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Journal of Cellular Biochemistry 118:2712–2721 (2017)

Molecular Modeling and Dynamic Simulation of Arabidopsis Thaliana Carotenoid Cleavage Dioxygenase Gene: A Comparison with Bixa orellana and Crocus Sativus R. Priya,1 Sneha P.,1 Renata Rivera Madrid,2 George Priya Doss C. and Ramamoorthy Siva

,1* Pooja Singh,3

1

School of Bio Sciences and Technology, VIT University, Vellore 632014 Tamil Nadu, India Cenro de Investigacion Cientifica de Yucatan A.C. Calle 43 No. 130, Col. Chuburnade Hidalgo, Merida 97200 Yucatan, Mexico 3 Centre for Research in Biotechnology for Agriculture, University of Malaya, Kuala Lumpur 50603, Malaysia 2

ABSTRACT Carotenoid cleavage dioxygenase (CCD) gene, ubiquitously found in numerous types of plants, are eminent in synthesizing the various volatile compounds (b-ionone, C13-norisoprenoid, geranylacetone) known as apocarotenoids. These apocarotenoids have various biological functions such as volatile signals, allelopathic interaction and plant defense. In Arabidopsis genome sequence, four potential CCD genes have been identified namely CCD1, CCD4, CCD7, and CCD8. These four genes give rise to diverse biological functions with almost similar sequence identity. In this investigation, an in silico analysis was proposed to study CCD proteins in Arabidopsis thaliana, aiming at constructing threedimensional (3D) structure for CCD1 proteins of Bixa orellana and Crocus sativus to observe the structural difference among AtCCD (A. thaliana CCD) proteins. The quality of modeled structures was evaluated using RAMPAGE, PSVS protein validation server and Q Mean server. Finally, we utilised molecular dynamics simulation to identify the stability of the predicted CCD protein structures. The molecular dynamic simulation also revealed that AtCCD4 protein showed lesser stability when compared to other CCD proteins. Overall results from molecular dynamics analysis predicted that BoCCD1, CsCCD1, and AtCCD1 show similar structural characteristics. J. Cell. Biochem. 118: 2712–2721, 2017. © 2017 Wiley Periodicals, Inc.

KEY WORDS:

C

CAROTENOID; APOCAROTENOID; CAROTENOID CLEAVAGE DIOXYGENASE; MOLECULAR MODELING; MOLECULAR DYNAMIC SIMULATION

arotenoids are fat-soluble terpenoid compounds synthesized by various groups of organisms like archaea, eubacteria to eukaryotes. It has numerous biological functions such as cell signaling molecules, pigmentation, antioxidant properties, as well as photosynthesis and photoprotection [Walter and Strack, 2011; Sui et al., 2013]. The apocarotenoids are generated by cleaving the

double bond of carotenoids through the reaction involving molecular oxygen, resulting in the formation of an aldehyde or ketone group at the point of cleavage site of each product [Olson and Hayaishi, 1965]. This metabolite performs essential biological functions in plants, animals and photosynthetic bacteria [Goodman and Huang, 1965; Krishnamurthy et al., 2002]. The biologically

