The thesis deals with bioinformatics analysis, the aim of which was to find a suitable producer of PHA for new generation industrial biotechnologies from the collection of found thermophilic bacteria. Part of experiments was the finding of several thermophilic bacteria based on the similarity of the protein sequence of the phaC gene of the bacterium Cupriavidus necator. The next part of thesis was a literature search of the abilities of these thermophilic bacteria focused on culture conditions and the spectrum of usable substrates. Subsequently, five bacteria were selected for use in NGBI based on the information obtained. Freely available databases were used during the experimental work, and evolutionary analysis were performed in MEGA X and Operon-mapper. Rubrobacter xylanophilus with collection number DSM 9941 was selected from the collection of bacterial strains as the most promising PHA producer for NGIB. The high culture temperature of up to 70 ° C and a large amount of utilized carbohydrate substrates were considered decisive. An interesting result of the analysis was to find the gene sequences of two classes of PHA synthase – I. and III. class, as for a single bacterial strain from the entire collection. Additional genes linked to PHA metabolism were found in genome analysis.

Background The automated retrieval and integration of information about protein point mutations in combination with structure, domain and interaction data from literature and databases promises to be a valuable approach to study structure-function relationships in biomedical data sets. Results We developed a rule- and regular expression-based protein point mutation retrieval pipeline for PubMed abstracts, which shows an F-measure of 87% for the mutation retrieval task on a benchmark dataset. In order to link mutations to their proteins, we utilize a named entity recognition algorithm for the identification of gene names co-occurring in the abstract, and establish links based on sequence checks. Vice versa, we could show that gene recognition improved from 77% to 91% F-measure when considering mutation information given in the text. To demonstrate practical relevance, we utilize mutation information from text to evaluate a novel solvation energy based model for the prediction of stabilizing regions in membrane proteins. For five G protein-coupled receptors we identified 35 relevant single mutations and associated phenotypes, of which none had been annotated in the UniProt or PDB database. In 71% reported phenotypes were in compliance with the model predictions, supporting a relation between mutations and stability issues in membrane proteins. Conclusion We present a reliable approach for the retrieval of protein mutations from PubMed abstracts for any set of genes or proteins of interest. We further demonstrate how amino acid substitution information from text can be utilized for protein structure stability studies on the basis of a novel energy model.