Geomfinder: a multi-feature identifier of similar three-dimensional protein patterns: a ligand-independent approach

Abstract

BackgroundSince the structure of proteins is more conserved than the sequence, the identification of conserved three-dimensional (3D) patterns among a set of proteins, can be important for protein function prediction, protein clustering, drug discovery and the establishment of evolutionary relationships. Thus, several computational applications to identify, describe and compare 3D patterns (or motifs) have been developed. Often, these tools consider a 3D pattern as that described by the residues surrounding co-crystallized/docked ligands available from X-ray crystal structures or homology models. Nevertheless, many of the protein structures stored in public databases do not provide information about the location and characteristics of ligand binding sites and/or other important 3D patterns such as allosteric sites, enzyme-cofactor interaction motifs, etc. This makes necessary the development of new ligand-independent methods to search and compare 3D patterns in all available protein structures.ResultsHere we introduce Geomfinder, an intuitive, flexible, alignment-free and ligand-independent web server for detailed estimation of similarities between all pairs of 3D patterns detected in any two given protein structures. We used around 1100 protein structures to form pairs of proteins which were assessed with Geomfinder. In these analyses each protein was considered in only one pair (e.g. in a subset of 100 different proteins, 50 pairs of proteins can be defined). Thus: (a) Geomfinder detected identical pairs of 3D patterns in a series of monoamine oxidase-B structures, which corresponded to the effectively similar ligand binding sites at these proteins; (b) we identified structural similarities among pairs of protein structures which are targets of compounds such as acarbose, benzamidine, adenosine triphosphate and pyridoxal phosphate; these similar 3D patterns are not detected using sequence-based methods; (c) the detailed evaluation of three specific cases showed the versatility of Geomfinder, which was able to discriminate between similar and different 3D patterns related to binding sites of common substrates in a range of diverse proteins.ConclusionsGeomfinder allows detecting similar 3D patterns between any two pair of protein structures, regardless of the divergency among their amino acids sequences. Although the software is not intended for simultaneous multiple comparisons in a large number of proteins, it can be particularly useful in cases such as the structure-based design of multitarget drugs, where a detailed analysis of 3D patterns similarities between a few selected protein targets is essential.Electronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0131-9) contains supplementary material, which is available to authorized users.

A New Strategy for Multitarget Drug Discovery/Repositioning Through the Identification of Similar 3D Amino Acid Patterns Among Proteins Structures: The Case of Tafluprost and its Effects on Cardiac Ion Channels

“…It is important to note that, beyond these functional and performance improvements, the core method for searching and comparison of 3D patterns is the same as in the original version (Núñez-Vivanco et al, 2016). The accuracy and precision of this core method has already been compared with those of computational tools such as PocketMatch and ClickTopology (Núñez-Vivanco et al, 2016). In addition, in the present work we confirmed the reliability of our algorithm with a case of use, in which the theoretical predictions were experimentally confirmed.…”

“…It also identifies 3D patterns formed by different protein chains and characterizes how druggable is the zone where the 3D patterns were detected. It is important to note that, beyond these functional and performance improvements, the core method for searching and comparison of 3D patterns is the same as in the original version (Núñez-Vivanco et al, 2016). The accuracy and precision of this core method has already been compared with those of computational tools such as PocketMatch and ClickTopology (Núñez-Vivanco et al, 2016).…”

“…These sites have recently gained attention for the rational design of polypharmacological allosteric modulators (Abdel-Magid, 2015;Meysman et al, 2015;Reyes-Parada and Iturriaga-Vasquez, 2016;Wakefield at al., 2019). Indeed, by using our in-house algorithm Geomfinder (Núñez-Vivanco et al, 2016), we have reported the finding of some similar 3D patterns among very different protein structures, which cannot be observed through other structural tools or with sequencebased methods. Noteworthy, in the original version of Geomfinder, the residues of each detected 3D pattern could only be part of one specific protein chain.…”

“…The new version of Geomfinder includes all geometrical characteristics and processes described previously (Núñez-Vivanco et al, 2016) and incorporates new features and substantial improvements. In the Supplementary Figure S1, the implemented architecture and essential components and services of Geomfinder are shown.…”

3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns

“…Thus, unveiling and comparing all local structural patterns (including those unknown or previously unobserved) into a set of protein structures could be more informative for the discovery, search and characterization of conserved 3D-patterns than exploring only previously known sites. In a recent report [32], we described a strategy for the exhaustive searching of similar 3D-patterns between two protein structures, which allowed the discovery of some conserved structural residue arrangements between proteins that differ in their function, structure and tissue localization but that share the same endogenous ligand and perform complementary physiological functions [33]. This type of finding, along with the increasing availability of structural data (more than 130,000 protein structures in the Protein Data Bank [34] and more than 3 million homology models in the SWISS-MODEL Repository [35]), represent an opportunity to use and develop structure-based methods for the classification, description and discovery of conserved 3D amino acid patterns among multiple protein structures.…”