No description available.

Site-specific tyrosine-type recombinases are effective tools for genome engineering, with the first engineered variants having demonstrated therapeutic potential. So far, adaptation to new DNA target site selectivity of designer-recombinases has been achieved mostly through iterative cycles of directed molecular evolution. While effective, directed molecular evolution methods are laborious and time consuming. To accelerate the development of designer-recombinases I evaluated two sequencing approaches and gathered the sequence information of over two million Cre-like recombinase sequences evolved for 89 different target sites. With this information I first investigated the sequence compositions and residue changes of the recombinases to further our understanding of their target site selectivity. The complexity of the data led me to a generative deep learning approach. Using the sequence data I trained a conditional variational autoencoder called RecGen (Recombinase Generator) that is capable of generating novel recombinases for a given target site. With computational evaluation of the sequences I revealed that known recombinases functional on the desired target site are generally more similar to the RecGen predicted recombinases than other recombinase libraries. Additionally, I could experimentally show that predicted recombinases for known target sites are at least as active as the evolved recombinases. Finally, I also experimentally show that 4 out of 10 recombinases predicted for novel target sites are capable of excising their respective target sites. As a bonus to RecGen I also developed a new method capable of accurate sequencing of recombinases with nanopore sequencing while simultaneously counting DNA editing events. The data of this method should enable the next development iteration of RecGen.

No description available.