Search for: All records

Various machine learning-assisted directed evolution (MLDE) strategies have been shown to identify high-fitness protein variants more efficiently than typical wet-lab directed evolution approaches. However, limited understanding of the factors influencing MLDE performance across diverse proteins has hindered optimal strategy selection for wet-lab campaigns. To address this, we systematically analyzed multiple MLDE strategies, including active learning and focused training using six distinct zeroshot predictors, across 16 diverse protein fitness landscapes. By quantifying landscape navigability with six attributes, we found that MLDE offers a greater advantage on landscapes which are more challenging for directed evolution, especially when focused training is combined with active learning. Despite varying levels of advantage across landscapes, focused training with zero-shot predictors leveraging distinct evolutionary, structural, and stability knowledge sources consistently outperforms random sampling for both binding interactions and enzyme activities. Our findings provide practical guidelines for selecting MLDE strategies for protein engineering. 

Obtaining annotations for large training sets is expen- sive, especially in settings where domain knowledge is re- quired, such as behavior analysis. Weak supervision has been studied to reduce annotation costs by using weak la- bels from task-specific labeling functions (LFs) to augment ground truth labels. However, domain experts still need to hand-craft different LFs for different tasks, limiting scal- ability. To reduce expert effort, we present AutoSWAP: a framework for automatically synthesizing data-efficient task-level LFs. The key to our approach is to efficiently represent expert knowledge in a reusable domain-specific language and more general domain-level LFs, with which we use state-of-the-art program synthesis techniques and a small labeled dataset to generate task-level LFs. Addition- ally, we propose a novel structural diversity cost that allows for efficient synthesis of diverse sets of LFs, further improv- ing AutoSWAP’s performance. We evaluate AutoSWAP in three behavior analysis domains and demonstrate that Au- toSWAP outperforms existing approaches using only a frac- tion of the data. Our results suggest that AutoSWAP is an effective way to automatically generate LFs that can signif- icantly reduce expert effort for behavior analysis.