Low-N Protein Activity Optimization with FolDE

• URL: http://arxiv.org/abs/2510.24053v1
• Date: Tue, 28 Oct 2025 04:24:39 GMT
• Title: Low-N Protein Activity Optimization with FolDE
• Abstract summary: We present FolDE, an ALDE method designed to maximize end-of-campaign success.<n>In simulations across 20 protein targets, FolDE discovers 23% more top 10% mutants than the best baseline method.<n>We also introduce a constant-liar batch selector, which improves batch diversity.
• Score: 15.733324207723328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Proteins are traditionally optimized through the costly construction and measurement of many mutants. Active Learning-assisted Directed Evolution (ALDE) alleviates that cost by predicting the best improvements and iteratively testing mutants to inform predictions. However, existing ALDE methods face a critical limitation: selecting the highest-predicted mutants in each round yields homogeneous training data insufficient for accurate prediction models in subsequent rounds. Here we present FolDE, an ALDE method designed to maximize end-of-campaign success. In simulations across 20 protein targets, FolDE discovers 23% more top 10% mutants than the best baseline ALDE method (p=0.005) and is 55% more likely to find top 1% mutants. FolDE achieves this primarily through naturalness-based warm-starting, which augments limited activity measurements with protein language model outputs to improve activity prediction. We also introduce a constant-liar batch selector, which improves batch diversity; this is important in multi-mutation campaigns but had limited effect in our benchmarks. The complete workflow is freely available as open-source software, making efficient protein optimization accessible to any laboratory.

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