Low N, High N Protein Engineering
Track: Biology: datasets and/or experimental results
Cell: I do not want my work to be considered for Cell Systems
Keywords: MLDE, Directed Evolution, High Throughput, Protein Engineering
TL;DR: Trading off high/low throughput and sequence-function relational information in machine learning directed evolution.
Abstract: Machine learning assisted directed evolution often involves experimentally collecting data from a relatively small number of variants to update a surrogate model, due to experimental limitations of characterisation and sequencing at high throughput. We propose an alternative approach, involving collecting high-throughput experimental data in a manner that results in a large number of characterised variants at the cost of reduced information: although the sequences and the measured fitness values are known, their correspondence is not. In particular we explore applying this method to the optimisation of a recently discovered phenomenon: magnetically sensitive fluorescent proteins.
Submission Number: 69
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