Prediction of Wild-type Enzyme Characteristics

Henrik Marcus Geertz-Hansen

Research output: Book/ReportPh.D. thesis

Abstract

Technological advances within massive parallel data generation techniques in biology have made bioinformatics an increasingly important part of biotechnology research. Data-driven research by means of integration and analysis of large biological data sets provide new opportunites across all areas of biotechnology, including enzyme discovery and characterization. This work presents two articles on sequence-based discovery and functional annotation of enzymes in environmental samples, and two articles on analysis and prediction of enzyme thermostability and cofactor requirements.
The first article presents a sequence-based approach to discovery of proteolytic enzymes in metagenomes obtained from the Polar oceans. We show that microorganisms living in these extreme environments of constant low temperature harbour genes encoding novel proteolytic enzymes with potential industrial relevance. The second article presents a web server for the processing and annotation of functional metagenomics sequencing data, tailored to meet the requirements of non-bioinformaticians. The third article presents analyses of the molecular determinants of enzyme thermostability, and a feature-based prediction method of the melting temperature of fungal wild-type enzymes from seven different glycoside hydrolase families. We exemplify family-specific stabilizing protein features, and show that our featurebased algorithm outperforms a sequence similarity-based approach to melting temperature prediction. Finally, the fourth article presents a sequence-based prediction method of the cofactor binding specificity of Rossmann folds. The algorithm predicts the specificity for the cofactors FAD(H2), NAD(H) and NADP(H), and we demonstrate its ability to reflect changes in cofactor preference upon introduction of two or more amino acid substitutions in Rossmann fold sequences.
In summary, this work presents novel methods for prediction of enzyme characteristics, and exemplify the largely untapped potential for discovery of new biocatalysts in environmental samples.
Original languageEnglish
Place of PublicationKgs. Lyngby
PublisherTechnical University of Denmark
Number of pages199
Publication statusPublished - 2014

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