Tree Search-Based Evolutionary Bandits for Protein Sequence Optimization
This work addresses protein engineering for biotechnologists by enhancing the iterative mutation process, though it appears incremental as it combines existing techniques like tree search and bandit learning.
The authors tackled the challenge of efficiently exploring vast protein sequence spaces for engineering by proposing a tree search-based bandit learning method, which demonstrated sample efficiency and the ability to find top designs with small mutation counts in simulated screens.
While modern biotechnologies allow synthesizing new proteins and function measurements at scale, efficiently exploring a protein sequence space and engineering it remains a daunting task due to the vast sequence space of any given protein. Protein engineering is typically conducted through an iterative process of adding mutations to the wild-type or lead sequences, recombination of mutations, and running new rounds of screening. To enhance the efficiency of such a process, we propose a tree search-based bandit learning method, which expands a tree starting from the initial sequence with the guidance of a bandit machine learning model. Under simplified assumptions and a Gaussian Process prior, we provide theoretical analysis and a Bayesian regret bound, demonstrating that the combination of local search and bandit learning method can efficiently discover a near-optimal design. The full algorithm is compatible with a suite of randomized tree search heuristics, machine learning models, pre-trained embeddings, and bandit techniques. We test various instances of the algorithm across benchmark protein datasets using simulated screens. Experiment results demonstrate that the algorithm is both sample-efficient and able to find top designs using reasonably small mutation counts.