Next Article in Journal
MicroRNA Expression Profiles and Breast Cancer Chemotherapy
Next Article in Special Issue
Light Tailoring: Impact of UV-C Irradiation on Biosynthesis, Physiognomies, and Clinical Activities of Morus macroura-Mediated Monometallic (Ag and ZnO) and Bimetallic (Ag–ZnO) Nanoparticles
Previous Article in Journal
Therapeutic Potential and Main Methods of Obtaining Selenium Nanoparticles
Previous Article in Special Issue
Silver Nanoparticles as a Tool for the Study of Spontaneous Aggregation of Immunoglobulin Monoclonal Free Light Chains
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJMS
Volume 22
Issue 19
10.3390/ijms221910813
ijms-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Possible Mechanism of Graphene Oxide to Enhance Thermostability of D-Psicose 3-Epimerase Revealed by Molecular Dynamics Simulations

by
Congcong Li
,
Zhongkui Lu
,
Min Wang
,
Siao Chen
,
Lu Han
* and
Weiwei Han
*
Key Laboratory for Molecular Enzymology and Engineering of Ministry of Education, School of Life Science, Jilin University, 2699 Qianjin Street, Changchun 130012, China
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2021, 22(19), 10813; https://doi.org/10.3390/ijms221910813
Submission received: 6 September 2021 / Revised: 2 October 2021 / Accepted: 4 October 2021 / Published: 6 October 2021
(This article belongs to the Special Issue Advances in Nanoparticles-Biomolecules Interactions)
Browse Figures
Versions Notes

Abstract

:
Thermal stability is a limiting factor for effective application of D-psicose 3-epimerase (DPEase) enzyme. Recently, it was reported that the thermal stability of DPEase was improved by immobilizing enzymes on graphene oxide (GO) nanoparticles. However, the detailed mechanism is not known. In this study, we investigated interaction details between GO and DPEase by performing molecular dynamics (MD) simulations. The results indicated that the domain (K248 to D268) of DPEase was an important anchor for immobilizing DPEase on GO surface. Moreover, the strong interactions between DPEase and GO can prevent loop α1′-α1 and β4-α4 of DPEase from the drastic fluctuation. Since these two loops contained active site residues, the geometry of the active pocket of the enzyme remained stable at high temperature after the DPEase was immobilized by GO, which facilitated efficient catalytic activity of the enzyme. Our research provided a detailed mechanism for the interaction between GO and DPEase at the nano–biology interface.

1. Introduction

In the past several decades, enzymes have been widely applied in industrial production, biopharmaceutical research, and medical diagnosis, due to its reproducibility and ability for increasing the reaction rate without changing the equilibrium [1,2]. However, the use of enzymes in industrial production has still had some limitations, such as operation stability, temperature, susceptibility to reaction condition, etc. [3]. In particular, high temperatures may result in structural denaturation of enzymes, leading to loss of catalytic performance of biological catalysts [4]. To address these difficulties, enzyme immobilization technology has been developed to improve thermal stability of enzymes in the wake of the development of nanoparticles [3,5]. At present, it has been reported that a variety of nanoparticles can successfully improve the thermal stability of enzymes [6,7,8,9].
Traditional surface analysis tools are commonly used to analyze the surface characteristics of nanomaterials, but it is generally difficult to capture the dynamic changes between GO and DPEase. However, MD simulation method could overcome this bottleneck. The combination of the two methods can more overall understand the characteristics of the nano−bio interface, providing favorable help for the study of the functions of enzymes [10,11,12,13].
Graphene oxide (GO) is a kind of nanomaterial that has attracted extensive attention in recent years [14,15,16,17]. It is a unique two-dimensional carbon network structure, which has some special properties, such as large surface area [18], extraordinary mechanic stability [19], and good biologic compatibility [20]. It can be used as an immobilization material without coupling reagents or surface modification due to the existence of oxidative functional groups [16,21]. Several enzymes have been successfully immobilized on GO with robust immobilization support by GO nanosheet [22,23,24,25,26].
Recently, interactions between GO and enzymes have attracted researchers’ attention [27,28]. The recent study revealed that the thermal stability of Agrobacterium tumefaciens (agtu) DPEase was enhanced by immobilizing enzymes on GO nanoparticles. The agtu-DPEase is most active at 50 °C, while the agtu-DPEase immobilized by GO shows maximum activity at 60 °C [29]. However, the mechanism of the DPEase adsorption on GO at atomic level is still unclear. In this study, four 300ns time-span MD simulations (Figure 1a) were carried out to explore the interaction between GO and DPEase. D-Psicose is a kind of sugar beneficial for good health but was rare found in nature [30,31]. It is difficult to synthesize by chemical methods [32,33]. DPEase can be used to produce D-Psicose from D-Fructose (FUD) [34]. The secondary structure of the enzyme was shown in Figure 1b. Figure 1c displayed the detailed structure features and active site residues of the enzyme, the important regions have been highlighted. Our research not only revealed the interaction mechanism of GO with DPEase but also provided clues for the design of mutant DPEase to improve the thermal stability of enzyme.

2. Results

2.1. Structural Stability and Flexibility Analysis

To explore the effect of GO on the thermal stability of DPEase, four models were constructed in the study. Model 1: DPEase complexed with the FUD was simulated at 50 °C. Model 2: the initial structure was the same as Model 1, but was performed at 60 °C. Model 3: the DPEase-FUD with randomly selected direction was placed above GO and was simulated at 50 °C. Model 4: The initial structure was the same as Model 3, but was carried out at 60 °C.
For the qualitative study on the stability and convergence of the simulated systems, the root mean square deviations (RMSD) of the protein backbone atoms in regard to the original structure were calculated and the results were shown in Figure 2a. Model 1 maintained a relatively stable RMSD fluctuation during the entire 300 ns of simulations, especially after 75 ns. However, when DPEase-FUD was simulated at higher temperature (Model 2) or were adsorbed on GO (Models 3 and 4), relatively large structural changes were observed. These three systems all reached equilibrium state in around 150 ns. Models 3 and 4 took longer to reach equilibrium, because they underwent the adsorption process on GO. As shown in Figure 2b, RMSD values were mainly distributed in 3.25–3.50 Å, except for Model 1, in which values were mostly concentrated at 2.26 Å.
Radius of gyration (Rg) can provide an insight into the overall size and dimensions of the proteins. Rg was calculated to examine structural drifts in protein complexes for four systems. Figure 2c,d illustrated the calculated results of Rg and corresponding probability for four systems. The Rg values of four systems were stable at 18.10, 18.55, 18.40, and 18.40 Å, respectively. The higher Rg was observed for Model 2, which meant that the protein in Model 2 took place greater conformational change. By contrast, the value of Model 1 was the smallest, indicating that the protein in Model 1 was the most compact during MD simulation. The protein adsorbed on GO (Model 4) presented more coherent than that of the enzyme without the presence of GO (Model 2) under high temperature simulation conditions.
Additionally, to explore the structural divergence of various important parts, the RMSD from different areas, including the loop α1′-α1 (E11-F18), loop β4-α4 (A107-D121), loop β5-α5 (V151-T162), α8′ (G251-D256), and α5 (A163-V173), which have great significance for the catalytic activity of enzyme, were also calculated (Table 1). In the case of the loop β5-α5 and α5, Model 1 exhibited similar trend in deviations with the other three systems. For other regions, Models 2, 3, and 4 presented significantly higher deviations than Model 1. Compared to Models 3 and 4, the average RMSD of the loop α1′-α1 showed larger deviations in Model 2. The average RMSD values for loop β4-α4 and α8′ had similar deviations in Model 2, 3, and 4.

