Predicting Stability and Functional Changes Induced by Protein Mutations with a Machine Learning Approach

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Author(s)
Primary Supervisor
Stantic, Bela
Sattar, Abdul
Other Supervisors
Zhou, Yaoqi
Year published
2015
Metadata
Show full item recordAbstract
Proteins form a group of one of the most vital macromolecules in living organisms. Yet, even a single mutation in a protein sequence may result in significant changes in protein stability, structure, and thus in protein function as well. Therefore, reliable prediction of stability changes induced by protein mutations is an important aspect of computational protein design, which can aid novel medical and technological discoveries. Also, many mutations have a functional impact which may lead to a disease. Therefore, a key component of personalised medicine is to fully annotate human genetic variations among different individuals. ...
View more >Proteins form a group of one of the most vital macromolecules in living organisms. Yet, even a single mutation in a protein sequence may result in significant changes in protein stability, structure, and thus in protein function as well. Therefore, reliable prediction of stability changes induced by protein mutations is an important aspect of computational protein design, which can aid novel medical and technological discoveries. Also, many mutations have a functional impact which may lead to a disease. Therefore, a key component of personalised medicine is to fully annotate human genetic variations among different individuals. Obviously, it would be infeasible to examine the impact of each possible variant experimentally. Instead, computational methods are needed for a quick and large-scale annotation of genetic variants. In this thesis, we proposed machine learning methods for predicting stability changes induced by single amino acid substitutions and for detecting disease-causing frameshifting indels (genetic variants caused by short insertions and deletions in the DNA sequence) and nonsense mutations (single nucleotide variants which truncate the protein sequence). The proposed methods can predict the effects of these mutations without the knowledge of the protein structure, which make them applicable universally to all proteins encoded in the human genome.
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View more >Proteins form a group of one of the most vital macromolecules in living organisms. Yet, even a single mutation in a protein sequence may result in significant changes in protein stability, structure, and thus in protein function as well. Therefore, reliable prediction of stability changes induced by protein mutations is an important aspect of computational protein design, which can aid novel medical and technological discoveries. Also, many mutations have a functional impact which may lead to a disease. Therefore, a key component of personalised medicine is to fully annotate human genetic variations among different individuals. Obviously, it would be infeasible to examine the impact of each possible variant experimentally. Instead, computational methods are needed for a quick and large-scale annotation of genetic variants. In this thesis, we proposed machine learning methods for predicting stability changes induced by single amino acid substitutions and for detecting disease-causing frameshifting indels (genetic variants caused by short insertions and deletions in the DNA sequence) and nonsense mutations (single nucleotide variants which truncate the protein sequence). The proposed methods can predict the effects of these mutations without the knowledge of the protein structure, which make them applicable universally to all proteins encoded in the human genome.
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Thesis Type
Thesis (PhD Doctorate)
Degree Program
Doctor of Philosophy (PhD)
School
School of Information and Communication Technology
Copyright Statement
The author owns the copyright in this thesis, unless stated otherwise.
Item Access Status
Public
Subject
Proteins
Macromolecules
Protean sequences
Protein mutations