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In silico modeling of human α2C-adrenoreceptor interaction with filamin-2.

Pawlowski M, Saraswathi S, Motawea HK, Chotani MA, Kloczkowski A - PLoS ONE (2014)

Bottom Line: To better understand the molecular nature and specificity of this interaction, in this study, we constructed comparative models of human α2C-AR and human filamin-2 proteins.We found that electrostatic interactions seem to play a key role in this complex formation which manifests in interactions between the C-terminal arginines of α2C-ARs (particularly R454 and R456) and negatively charged residues from filamin-2 region between residues 1979 and 2206.Phylogenetic and sequence analysis showed that these interactions have evolved in warm-blooded animals.

View Article: PubMed Central - PubMed

Affiliation: Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio, United States of America.

ABSTRACT
Vascular smooth muscle α2C-adrenoceptors (α2C-ARs) mediate vasoconstriction of small blood vessels, especially arterioles. Studies of endogenous receptors in human arteriolar smooth muscle cells (referred to as microVSM) and transiently transfected receptors in heterologous HEK293 cells show that the α2C-ARs are perinuclear receptors that translocate to the cell surface under cellular stress and elicit a biological response. Recent studies in microVSM unraveled a crucial role of Rap1A-Rho-ROCK-F-actin pathways in receptor translocation, and identified protein-protein interaction of α2C-ARs with the actin binding protein filamin-2 as an essential step in the process. To better understand the molecular nature and specificity of this interaction, in this study, we constructed comparative models of human α2C-AR and human filamin-2 proteins. Finally, we performed in silico protein-protein docking to provide a structural platform for the investigation of human α2C-AR and filamin-2 interactions. We found that electrostatic interactions seem to play a key role in this complex formation which manifests in interactions between the C-terminal arginines of α2C-ARs (particularly R454 and R456) and negatively charged residues from filamin-2 region between residues 1979 and 2206. Phylogenetic and sequence analysis showed that these interactions have evolved in warm-blooded animals.

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Predicted models of filamin-2 (FLN2) and α2C-adrenoceptor (ADRA2C) proteins, and their complex.Panels A and B present cartoon diagram of FLN2 (region between residues 1982 and 2183) and ADRA2C protein models. Positively and negatively charged regions are indicated by blue and red colors, respectively. Panel C presents whole protein-protein complex predicted by HADDOCK program. Panel D shows the interaction between receptor's C-terminal helix and the filamin-2 region that is responsible for binding the receptor.
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pone-0103099-g003: Predicted models of filamin-2 (FLN2) and α2C-adrenoceptor (ADRA2C) proteins, and their complex.Panels A and B present cartoon diagram of FLN2 (region between residues 1982 and 2183) and ADRA2C protein models. Positively and negatively charged regions are indicated by blue and red colors, respectively. Panel C presents whole protein-protein complex predicted by HADDOCK program. Panel D shows the interaction between receptor's C-terminal helix and the filamin-2 region that is responsible for binding the receptor.