2.2. Adsorption of DPEase onto GO

To compare the structural differences between Model 1 and Model 2 during MD simulations under different temperature conditions, the representative structures of the two complexes were plotted in Figure 3.
As a result, the two residues (W14 and W112) in active pocket produced distinct alterations in the conformation for two complexes. In contrast, the other residues of active pocket showed minor difference. W14 and W112 were away from the ligand upon high temperature (60 °C) simulation, while at 50 °C, two residues were still tightly bound to the ligand. Since W14 and W112 belong to loop α1′-α1 and loop β4-α4, respectively, the dissimilar displacement of these two residues may be caused by the different dynamic characteristics of loop α1′-α1 and loop β4-α4. According to the above results, it can be speculated that GO could help enhances the thermal stability of enzyme probably by enhancing the stability of these two loops. In order to confirm this hypothesis, we next analyzed the protein adsorption process on GO. Figure 4 showed representative snapshots of the adsorption process for DPEase onto the GO surface under the condition of 60 °C.
As shown in Figure 4a, the initial distance between GO surface and the center of mass (COM) of enzyme that randomly selected orientations was 4.0 nm (Figure 4a). Along the MD simulations, DPEase was gradually adsorbed onto the surface of GO. Firstly, the enzyme moved close to the surface of GO (∼30 ns) through long-range Coulomb interactions between cationic residue of DPEase (R261) and GO (Figure 4b). Secondly, the α8′ and its near loop (from to K248 to D268) served as an anchor (Figure 4c) and was adsorbed onto the surface of GO (70 ns). Finally, the loop β4-α4 was also adsorbed onto the GO surface at 100 ns (Figure 4d), so the protein had larger contact area with GO. The distance between them decreased to ∼0.6 Å. As shown in Figure 4, when the protein was immobilized by GO, although the MD simulation was performed at high temperature, the W14 and W112 were finally tightly bound to the ligand, whereas, in the absence of GO, the two residues were far away from the ligand. Representative conformation for the adsorption process of DPEase on GO surface at 50 °C were displayed in Figure S1, the results were similar to those at 60 °C.
In summary, DPEase can be successfully adsorbed on the surface of GO, resulting in enhancing the stability of the enzyme’s active sites. Table 2 listed the occupancies of formed hydrogen bonds between proteins and ligands in the MD simulations for different systems. Table 2 showed that the hydrogen bond interactions between proteins and ligands significantly were weakened when the temperature increased. While the protein was adsorbed by GO, the hydrogen bond interactions between them were significantly enhanced. The stronger the hydrogen bond interactions, the more stable the interaction between DPEase and FUD. Stable systems will help maintain the catalytic activities of enzymes. So, GO could help improve the stability of DPEase-FUD at high temperature.
With the purpose of investigating the interaction mechanism of GO with DPEase, the critical residues at the nano–bio interface were discussed in detail. At first, almost all residues (residues K248 to D268) directly interacting with GO were hydrophilic residues, especially lysine and aspartic acid. These residues consisted of K248, S255, D256, K258, W260, R261, D262, S264, D268, D115, and Q118. These key residues had been annotated in Figure 5c. These hydrophilic residues could form strong hydrogen bonds with epoxide and/or carbonyl groups on the surface of GO. Among these residues, K248, K258, and R261 were cationic residues, which can form favorable interactions with anionic GO. Moreover, W260 was aromatic residues, which contributed the π–π stacking and CH–π interactions. Therefore, DPEase was susceptible to be effectively adsorbed onto the GO surface with the assistance of these residues during MD simulations. W252 in α8′ had strong interaction with W13 (Figure 5a), while W112 in loop β4–α4 can form a strong hydrogen bond with E156 (Figure 5b). From Figure 5a,b, we found that W14 and W112 were closed to the adsorbed regions (loop β4–α4, K248 to D268) of the enzyme.

