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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.


Similar 3D patterns between MAO-A and SERT. The binding sites in SERT (S1 and S2) and MAO-A (MLG) are depicted in red. The circles represent the identical 3D pattern detected by Geomfinder
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Fig11: Similar 3D patterns between MAO-A and SERT. The binding sites in SERT (S1 and S2) and MAO-A (MLG) are depicted in red. The circles represent the identical 3D pattern detected by Geomfinder

Mentions: In an additional evaluation, we analyzed the crystal structure of the human monoamine oxidase A (MAO-A) [PDBid: 2BXS] co-crystallized with the selective and irreversible inhibitor clorgiline (MLG), and a homology model of the human serotonin transporter (SERT), built using the structure of the Drosophila melanogaster dopamine transporter (DAT) [PDBid: 4M48], as template. It has been shown that two putative ligand binding sites exist in SERT, named S1 and S2 [57–59], whereas a single substrate binding site is found in MAO-A [47]. Both proteins are considerably different from a structural point of view, and while SERT is a transmembrane protein belonging to SLC6 family, MAO-A is an outer mitochondrial membrane bound flavoprotein, with the FAD cofactor covalently bound to the enzyme. Their global sequence identity is only of 3.9 % while the local sequence similarity shows a 34 % in a segment of 55 aligned residues including 19 gaps. Nevertheless, the neurotransmitter serotonin (5-HT) is a common ligand and the physiological actions of both proteins are related with the regulation of adequate levels of 5-HT in the synaptic cleft. In spite of the low sequence similarity, Geomfinder was able to detect several similar 3D patterns between SERT y MAO-A, one of which correspond to the MLG binding site in MAO-A and the binding site S2 of SERT. These 3D patterns, defined by the virtual reference Atom1393 (in MAO-A) and Atom12422 (in SERT), have a GScore value of 100 % (Fig. 11). We designated this case as “uncommon” since it shows that Geomfinder is able to find identical 3D patterns in binding sites belonging to proteins with highly different sequences, structures, genetic origin, tissue distribution and catalytic activities. In this particular case, SERT and MAO-A similarities found suggest the existence of some degree of structural convergence between both proteins, which could be related with the recognition of the common substrate.


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)

Similar 3D patterns between MAO-A and SERT. The binding sites in SERT (S1 and S2) and MAO-A (MLG) are depicted in red. The circles represent the identical 3D pattern detected by Geomfinder
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig11: Similar 3D patterns between MAO-A and SERT. The binding sites in SERT (S1 and S2) and MAO-A (MLG) are depicted in red. The circles represent the identical 3D pattern detected by Geomfinder
Mentions: In an additional evaluation, we analyzed the crystal structure of the human monoamine oxidase A (MAO-A) [PDBid: 2BXS] co-crystallized with the selective and irreversible inhibitor clorgiline (MLG), and a homology model of the human serotonin transporter (SERT), built using the structure of the Drosophila melanogaster dopamine transporter (DAT) [PDBid: 4M48], as template. It has been shown that two putative ligand binding sites exist in SERT, named S1 and S2 [57–59], whereas a single substrate binding site is found in MAO-A [47]. Both proteins are considerably different from a structural point of view, and while SERT is a transmembrane protein belonging to SLC6 family, MAO-A is an outer mitochondrial membrane bound flavoprotein, with the FAD cofactor covalently bound to the enzyme. Their global sequence identity is only of 3.9 % while the local sequence similarity shows a 34 % in a segment of 55 aligned residues including 19 gaps. Nevertheless, the neurotransmitter serotonin (5-HT) is a common ligand and the physiological actions of both proteins are related with the regulation of adequate levels of 5-HT in the synaptic cleft. In spite of the low sequence similarity, Geomfinder was able to detect several similar 3D patterns between SERT y MAO-A, one of which correspond to the MLG binding site in MAO-A and the binding site S2 of SERT. These 3D patterns, defined by the virtual reference Atom1393 (in MAO-A) and Atom12422 (in SERT), have a GScore value of 100 % (Fig. 11). We designated this case as “uncommon” since it shows that Geomfinder is able to find identical 3D patterns in binding sites belonging to proteins with highly different sequences, structures, genetic origin, tissue distribution and catalytic activities. In this particular case, SERT and MAO-A similarities found suggest the existence of some degree of structural convergence between both proteins, which could be related with the recognition of the common substrate.

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.