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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