2.3. Dynamics of Regions Adsorbed by GO

2.3.1. Dynamic Analysis of Interaction between Loop α1′-α1 and α8′

Previous analysis had identified that K248 to D268 were key residues for protein anchoring on GO. Therefore, we analyzed the dynamic change characteristics of this region. This region includes two parts, α8′and its near loops. The regional fluctuation characteristics can be estimated by computing the root-mean-square fluctuation (RMSF) of Cα atoms for each residue based on MD trajectories. We adopted the equilibrium part of each trajectory to calculate RMSF to ensure the reliability of the results. The calculated RMSF results for this region (K248 to D268) were shown in Figure 6a. It can be seen in Figure 6a that this region in Model 2 was more flexible compared to the other three Models. The reason may be that a high simulated temperature will destroy the stability of the region, resulting in increased atomic fluctuation. Figure 6b–e visually exhibit large fluctuations in Model 2 compared to the other systems. For the complex with GO, whether at 50 °C or 60 °C, atomic volatility was significantly lower than that of Model 2. Based on the above analysis, it can be concluded that this region was successfully adsorbed on GO, resulting in reduced atomic volatility. It can be seen in Figure 6a that R261 and D262 in Model 2 had the largest fluctuation, while the RMSF values decreased significantly in Models 3 and 4, becoming even lower than that in Model 1. This was because R261 and D262 were important residues for protein anchoring on GO, and they can interact strongly with GO. Furthermore, this region contains an α8′. For the RMSF values of this α-helix, those of Model 2 were the largest (2.10–4.00 Å), those of Models 1 and 3 were similar and minimal (0.90–1.50 Å), and those of Model 4 fluctuated between 1.00–2.10 Å.
In order to explain the reasons for this change, we analyzed the time-dependent secondary structure of α8′. Figure 7a presented the detailed changes of secondary structures for the four systems during the 300 ns MD simulations. It was obvious that the helical structure remained stable throughout the simulation process when the complex without GO was simulated at 50 °C, whereas the helix structure developed turns and bends when the simulation was performed at 60 °C. Thus, temperature had an important effect on the stability of helix for DPEase. At 50 °C, the helical structure was essentially maintained. However, after 20 ns, some of the helix became loose, which may be related to the strong adsorption of GO. In contrast, the helix completely coiled and bent after 20 ns at 60 °C.
Figure 7b–e showed overlapping graph of structural changes over time in each system. Model 1 always maintained stable spiral structure with slightly structural change displacement (Figure 7b). Compared with the structures in Figure 7c,d, it can be concluded that, although the simulation at high temperature made the helix uncoiled, the stability of the uncoiled structure was significantly enhanced and the volatility of the structure reduced after the complex was immobilized by GO. For Model 3, even though the helix should remain stable at 50 °C, the helix structure may change slightly due to the strong adsorption of GO (Figure 7d).
Subsequently, we further analyzed why this region was immobilized on GO to improve the thermal stability and catalytic activity of the enzyme. E13 in loop α1′-α1 can form a strong hydrogen bond interaction with T252, which belongs to α8′. The W14 next to E13 is an active residue that contributed to enzymatic reaction [35]. Therefore, the movement of E13 will affect the dynamic change of E14. We analyzed the variation of hydrogen bond distance between E13 and T252 in the MD simulations. The relative frequency plots for four systems are displayed in Figure 8a. Meanwhile, the representative structure of the distance between the two residues for each system is shown in Figure 8c. The relative position diagram of W14 and ligand is represented in Figure 8d. From Figure 8a, it can be seen that, for Model 1 and 3, the variation trend of distance between E13 and T252 were similar, and the distance between them was mainly distributed about 2.50–2.80 Å, indicating that the stable hydrogen bond can be formed between E13 and T252 during MD simulations. However, the results for Models 2 and 4 were quite different. When the complex was at 60 °C, the distance between the two residues was mainly distributed at about 6.90 Å, so the probability of forming hydrogen bonds was relatively small.
However, when the complex was adsorbed on GO, the distance between E13 and T252 was mainly concentrated in 2.00 Å, which can form stable hydrogen bonds. Loop α1′-α1 is an important structure for enzyme catalysis, in which E13 can form hydrogen bonds with T252, and W13 is an active site residue. The dynamic study for loop α1′-α1 was conducive to a better explanation of the catalytic mechanism. Therefore, RMSF calculation of the loop was performed. The residues contained in loop α1′-α1 were colored mapped from green (most flexible) through white to blue (least flexible), according to their calculated Cα RMSF. Figure 8b showed that the loop α1′-α1 of Model 2 had the largest fluctuation under the influence of high temperature, while the stability of Model 4 was significantly enhanced. Therefore, GO-immobilized enzymes could enhance the stability of loop α1′-α1 through the interaction of hydrogen bonds, thereby helping to improve the stability of the complex.

2.3.2. Dynamic Analysis of Interaction between Loop β4-α4 and β5-α5

From the above analysis for the adsorption process of enzymes on GO (Figure 5b), it can be seen that, with the adsorption process proceeds, the loop β4-α4 also moved close to the surface of GO. It was reported that the loop β4-α4 serves as the lid to the active site [35]. This showed that the importance of the loop β4-α4 on enzyme catalytic. W112 and E156 belong to loop β4-α4 and β5-α5, respectively, and a strong hydrogen bond formed between W112 and E156 during MD simulations. Our analysis of the interaction between loop β4-α4 and loop β5-α5 could help explain the mechanism of thermal stability improvement of GO immobilized enzyme.
To further decode loop β4-α4 and loop β5-α5 conformational changes of DPEase due to the effect of temperature and GO immobilization, the motion directions and magnitudes of the first eigenvector in the four systems were visually displayed using porcupine plots by means of PC analysis (Figure 9).
As shown in Figure 9a, loop β4-α4 and loop β5-α5 in Model 1 represented strong motion consistency, and they all moved toward the catalytic center. Additionally, α5 was consistent with their direction of motion and the degree of curvature was relatively low. In Figure 9b, the complex without GO was carried out at 60 °C, it was obviously that both loop β4-α4 and loop β5-α5 moved away from the active center. Meanwhile, the degree of curvature for α5 was highest in four systems, this may be the reason why the loop moved away from the active center. For Model 3 (Figure 9c), two loops moved away from GO, and they also moved away from the catalytic center. Notably, α5 broke into two parts. As shown in Figure 9d, both loop β4-α4 and loop β5-α5 moved toward the catalytic center and α5 showed smaller curvature than the others.
The dynamical network for interfaces between loop β4-α4 and loop β5-α5 were obtained to describe the stable interaction communities. Figure 10 showed the number of interacting residues in two loops in four systems. More residues participated in the interaction between loop1 and loop2 in Model 1 (Figure 10a) and Model 3 (Figure 10c) than the other two systems (Figure 10b,d). Compared with the Model 1, a weaker interaction between loop1 and loop2 was shown in Model 2 due to the influence of high temperature conditions, so the number of interaction residues was relatively low. The results of comparison between Model 3 and Model 4 showed that only at high temperature, DPEase adsorption on GO resulting in stability of loop β4-α4 be significantly increased, and it was not necessarily good at 50 °C.
Cross-correlation maps for residues were computed by utilizing normal mode wizard plug-in module to expose impacts of immobilization of GO on the internal dynamics of related regions (Figure 11). The black and red represent the movement having strong positive correlation, while the light blue color represents the intensely anticorrelated motions. The diagonal region represented the motion correlation of the residue itself, so the diagonal part had the positive strongest correlation. The regions outside the diagonal described the motions of a residue relative to the other residues. The region R1 displayed the motion correlation of residues between loop β4-α4 and loop β5-α5, and the movement relativity between loop α1′-α1 and α8′ was represented by region R2. The region R3 yielded the strongly positive correlated movements in Model 1 (Figure 11a) and Model 3 (Figure 11c). However, the positive correlation of this region was greatly weakened in Model 2 (Figure 11b) and 4 (Figure 11d). This result was consistent with previous PCA and network results. The region R2 showed strong positive correlation movement except for Model 2. MD simulation at high temperatures weakened the positive correlation motions of residues in this region.