Abbreviations: CCD, Carotenoid cleavage dioxygenase; ABA, abscisic acid; CCO, Carotenoid cleavage oxygenases; NCED, 9 cis epoxycarotenoid dioxygenase; MSA, Multiple Sequence Alignment; APO, apocarotenoid oxygenase; VP14, viviparous14; PDB, Protein Data Bank; MD, Molecular Dynamics; SPC, simple-point-charge; PME, Particle Mesh Ewald; LINCS, Linear Constraint Solver; RMSD, Root Mean Square Deviation; H-bond, Hydrogen bonds; ED, Essential Dynamics; PC, Principal Component; ns, nanoseconds; ps, picoseconds; 3D, three dimensional; OPLSAA, Optimized Potentials for Liquid Simulations (All Atoms). Conflict of interest: The authors declares no conflicts of interest. R. Priya and P. Sneha contributed equally. Grant sponsor: Science and Engineering Research Board—Department Science and Technology, New Delhi, India; Grant number: SR/FT/LS-75/2011. *Correspondence to: Ramamoorthy Siva and C. George Priya Doss, School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India. E-mail: [email protected]; [email protected] Manuscript Received: 3 January 2017; Manuscript Accepted: 30 January 2017 Accepted manuscript online in Wiley Online Library (wileyonlinelibrary.com): 1 February 2017 DOI 10.1002/jcb.25919
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active apocarotenoid phytohormone abscisic acids (ABA) are involved in plant defense [Auldridge et al., 2006]. Economically, apocarotenoids are valued as colorants in food and used in cosmetics, for example, bixin from Bixa orellana [Siva, 2003; Siva et al., 2010] and crocin from Crocus sativus, a spice extracted from the styles of Crocus flowers [Frusciante et al., 2014]. In Arabidopsis, the CCD gene family consists of nine members; of which five are 9-cis epoxycarotenoid dioxygenase (NCEDs) and four are CCDs. The NCEDs are most often found to be involved in the abscisic acid (ABA) biosythietic pathway, whereas CCDs are involved in the cleaving of carotenoids to generate multiple apocarotenoid products [Rubio et al., 2008]. The four members in Arabidopsis, two CCD genes, AtCCD1 [Schwartz et al., 2001; Chang et al., 2012], and AtCCD7 [Booker et al., 2004; Schwartz et al., 2001] are specific to their site of cleavage. AtCCD1 cleaves symmetrically at the 9, 10, and 9’, 10’ bonds, whereas AtCCD7 cleave asymmetrically. Thus, AtCCD1 cleaves b-carotene to produce two C13 products (both b-ionone) and one central C14 dialdehyde [Schwartz et al., 2001] whereas AtCCD7 produces one b-ionone and a C27 10’-apo-b-carotenal. AtCCD8 has been shown to catabolize further the C27 apocarotenoid derived from AtCCD7 cleavage of b-carotene [Schwartz et al., 2004]. Carotenoid cleavage oxygenases (CCOs), also sometimes referred to as CCDs are a new class of nonheme iron-type enzymes that can oxidatively cleave double bonds present in the conjugated carbon chain of carotenoidsGiuliano et al., 2004 has been changed to Giuliano, 2014 to match with the reference list. Please check for correctness. [Auldridge et al., 2006; Marasco and Schmidt-Dannert, 2008; Giuliano, 2014]. Till date, three diverse CCOs crystal structures have been solved such as apocarotenoid oxygenase (ACO) [Kloer et al., 2013], Retinal Pigment Epithelium-Specific Protein 65 kDa (RPE65) [Kiser et al., 2009], and maize viviparous14 (VP14) [Messing et al., 2010]. ACO reveals a complicated protein structure with

Fig. 1. Three-Dimensional structure of ACO elucidating different regions present.

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seven-blade b-propeller and three a-helices (Fig. 1). The b-propeller was found to be conserved evolutionarily between the bacteria and eukaryotes. The protein structure contains Fe2þ that coordinates with four Histidine residues at the active site [Marasco and SchmidtDannert, 2008; Bertoni, 2010; Frusciante et al., 2014]. This structural information offers unprecedented insights into the structural basis for the functional diversity of this protein family. Hence, it is important to elucidate the three-dimensional (3D) structures of CCD genes to identify their functional diversity. Earlier, we have reported the occurrence of duplication in CCD4 genes that evolved into two new genes CCD7/8. The site-specific profile and coefficient of type-I functional divergences revealed critical amino acid residues, leading to sub group-specific functional evolution after their phylogenetic diversification [Priya and Siva, 2014]. A hierarchical clustering of the CCD genes revealed better relationships with closeness in function among the various CCD genes of different plants except for BoCCD1 gene [Priya et al., 2016]. In the present in silico study, we designed and constructed four CCD sub-class genes of A. onal variants of the CCD protein through structural conformation changes and stability of each prthaliana to evaluate the functiotein through dynamic simulation. Also, a comparative analysis between the CCD proteins from B. orellana and C. sativus was attempted to predict a probable function of the CCD proteins of the A. Thaliana. Consequently, 3D structure of CCD1, CCD4, CCD7, and CCD8 proteins of A. thaliana along with the CCD proteins from B. orellana and C. sativus were constructed and compared to reveal functional diversity which might render insights and aid in near future applications.

