3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns
Center for Bioinformatics, Simulations and Modelling, Universidad de Talca, 3460000 Talca, Chile
PhD Program on Computer Architecture, Universitat Politécnica de Catalunya, 08034 Barcelona, Spain
DAMA-UPC, Universitat Politécnica de Catalunya BarcelonaTech, 08034 Barcelona, Spain
Facultad de Ciencias de la Salud, Universidad Autonóma de Chile, 3467987 Talca, Chile
School of Medicine, Faculty of Medical Sciences, Universidad de Santiago de Chile, 9170022 Santiago, Chile
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(13), 3174; https://doi.org/10.3390/ijms20133174
Received: 11 May 2019 / Revised: 19 June 2019 / Accepted: 20 June 2019 / Published: 28 June 2019
(This article belongs to the Special Issue Recent Developments on Protein–Ligand Interactions: From Structure, Function to Applications)
Discovering conserved three-dimensional (3D) patterns among protein structures may provide valuable insights into protein classification, functional annotations or the rational design of multi-target drugs. Thus, several computational tools have been developed to discover and compare protein 3D-patterns. However, most of them only consider previously known 3D-patterns such as orthosteric binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possible 3D-patterns that exist in protein structures (allosteric sites, enzyme-cofactor interaction motifs, among others). In this work, we present 3D-PP, a new free access web server for the discovery and recognition all similar 3D amino acid patterns among a set of proteins structures (independent of their sequence similarity). This new tool does not require any previous structural knowledge about ligands, and all data are organized in a high-performance graph database. The input can be a text file with the PDB access codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico homology modeling. The results are presented as lists of sequence patterns that can be further analyzed within the web page. We tested the accuracy and suitability of 3D-PP using two sets of proteins coming from the Protein Data Bank: (a) Zinc finger containing and (b) Serotonin target proteins. We also evaluated its usefulness for the discovering of new 3D-patterns, using a set of protein structures coming from in silico homology modeling methodologies, all of which are overexpressed in different types of cancer. Results indicate that 3D-PP is a reliable, flexible and friendly-user tool to identify conserved structural motifs, which could be relevant to improve the knowledge about protein function or classification. The web server can be freely utilized at https://appsbio.utalca.cl/3d-pp/.