3. Discussion

Understanding the interaction mechanism of GO with DPEase at the GO-DPEase interface has great significance for promoting DTEase enzymes applications in bioproduction of D-psicose from D-fructose. Four initial models were constructed and performed 300 ns molecular dynamic simulations for each model in our work. Dedania et al. have immobilized agtu-DPEase on GO through experiments [29]. MD simulations showed that the agtu-DPEase can gradually be adsorbed onto GO surface over time. Similar phenomenon between proteins and nanoparticles have been observed in recent MD simulations. Different kinds of proteins could be adsorbed on different nanomaterials [36,37,38,39,40], which suggested that the MD simulation method can simulate the adsorption process of protein on GO, which provided a reliable guarantee for our further research. We found that several positively charged or hydrophilic residues were important anchors, inducing the adsorption of DPEase by interacting with GO. These residues had strong interactions with negative charged groups on the surface of GO.
The agtu-DPEase immobilized by GO (GO-agtu-DPEase) shows maximum activity at 60 °C [29]. By contrast, agtu-DPEase without GO is most active at 50 °C [41]. Experimental data has shown that the half-life of GO-agtu-DPEase enzyme at 60 °C was much higher than that of agtu-DPEase and other DPEase family enzyme [41,42,43,44]. Furthermore, the half-life of GO-agtu-DPEase enzyme was also higher than that of other immobilization materials [33,45]. Conformation of active sites are important factors affecting the specificity and efficiency of enzymes. We observed that two active site residues of agtu-DPEase were far away from the catalytic center when the simulations performed at 60 °C; however, there were no significant changes in the conformation of active site residues for GO-agtu-DPEase under the same temperature condition. To further investigate the underlying mechanism of GO enhancing the thermal stability of DPEase, we found that the DPEase only simulated at high temperature (60 °C) underwent significant conformational changes, especially in the loop α1′-α1, loop β4-α4, and α8′, compared to that performed at 50 °C. These findings were consistent with the crystallographic study [35]. It is noteworthy that the reasons for the structural changes in Model 2 were differ from Models 3 and 4. The dramatic structural changes in Model 2 was due to the stability of DPEase was destroyed at high temperatures. By comparison, Models 3 and 4 were due to the proteins successfully adsorbed on the surface of GO, resulting in inevitable changes in protein structure.
W14 in loop α1′-α1 shifted the positions obviously when binding substrate, so it is an important catalytic residue [35]. Our simulations showed that α8′ adsorbed on GO could prevent the loop α1′-α1 region from deviating the active center through hydrogen bond interaction. Crystallographic study manifested that W112 in the β4-α4 loop plays an important role in closing the active center and has obvious conformational changes. W112 is stabilized by a strong hydrogen bond forming with E156 in loop β5-α5 [35]. We observed that part of the β4-α4 loop region can be adsorbed on the surface of GO. Moreover, we found that there was motion correlation between loop β4-α4 and loop β5-α5 by analyzing the trajectory of MD simulations. The interactions between two loops helped W112 keep stable at high temperature. Interestingly, although DPEase can be adsorbed on GO at 50 °C, the loop β5-α5 moves away from the active center, so the enzyme activity was affected. The immobilization nanomaterial can protect enzymes from denaturation at high temperature [46]. In the meantime, immobilization could enhance enzyme rigidity and reduce conformation flexibility [47]. When the GO-agtu-DPEase enzyme was at 50 °C, the protein itself did not fluctuate greatly. Strong adsorption of GO led to excessive rigidity of protein, which will promote the loop to move in the opposite direction of GO and far away from the active center. So, it was not conducive to the occurrence of catalytic reaction. This could help explain why the GO-agtu-DPEase enzyme has the highest activity at 60 °C, not at 50 °C.
MD simulation is conducive to explain the interaction mechanisms between proteins and nanostructured materials when it is related to experimental data [12,48,49,50,51]. So, new insights could be provided for the comprehension of experimental results from the perspective of the dynamic behavior at atomic level. In case of our work, the above analysis results were consistent with the experimental data of enzyme activity [29]. So, MD simulations not only indicated that GO could enhance the thermal stability of DPEase, but also theoretically elaborated the mechanism of how GO interacted with DPEase and enhanced its thermal stability.

4. Methods

The initial coordinates of agtu-DPEase complexed with manganese ion and the frucose (FUD) were obtained from the RCSB Protein Data Bank (https://www.rcsb.org/, accessed on 3 July 2006) (PDB code: 2HK1) [35]. This crystal structure contains ligand (D-Fructose), so can be directly used for MD simulations. A nanostructure model of GO with the dimensions of 10 × 11 nm2 was prepared from the website (https://jerkwin.github.io/gmxtool/model/graphene.html, accessed on 5 April 2020), which is an online tool created by Jicun Li. The structure created with this tool has been optimized using xTB. The ratio of carbon to oxygen in GO is 4:1; this proportion of GO has been experimentally obtained [52]. Epoxy and hydroxyl groups were randomly attached to the carbon atoms of graphene basal plane.
The initial distance between the GO surface and the center of mass (COM) of the DPEase was 4.0 nm. The DPEase-FUD complex was positioned at the center, and the direction was randomly selected. To explore the dynamic characteristics of the DPEase-GO interaction, 300 ns MD simulations applying explicit water model were carried out for four systems. Four starting structures for MD simulations were prepared. Model 1: DPEase + FUD (50 °C), Model 2: DPEase + FUD (60 °C), Model 3: DPEase + FUD + GO (50 °C), Model 4: DPEase + FUD + GO (60 °C). Each system contains ligand and manganese ion. Four MD simulations were performed by the GROMACS 2018.3 [53] MD software package with Gromos54a7 force field [54,55,56]. The topology files of the GO generated by x2top suite embedded in the GROMACS [53] software package. GO sheet and the complex were embedded into the periodic boundary simulation box with SPC water molecules [57]. The size of the box was 10 × 11 × 9 nm3. The simulations of Model 1 and 3 were carried out at 50 °C, and Models 2 and 4 were simulated at 60 °C. Additionally, 100 ps pre-equilibrated MD simulations were performed in the NVT ensemble at 50/60 °C using a Berendsen thermostat [58], and the NPT ensemble was used for four systems where the Berendsen thermostat and the Parrinello–Rahman barostat [59] were applied, respectively, to maintain the pressure at 1 bar and the temperature at 50/60 °C. Four systems were performed in an NPT ensemble. The time steps of all simulations were set to 2 femtoseconds (fs), and the snapshots were recorded every 10 picoseconds (ps), setting the cutoff radius for the van der Waals interaction as 1.0 nm. The trajectory analysis were performed with GROMACS suite of programs [53] and VMD [60].

5. Conclusions

In this study, we performed MD simulations to elucidate how GO stabilized the DPEase at high temperature. Our results indicated that DPEase can be adsorbed onto the surfaces of GO surface through its cationic residues and hydrophilic residues. The active site of DPEase was stabilized at high temperature in the presence of GO, which was beneficial to maintain the catalytic activity of the enzyme. Our simulation results corroborated experimental observations and offered molecular insights into the interactions between DPEase and GO. The mechanistic understanding gained from this study should be useful for the design and development of DPEase.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijms221910813/s1.

