DOI: 10.5281/zenodo.14544486
title: lrDMS datasets

description: This zenodo archive contains the processed data for the publication "Microdroplet screening rapidly profiles a biocatalyst to enable its AI-assisted engineering". The unprocessed nanopore and illumina data are available via the European Nucleotide Archive (ENA).

Code is available at github.com/Hollfelder-Lab/lrDMS-IRED

If you use this data in your work, please cite the paper. 

Fitness scores in this dataset correspond to log-enrichment factors over wild type. For a detailed description of the fitness values, please refer to the paper.

We use the following file naming convention: `<enzyme>-<substrates>-<active/inactive>_data.csv`    - `<enzyme>`: The name of the wildtype enzyme.    - `<substrates>`: The names of the key substrates seperated by underscores (Co-factors like NADPH are not included)    - `<active/inactive>`: Whether the dataset summarizes active or inactive variants.



`srired-mepy-active_data.csv`: Variants with measurable lrDMS fitness scores for variants derived from the SrIRED wildtype enzyme on MePy.

`srired-chx_cpa-active_data.csv`: Variants with measurable lrDMS fitness scores for variants derived from the SrIRED wildtype enzyme on CHX and CPA.

`srired-chx_cpa-inactive_data.csv`: Variants with no discernable activity (no output counts despite sufficient input counts) for variants derived from the SrIRED wildtype enzyme on CHX and CPA.

`srired-chx_menh2-active_data.csv`: Variants with measurable lrDMS fitness scores for variants derived from the SrIRED wildtype enzyme on CHX and MeNH2.

`pcired-tecalcet-active_data.csv`: Variants with measurable lrDMS fitness scores for variants derived from the PcIRED wildtype enzyme on the tecalcet drug candidate substrate.


For more information on these enzymes, substrates and study setup, please refer to the paper.
description: Engineering enzyme biocatalysts for higher efficiency is key to enabling sustainable, ‘green’ production processes for the chemical and pharmaceutical industry. This challenge can be tackled from two angles: by directed evolution, based on labor-intensive experimental testing of enzyme variant libraries, or by computational methods, where sequence-function data are used to predict biocatalyst improvements. Here, we combine both approaches into a two-week workflow, where ultra-high throughput screening of a library of imine reductases (IREDs) in microfluidic devices provides not only selected ‘hits’, but also long-read sequence data linked to fitness scores of >17 thousand enzyme variants. We demonstrate engineering of an IRED for chiral amine synthesis by mapping functional information in one go, ready to be used for interpretation and extrapolation by protein engineers with the help of machine learning (ML). We calculate position-dependent mutability and combinability scores of mutations and comprehensively illuminate a complex interplay of mutations driven by synergistic, often positively epistatic effects. Interpreted by easy-to-use regression and tree-based ML algorithms designed to suit the evaluation of random whole-gene mutagenesis data, 3-fold improved ‘hits’ obtained from experimental screening are extrapolated further to give up to 23-fold improvements in catalytic rate after testing only a handful of designed mutants. Our campaign is paradigmatic for future enzyme engineering that will rely on access to large sequence-function maps as profiles of the way a biocatalyst responds to mutation. These maps will chart the way to improved function by exploiting the synergy of rapid experimental screening combined with ML evaluation and extrapolation.