Het onderzoeken van de sequentie en functionele landschappen van natuurlijke en gemanipuleerde polymerasen

Xenobiotic Nucleic Acids (XNAs), chemical analogs of nature's only genetic polymers (DNA and RNA), are not only able to store and propagate genetic information, but also broaden the chemical diversity of genetic polymers and their biotechnological and pharmaceutical applications. Although solid-phase synthesis is the current gold standard for XNA production, it presents limitations in oligonucleotide length, yields, and purity due to constraints inherent in solid supports and the complexities of sequential reactions, particularly for longer oligonucleotides. Enzymatic synthesis is gradually emerging as a promising alternative; however, the synthesis of XNA is limited by the low efficiency and lack of selectivity of the current generation of engineered XNA polymerases. These limitations stem from gaps in our understanding of polymerase specificity, selectivity, and catalytic mechanisms, despite decades of rigorous biophysical and biochemical investigation. Directed evolution has proved to be a powerful approach to engineer proteins and isolate novel functions and has been highly successful in enabling the development of XNA synthetases (i.e. DNA-templated XNA polymerases). However, the field lacks an in-depth analysis of the effect of selection parameters, library construction biases and sampling biases. Furthermore, there are limited analytical tools that have been developed to determine the sequence-functional landscape of natural and engineered polymerases from directed evolution datasets. This work aims to improve the directed evolution pipeline for DNA and XNA polymerase engineering through the automatization of complex protein library construction and exploration of the impact of selection conditions on selection success and efficiency. Additionally, this work developed and explored approaches to retrieve significant information from selections such as co-variation and epistatic interactions for network reconstruction. Overall, this work aims to provide a powerful platform for efficient XNA polymerase engineering that can be adapted to other enzymes.