Thermus bacteria are of special interest because of their ability to live in high temperature environments. Their enzymes exhibit higher and stable activity in industry as compared to mesophilic or synthetic counterparts. Thermus bacteria are capable of reducing heavy metals which is essential in eradication of heavy metal pollution and controlling global warming. Genome rearrangements were investigated in Thermus species and the extent to which they affected the distribution of functionally related genes on the chromosome and its implication on the coherence of the metabolic network. The contribution of horizontal gene transfer to genome rearrangements and the shuffling of genes on the chromosome were analysed. Horizontally transferred genes were identified alongside their donors and recipients, their age and relative time of insertion. Metabolic networks were clustered and compared to determine the extent to which they were affected by rearrangements as a measure of evolutionary pressures experienced by organisms. Factors that enhance protein thermostability were analysed by determining dominant substitutions for amino acid residues and their properties. Protein thermostability was measured using the UNAFold algorithm. Amino acid substitutions were compared between less and highly thermophilic orthologous sequences in T. scotoductus SA-01 and T. thermophilus HB27 respectively. Protein structures were modelled for orthologs that met a defined selection criterion. Dominant amino acid substitutions were analysed in the structures to determine their locations. The contribution of dominant substitutions to energy changes in structures was analysed using FoldX program. Results revealed a uniform distribution of functionally related genes among thermophilic and mesophilic organisms. The contribution of horizontal gene transfer to genome rearrangements was found to be insignificant. Metabolic networks for Thermus species were poorly clustered in correlation with their optimum environmental growth temperatures. Non-polar, small and charged amino acids were found to significantly enhance thermostability. Higher occurrence of alanine substituted by serine and threonine; and arginine substituted by glutamine and lysine were observed to influence thermostability. Structural comparison showed that mutations were mostly located on the surfaces and helices of structures. The positions of mutations appeared to influence their energy contribution to the overall structure as measured by FoldX algorithm. / Thesis (PhD)--University of Pretoria, 2013. / gm2014 / Biochemistry / unrestricted

This thesis is focused on predicting the impact of amino acid substitution on protein stability. The main goal is to create a consensual predictor that uses the outputs of chosen existing tools in order to improve accuracy of prediction. The optimal consensus of theese tools was designed using evolution strategies in three variants: 1/5 success rule, self-adaptation variant and the CMA-ES method. Then, the quality of calculated weight vectors was tested on the independent dataset. Although the highest prediction performance was attained by self-adaptation method, the differences between all three variants were not significant. Compared to the individual tools, the predictions provided by consensual methods were generally more accurate - the self-adaptation variant imporved the Pearson's corelation coeficient of the predictions by 0,057 on the training dataset. On the testing dataset, the improvement of designed method was smaller (0,040). Relatively low improvement of prediction performance (both on the training and the testing dataset) were caused by the fact, that for some records of testing dataset, some individual tools vere not able to provide their results. When omitting these records, consensual method improved the Pearson's corelations coeficient by 0,118.

This thesis discusses the issue of predicting of the effect of amino acid substitutions on protein funkcion, based on phylogenetic analysis method, inspired by tool MAPP. Significant number of genetic diseases is caused by nonsynonymous SNPs manifested as single point mutations on the protein level. The ability to identify deleterious substitutions could be useful for protein engineering to test whether the proposed mutations do not damage protein function same as for targeting disease causing harmful mutations. However the experimental validation is costly and the need of predictive computation methods has risen. This thesis describes desing and implementation of a new in silico predictor based on the principles of evolutionary analysis and dissimilarity between original and substituting amino acid physico-chemical properties. Developed algorithm was tested on four datasets with 74,192 mutations from 16,256 sequences in total. The predictor yields up to 72 % accuracy and in the comparison with the most existing tools, it is substantially less time consuming. In order to achieve the highest possible efficiency, the optimization process was focused on selection of the most suitable (a) third-party software for calculation of a multiple sequence alignment, (b) overall decision threshold and (c) a set of physico-chemical properties.

This thesis discusses the issue of predicting the effect of amino acid substitutions on protein function using MAPP method. This method requires the multiple sequence alignment and phylogenetic tree constructed by third-party tools. Main goal of this thesis is to find the combination of suitable tools and their parameters to generate the inputs of MAPP method on the basis of analysis on one massively mutated protein. Then, the MAPP method is tested with chosen combination of parameters and tools on two large independent datasets and consequently is compared with the other tools focused on prediction of the effect of mutations. Apart from this the web interface for the MAPP method was created. This interface simplifies the use of the method since the user need not to install any tools or set any parameters.