Improving few-shot learning-based protein engineering with evolutionary sampling

Muhammad Zaki Jawaid, Aayushma Gautam, T. Gainous, Dan Hart, Robin Yeo, Timothy Daley

Keywords: Large Protein Language Models, Generative AI, ML-guided Protein Engineering, MCMC, Evolutionary Monte Carlo, Protein Fitness Exploration, Transfer Learning, Few-Shot Learning

Abstract: Designing novel functional proteins remains a slow and expensive process due to a variety of protein engineering challenges; in particular, the number of protein variants that can be experimentally tested in a given assay pales in comparison to the vastness of the overall sequence space, resulting in low hit rates and expensive wet lab testing cycles. ML-guided protein engineering promises to accelerate this process through computational screening of proposed variants in silico. However, exploring the prohibitively large protein sequence space presents a significant challenge for the design of novel functional proteins using ML-guided protein engineering. Here, we propose using evolutionary Monte Carlo search (EMCS) to efficiently explore the fitness landscape and accelerate novel protein design. As a proof-of-concept, we use our approach to design a library of peptides predicted to be functionally capable of transcriptional activation and then experimentally screen them, resulting in a dramatically improved hit rate compared to existing methods. Our method can be easily adapted to other protein engineering and design problems, particularly where the cost associated with obtaining labeled data is significantly high. We have provided open source code for our method at https://github.com/SuperSecretBioTech/evolutionary_monte_carlo_search.

Submission Number: 34