The relationships between genetic diseases and the structures of protein and their interactions
File(s)
Author(s)
Ittisoponpisan, Sirawit
Type
Thesis
Abstract
The rapid growth in the number of human genome sequences is leading to the discovery of numerous single amino acid variants (SAVs), some of which are associated with genetic disease. Understanding these variants at both the sequence level and the structural level is of great benefits particularly for clinical researchers.
The first part of this thesis focuses on characterising pleiotropic proteins (proteins associated with multiple disease phenotypes). This study shows that pleiotropic proteins have unique properties: 1) they are enriched in disease-associated SAVs and rare neutral SAVs, 2) they tend to contain disordered regions, 3) they tend to have a higher number of protein-protein interactions, and 4), they are more specific to diseases of certain classes such as neoplasm, nervous system, and congenital malformation. This new understanding of pleiotropic proteins could have important implications for evolutionary studies, genetic studies, and any other related field.
At the structural level, the effects of SAVs on protein structures were analysed by remodelling high-quality PDB coordinates of proteins to obtain their corresponding mutant structures. By considering 16 structural features affecting protein stability, 40% of disease-associated SAVs and 11.5% neutral SAVs were shown to cause at least one of the 16 structural effects. The limited number of available experimental PDB structures poses a major challenge in structural analysis. However, incorporating homology models allows more SAVs to be analysed as the residue coverage increases from 17% to approximately 50%. The same analysis was repeated on homology models with sequence identity ranging from 30% to 95%. Similar percentages of disease-associated and neutral SAVs causing structural effects were observed even on models with low sequence identity (<40%). Subsequently, a web server Missense3D was developed to help analyse the structural effect of SAVs on protein structures. A better understanding of genetic variants could help researchers unveil the molecular mechanisms underlying human disease, which could support further research such as in drug design and personalised medicine.
The first part of this thesis focuses on characterising pleiotropic proteins (proteins associated with multiple disease phenotypes). This study shows that pleiotropic proteins have unique properties: 1) they are enriched in disease-associated SAVs and rare neutral SAVs, 2) they tend to contain disordered regions, 3) they tend to have a higher number of protein-protein interactions, and 4), they are more specific to diseases of certain classes such as neoplasm, nervous system, and congenital malformation. This new understanding of pleiotropic proteins could have important implications for evolutionary studies, genetic studies, and any other related field.
At the structural level, the effects of SAVs on protein structures were analysed by remodelling high-quality PDB coordinates of proteins to obtain their corresponding mutant structures. By considering 16 structural features affecting protein stability, 40% of disease-associated SAVs and 11.5% neutral SAVs were shown to cause at least one of the 16 structural effects. The limited number of available experimental PDB structures poses a major challenge in structural analysis. However, incorporating homology models allows more SAVs to be analysed as the residue coverage increases from 17% to approximately 50%. The same analysis was repeated on homology models with sequence identity ranging from 30% to 95%. Similar percentages of disease-associated and neutral SAVs causing structural effects were observed even on models with low sequence identity (<40%). Subsequently, a web server Missense3D was developed to help analyse the structural effect of SAVs on protein structures. A better understanding of genetic variants could help researchers unveil the molecular mechanisms underlying human disease, which could support further research such as in drug design and personalised medicine.
Version
Open Access
Date Issued
2018-09
Date Awarded
2019-03
Copyright Statement
Creative Commons Attribution NonCommercial NoDerivatives Licence
Advisor
Sternberg, Michael
Sponsor
Thailand
Publisher Department
Life Sciences
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)