MATERIALS AND METHODS SEQUENCE RETRIEVAL OF CCD PROTEIN The protein sequence of CCD genes (AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1) was obtained from the UniProt database (Acession no: AtCCD1: CAA06712.1, AtCCD4: O49675.1, AtCCD7: NP_182026.4, AtCCD8: NP_195007.2, BoCCD1: CAD71148.1, and CsCCD1: Q84KG5.1) [Apweiler et al., 2004]. Using the protein BLAST [Altschul et al., 1997] through NCBI, the homologous template structures were identified and obtained from protein databank (PDB). HOMOLOGY MODELING OF CCD PROTEINS The homologous structure (PDB ID: 2BIW) was used as a template for building a 3D model of the target CCD proteins. The alignment of CCD sequences with the templates was done using the online version of Clustal Omega [Sievers et al., 2011]. The academic version of
and Blundell, 1993] was employed to construct MODELER 9v7 [Sali the initial 3D model of CCD proteins. This tool specifically employs the molecular probability density function (PDF) [Sali and Blundell, 1993]. The results were analyzed and ranked based on the Modeler scoring function. The best models for each CCD protein structure with a high score were used for the refinement and quality validation. Optimization and refinement of model structures of CCD protein were performed using Swiss-PDB Viewer [Guex and Peitsch, 1997]. Further validation of the modeled structures was made using

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various computational tools to obtain a superior structure that may not hinder with results. RAMPAGE, PSPV, and Q MEAN server were used to validate the modeled protein structure. The results from the structure validation were compared to measure the level of accuracy of each predicted model. The obtained 3D model structures were analyzed, visualized and superimposed using PyMOL [DeLano, 2002] . MOLECULAR DYNAMICS SIMULATION The MD (Molecular Dynamics) simulation of model structures of AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 was performed using Gromacs 4.6. 3 [Hess et al., 2008]. The OPLSAA force field was utilized for protein simulation analysis [Robertson et al., 2015]. All the model structures were solvated in a simplepoint-charge (SPC) [Pullman, 1981] under periodic boundary conditions using 1.0 nm distance from the protein to the box faces. To ensure that the simulation system was electrically neutral, each system was neutralized with three Naþ ions. Since SPC water was used, the added hydrogens and broken hydrogen bond network in water would lead to quite large forces and structure distortions. To remove these forces, energy minimization using steepest descent was performed until a minimum energy of 1,000 kJ/mol/nm achieved by the systems. Following to energy minimization, equilibration of the system was carried out. Canonical Ensemble (NVT) and IsobaricIsothermal Ensemble (NPT) were also carried out for 50,000 steps each to obtain a well-equilibrated system. Since a well-equilibrated system produce reliable predictions [Walton and Vanvliet, 2006]. All the covalent bonds were constrained using the LINCS (Linear Constraint Solver) algorithm [Hess et al., 1997]. Particle Mesh Ewald (PME) method [Essmann et al., 1995] was used to treat the electrostatic interactions. The cut-off radii for van der Waals interactions and Coulomb within the system were set to 14.0 Å and 10.0 Å , respectively. Finally, 50 ns MD simulation was performed for AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 in order to analyze the stability of each system. The trajectories of the simulation, which were saved every 2.0 ps, were analyzed with the utilities available in the GROMACS package. g_rms, g_gyrate, and g_hbond of GROMACS utilities were used to obtain the Root Mean Square Deviation (RMSD), Radius of Gyration (Rg), and the number of Hydrogen bonds (H-bond). The differences in the kinetic, potential and total energies, pressure and temperature were computed as a function of simulation time to check whether the systems obey NVT or NPT ensemble throughout the simulation period. Finally, to understand the motional changes that could have taken place throughout the simulation was calculated using Essential Dynamics (ED) [Amadei et al., 1993]. The first two eigenvectors (principal components PC1 and PC2) with largest eigenvectors were used to make a 2D projection for each of independent trajectories. All the Graphs were generated using GRACE software.

