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GSP4PDB : a web tool to visualize, search and explore protein-ligand structural patterns.

Angles, Renzo and Arenas-Salinas, Mauricio and García, Roberto and Reyes-Suarez, Jose Antonio and Pohl, Ehmke (2020) 'GSP4PDB : a web tool to visualize, search and explore protein-ligand structural patterns.', BMC bioinformatics., 21 (S2). p. 85.

Abstract

Background: In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein Data Bank. Results: We introduce the notion of graph-based structural pattern (GSP) as an abstract model for representing protein-ligand interactions. A GSP is a graph where the nodes represent entities of the protein-ligand complex (amino acids and ligands) and the edges represent structural relationships (e.g. distances ligand - amino acid). The novel feature of GSP4PDB is a simple and intuitive graphical interface where the user can “draw” a GSP and execute its search in a relational database containing the structural data of each PDB entry. The results of the search are displayed using the same graph-based representation of the pattern. The user can further explore and analyse the results using a wide range of filters, or download their related information for external post-processing and analysis. Conclusions: GSP4PDB is a user-friendly and efficient application to search and discover new patterns of protein-ligand interaction.

Item Type:Article
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Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1186/s12859-020-3352-x
Publisher statement:This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Date accepted:No date available
Date deposited:02 April 2020
Date of first online publication:11 March 2020
Date first made open access:02 April 2020

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