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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:89093 |
| Date | 17 January 2024 |
| Creators | Schmitt, Lukas Theo |
| Contributors | Buchholz, Frank, Hiller, Michael, Technische Universität Dresden, TU Dresden |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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