Author Contributions

Conceptualization, C.L.; methodology, C.L.; software, M.W.; validation, W.H. and L.H.; formal analysis, C.L.; investigation, Z.L.; resources, W.H.; data curation, S.C.; writing—original draft preparation, C.L.; writing—review and editing, C.L.; visualization, W.H.; supervision, W.H.; project administration, L.H.; funding acquisition, W.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Graduate Innovation Fund of Jilin University, grant number 101832020CX097, the Overseas Cooperation Project of Jilin Province, grant number 20200801069GH, and the National Natural Science Foundation of China, grant number 31870201.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brena, B.; González-Pombo, P.; Batista-Viera, F. Immobilization of enzymes: A literature survey. Methods Mol. Biol. 2013, 1051, 15–31. [Google Scholar] [PubMed]
  2. Hermanová, S.; Zarevúcká, M.; Bouša, D.; Pumera, M.; Sofer, Z. Graphene oxide immobilized enzymes show high thermal and solvent stability. Nanoscale 2015, 7, 5852–5858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Nawani, N.; Singh, R.; Kaur, J. Immobilization and stability studies of a lipase from thermophilic Bacillus sp: The effect of process parameters on immobilization of enzyme. Electron. J. Biotechnol. 2006, 9, 559–565. [Google Scholar] [CrossRef] [Green Version]
  4. Cherry, J.R.; Fidantsef, A.L. Directed evolution of industrial enzymes: An update. Curr. Opin. Biotechnol. 2003, 14, 438–443. [Google Scholar] [CrossRef]
  5. Su, R.; Zhang, W.; Zhu, M.; Xu, S.; Ming, Y.; Li, D. Alkaline protease immobilized on graphene oxide: Highly efficient catalysts for the proteolysis of waste-activated sludge. Pol. J. Environ. Stud. 2013, 22, 885–891. [Google Scholar]
  6. Kao, K.C.; Lin, T.S.; Mou, C.Y. Enhanced activity and stability of lysozyme by immobilization in the matching nanochannels of mesoporous silica nanoparticles. J. Phys. Chem. C 2014, 118, 6734–6743. [Google Scholar] [CrossRef]
  7. Li, C.; Huang, M.; Gu, Z.; Hong, Y.; Cheng, L.; Li, Z. Nanosilica sol leads to further increase in polyethylene glycol (PEG) 1000-enhanced thermostability of β-cyclodextrin glycosyltransferase from Bacillus circulans. J. Agric. Food Chem. 2014, 62, 2919–2924. [Google Scholar] [CrossRef]
  8. Ju, L.; Wang, J.; Gavalas, V.G.; Atwood, D.; Ba Chas, L.G. Alumina−pepsin hybrid nanoparticles with orientation-specific enzyme coupling. Nano Lett. 2015, 3, 55–58. [Google Scholar]
  9. Li, D.; He, Q.; Cui, Y.; Duan, L.; Li, J. Immobilization of glucose oxidase onto gold nanoparticles with enhanced thermostability. Biochem. Biophys. Res. Commun. 2007, 355, 488–493. [Google Scholar] [CrossRef]
  10. Xi, L.; Li, S.; Yao, X.; Wei, Y.; Li, J.; Liu, H.; Wu, X. In silico study combining docking and QSAR methods on a series of matrix metalloproteinase 13 inhibitors. Arch. Pharm. 2014, 347, 825–833. [Google Scholar] [CrossRef]
  11. Guo, J.; Li, J.; Zhang, Y.; Jin, X.; Liu, H.; Yao, X. Exploring the influence of carbon nanoparticles on the formation of β-sheet-rich oligomers of IAPP₂₂₋₂₈ peptide by molecular dynamics simulation. PLoS ONE 2013, 8, e65579. [Google Scholar]
  12. Sun, X.; Feng, Z.; Hou, T.; Li, Y. Mechanism of graphene oxide as an enzyme inhibitor from molecular dynamics simulations. ACS Appl. Mater. Interfaces 2014, 6, 7153–7163. [Google Scholar] [CrossRef]
  13. Ge, C.; Du, J.; Zhao, L.; Wang, L.; Liu, Y.; Li, D.; Yang, Y.; Zhou, R.; Zhao, Y.; Chai, Z.; et al. Binding of blood proteins to carbon nanotubes reduces cytotoxicity. Proc. Nati. Acad. Sci. USA 2011, 108, 16968–16973. [Google Scholar] [CrossRef] [Green Version]
  14. Shih, C.; Lin, S.; Sharma, R.; Strano, M.; Blankschtein, D. Understanding the pH-dependent behavior of graphene oxide aqueous solutions: A comparative experimental and molecular dynamics simulation study. Langmuir 2012, 28, 235–241. [Google Scholar] [CrossRef]
  15. Lu, N.; Li, Z.; Yang, J. Structure of Graphene Oxide: Thermodynamics versus Kinetics. J. Phys. Chem. C 2011, 115, 11991–11995. [Google Scholar] [CrossRef] [Green Version]
  16. Zhang, J.; Zhang, F.; Yang, H.; Huang, X.; Liu, H.; Zhang, J.; Guo, S. Graphene oxide as a matrix for enzyme immobilization. Langmuir 2010, 26, 6083–6085. [Google Scholar] [CrossRef] [PubMed]
  17. Whitby, R.L.; Gun’ko, V.M.; Korobeinyk, A.; Busquets, R.; Cundy, A.B.; László, K.; Skubiszewska-Zięba, J.; Leboda, R.; Tombácz, E.; Toth, I.Y.; et al. Driving forces of conformational changes in single-layer graphene oxide. ACS Nano 2012, 6, 3967–3973. [Google Scholar] [CrossRef] [PubMed]
  18. Luan, V.H.; Tien, H.N.; Le, T.H.; Hien, N.; Hur, S.H. Synthesis of a highly conductive and large surface area graphene oxide hydrogel and its use in a supercapacitor. J. Mater. Chem. A 2013, 1, 208–211. [Google Scholar] [CrossRef]
  19. Zhu, P.; Sumpter, B.G.; Meunier, V. Electronic, thermal, and structural properties of graphene oxide frameworks. J. Phys. Chem. B 2013, 117, 8276–8281. [Google Scholar] [CrossRef]
  20. Zhuang, Z.; Peng, Q.; Zhang, B.; Li, Y. Controllable synthesis of Cu2S nanocrystals and their assembly into a superlattice. J. Am. Chem. Soc. 2008, 130, 10482–10483. [Google Scholar] [CrossRef]
  21. Zhang, Y.; Wu, C.; Guo, S.; Zhang, J. Interactions of graphene and graphene oxide with proteins and peptides. Nanotechnol. Rev. 2013, 2, 27–45. [Google Scholar] [CrossRef]
  22. Jin, L.; Yang, K.; Yao, K.; Zhang, S.; Tao, H.; Lee, S.T.; Liu, Z.; Peng, R. Functionalized graphene oxide in enzyme engineering: A selective modulator for enzyme activity and thermostability. ACS Nano 2012, 6, 4864–4875. [Google Scholar] [CrossRef] [PubMed]