Mentions: In the absence of experimentally determined structure for functionally characterized human filamin-2, we constructed a comparative model of a human filamin-2 region (amino acids 1979–2206) found to bind α2C-adrenoceptor. First, to perform initial sequence analysis the sequence of FLN2 (amino acids 1979–2206) was submitted to GeneSilico metaserver [23]. This analysis revealed that this region is composed of three domains (roughly residues 1982–2100, 2101–2178 and 2179–2183). Both the N-terminal and C-terminal domains of FLN2 were found to exhibit significant similarity to Filamin/ABP280 repeat family, whose members have been found to interact with such proteins like: β-Integrin, Rho, Rho-associated kinase (ROCK), and many others [43]. In contrast to the N-terminal and C-terminal domains of FLN2, the domain in the middle (2101–2178 residues) exhibited no evident similarity to any known protein family. Nearly all individual fold-recognition methods (e.g., HHSEARCH: score 100, FFAS score -50.1, COMPASS score: 2.72e-59, PHYRE score: 1e-19) reported the structure of the protein with PDB code 2j3s [crystal structure of filamin A Ig domains 19–21] as the potentially best template to model FLN2 region 1982–2183 (i.e. its closest homologs among proteins of known structure); the sequences of 2j3s and the target proteins share 54% sequence identity. In the next step, the sequence of FLN2 (amino acids 1979–2206) was submitted also to Zhang-Server [24], Robetta Server [25], HHpred [22], and Multicom [26] server; these servers have been shown to be the best automatic methods for proteins structure modeling [44]. In total we collected 145 initial models, which were submitted to the QA-RecombineIT [27] server that operates through following two stages. In the first stage (QA-mode), the server predicts both the global quality of input models and provides estimates of local quality as the deviation between C-α atoms in the models and corresponding atoms in the unknown native structure. The input models and the predictions of the models' correctness become the input for the second stage (RecombineIt-mode), in which fragments predicted to be better than others are judiciously combined to generate hybrid (consensus) models. Finally, hybrid models are scored by the MQAPs implemented in the QA-mode and then presented to the user. By using recombination of the initial models, the QA-RecombineIt generated 100 additional consensus models for the filamin-2 region between residue 1982 and 2183. From these models, the final model was selected by using Model Quality Assessment Programs, such as MetaMQAP [28], ProQ2 [29], and MQAPmulti (M Pawlowski, unpubl.). These methods predict GDT_TS score of a protein model without the knowledge about the true native structure of the protein. Global Distance Test (GDT_TS) corresponds to the average value of fractions of C-α atoms in the model that are placed within the distances of 1, 2, 4 or 8 Å from corresponding C-α atoms in the experimentally determined structure. This metric has values in the [0,1] range, where 1 corresponds to the highest quality model. In contrast to RMSD (root-mean-square-deviation) score, GDT_TS-score is insensitive to local structure variation. In general, two structures with GDT_TS-score lower than 0.3 correspond to random similarity and those with GDT_TS-score at least 0.5 indicate high similarity between the predicted model and native structure. Model Quality Assessment Programs, may be divided into two main classes: 1) single-model MQAPs - methods capable of assessing quality for single models, without using any alternative models (decoys) generated for the same protein; 2) clustering MQAPs – methods that perform all against- all structural comparisons to obtain mean similarity scores for ranking models. Moreover, it was shown that a linear combination of scores provided by clustering and single model MQAP perform well for selection of the most accurate model from a set of alternative models for the target protein [29]. Thus, to select the final model of filamin-2 region (amino acids 1979–2206) we applied a linear combination of MQAPmulti (a clustering MQAP, weight: 0.8) and two single model MQAPs MetaMQAP, and ProQ2 (weight: 0.1 each), then the model with the highest score was selected as the final model. The selection procedure was inspired by the findings that a linear combination of scores provided by clustering and single model MQAPs perform well for selection of the most accurate model from a set of alternative models for the target protein [29]. For the best-scoring structure the MetaMQAP, ProQ2 and MQAPmulti GDT_TS scores were as follows, 0.51, 0.43 and 0.78. This model is presented in Fig. 3 panel A.


In silico modeling of human α2C-adrenoreceptor interaction with filamin-2.

Pawlowski M, Saraswathi S, Motawea HK, Chotani MA, Kloczkowski A - PLoS ONE (2014)

Predicted models of filamin-2 (FLN2) and α2C-adrenoceptor (ADRA2C) proteins, and their complex.Panels A and B present cartoon diagram of FLN2 (region between residues 1982 and 2183) and ADRA2C protein models. Positively and negatively charged regions are indicated by blue and red colors, respectively. Panel C presents whole protein-protein complex predicted by HADDOCK program. Panel D shows the interaction between receptor's C-terminal helix and the filamin-2 region that is responsible for binding the receptor.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4128582&req=5

