Key Points

• We developed an automated script to generate chemical structures and corresponding masses via SMILES concatenation, streamlining downstream MS data analysis.

• We identified 35 donor exchange units through phylogenetic analysis, enabling a significantly higher engineering success rate compared to random selection.

• We developed mATChmaker, the first tool designed to significantly improve NRPS engineering success rates through phylogeny-guided donor selection and structural predictions.

• Our Software tool enables NRPS donor selection by calculating cluster similarity scores, improving the predictability of module compatibility in engineered NRPS.

• It provides an integrated 3D structure prediction pipeline that enables the high-throughput modeling of NRPS condensation complexes.

• Using this pipeline, we generated a library of over 2,000 predicted structures of condensation complexes from our engineered NRPS.

We developed three Drylab approaches to boost, speed up, and improve the success of our Wetlab work.

High-throughput Scripts

Phylogenetics

mATChmaker Software

SMILES structure generator

Our high-throughput scripts streamline the analysis of large LC–MS datasets from NRP libraries, but they can also be applied to any peptides by other iGEM teams. We provide two separate scripts that can be used sequentially:

• The SMILES structure generator automates the generation of chemical structures using only plasmid names and corresponding amino acids incorporated by the encoded NRPS as input. It automatically generates the chemical structures of the produced non-ribsomal peptides as SMILES, including mass and sum formulas. The output is provided as an Excel sheet. This Python-based script is available as a Jupyter Notebook and can, for example, be executed in Google Colab.

• Our MS Data Analysis script can take this excel sheet generated by the “SMILES structure generator” as input and use it for the automated analysis of MS spectra. It automatically detects if a peak of the mass of interest as defined in the Excel sheet is present in the chromatogram and extracts peak area and retention time. This script is VBScript-based and runs in Bruker DataAnalysis. The output is the same Excel sheet.

Phylogenetic Approach

In the second part of our drylab we focused on improving NRPS engineering, by trying to predict functionality of NRPS hybrid enzymes. To achieve this we tested an approach guided by phylogenetic analysis of thioesterase (TE) domains to maximize module compatibility and successful peptide production. By selecting donor modules by aligning TE domains, we overcome the poor success rates seen with random donor selection and achieving a much higher peptide synthesis rate. This rational, TE domain–guided donor selection strategy tripled the success rate of hybrid NRPS constructs versus random selection. This result of our wetlab data (results) inspired the implementation of TE scoring feature for donor modules implemented in our software.

The Core outcome

The core outcome of our drylab work is the mATChmaker software, mATChmaker is a cross-platform, Docker-based software that automates NRPS donor selection by calculating sequence similarity scores between clusters and predicts 3D structures of condensation complexes from annotated GenBank files, streamlining NRPS engineering and compatibility assessment.

• The sequence similarity scoring feature in mATChmaker quantifies relatedness between NRPS clusters by comparing their TE regions, enabling rational selection of donor modules that are more likely to be compatible and productive in hybrid NRPS assemblies.

• The 3D structure prediction feature generates high-throughput models of condensation complexes, allowing users to visualize domain-domain and substrate-protein interactions, thereby providing insight into mechanistic causes of NRPS unit incompatibility and supporting rational experiment design.

In summary, we successfully developed and experimentally validated software, which enables rational NRPS donor selection and high-throughput structure prediction, significantly improving NRPS engineering success rates.