Abstract
According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from biorxiv.org: “Tailored enzymes hold great potential to accelerate the transition to a sustainable bioeconomy. Yet, enzyme engineering remains challenging as it relies largely on serendipity and is, therefore, highly laborious and prone to failure. The efficiency and success rates of engineering campaigns may be improved substantially by applying machine learning to construct a comprehensive representation of the sequence-activity landscape from small sets of experimental data. “However, it often proves challenging to reliably model a large protein sequence space while keeping the experimental effort tractable.
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