RESULTS AND DISCUSSION HOMOLOGY MODELING Crystallized 3D structures were not available in in public protein structure databases for Arabidopsis, Bixa, and Crocus CCD

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proteins. Consequently, modeling techniques was used to model the 3D structure of the CCD proteins. A multiple sequence alignment (MSA) using Clustal Omega predicted the probable template for modeling the different CCD proteins. Consequently, we constructed a model for the AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 proteins using ACO as the template. A previous study by Dominik and Ulrich [2011] explained the use of apocarotenoid cleavage oxygenase from Synechocystis as a probable template for modeling the CCD proteins [Dominik and Ulrich, 2011]. Based on the obtained results and literature survey, we considered 2BIW as a template to model the CCD proteins. The protein modeling was followed by structure validation. A fully validated structure would provide more reliable results, and hence modeled proteins were further subjected to three different in silico prediction tools that validates based on the Ramachandran plot analysis, Q-mean score, and a Z-score (calculated by comparing with the other models in the database). Individual tools have been evolved over the decades based on different algorithms and are believed that some of them when combined provides a single consensus prediction with reliable results of better accuracy. Using combinations of geometric validation criteria to restraints or constraints by the refinement programs, errors are known to be masked to some extent [Read et al., 2011]. Vuister et al., [2014], reports that the assessment of the structural quality of a combined set of different parameters were found to be a viable approach for the identification of incorrect structures. Consequently, in the current study, we used a series of validation tools to refine the level of prediction. The stereochemical qualities of the protein models assessed by RAMPAGE were found to be good. The Ramachandran plot of the CCD models obtained from the RAMPAGE revealed 90% residues are present in most favored regions. The higher percentage of CCD protein residues were in the most allowed regions indicated that the AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 models obey the basic protein conformation (Fig. 2A–F). The Q-mean score is a useful measure to estimate the absolute quality of the predicted 3D models. All of our models showed better scores and believed to be constructed with accuracy. Further PSVS server was used to predict the precision of the constructed models by comparing the models present in the database. A positive mean predicts that the model is more accurate and all of our models presented a positive mean score (Table I). In general, all the constructed six models exhibited seven-blade b-propeller (Fig. 2A–F). The b-propeller region of the structure may be conserved throughout the proteins in CCD family. This region is present in the prokaryotic apocarotenoid 15, 150 -oxygenase (template for modeling), as well as observed in our CCD proteins (Fig. 3). The secondary structure prediction shows all the modeled proteins to have three a-helix, b-sheets, and turns that were similar to the template protein. The four His residues located on the inner strands are shown in the structural alignment (His-95, His-222, His270, and His-336). The structural variation was estimated using a carbons and backbone atoms of the modeled structures through PyMOL program. The superimposed AtCCD1, AtCCD4, AtCCD7, and AtCCCD8 structures showed RMSD value of 0.2Å , 0.8Å , 0.1Å , and 0.1Å (Fig. 4A). The RMSD value for supposed BoCCD1/CsCCD1 structure is 0.1Å (Fig. 4B). Hence, we can argue that structural

