Interactive Analysis and Visualization of Macromolecular Interfaces between Proteins.
ABSTRACT Molecular interfaces between proteins are of high importance for understanding their interactions and functions. In this paper
protein complexes in the PDB database are used as input to calculate an interface contact matrix between two proteins, based
on the distance between individual residues and atoms of each protein. The interface contact matrix is linked to a D visualization of the macromolecular structures in that way, that mouse clicking
on the appropriate part of the interface contact matrix highlights the corresponding residues in the 3D structure. Additionally,
the identified residues in the interface contact matrix are used to define the molecular surface at the interface. The interface
contact matrix allows the end user to overview the distribution of the involved residues and an evaluation of interfacial
binding hot spots. Theinteractive visualization of the selected residues in a 3D view via interacting windows allows realistic analysis of the
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ABSTRACT: Biomedical experts are confronted with "Big data", driven by the trend towards precision medicine. Despite the fact that humans are excellent at pattern recognition in dimensions of ≤ 3, most biomedical data is in dimensions much higher than 3, making manual analysis often impossible. Experts in daily routine are decreasingly capable of dealing with such data. Efficient, useable and useful computational methods, algorithms and tools to interactively gain insight into such data are a commandment of the time. A synergistic combination of methodologies of two areas may be of great help here: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with machine learning. Mapping higher dimensional data into lower dimensions is a major task in HCI, and a concerted effort including recent advances from graph-theory and al-gebraic topology may contribute to finding solutions. Moreover, much biomedical data is sparse, noisy and time-dependent, hence entropy is also amongst promising topics. This tutorial gives an overview of the HCI-KDD approach and focuses on 3 topics: graphs, topology and en-tropy. The goal of this intro tutorial is to motivate and stimulate further research.Brain Informatics and Health, Edited by Dominik Slezak, Ah-Hwee Tan, James F. Peters, Lars Schwalbe, 01/2014: pages 502-515; Springer., ISBN: 978-3-319-09890-6
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ABSTRACT: Proteins are the molecules of life which are involved in cellular processes. The functional specificity of a pro-tein is linked to its structure. A great section of bioinformatics deals with the prediction, analysis and visualization of pro-tein 3D structures. High-throughput methods for the determination of protein structures provide the information needed to build structure-activity relationships. The accessibility of these structural data together with genomic and clinical data is of crucial importance for the application of bioinformatics in medical research. The experimental methods are supple-mented by homology modelling, where new protein structures are predicted by exploiting structural information from known configurations. Computer visualization of protein models provide insights into biological processes which can not be adequately explained otherwise. For the analysis of protein-protein interactions, Voronoi tessellations are used to quan-tify the macromolecular interfaces. Details at the atomic and electronic levels of the protein molecules, needed for a deeper understanding of properties that remain unrevealed after structural elucidation, are provided by methods based on quantum theoretical calculations. Many proteins are of immediate medical and pharmacological relevance. The structural analysis is therefore of special interest to understand diseases at a molecular level, which is the prerequisite for new de-velopments in diagnosis and therapy.Current Bioinformatics. 01/2009; 4:54-87.