| Autor: |
Inhester T; ZBH - Center for Bioinformatics, Universität Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany., Nittinger E; ZBH - Center for Bioinformatics, Universität Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany., Sommer K; ZBH - Center for Bioinformatics, Universität Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany., Schmidt P; ZBH - Center for Bioinformatics, Universität Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany., Bietz S; ZBH - Center for Bioinformatics, Universität Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany., Rarey M; ZBH - Center for Bioinformatics, Universität Hamburg , Bundesstrasse 43, 20146 Hamburg, Germany. |
| Abstrakt: |
Noncovalent interactions play an important role in macromolecular complexes. The assessment of molecular interactions is often based on knowledge derived from statistics on structural data. Within the last years, the available data in the Brookhaven Protein Data Bank has increased dramatically, quantitatively as well as qualitatively. This development allows the derivation of enhanced interaction models and motivates new ways of data analysis. Here, we present a method to facilitate the analysis of noncovalent interactions enabling detailed insights into the nature of molecular interactions. The method is integrated into a highly variable framework enabling the adaption to user-specific requirements. NAOMInova, the user interface for our method, allows the generation of specific statistics with respect to the chemical environment of substructures. The substructures as well as the analyzed set of protein structures can be chosen arbitrarily. Although NAOMInova was primarily made for data exploration in protein-ligand crystal structures, it can be used in combination with any structure collection, for example, analysis of a carbonyl in the neighborhood of an aromatic ring on a set of structures resulting from a MD simulation. Additionally, a filter for different atom attributes can be applied including the experimental support by electron density for single atoms. In this publication, we present the underlying algorithmic techniques of our method and show application examples that demonstrate NAOMInova's ability to support individual analysis of noncovalent interactions in protein structures. NAOMInova is available at http://www.zbh.uni-hamburg.de/naominova . |
