Data mining for important amino acid residues in multiple sequence alignments and protein structures

Enzymes are highly efficient bio-catalysts interesting for industries and medicine. Therefore, a goal of utmost importance in biochemical research is to understand how an enzyme catalyzes a chemical reaction. Here, the computational identification of functionally or structurally important residue positions can be of tremendous help. The datasets that are most informative for the algorithms are the 3D structure of a protein and a multiple sequence alignment (MSA) composed of homologous sequences. For example, an MSA allows for the quantification of residue conservation. Residue conservation at a given position indicates that only one type of amino acid fulfills all constraints imposed by protein structure or function. Furthermore, a detailed analysis of less strictly conserved residue positions may identify pairs, whose orchestration is mutually dependent and induces correlated mutations. Both of these conservation signals are indicative of functionally or structurally important positions. In the first part of this thesis, methods of machine learning were used to identify and classify these residue positions. It was the aim to predict in a mutually exclusively manner a role in catalysis, ligand-binding or protein stability for each residue position of a protein. Unfortunately, for many proteins the 3D structure is unknown. For other proteins, the number of known homologs is not sufficient to compile a meaningful MSA. Therefore, three variants of a classifier were designed and implemented, named CLIPS-1D, CLIPS-3D, and CLIPS-4D. These multi-class support vector machines allow for a classification based on an MSA (CLIPS-1D), a 3D structure (CLIPS-3D), and a combination of both (CLIPS-4D). CLIPS-1D exploits seven sequence-based features, whereas CLIPS-3D utilizes seven structure-based features. CLIPS-4D combines the seven sequence-based features of CLIPS-1D with those two structure-based features that increased its classification performance. A comparison with existing methods and a detailed analysis on a well-studied enzyme confirmed state-of-the-art prediction quality for CLIPS-1D and CLIPS-4D. In the second part of this thesis an algorithm for the identification of correlated mutations was improved. A common method for the identification of correlated mutations is to deduce the mutual information (MI) of a pair of residue positions from an MSA. The classical MI is based on Shannon’s information theory that utilizes probabilities only. Consequently, these approaches do not consider the similarity of residue pairs, which is a severe limitation. In order to improve these algorithms, H2rs was developed for this thesis. Thus, the MIvalues originate from the von Neumann entropy (vNE), which takes into account amino acid similarities modeled by means of a substitution matrix. To further improve the specificity of H2rs, the significance of MIvNE-values was assessed with a bootstrapping approach. The analysis of a large in silico testbed and the detailed assessment of five well-studied enzymes demonstrated state-of-the-art performance