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Fig. 2. Modeled structure of (A) AtCCD1, (B) AtCCD4, (C) AtCCD7, (D) AtCCD8, (E) BoCCD1, (F) CsCCD1.

variation occurs only in AtCCD4 protein rather than other CCD protein structure. MD SIMULATIONS MD simulations were conducted to understand the conformational stability within nanosecond timescale for AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 proteins. Every protein reacts

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differently at different environmental conditions. Bertosa et al. [2008], in their work have explicitly discussed the activity of proteins (Auxin) from A. thalaina at different temperature using MD simulation analysis. Results obtained from their comparative analysis predicted that the auxin protein present in A. thaliana showed better results at 300 K. Accordingly, in the current study, the temperature was set to 300 K to assess the structural similarity and

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TABLE I. Validation Scores for the Proteins Using Different in Silico Tools

Protein

RAMPAGE (favored region) (%)

Q-mean server

PSPV server (Z-scores)

AtCCD1 AtCCD4 AtCCD7 AtCCD8 BoCCD1 CsCCD1

89.3 90.3 88.9 89.5 91.9 89.0

1.55 2.32 1.87 1.68 -1.20 -1.81

2.47 0.87 1.89 1.64 2.11 1.99

differences that could have occurred in the protein molecules. Analysis such as RMSD and Radius of gyration can help us understand the structural stability throughout the simulation [Carugo and Pongor, 2001]. The tertiary structure of a protein is managed by various bonds and forces that includes; hydrogen bonds, ionic bonds interactions, hydrophobic interactions, and

covalent bonds. Of all these forces, hydrogen bond interactions are the significant interaction patterns that aid in maintaining the stability of the tertiary structure of the protein [Leucke and Quiocho, 1990]. To understand this phenomenon, we further utilized hydrogen bond analysis to compare the number of hydrogen bonds present in the protein.

Fig. 3. Structure-based sequence alignment of selected carotenoid cleavage oxygenases, the red background indicates sequence identity, and red letters denote sequence similarity. a-helices and b-strands are displayed as blue squiggles and black arrows, respectively. The strictly conserved iron-coordinating his residues are marked in blue asterisk. The sequences were aligned with Clustal Omega and the figure was generated with ESPript.

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Fig. 5. Backbone RMSDs are shown for AtCCD1 (Green), AtCCD4 (Blue), AtCCD7(Violet), AtCCD8 (Yellow), BoCCD1(Black), and CsCCD1(Red).

Fig. 4. Modeled 3D structure superimposed through PyMol. (A) AtCCD1 (pink), AtCCD4 (Blue), AtCCD7 (Red), and AtCCCD8 (green) (B) BoCCD1(Blue) and CsCCD1(Red).

ROOT MEAN SQUARE DEVIATION The RMSD is a crucial parameter to check the equilibration of MD trajectories. The RMSD backbone values for CCD proteins are calculated against the time simulation between 0 and 50 ns. At the beginning of the simulation, AtCCD1 and CsCCD1 showed similar deviation patterns, but after 7 ns, AtCCD1 deviated from the CsCCD1 protein. Parallel deviation patterns were observed between BoCCD1 and CsCCD1 and converged at 0.4 and 0.5 nm, respectively. This parallel deviation illustrates that both the proteins may have resembling structures although originating from different plant classes [Reva et al., 1998]. Figure 5 portrays that, AtCCD4 showed highest RMSD value (0.85 nm) and BoCCD1 having the least value (0.4 nm) elucidating that, both the proteins may not share much of structural similarities. The AtCCD4 protein deviation pattern suggests that the protein has a comparatively less rigid structure in comparison with other CCD protein (Fig. 4A), this further correlates from the RMSD values obtained from PyMOL. Converging patterns of AtCCD7, AtCCD8 were observed to be similar between 20 and 40 ns. Maiorov and Crippen, (1994) have extensively described the use of RMSD to describe and compare the structural similarities of the protein. A similar analysis in the current study provides an overall insight that, BoCCD1 and CsCCD1 should have a similar structural

