Thesis Details

Computational Design of Stable Proteins

Ph.D. Thesis Student: Musil Miloš Academic Year: 2021/2022 Supervisor: Zendulka Jaroslav, doc. Ing., CSc.
Czech title
Automatizovaný návrh stabilních proteinů
Language
English
Abstract

Stable proteins are utilized in a vast number of medical and biotechnological applications. However, the native proteins have mostly evolved to function under mild conditions inside the living cells. As a result, there is a great interest in increasing protein stability to enhance their utility in the harsh industrial conditions. In recent years, the field of protein engineering has matured to the point that enables tailoring of native proteins for specific practical applications. However, the identification of stable mutations is still burdened by costly and laborious experimental work. Computational methods offer attractive alternatives that allow a rapid search of the pool of potentially stabilizing mutations to prioritize them for further experimental validation. A plethora of the computational strategies was developed: i) force-field-based energy calculations, ii) evolution-based techniques, iii) machine learning, or iv) the combination of several approaches. Those strategies are usually limited in their predictions to less impactful single-point mutations, while some more sophisticated methods for prediction of multiple-point mutations require more complex inputs from the side of the user.The main aim of this Thesis is to provide users with a fully automated workflow that would allow for the prediction of the highly stable multiple-point mutants without the requirement of the extensive knowledge of the bioinformatics tools and the protein of interest.

FireProt is a fully automated workflow for the design of the highly stable multiple-point mutants. It is a hybrid method that combines both energy- and evolution-based approaches in its calculation core, utilizing sequence information as a filter for robust force-field calculations. FireProt workflow not only detects a pool of potentially stabilizing mutations but also tries to combine them together while reducing the risk of antagonistic effects.

FireProt-ASR is a fully automated workflow for ancestral sequence reconstruction, allowing users to utilize this protein engineering strategy without the need for the laborious manual work and the knowledge of the system of interest. It resolves all the steps required during the process of ancestral sequence reconstruction, including the collection of the biologically relevant homologs, construction of the rooted tree, and the reconstruction of the ancestral sequences and ancestral gaps.HotSpotWizard is a workflow for the automated design of mutations and smart libraries for the engineering of protein function and stability. It allows for a wider analysis of the protein of interest by utilizing four different protein engineering strategies: i) identification of the highly mutable residues located in the catalytic pockets and tunnels, ii) identification of the flexible regions, iii) calculation of the sequence consensus, and iv) identification of the correlated residues.FireProt-DB is a database of the known experimental data quantifying a protein stability. The main aim of this database is to standardize protein stability data, provide users with well-manageable storage, and allow them to construct protein stability datasets to use them as training sets for various machine learning applications.

Keywords

protein engineering, protein stability, stability prediction, multiple-point mutants, ancestral sequence reconstruction

Department
Degree Programme
Status
delivered
Date
15 December 2021
Citation
MUSIL, Miloš. Computational Design of Stable Proteins. Brno, 2021. Ph.D. Thesis. Brno University of Technology, Faculty of Information Technology. 2021-12-15. Supervised by Zendulka Jaroslav. Available from: https://www.fit.vut.cz/study/phd-thesis/967/
BibTeX
@phdthesis{FITPT967,
    author = "Milo\v{s} Musil",
    type = "Ph.D. thesis",
    title = "Computational Design of Stable Proteins",
    school = "Brno University of Technology, Faculty of Information Technology",
    year = 2021,
    location = "Brno, CZ",
    language = "english",
    url = "https://www.fit.vut.cz/study/phd-thesis/967/"
}
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