Využití variačních autoenkodérů pro ancestrální rekonstrukci sekvencí

Protein engineering is an interdisciplinary science concerned with the design of improved proteins. A successful method used to design more stable and active proteins is ancestral sequence reconstruction. This method explores the evolutionary relationships between existing proteins and uses phylogenetic trees to generate their evolutionary ancestors, which often exhibit the desired improved properties. Therefore, new and more robust methods using mathematical models together with huge amounts of sequence data could become a powerful tool for protein engineering. This thesis explores the use of variational autoencoders as an alternative approach to ancestral sequence design compared to conventional methods using phylogenetic trees. Experiments were performed to optimize the architecture and statistical methods were proposed to evaluate the quality of the models and the sequences generated. At the same time, robustness tests of the whole method were performed and strategies for ancestral sequence generation were proposed and implemented.

bioinformatics, machine learning, generative models, variational autoencoders, protein engineering, ancestral reconstruction, protein optimization