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Thesis details
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Enzyme optimization using sequence homology and machine learning
Thesis title in Czech: Optimalizace enzymů ze sekvenční homologie za pomoci strojového učení
Thesis title in English: Enzyme optimization using sequence homology and machine learning
Academic year of topic announcement: 2021/2022
Thesis type: diploma thesis
Thesis language: angličtina
Department: Department of Cell Biology (31-151)
Supervisor: David Příhoda
Author: hidden - assigned by the advisor
Date of registration: 18.11.2021
Date of assignment: 14.02.2022
Date of electronic submission:09.08.2022
Date of proceeded defence: 07.09.2022
Opponents: doc. RNDr. David Hoksza, Ph.D.
Preliminary scope of work
Práce je popsána v anglické verzi
Preliminary scope of work in English
In pharmaceutical research and development, enzymes play an important role in the synthesis of drugs and drug-related molecules. For higher efficiency and increased production, it is important to optimize the yield of these enzymes, a task often addressed by protein engineering and design. This process however can become tedious with the vast options of mutations for each single protein.

To improve the process of enzyme optimization, sequence homology and machine learning methods can be used. These greatly reduce the manual effort of protein redesign and can assist in finding the most fit enzyme for the given task, increasing the efficiency of the overall drug development pipeline.

The aim of this thesis is to: 1) Summarize existing methods for diversifying and selecting enzymes for drug synthesis 2) Develop a novel machine learning method and a baseline method based on sequence homology 3) Computationally evaluate the implemented methods using previously published experimental data
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