Figure 4.

A fully self-driven protein engineering system as an active learning “design-build-test-learn” cycle assisted by machine learning. Emerging ML-assisted methods will provide an increased diversity of protein starting points that possess desired function and are highly evolvable. Automated robotic systems will synthesize protein variants and test them for various properties using experimental assays. Supervised ML models will then be trained to learn a mapping between protein features and their properties. Finally, design algorithms will propose new variants to test in the next iteration and update robotic scripts on the fly. This protein engineering system will perform automated end-to-end discovery and optimization of proteins for desired functions.