  23. Zhang, F.; Zheng, B.; Zhang, J.; Huang, X.; Liu, H.; Guo, S.; Zhang, J. Horseradish peroxidase immobilized on graphene oxide: Physical properties and applications in phenolic compound removal. J. Phys. Chem. C 2010, 114, 8469–8473. [Google Scholar] [CrossRef]
  24. Pavlidis, I.V.; Vorhaben, T.; Tsoufis, T.; Rudolf, P.; Bornscheuer, U.T.; Gournis, D.; Stamatis, H. Development of effective nanobiocatalytic systems through the immobilization of hydrolases on functionalized carbon-based nanomaterials. Bioresour. Technol. 2012, 115, 164–171. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Li, Q.; Fan, F.; Wang, Y.; Feng, W.; Ji, P. Enzyme immobilization on carboxyl-functionalized graphene oxide for catalysis in organic solvent. Ind. Eng. Chem. Res. 2013, 52, 6343–6348. [Google Scholar] [CrossRef]
  26. Su, R.; Shi, P.; Zhu, M.; Hong, F.; Li, D. Studies on the properties of graphene oxide-alkaline protease bio-composites. Bioresour. Technol. 2012, 115, 136–140. [Google Scholar] [CrossRef] [PubMed]
  27. Wen, Y.; Peng, C.; Li, D.; Zhuo, L.; He, S.; Wang, L.; Huang, Q.; Xu, Q.H.; Fan, C. Metal ion-modulated graphene-DNAzyme interactions: Design of a nanoprobe for fluorescent detection of lead(II) ions with high sensitivity, selectivity and tunable dynamic range. Chem. Commun. 2011, 47, 6278–6280. [Google Scholar] [CrossRef]
  28. Wang, W.; Donini, O.; Reyes, C.M.; Kollman, P.A. Biomolecular simulations: Recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu. Rev. Biophys. Biomol. Struct. 2001, 30, 211–243. [Google Scholar] [CrossRef] [Green Version]
  29. Dedania, S.R.; Patel, M.J.; Patel, D.M.; Akhani, R.C.; Patel, D.H. Immobilization on graphene oxide improves the thermal stability and bioconversion efficiency of D-psicose 3-epimerase for rare sugar production. Enzyme Microb. Technol. 2017, 107, 49–56. [Google Scholar] [CrossRef]
  30. Zhang, L.; Mu, W.; Jiang, B.; Zhang, T. Characterization of D-tagatose-3-epimerase from Rhodobacter sphaeroides that converts D-fructose into D-psicose. Biotechnol. Lett. 2009, 31, 857–862. [Google Scholar] [CrossRef]
  31. Zhang, W.; Fang, D.; Xing, Q.; Zhou, L.; Jiang, B.; Mu, W. Characterization of a novel metal-dependent D-psicose 3-epimerase from Clostridium scindens 35704. PLoS ONE 2013, 8, e62987. [Google Scholar] [CrossRef]
  32. Oh, D.K.; Kim, N.H.; Kim, H.J.; Park, C.S.; Kim, S.W.; Ko, M.; Park, B.W.; Min, J. d-Psicose production from d-fructose using an isolated strain, Sinorhizobium sp. World J. Microbiol. Biotechnol. 2007, 23, 559–563. [Google Scholar] [CrossRef]
  33. Tseng, C.W.; Liao, C.Y.; Sun, Y.; Peng, C.C.; Tzen, J.T.; Guo, R.T.; Liu, J.R. Immobilization of Clostridium cellulolyticum D-psicose 3-epimerase on artificial oil bodies. J. Agric. Food Chem. 2014, 62, 6771–6776. [Google Scholar] [CrossRef]
  34. Itoh, H.; Okaya, H.; Khan, A.R.; Tajima, S.; Hayakawa, S.; Izumori, K. Purification and Characterization of D-Tagatose 3-Epimerase from Pseudomonas sp. ST-24. Biosci. Biotechnol. Biochem. 1994, 58, 2168–2171. [Google Scholar] [CrossRef]
  35. Kim, K.; Kim, H.-J.; Oh, D.; Cha, S.; Rhee, S. Crystal structure of D-psicose 3-epimerase from Agrobacterium tumefaciens and its complex with true substrate D-fructose: A pivotal role of metal in catalysis, an active site for the non-phosphorylated substrate, and its conformational changes. J. Mol. Biol. 2006, 361, 920–931. [Google Scholar] [CrossRef]
  36. Thompson, A.B.; Calhoun, A.K.; Smagghe, B.J.; Stevens, M.D.; Wotkowicz, M.T.; Hatziioannou, V.M.; Bamdad, C. A gold nanoparticle platform for protein-protein interactions and drug discovery. ACS Appl. Mater. Interfaces 2011, 3, 2979–2987. [Google Scholar] [CrossRef]
  37. Sahoo, D.; Bhattacharya, P.; Patra, H.K.; Mandal, P.; Chakravorti, S. Gold nanoparticle induced conformational changes in heme protein. J. Nanopart. Res. 2011, 13, 6755–6760. [Google Scholar] [CrossRef] [Green Version]
  38. Zuo, G.; Huang, Q.; Wei, G.; Zhou, R.; Fang, H. Plugging into proteins: Poisoning protein function by a hydrophobic nanoparticle. ACS Nano 2010, 4, 7508–7514. [Google Scholar] [CrossRef]
  39. Katoch, J.; Kim, S.N.; Kuang, Z.; Farmer, B.L.; Naik, R.R.; Tatulian, S.A.; Ishigami, M. Structure of a peptide adsorbed on graphene and graphite. Nano Lett. 2012, 12, 2342–2346. [Google Scholar] [CrossRef]
  40. Zuo, G.; Zhou, X.; Huang, Q.; Fang, H.; Zhou, R. Adsorption of villin headpiece onto graphene, carbon nanotube, and C60: Effect of contacting surface curvatures on binding affinity. J. Phys. Chem. C 2011, 115, 23323–23328. [Google Scholar] [CrossRef]
  41. Kim, H.J.; Hyun, E.K.; Kim, Y.S.; Lee, Y.J.; Oh, D.K. Characterization of an Agrobacterium tumefaciens D-psicose 3-epimerase that converts D-fructose to D-psicose. Appl. Environ. Microbiol. 2006, 72, 981–985. [Google Scholar] [CrossRef] [Green Version]
  42. Zhang, W.; Zhang, T.; Jiang, B.; Mu, W. Biochemical characterization of a D-psicose 3-epimerase from Treponema primitia ZAS-1 and its application on enzymatic production of D-psicose. J. Sci. Food Agric. 2016, 96, 49–56. [Google Scholar] [CrossRef]
  43. Zhang, W.; Fang, D.; Zhang, T.; Zhou, L.; Jiang, B.; Mu, W. Characterization of a metal-dependent D-psicose 3-epimerase from a novel strain, Desmospora sp. 8437. J. Agric. Food Chem. 2013, 61, 11468–11476. [Google Scholar] [CrossRef] [PubMed]
