# Comparative Analysis of Signature Sequences from Adenylation Domains Situated within Bacterial-Origin Nonribosomal Peptide Synthetase Modules

## TL;DR

This study analyzes adenylation domains in bacterial nonribosomal peptide synthetases to identify sequences that determine amino acid specificity and modification patterns.

## Contribution

The study identifies 508 signature sequences and their associations with specific amino acid modifications in bacterial NRPS modules.

## Key findings

- Over 80% of the identified signature sequences show distinct specificity for 36 α-amino acid moieties.
- Modifications like N-methylation and β-hydroxylation show strong preferences for certain amino acid moieties.
- 41 modules were found to be used iteratively, aiding in the prediction of uncharacterized NRPS systems.

## Abstract

Nonribosomal peptides are assembled by large enzymes that contain multiple active sites, which function in a modular manner. The adenylation (A) domains present within typical nonribosomal peptide synthetase (NRPS) modules contain specificity-conferring codes or signature sequences (SNSs). In this study, we obtained 2051 A domain sequences from 67 bacterial species. Their alignment and clustering identified 508 SNSs. Over 80% of the SNSs displayed distinct specificity for 36 proteinogenic and nonproteinogenic α-amino acid moieties (α-AAMs). Furthermore, modifications such as N-methylation, monooxygenase activity, and oxidation contributed to the elongation of the A domains, while conferring pronounced affinities for certain α-AAMs. Notably, β-hydroxylation demonstrated particular preferences. Specifically, ornithine, threonine, tyrosine, and phenylalanine moieties frequently underwent atypical covalent modifications, and 41 modules were used iteratively. These insights significantly facilitate the identification of uncharacterized NRPS systems—expediting traditional identification processes—although novel modifications, unusual domain organizations, and dormant domains pose challenges for their accurate prediction.