Nonsynonymous Single-Nucleotide Variations on Some Posttranslational Modifications of Human Proteins and the Association with Diseases

Protein posttranslational modifications (PTMs) play key roles in a variety of protein activities and cellular processes. Different PTMs show distinct impacts on protein functions, and normal protein activities are consequences of all kinds of PTMs working together. With the development of high throughput technologies such as tandem mass spectrometry (MS/MS) and next generation sequencing, more and more nonsynonymous single-nucleotide variations (nsSNVs) that cause variation of amino acids have been identified, some of which result in the damage of PTMs. The damaged PTMs could be the reason of the development of some human diseases. In this study, we elucidated the proteome wide relationship of eight damaged PTMs to human inherited diseases and cancers. Some human inherited diseases or cancers may be the consequences of the interactions of damaged PTMs, rather than the result of single damaged PTM site

Patterns and Signals of Biology: An Emphasis On The Role of Post Translational Modifications in Proteomes for Function and Evolutionary Progression

After synthesis, a protein is still immature until it has been customized for a specific task. Post-translational modifications (PTMs) are steps in biosynthesis to perform this customization of protein for unique functionalities. PTMs are also important to protein survival because they rapidly enable protein adaptation to environmental stress factors by conformation change. The overarching contribution of this thesis is the construction of a computational profiling framework for the study of biological signals stemming from PTMs associated with stressed proteins. In particular, this work has been developed to predict and detect the biological mechanisms involved in types of stress response with PTMs in mitochondrial (Mt) and non-Mt protein. Before any mechanism can be studied, there must first be some evidence of its existence. This evidence takes the form of signals such as biases of biological actors and types of protein interaction. Our framework has been developed to locate these signals, distilled from “Big Data” resources such as public databases and the the entire PubMed literature corpus. We apply this framework to study the signals to learn about protein stress responses involving PTMs, modification sites (MSs). We developed of this framework, and its approach to analysis, according to three main facets: (1) by statistical evaluation to determine patterns of signal dominance throughout large volumes of data, (2) by signal location to track down the regions where the mechanisms must be found according to the types and numbers of associated actors at relevant regions in protein, and (3) by text mining to determine how these signals have been previously investigated by researchers. The results gained from our framework enable us to uncover the PTM actors, MSs and protein domains which are the major components of particular stress response mechanisms and may play roles in protein malfunction and disease

Analysing Genetic Variation in Ebolaviruses and Cancer Cell Lines

With the arrival of the -omics era and the democratisation of genome sequencing the amount of genetic data is escalating in magnitude orders every year. However, despite all this raw data, the effect prediction of genetic variations in disease remains an open question. The future machine learning algorithms which could solve the problem still require lots of information to feed their development, and it is our mission as bioinformaticians to extract it from the oceans of data. This Thesis focusses in the analysis of genetic variation in two complete different diseases: Ebolavirus and neuroblastoma. After the last Ebolavirus outbreak in West Africa (2014), the deadliest one in history, researchers sequenced lots of viral genomes for both surveillance and study of the pathogenic strain. There are still lots to learn from this virus and this Thesis wants to contribute with the study of how it becomes human pathogenic. By comparing different Ebolavirus species, four pathogenic to humans and one not, and looking into functionally important residues called Specificity Determining Positions (SDPs) in their genomes, we predict protein residues which may be key to the host-specifity pathogenicity. Neuroblastoma is one of the most common cancers in infancy, and the high-risk variety remains a challenging and deadly disease. Chemotherapy is a key treatment for this cancer, so diagnostic of the right drug and effective monitoring of drug resistance emergence could increase the cure ratio of patients. In order to learn more about the genetic variance of this cancer in response to treatment and the effect of these variants in drug resistance emergence, we study the genome of the neuroblastoma cell line UKF-NB-3 and its clonal sub-lines

A Computational study of Ebolavirus Pathogenicity and a Modeling approach for human non-synonymous variants

Recent advances in genome sequencing are improving our better understanding of genetic variation. However, the investigation of the genotype-phenotype relationship is still challenging, especially for the interpretation of the myriad of discovered genetic variants that weakly relate to disease. Recently, researchers have confirmed that disease causing genetic variants typically occur at functional sites, such as protein-protein or protein-ligand interaction sites. Giving this observation, several bioinformatics tools have been developed. This thesis first details VarMod (Variant Modeller), an algorithm that predicts whether nonsynonymous single nucleotide variants (nsSNVs) affect protein function. The recent Ebola virus outbreak in West Africa demonstrated the potential for the virus to cause edipdemics and highlighted our limited understanding of Ebola virus biology. The second part of this thesis focuses on the investigation of the molecular determinants of Ebolavirus pathogenicity. In two related analyses knowledge of differing pathogenicity of Ebolavirus species is used. Firstly, comparison of the sequences of Reston viruses (the only Ebolavirus species that is not pathogenic in humans) with the four pathogenic Ebolavirus species, enabled the identification of Specificity Determining Positions (SDPs) that are differentially conserved between these two groups. These SDPs were further investigated using analysis of protein structure and identified variation in the Ebola virus VP24 as likely to have a role in determining species-specific pathogenicity. The second approach investigated rodent-adapted Ebola virus. Ebola virus is not pathogenic in rodents but it can be passaged to induce pathogenicity. Analysis of the mutations identified in four adaption studies identified that very few mutations are required for adaptation to a new species and once again the VP24 is likely to have a central role. Subsequent molecular dynamics simulations compared the interaction of Ebola and Reston virus VP24 with human karyopherin alpha5. The analysis suggests that Reston virus VP24 has weaker binding with karyopherins and we propose that this change in binding may reduce the ability of Reston VP24 to inhibit human interferon signaling