Hollfelder-Lab/lrDMS-IRED
Code, data and notebooks for engineering enzymes with ultra-high throughput microfluidics. Please read our accompanying paper for details.
This project helps enzyme engineers and biochemists analyze high-throughput microfluidic screening data to understand and improve biocatalyst performance. It takes raw Oxford Nanopore sequencing data from microdroplet experiments as input and produces fitness scores for individual enzyme variants, alongside insights into their combinability and mutability. The output can directly inform AI-assisted enzyme engineering efforts.
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Use this if you are performing long-read deep mutational scanning (lrDMS) with microdroplet screening and need to process raw sequencing data to derive enzyme fitness scores and mutational profiles for engineering campaigns.
Not ideal if you are looking for a general-purpose tool for molecular dynamics simulations or protein structure prediction, as this focuses specifically on enzyme variant screening data analysis.
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Dec 22, 2024
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