric graph of NRPs from their atomic structure in SMILES format. This prediction is achieved through the “in silico” fragmentation of a chemical structure and matching the resulting fragments against the monomers of Norine for identification. Structures containing monomers not yet recorded in Norine, are processed in a “discovery mode” that uses the RESTful service from PubChem to search the unidentified substructures and suggest new monomers. rBAN was integrated in a pipeline for the curation of Norine data in which it was used to check the correspondence between the monomeric graphs annotated in Norine and SMILES-predicted graphs. The process concluded with the validation of the 97.26% of the records in Norine, a two-fold extension of its SMILES data and the introduction of 11 new monomers suggested in the discovery mode. The accuracy, robustness and high-performance of rBAN were demonstrated in benchmarking it against other tools with the same functionality: Smiles2Monomers and GRAPE.

中文翻译：

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rBAN：非核糖体肽的逆向生物合成分析。

蛋白原和非蛋白原氨基酸，脂肪酸或聚糖是非核糖体肽（NRP）的一些主要结构单元，因此可以深入了解其构成肽的起源，生物合成和生物活性。因此，使用单体的NRP的结构表示提供了这些次级代谢产物的生物学意义的骨架。专门用于NRP的数据库（例如Norine）已经集成了基于单体的注释，以促进结构分析工具的开发。在本文中，我们介绍了rBAN（非核糖体肽的复古生物合成分析），一种新的计算工具，旨在根据SMILES格式的原子结构预测NRP的单体图。这种预测是通过化学结构的“计算机内”断裂并将所得片段与Norine的单体进行匹配来鉴定来实现的。包含尚未记录在Norine中的单体的结构以“发现模式”进行处理，该模式使用PubChem的RESTful服务搜索未识别的子结构并提出新的单体。rBAN集成在用于Norine数据管理的管道中，其中用于检查Norine注释的单体图和SMILES预测的图之间的对应关系。该过程以Norine中97.26％的记录得到验证，其SMILES数据的两倍扩展以及在发现模式下建议引入11种新单体而结束。准确性