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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.

Diese Arbeit beschreibt die Kombination von Phagen-Display-Biopanning und Illumina Next-Generation DNA-Sequencing (NGS) zur Identifizierung peptidbasierter Adhäsionsdomänen für Polypropylen (PP). Eine Biopanning-Runde gefolgt von NGS liefert PP-bindende Peptide, die durch Sanger-Sequenzierung nicht erkennbar sind. NGS bietet den Vorteil eines enorm umfangreichen Datensatzes, welcher tiefgreifende Sequenzanalysen erlaubt. Die selektierten Sequenzen werden als wasserbasierte Primer für PP–Metallhaftung zur Vorbehandlung von PP-Oberflächen eingesetzt und erhöhen die Haftfestigkeit um 100 % gegenüber nicht vorbehandeltem PP. / This thesis describes the combination of phage display biopanning and Illumina Next-Generation DNA-Sequencing (NGS) to identify peptide-based adhesion domains for polypropylene (PP). One round of biopanning followed by NGS yields PP-binding peptides that are undetectable by Sanger sequencing. NGS has the advantage of an extensive data set, which allows in-depth sequence analysis. The selected peptide sequences are then used as water-based primers for PP metal adhesion for the pretreatment of PP surfaces and increase the adhesion by 100% compared to non-pretreated PP.