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

Núñez-Vivanco G, Valdés-Jiménez A, Besoaín F, Reyes-Parada M - J Cheminform (2016)

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: Escuela de Ingeniería Civil en Bioinformática, Universidad de Talca, Avenida Lircay s/n, Talca, Chile ; Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Talca, Chile.

ABSTRACT

Background: Since 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.

Results: Here 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.

Conclusions: Geomfinder 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.

No MeSH data available.


Similarity of the 3D patterns in the Pyridoxal Phosphate (PLP) binding proteins. In red is represented the higher value of the GScore (Geomfinder) between each pair of 3D patterns related with the PLP binding sites. The Sequence Component Similarity between each pair of PLP target proteins is shown in blue. All pair of the evaluated proteins are described in this scatter plot
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Fig8: Similarity of the 3D patterns in the Pyridoxal Phosphate (PLP) binding proteins. In red is represented the higher value of the GScore (Geomfinder) between each pair of 3D patterns related with the PLP binding sites. The Sequence Component Similarity between each pair of PLP target proteins is shown in blue. All pair of the evaluated proteins are described in this scatter plot

Mentions: BEN is a reversible competitive inhibitor employed as a ligand to prevent proteases degrading the product of interest in protein crystallography [53], PLP is the most common enzymatic co-factor, being present in a wide number of diverse of proteins and organisms [54] and, as it is well known, ATP plays a fundamental role in a vast amount of chemical reactions in biological systems. Furthermore, these compounds have been co-crystallized with hundreds of proteins such as hydrolases, oxidoreductases, isomerases, ligases, transmembrane proteins, globular proteins, transporters and receptors. In our evaluation, we randomly compared 102 protein targets of BEN, 234 protein targets of ATP and 674 protein targets of PLP (Additional file 2: Table S1). Our results showed that almost in the 70 % of the cases (73 % for BEN, 72.5 % for ATP and 70 % for PLP), Geomfinder found 3D patterns located in the BEN/ATP/PLP binding sites exhibiting GScore values higher than 50 %. For comparative purposes, we measured the sequence component similarity of the same pairs of proteins. In this case, to make a fair comparison, only the residues located up to 5 Å from the ligands (i.e. those lining the BEN/ATP/PLP binding sites) were considered. Thus, the sequence component similarity of each pair of proteins was calculated as the percentage of similar residues occurring in both binding sites. Interestingly, these values were in most cases lower than those detected by Geomfinder (Figs. 6, 7, 8). As previously mentioned, the sequence identity in two proteins does not necessarily imply that the spatial organization of the amino acids in each protein site is preserved. Therefore, it is not surprising that Geomfinder (which determines 3D similarities) performed better than a sequence-based method regarding the identification of similar 3D patterns in two given protein structures. Indeed, Geomfinder identified a high degree of similarity between protein 3D patterns showing low sequence identity (Figs. 6, 7, 8), which implies that the residues forming the 3D patterns exhibit Dist, NbA and/or TSP parameter values, that allow to identify them as similar. Thus, this is an important case of evaluation since the similar 3D patterns found by Geomfinder might underlie the binding properties of the ligands analyzed (BEN, ATP, PLP) to structurally unrelated proteins, and also show that the software is able to identify local structural similarities, which cannot be observed at sequence level.


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

Núñez-Vivanco G, Valdés-Jiménez A, Besoaín F, Reyes-Parada M - J Cheminform (2016)

Similarity of the 3D patterns in the Pyridoxal Phosphate (PLP) binding proteins. In red is represented the higher value of the GScore (Geomfinder) between each pair of 3D patterns related with the PLP binding sites. The Sequence Component Similarity between each pair of PLP target proteins is shown in blue. All pair of the evaluated proteins are described in this scatter plot
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4834829&req=5

Fig8: Similarity of the 3D patterns in the Pyridoxal Phosphate (PLP) binding proteins. In red is represented the higher value of the GScore (Geomfinder) between each pair of 3D patterns related with the PLP binding sites. The Sequence Component Similarity between each pair of PLP target proteins is shown in blue. All pair of the evaluated proteins are described in this scatter plot
Mentions: BEN is a reversible competitive inhibitor employed as a ligand to prevent proteases degrading the product of interest in protein crystallography [53], PLP is the most common enzymatic co-factor, being present in a wide number of diverse of proteins and organisms [54] and, as it is well known, ATP plays a fundamental role in a vast amount of chemical reactions in biological systems. Furthermore, these compounds have been co-crystallized with hundreds of proteins such as hydrolases, oxidoreductases, isomerases, ligases, transmembrane proteins, globular proteins, transporters and receptors. In our evaluation, we randomly compared 102 protein targets of BEN, 234 protein targets of ATP and 674 protein targets of PLP (Additional file 2: Table S1). Our results showed that almost in the 70 % of the cases (73 % for BEN, 72.5 % for ATP and 70 % for PLP), Geomfinder found 3D patterns located in the BEN/ATP/PLP binding sites exhibiting GScore values higher than 50 %. For comparative purposes, we measured the sequence component similarity of the same pairs of proteins. In this case, to make a fair comparison, only the residues located up to 5 Å from the ligands (i.e. those lining the BEN/ATP/PLP binding sites) were considered. Thus, the sequence component similarity of each pair of proteins was calculated as the percentage of similar residues occurring in both binding sites. Interestingly, these values were in most cases lower than those detected by Geomfinder (Figs. 6, 7, 8). As previously mentioned, the sequence identity in two proteins does not necessarily imply that the spatial organization of the amino acids in each protein site is preserved. Therefore, it is not surprising that Geomfinder (which determines 3D similarities) performed better than a sequence-based method regarding the identification of similar 3D patterns in two given protein structures. Indeed, Geomfinder identified a high degree of similarity between protein 3D patterns showing low sequence identity (Figs. 6, 7, 8), which implies that the residues forming the 3D patterns exhibit Dist, NbA and/or TSP parameter values, that allow to identify them as similar. Thus, this is an important case of evaluation since the similar 3D patterns found by Geomfinder might underlie the binding properties of the ligands analyzed (BEN, ATP, PLP) to structurally unrelated proteins, and also show that the software is able to identify local structural similarities, which cannot be observed at sequence level.

Bottom Line: 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.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.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.

View Article: PubMed Central - PubMed

Affiliation: Escuela de Ingeniería Civil en Bioinformática, Universidad de Talca, Avenida Lircay s/n, Talca, Chile ; Centro de Bioinformática y Simulación Molecular, Universidad de Talca, 2 Norte 685, Talca, Chile.

ABSTRACT

Background: Since 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.

Results: Here 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.

Conclusions: Geomfinder 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.

No MeSH data available.