Abstract
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/.
Highlights
Most drugs interact with more than one molecular target [1,2]
To demonstrate the applicability of 3D-PP, we show the results of two different examples in which the existence of known and unknown 3D-patterns are assessed in a set of proteins
We present 3D-PP, a new free access web server for discovering and recognition of all similar 3D amino acid patterns among a set of protein structures
Summary
Most drugs interact with more than one molecular target [1,2]. This fact is usually considered an undesired feature since it might be related to the side effects of pharmacological treatments. Current trends in drug discovery have put hope and considerable effort into the development of multitarget compounds, due to the improved efficacy and safety profiles shown by some promiscuous drugs [3,4,5,6,7,8]. In this context, several computational approaches to predict the polypharmacological profile of either novel or known drugs have been developed, most of which are based on two main methodological strategies. The second approach is centered on target(s) features and involves methods that use the known 3D structure of proteins to perform inverted docking, structure-based pharmacophore searching and the evaluation of binding sites similarities [8,9,10]
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