pone-0103099-g003: Predicted models of filamin-2 (FLN2) and α2C-adrenoceptor (ADRA2C) proteins, and their complex.Panels A and B present cartoon diagram of FLN2 (region between residues 1982 and 2183) and ADRA2C protein models. Positively and negatively charged regions are indicated by blue and red colors, respectively. Panel C presents whole protein-protein complex predicted by HADDOCK program. Panel D shows the interaction between receptor's C-terminal helix and the filamin-2 region that is responsible for binding the receptor.
Mentions: In the absence of experimentally determined structure for functionally characterized human filamin-2, we constructed a comparative model of a human filamin-2 region (amino acids 1979–2206) found to bind α2C-adrenoceptor. First, to perform initial sequence analysis the sequence of FLN2 (amino acids 1979–2206) was submitted to GeneSilico metaserver [23]. This analysis revealed that this region is composed of three domains (roughly residues 1982–2100, 2101–2178 and 2179–2183). Both the N-terminal and C-terminal domains of FLN2 were found to exhibit significant similarity to Filamin/ABP280 repeat family, whose members have been found to interact with such proteins like: β-Integrin, Rho, Rho-associated kinase (ROCK), and many others [43]. In contrast to the N-terminal and C-terminal domains of FLN2, the domain in the middle (2101–2178 residues) exhibited no evident similarity to any known protein family. Nearly all individual fold-recognition methods (e.g., HHSEARCH: score 100, FFAS score -50.1, COMPASS score: 2.72e-59, PHYRE score: 1e-19) reported the structure of the protein with PDB code 2j3s [crystal structure of filamin A Ig domains 19–21] as the potentially best template to model FLN2 region 1982–2183 (i.e. its closest homologs among proteins of known structure); the sequences of 2j3s and the target proteins share 54% sequence identity. In the next step, the sequence of FLN2 (amino acids 1979–2206) was submitted also to Zhang-Server [24], Robetta Server [25], HHpred [22], and Multicom [26] server; these servers have been shown to be the best automatic methods for proteins structure modeling [44]. In total we collected 145 initial models, which were submitted to the QA-RecombineIT [27] server that operates through following two stages. In the first stage (QA-mode), the server predicts both the global quality of input models and provides estimates of local quality as the deviation between C-α atoms in the models and corresponding atoms in the unknown native structure. The input models and the predictions of the models' correctness become the input for the second stage (RecombineIt-mode), in which fragments predicted to be better than others are judiciously combined to generate hybrid (consensus) models. Finally, hybrid models are scored by the MQAPs implemented in the QA-mode and then presented to the user. By using recombination of the initial models, the QA-RecombineIt generated 100 additional consensus models for the filamin-2 region between residue 1982 and 2183. From these models, the final model was selected by using Model Quality Assessment Programs, such as MetaMQAP [28], ProQ2 [29], and MQAPmulti (M Pawlowski, unpubl.). These methods predict GDT_TS score of a protein model without the knowledge about the true native structure of the protein. Global Distance Test (GDT_TS) corresponds to the average value of fractions of C-α atoms in the model that are placed within the distances of 1, 2, 4 or 8 Å from corresponding C-α atoms in the experimentally determined structure. This metric has values in the [0,1] range, where 1 corresponds to the highest quality model. In contrast to RMSD (root-mean-square-deviation) score, GDT_TS-score is insensitive to local structure variation. In general, two structures with GDT_TS-score lower than 0.3 correspond to random similarity and those with GDT_TS-score at least 0.5 indicate high similarity between the predicted model and native structure. Model Quality Assessment Programs, may be divided into two main classes: 1) single-model MQAPs - methods capable of assessing quality for single models, without using any alternative models (decoys) generated for the same protein; 2) clustering MQAPs – methods that perform all against- all structural comparisons to obtain mean similarity scores for ranking models. Moreover, it was shown that a linear combination of scores provided by clustering and single model MQAP perform well for selection of the most accurate model from a set of alternative models for the target protein [29]. Thus, to select the final model of filamin-2 region (amino acids 1979–2206) we applied a linear combination of MQAPmulti (a clustering MQAP, weight: 0.8) and two single model MQAPs MetaMQAP, and ProQ2 (weight: 0.1 each), then the model with the highest score was selected as the final model. The selection procedure was inspired by the findings that a linear combination of scores provided by clustering and single model MQAPs perform well for selection of the most accurate model from a set of alternative models for the target protein [29]. For the best-scoring structure the MetaMQAP, ProQ2 and MQAPmulti GDT_TS scores were as follows, 0.51, 0.43 and 0.78. This model is presented in Fig. 3 panel A.

Bottom Line: To better understand the molecular nature and specificity of this interaction, in this study, we constructed comparative models of human α2C-AR and human filamin-2 proteins.We found that electrostatic interactions seem to play a key role in this complex formation which manifests in interactions between the C-terminal arginines of α2C-ARs (particularly R454 and R456) and negatively charged residues from filamin-2 region between residues 1979 and 2206.Phylogenetic and sequence analysis showed that these interactions have evolved in warm-blooded animals.

View Article: PubMed Central - PubMed

Affiliation: Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, Ohio, United States of America.

ABSTRACT
Vascular smooth muscle α2C-adrenoceptors (α2C-ARs) mediate vasoconstriction of small blood vessels, especially arterioles. Studies of endogenous receptors in human arteriolar smooth muscle cells (referred to as microVSM) and transiently transfected receptors in heterologous HEK293 cells show that the α2C-ARs are perinuclear receptors that translocate to the cell surface under cellular stress and elicit a biological response. Recent studies in microVSM unraveled a crucial role of Rap1A-Rho-ROCK-F-actin pathways in receptor translocation, and identified protein-protein interaction of α2C-ARs with the actin binding protein filamin-2 as an essential step in the process. To better understand the molecular nature and specificity of this interaction, in this study, we constructed comparative models of human α2C-AR and human filamin-2 proteins. Finally, we performed in silico protein-protein docking to provide a structural platform for the investigation of human α2C-AR and filamin-2 interactions. We found that electrostatic interactions seem to play a key role in this complex formation which manifests in interactions between the C-terminal arginines of α2C-ARs (particularly R454 and R456) and negatively charged residues from filamin-2 region between residues 1979 and 2206. Phylogenetic and sequence analysis showed that these interactions have evolved in warm-blooded animals.

Show MeSH
Related in: MedlinePlus