  44. Zhu, Y.; Men, Y.; Bai, W.; Li, X.; Zhang, L.; Sun, Y.; Ma, Y. Overexpression of D-psicose 3-epimerase from Ruminococcus sp. in Escherichia coli and its potential application in D-psicose production. Biotechnol. Lett. 2012, 34, 1901–1906. [Google Scholar] [CrossRef] [PubMed]
  45. Lim, B.C.; Kim, H.J.; Oh, D.K. A stable immobilized d -psicose 3-epimerase for the production of d -psicose in the presence of borate. Process Biochem. 2009, 44, 822–828. [Google Scholar] [CrossRef]
  46. Srivastava, G.; Singh, K.; Talat, M.; Srivastava, O.N.; Kayastha, A.M. Functionalized graphene sheets as immobilization matrix for fenugreek β-amylase: Enzyme kinetics and stability studies. PLoS ONE 2014, 9, e113408. [Google Scholar] [CrossRef] [Green Version]
  47. Chang, M.Y.; Juang, R.S. Use of chitosan–clay composite as immobilization support for improved activity and stability of β-glucosidase. Biochem. Eng. J. 2007, 35, 93–98. [Google Scholar] [CrossRef]
  48. Calvaresi, M.; Hoefinger, S.; Zerbetto, F. Probing the structure of lysozyme-carbon-nanotube hybrids with molecular dynamics. Chemistry 2012, 18, 4308–4313. [Google Scholar] [CrossRef] [PubMed]
  49. Vaitheeswaran, S.; Garcia, A.E. Protein stability at a carbon nanotube interface. J. Chem. Phys. 2011, 134, 125101. [Google Scholar] [CrossRef]
  50. Sun, X.; Feng, Z.; Zhang, L.; Hou, T.; Li, Y. The selective interaction between silica nanoparticles and enzymes from molecular dynamics simulations. PLoS ONE 2014, 9, e107696. [Google Scholar] [CrossRef] [PubMed]
  51. De, M.; Chou, S.S.; Dravid, V.P. Graphene oxide as an enzyme inhibitor: Modulation of activity of α-chymotrypsin. J. Am. Chem. Soc. 2011, 133, 17524–17527. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Yan, J.A.; Chou, M.Y. Oxidation functional groups on graphene: Structural and electronic properties. Phys. Rev. 2010, 82, 125401–125403. [Google Scholar] [CrossRef]
  53. Bogetti, A.T.; Mostofian, B.; Dickson, A.; Pratt, A.J.; Saglam, A.S.; Harrison, P.O.; Adelman, J.L.; Dudek, M.; Torrillo, P.A.; DeGrave, A.J.; et al. A suite of tutorials for the WESTPA rare-events sampling software [Article v1.0]. Living J. Comput. Mol. Sci. 2019, 1, 10607. [Google Scholar] [CrossRef] [PubMed]
  54. Schmid, N.; Eichenberger, A.P.; Choutko, A.; Riniker, S.; Winger, M.; Mark, A.E.; van Gunsteren, W.F. Definition and testing of the GROMOS force-field versions 54A7 and 54B7. Eur. Biophys. J. 2011, 40, 843–856. [Google Scholar] [CrossRef]
  55. Oostenbrink, C.; Villa, A.; Mark, A.E.; van Gunsteren, W.F. A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6. J. Comput. Chem. 2004, 25, 1656–1676. [Google Scholar] [CrossRef]
  56. Domański, J.; Stansfeld, P.J.; Sansom, M.S.; Beckstein, O. Lipidbook: A public repository for force-field parameters used in membrane simulations. J. Membr. Biol. 2010, 236, 255–258. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Wu, Y.; Tepper, H.L.; Voth, G.A. Flexible simple point-charge water model with improved liquid-state properties. J. Chem. Phys. 2006, 124, 024503. [Google Scholar] [CrossRef] [PubMed]
  58. Eslami, H.; Mojahedi, F.; Moghadasi, J. Molecular dynamics simulation with weak coupling to heat and material baths. J. Chem. Phys. 2010, 133, 084105. [Google Scholar] [CrossRef]
  59. Martonák, R.; Laio, A.; Parrinello, M. Predicting crystal structures: The Parrinello-Rahman method revisited. Phys. Rev. Lett. 2003, 90, 075503. [Google Scholar] [CrossRef] [Green Version]
  60. Humphrey, W.; Dalke, A.; Schulten, K. VMD: Visual molecular dynamics. J. Mol. Graph. 1996, 14, 33–38. [Google Scholar] [CrossRef]
Ijms 22 10813 g001 550
Figure 1. General overview of the initial structure. (a) MD simulation box of GO and DPEase-FUD complex with water (the orientation of complex is randomly selected); (b) secondary structural elements of DPEase, the helices, and β-strands are displayed in different symbols. Active site residues highlighted in orange; (c) molecular architecture of DPEase-FUD and the detailed close-up of the residues interacting with FUD.
Figure 1. General overview of the initial structure. (a) MD simulation box of GO and DPEase-FUD complex with water (the orientation of complex is randomly selected); (b) secondary structural elements of DPEase, the helices, and β-strands are displayed in different symbols. Active site residues highlighted in orange; (c) molecular architecture of DPEase-FUD and the detailed close-up of the residues interacting with FUD.
Ijms 22 10813 g001
Ijms 22 10813 g002 550
Figure 2. (a) Time evolution of RMSDs for backbone atoms in four systems throughout the 300 ns MD simulations; (b) corresponding frequency for RMSD; (c) radius of gyration changes in four systems during simulations; (d) relative frequency for radius of gyration.
Figure 2. (a) Time evolution of RMSDs for backbone atoms in four systems throughout the 300 ns MD simulations; (b) corresponding frequency for RMSD; (c) radius of gyration changes in four systems during simulations; (d) relative frequency for radius of gyration.
Ijms 22 10813 g002
Ijms 22 10813 g003 550
Figure 3. Representative structure for the complex at 50 °C and 60 °C. Additionally, the superimposition of two structures, in which DPEase are displayed in cartoon and active pockets, are represented in different color sticks.
Figure 3. Representative structure for the complex at 50 °C and 60 °C. Additionally, the superimposition of two structures, in which DPEase are displayed in cartoon and active pockets, are represented in different color sticks.
Ijms 22 10813 g003
Ijms 22 10813 g004 550
