A Deep Mining Strategy for Peptide Rapid Identification in <i>Lactobacillus reuteri</i> Based on LC–MS/MS Integrated with FBMN and De Novo Sequencing

Abstract

Lactobacillus reuteri (L. reuteri) is widely recognized as a probiotic that produces prebiotics. However, studies on bioactive peptides or amino acid (AA) derivatives produced by L. reuteri are still lacking, whereas many bioactive peptides and AA derivatives have been found in other Lactobacillus species. In addition, rapid identification of peptides is challenged by the large amount of data and is limited by the coverage of protein databases. In this study, we performed a rapid and thorough profile of peptides in L. reuteri incorporating Global Natural Products Social Molecular Networking (GNPS) platform database searching, de novo sequencing, and deep mining, based on feature-based molecular networking (FBMN). According to FBMN, it was found that peptides containing identical or similar AA compositions were grouped into the same clusters, especially cyclic dipeptides (CDPs). Therefore, the grouping characteristics of clusters, differences in precursor ions, and characteristic fragment ions were utilized for the mining of deeply unknown compounds. Through this strategy, a total of 192 compounds, including 184 peptides, were rapidly identified. Among them, 53 CDPs, including four novel ones, were found for the first time in L. reuteri. Then, one of the novel CDPs, cyclo(5-OMe-Glu-4-OH-Pro), was isolated and characterized, which was consistent with the identification results. Moreover, some of the identified peptides exhibited considerable interactions with seven anti-inflammatory-related target proteins through molecular docking. According to the binding energies of peptides with different AA consistencies, it was considered that the existence of unnatural AAs in CDPs might contribute to their anti-inflammatory activity. These results provide a valuable strategy for the rapid identification of peptides, including CDPs. This study also reveals the substance basis for the potential anti-inflammatory effects exerted by L. reuteri.

Keywords

• <i>Lactobacillus reuteri</i>
• peptides
• cyclic dipeptides
• feature-based molecular networking
• molecular docking
• anti-inflammatory