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GIANT: pattern analysis of molecular interactions in 3D structures of protein–small ligand complexes

Kota Kasahara14 and Kengo Kinoshita123*

Author Affiliations

1 Graduate School of Information Sciences, Tohoku University, 6-3-09 Aoba, Aramaki, Aoba-ku, Sendai, Miyagi 980-8597, Japan

2 Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-cho, Aoba-ku, Sendai, Miyagi 980-8573, Japan

3 Institute of Development, Aging, and Cancer, Tohoku University, 4-1 Seiryo-cho, Aoba-ku, Sendai, Miyagi 980-8575, Japan

4 Present addresses: Institute for Protein Research, Osaka University, 3-2 Yamada-oka, Suita, Osaka 565-0871, Japan

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BMC Bioinformatics 2014, 15:12  doi:10.1186/1471-2105-15-12

Published: 14 January 2014

Abstract

Background

Interpretation of binding modes of protein–small ligand complexes from 3D structure data is essential for understanding selective ligand recognition by proteins. It is often performed by visual inspection and sometimes largely depends on a priori knowledge about typical interactions such as hydrogen bonds and π-π stacking. Because it can introduce some biases due to scientists’ subjective perspectives, more objective viewpoints considering a wide range of interactions are required.

Description

In this paper, we present a web server for analyzing protein–small ligand interactions on the basis of patterns of atomic contacts, or “interaction patterns” obtained from the statistical analyses of 3D structures of protein–ligand complexes in our previous study. This server can guide visual inspection by providing information about interaction patterns for each atomic contact in 3D structures. Users can visually investigate what atomic contacts in user-specified 3D structures of protein–small ligand complexes are statistically overrepresented. This server consists of two main components: “Complex Analyzer”, and “Pattern Viewer”. The former provides a 3D structure viewer with annotations of interacting amino acid residues, ligand atoms, and interacting pairs of these. In the annotations of interacting pairs, assignment to an interaction pattern of each contact and statistical preferences of the patterns are presented. The “Pattern Viewer” provides details of each interaction pattern. Users can see visual representations of probability density functions of interactions, and a list of protein–ligand complexes showing similar interactions.

Conclusions

Users can interactively analyze protein–small ligand binding modes with statistically determined interaction patterns rather than relying on a priori knowledge of the users, by using our new web server named GIANT that is freely available at http://giant.hgc.jp/ webcite.

Keywords:
Molecular recognition; Ligand binding site; Protein–ligand interactions; Protein structure; Protein function; Pattern recognition; Database; Web-server