Discovering conserved 3D-patterns among protein structures may provide valuable insights into protein classification, functional annotations or rational drug design. Thus, several tools have been developed to compare 3D-patterns; however, most of them only consider the previously known 3D-patterns such as binding sites or structural motifs. This fact makes necessary the development of new methods for the identification of all possibles 3D-patterns available 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 discovering and recognition all similar 3D amino acid patterns among a set of proteins structures (independently of their sequence similarity). This new tool does not require any previous knowledge about ligands or motifs, and all data is organizing in a high-performance graph database. The input can be a text file with the PDB accession codes or a zip file of PDB coordinates regardless of the origin of the structural data: X-ray crystallographic experiments or in silico comparative modeling. Finally, the results are lists of sequence patterns that can be further analyses within the web page. We probe the confidence of 3D-PP using a C3H1-type Zinc Finger set of proteins, and we sketch their utility for the discovering of new 3D-patterns using a set of serotonin protein targets.

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Valdés-Jiménez A, Larriba-Pey JL, Núñez-Vivanco G, Reyes-Parada M. 3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns. Int J Mol Sci. 2019;20(13):3174 doi: 10.3390/ijms20133174