This project focuses on developing a Bayesian optimization workflow for protein mutagenesis enhancing protein binding affinity. Using predictions generated by the model such as the one outlined in the study from from Rube et al. (Nat. Biotech., 2022), our approach aims to optimize protein mutagenesis for biologics and enzyme engineering applications. By integrating predictive models with Bayesian optimization, we seek to efficiently guide the design of high-affinity protein variants through the vast search space of potential nucleotides mutations.

Check out our social media post on LinkedIn for an overview of the project!

References:

1. Rube, H.T., Rastogi, C., Feng, S. et al. Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning. Nat Biotechnol 40, 1520–1527 (2022). https://doi.org/10.1038/s41587-022-01307-0
2. Blanchard, Andrew E., et al. “Language models for the prediction of SARS-CoV-2 inhibitors.” The International Journal of High Performance Computing Applications 36.5-6 (2022): 587-602.
3. Matthew J Bick, Per J Greisen, Kevin J Morey, Mauricio S Antunes, David La, Banumathi Sankaran, Luc Reymond, Kai Johnsson, June I Medford, David Baker (2017) Computational design of environmental sensors for the potent opioid fentanyl eLife 6:e28909