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Proteins are macromolecules that consist of linear-bonded amino acids. They are essential elements in various metabolic processes. The three-dimensional structure of a protein is determined by the order of amino acids, also referred to as the protein sequence. This conformation corresponds to the structural state in which the protein is functionally active. However, relationships between protein sequence, structure and function have not been fully understood yet. Additionally, information about structural properties or even the entire protein structure are crucial for understanding the dynamics that define protein functionality and mechanisms. From this, the role of a protein in its molecular context can be described closely. For instance, interactions can be investigated and comprehended as a biological dynamic network that is sensitive to alternations, i.e. changes which are caused by diseases. Such knowledge can aid in drug design, whereas compounds need to be specifically tailored and adjusted to their molecular targets. Protein energy profile-basedmethods can be applied to investigate protein structures concerning dynamics and alternations. The publications enclosed to this work discuss in general the scientific potentials of energy profilebased techniques and algorithms. On the one hand, changes in stability caused by protein mutations and proteinligand interactions are discussed in the context of energy profiles. On the other hand, energetic relations to protein sequence, structure and function are elucidated in detail. Finally, the presented discussions focus on recent enhancements of the eProS (energy profile suite) database and toolbox. eProS freely provides all elucidated methodologies to the scientific community. Thus, one can address biological questions with the presented methods at hand. Additionally, eProS provides annotations related to foreign databases. This ensures a broad view on biological data and information. In particular, energetic characteristics can be identified which contribute to a protein’s structure and function.
In this work a new method for the prediction of the Xaa-proline (where Xaa is any amino acid) cis/trans isomerization was investigated. By extraction of twelve structural features (real secondary structure, inside/outside classification, properties of the environment around proline and proline itself) a support vector machine (SVM) based prediction approach was evolved. The Java software Xaa-PIPT for structural feature extraction was developed. Based on 4397 (2199 cis and 2198 trans) prolines extracted from non-redundant, globular proteins a classifier was trained using the radial basis function (RBF) kernel. In ten-fold cross-validation it achieved an accuracy of 70.0478 % and a Matthews correlation coefficient (MCC) of 0.4223, a sensitivity of 0.5433 and a specificity of 0.8576. Based on this classifier a lightweight and easy-to-use Java software tool, called m Xaa-PIPT, for the prediction of the Xaa-proline cis/trans isomerization was devel-oped. It was shown that there are correlations between the proline surrounding environment and the isomerization state. m Xaa-PIPT can be used for the evaluation of low-resolution protein structures and theoretical models to improve their quality by the prediction of the Xaa-proline isomerization.