Substrate Sequence Controls Regioselectivity of Lanthionine Formation by ProcM

• Published in: Journal of the American Chemical Society Vol. 143; no. 44; pp. 18733 - 18743
• Main Authors: Le, Tung, Jeanne Dit Fouque, Kevin, Santos-Fernandez, Miguel, Navo, Claudio D, Jiménez-Osés, Gonzalo, Sarksian, Raymond, Fernandez-Lima, Francisco Alberto, van der Donk, Wilfred A
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 10.11.2021
• Subjects: Alanine - analogs & derivatives; Alanine - chemistry; Amino Acid Sequence; biosynthesis; crosslinking; Cyanobacteria; enzymes; liquid chromatography; Models, Molecular; peptides; Peptides - chemistry; Peptides - metabolism; prediction; Protein Conformation; regioselectivity; Sulfides - chemistry
• ISSN: 0002-7863; 1520-5126; 1520-5126
• DOI: 10.1021/jacs.1c09370

Lanthipeptides belong to the family of ribosomally synthesized and post-translationally modified peptides (RiPPs). The (methyl)­lanthionine cross-links characteristic to lanthipeptides are essential for their stability and bioactivities. In most bacteria, lanthipeptides are maturated from single precursor peptides encoded in the corresponding biosynthetic gene clusters. However, cyanobacteria engage in combinatorial biosynthesis and encode as many as 80 substrate peptides with highly diverse sequences that are modified by a single lanthionine synthetase into lanthipeptides of different lengths and ring patterns. It is puzzling how a single enzyme could exert control over the cyclization processes of such a wide range of substrates. Here, we used a library of ProcA3.3 precursor peptide variants and show that it is not the enzyme ProcM but rather its substrate sequences that determine the regioselectivity of lanthionine formation. We also demonstrate the utility of trapped ion mobility spectrometry–tandem mass spectrometry (TIMS-MS/MS) as a fast and convenient method to efficiently separate lanthipeptide constitutional isomers, particularly in cases where the isomers cannot be resolved by conventional liquid chromatography. Our data allowed identification of factors that are important for the cyclization outcome, but also showed that there are no easily identifiable predictive rules for all sequences. Our findings provide a platform for future deep learning approaches to allow such prediction of ring patterns of products of combinatorial biosynthesis.