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characteristic as of AtCCD1 since their converging patterns were observed to be similar. Interestingly, we have also found that AtCCD7 and AtCCD8 showed similar converging patterns which might be associated with the similar structural activity. Overall, the results of the RMSD propose an evolutionary based prediction with AtCCD7 and AtCCD8 being closely associated and the CCD1 proteins from A. thaliana (AtCCD1), B. orellana (BoCCD1), and C. sativus (CsCCD1) showing similar structural conformations (Fig. 3). RADIUS OF GYRATION The radius of Gyration (Rg or gyradius) depicts the compactness of the protein. Comparative analyses between the proteins were made based on their compacting natures for a simulation period of 50 ns. Figure 6, depicts that, BoCCD1 showed least Rg value (1.65 nm) followed by CsCCD1 (1.75 nm) elucidating that, these two proteins are the most tightly packed than other proteins [Lobanov et al., 2008]. AtCCD1 and CsCCD1 were found to be having similar deviating patterns in Rg plot and therefore showing similar compactness. From the Rg plot, we observed that AtCCD1, AtCCD7, and AtCCD8 were converging approximately at a similar point (nearing to 1.7 nm) at the end of 50 ns simulation. Similar to RMSD results, AtCCD4 showed highest Rg value depicting poor compactness. The results from RMSD and Rg clearly interpret that, AtCCD1, BoCCD1, and CsCCD1 have a similar structure, whereas AtCCD7 shows similarity with AtCCD8. As a further step to confirm this stability and compactness, H-bond analysis for all the trajectories was carried out. HYDROGEN BONDS Hydrogen bonds (H-bond) play a crucial role in molecular recognition and the overall stability of the protein structure. Intramolecular H-bonds were analyzed for the modeled structures of the AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 protein during the simulation period. The number of H-bonds formed

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had a similar number of average H-bonds with 43.42246 and 42.61702, respectively (Table II). The results of H-bond analysis predict that BoCCD1 and CsCCD1 show similar hydrogen bond formation as of AtCCD1. Observed results from intramolecular Hbonds analysis suggest a structural similarity between the three proteins. AtCCD7 and AtCCD8 showed similar number of hydrogen bonds formation throughout the entire simulation period of time (Fig 3), and also lesser in comparison to the number of hydrogen bonds formed by CCD1 proteins of Bixa (BoCCD1) and Crocus (CsCCD1). This analysis renders valuable information that these two proteins must have a similar evolutionary pattern which is in accordance an already reported by Priya and Siva [2014].

Fig. 6. Radius of Gyration AtCCD1 (Green), AtCCD4 (Blue), AtCCD7(Violet), AtCCD8 (Yellow), BoCCD1(Black), and CsCCD1(Red).

within the BoCCD1, CsCCD1, and AtCCD1 were comparatively similar (Fig. 7), whereas AtCCD7, AtCCD8 showed the lesser number formation of H-bonds. An average number of hydrogen bonds calculated at the end of 50 ns simulation predicted AtCCD4 with least number of hydrogen bonds formed (36.15352). BoCCD1 and CsCCD1

