Deep learning-guided evolutionary optimization for protein design
This work addresses a significant challenge in protein design, which is crucial for advancing therapeutics and biotechnology, particularly for researchers and scientists in the field of bioengineering and biotechnology.
The authors tackled the challenge of designing novel proteins with desired characteristics, achieving efficient exploration of the sequence space and identifying high-confidence binders, such as peptide binders against pneumolysin. BoGA accelerated the discovery of these binders, demonstrating potential for efficient protein design across diverse objectives.
Designing novel proteins with desired characteristics remains a significant challenge due to the large sequence space and the complexity of sequence-function relationships. Efficient exploration of this space to identify sequences that meet specific design criteria is crucial for advancing therapeutics and biotechnology. Here, we present BoGA (Bayesian Optimization Genetic Algorithm), a framework that combines evolutionary search with Bayesian optimization to efficiently navigate the sequence space. By integrating a genetic algorithm as a stochastic proposal generator within a surrogate modeling loop, BoGA prioritizes candidates based on prior evaluations and surrogate model predictions, enabling data-efficient optimization. We demonstrate the utility of BoGA through benchmarking on sequence and structure design tasks, followed by its application in designing peptide binders against pneumolysin, a key virulence factor of \textit{Streptococcus pneumoniae}. BoGA accelerates the discovery of high-confidence binders, demonstrating the potential for efficient protein design across diverse objectives. The algorithm is implemented within the BoPep suite and is available under an MIT license at \href{https://github.com/ErikHartman/bopep}{GitHub}.