ESSENTIAL DYNAMICS The confined fluctuation and structural motion of AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 were investigated by using Essential dynamics (ED) analysis. The molecular dynamics simulation snapshots at every two ps were projected onto the first two eigenvectors. The projections of trajectories obtained at 300 K onto the first two principal components (PC1 and PC2) showed the protein structural motion of the AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 model protein structures in phase space. The 2D plots of two principal components (PC1 and PC2 with largest eigenvectors) for CCD model structures were depicted in Figure 8. More distribution of atoms indicates the more conformational changes in protein structure. Highest scattering of the atoms was observed with AtCCD4 illustrates the occurrence of larger conformational changes with this protein. It is observed that the internal motions of AtCCD4 are represented by a subspace whose dimension is much higher than other model structures. It is also revealed that the concerted motions increased in the AtCCD4 model protein structure in agreement with MD analysis. As already described from the results of RMSD (Fig. 5) and Rg (Fig. 6), we found correlating results from PCA analysis depicts the similarity between BoCCD1 and AtCCD1. Both the protein showed similar patterns of conformational changes throughout the simulation. This conformational analysis study supports the results of structural similarity stated previously. The stability and function of proteins are the two important properties that are interdependent and also a crucial phenomenon to be considered while studying a protein [Shoichet et al., 1995]. Stability plays a significant role in conserving the function of a protein [Hamill et al., 2000]. With this knowledge, in the current study, the similarity and differences in the stability of the proteins were investigated. Computational tools, of late, have proven its efficiency widely in recognizing these phenomena [Gupta et al., 2010; Yin et al., 2010; Sneha and George Priya Doss, 2016; Sujitha

TABLE II. Average Hydrogen Bonds Formed After 50 ns Simulation

Fig. 7. Average number of intramolecular hydrogen bonds in AtCCD1 (Green), AtCCD4 (Blue), AtCCD7(Violet), AtCCD8 (Yellow), BoCCD1(Black), and CsCCD1 (Red).

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Protein

Average hydrogen bonds

AtCCD1 AtCCD4 AtCCD7 AtCCD8 B0CCD1 CsCCD1

43.69588 36.15352 39.8347 38.94898 43.42246 42.61702

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Fig. 8. Projection of most significant principal components of motion of the Ca-atoms of CCD model proteins, the trajectory projected to the two-dimensional space. (A) AtCCD1, (B) AtCCD4, (C) AtCCD7, (D) AtCCD8, (E) BoCCD1, (F) CsCCD1.

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et al., 2016]. Consequently, molecular dynamics studies were used to highlight the phenomena between the CDD proteins. RMSD, Radius of gyration explained the level of stability achieved by the protein, in our study, the same phenomenon was used as a comparative analysis to explain the functional similarities. The results from RMSD and Rg suggest that there is a structural similarity observed between BoCCD1, CsCCD1, and AtCCD1. The H-bond analysis also confers results that correlate with RMSD and Rg results. Another interesting pattern observed in our study is, AtCCD7, AtCCD8 showed very similar patterns of deviations (RMSD) and H-bond formation. This fascinating fact correlates with our evolutionary analysis and suggests that AtCCD7 and AtCCD8 proteins should have a similar evolutionary pattern. Also, a recent study by Sankari et al. [2016] reports the evolutionary closeness between the CCD4 genes of B. orellana (BoCCD4a) and C. sativus (CsCCD4a). Similarly, through our molecular dynamics simulation analysis, structural similarity was observed between the CCD1 proteins of B. orellana (BoCCD1) and C. sativus (CsCCD1). It is very essential to understand the structural behavior of biologically and industrially important CCD genes owing to their significant role in plants. Current investigation is such of a kind in exploring the probable structure-function relationship of AtCCD1, AtCCD4, AtCCD7, AtCCD8, BoCCD1, and CsCCD1 proteins using high-end computational methods. Result from these studies confirmed that duplication in CCD4 affects structural conformation which might lead to functional variation. Nevertheless, the dynamic behavior and structural information provides an interesting point that AtCCD7, AtCCD8 must have similar evolutionary patterns. We believe that the structural similarities of Arabidopsis CCD genes with Bixa and Crocus species may be useful in improved production of industrially important bixin and crocin using metabolic engineering and synthetic biology approach.

ACKNOWLEDGMENTS

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We express our sincere gratitude to Science and Engineering Research Board—Department of Science and Technology, New Delhi, India for the support extended through the project [SR/FT/ LS-75/2011]. The authors are grateful to VIT University management for their constant support. The authors thank the anonymous reviewer for their insightful review of an earlier version of this manuscript.

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