Figure 4. Adsorption process of enzyme on GO at 60 °C. The representative snapshots of (a) 0 ns; (b) 30 ns; (c) 70 ns; and (d) 100 ns.
Figure 4. Adsorption process of enzyme on GO at 60 °C. The representative snapshots of (a) 0 ns; (b) 30 ns; (c) 70 ns; and (d) 100 ns.
Ijms 22 10813 g004
Ijms 22 10813 g005 550
Figure 5. The detailed close-up of the interaction surface between DPEase and GO. (a) The interaction between loop α1′-α1 and α8′; (b) the interaction between loop β4-α4 and loop β5-α5′; (c) structure diagram of the adsorbed region. The key residues have been marked in the picture with different color sticks.
Figure 5. The detailed close-up of the interaction surface between DPEase and GO. (a) The interaction between loop α1′-α1 and α8′; (b) the interaction between loop β4-α4 and loop β5-α5′; (c) structure diagram of the adsorbed region. The key residues have been marked in the picture with different color sticks.
Ijms 22 10813 g005
Ijms 22 10813 g006 550
Figure 6. (a) The RMSF values of residues 248–268 for four systems; visualizations of the backbone flexibility for (b) DPEase + FUD (50 °C); (c) DPEase + FUD (60 °C); (d) DPEase + FUD + GO (50 °C); (e) DPEase + FUD + GO (60 °C).
Figure 6. (a) The RMSF values of residues 248–268 for four systems; visualizations of the backbone flexibility for (b) DPEase + FUD (50 °C); (c) DPEase + FUD (60 °C); (d) DPEase + FUD + GO (50 °C); (e) DPEase + FUD + GO (60 °C).
Ijms 22 10813 g006
Ijms 22 10813 g007 550
Figure 7. (a) DSSP results of α8′ residues; α8′ region is colored mapped from red (0 ns) through white and to blue (300 ns) along with time for (b) DPEase + FUD (50 °C); (c) DPEase + FUD (60 °C); (d) DPEase + FUD + GO (50 °C); (e) DPEase + FUD + GO (60 °C).
Figure 7. (a) DSSP results of α8′ residues; α8′ region is colored mapped from red (0 ns) through white and to blue (300 ns) along with time for (b) DPEase + FUD (50 °C); (c) DPEase + FUD (60 °C); (d) DPEase + FUD + GO (50 °C); (e) DPEase + FUD + GO (60 °C).
Ijms 22 10813 g007
Ijms 22 10813 g008 550
Figure 8. (a) Relative frequency of distance distribution for the 300 ns MD simulation; (b) loop α1′-α1 residues are colored mapped from green (most flexible) through white to blue (least flexible) according to their calculated Cα RMSF; (c) representational structure diagram of the distance between E13 and T252; (d) representation snapshots of relative positions between W14 and FUD.
Figure 8. (a) Relative frequency of distance distribution for the 300 ns MD simulation; (b) loop α1′-α1 residues are colored mapped from green (most flexible) through white to blue (least flexible) according to their calculated Cα RMSF; (c) representational structure diagram of the distance between E13 and T252; (d) representation snapshots of relative positions between W14 and FUD.
Ijms 22 10813 g008
Ijms 22 10813 g009 550
Figure 9. Collective motions along the first eigenvector obtained from principal component analysis for four systems: (a) DPEase + FUD (50 °C); (b) DPEase + FUD (60 °C); (c) DPEase + FUD + GO (50 °C); (d) DPEase + FUD + GO (60 °C). Loop β4-α4 is shown in green tube, while loop β5-α5 and α5 are displayed in skybule tube. Red arrows represented movement trends. α5 residues were colored mapped from blue (most curved) through white and to red (least curved) according to their calculated curvature.
Figure 9. Collective motions along the first eigenvector obtained from principal component analysis for four systems: (a) DPEase + FUD (50 °C); (b) DPEase + FUD (60 °C); (c) DPEase + FUD + GO (50 °C); (d) DPEase + FUD + GO (60 °C). Loop β4-α4 is shown in green tube, while loop β5-α5 and α5 are displayed in skybule tube. Red arrows represented movement trends. α5 residues were colored mapped from blue (most curved) through white and to red (least curved) according to their calculated curvature.
Ijms 22 10813 g009
Ijms 22 10813 g010 550
Figure 10. Dynamical network analysis of the interaction between loop β4-α4 and loop β5-α5 in (a) DPEase + FUD (50 °C); (b) DPEase + FUD (60 °C); (c) DPEase + FUD + GO (50 °C); (d) DPEase + FUD + GO (60 °C). The nodes (blue) are αC atoms, the red and green edges represent the interactions between αC atoms of the residues for loop β4-α4, loop β5-α5, respectively, and the silver edges show the interactions between αC atoms of two loops.
Figure 10. Dynamical network analysis of the interaction between loop β4-α4 and loop β5-α5 in (a) DPEase + FUD (50 °C); (b) DPEase + FUD (60 °C); (c) DPEase + FUD + GO (50 °C); (d) DPEase + FUD + GO (60 °C). The nodes (blue) are αC atoms, the red and green edges represent the interactions between αC atoms of the residues for loop β4-α4, loop β5-α5, respectively, and the silver edges show the interactions between αC atoms of two loops.
Ijms 22 10813 g010
Ijms 22 10813 g011 550
Figure 11. Cross-correlation matrices between residues calculated for four systems: (a) DPEase + FUD (50 °C); (b) DPEase + FUD (60 °C); (c) DPEase + FUD + GO (50 °C); (d) DPEase + FUD + GO (60 °C). The regions R1 and R2 marked with cyan color rectangles represent obvious variation in motion modes.
Figure 11. Cross-correlation matrices between residues calculated for four systems: (a) DPEase + FUD (50 °C); (b) DPEase + FUD (60 °C); (c) DPEase + FUD + GO (50 °C); (d) DPEase + FUD + GO (60 °C). The regions R1 and R2 marked with cyan color rectangles represent obvious variation in motion modes.
Ijms 22 10813 g011
Table
Table 1. Average backbone atoms RMSD of loop α1′-α, loop β4-α4, loop β5-α5, α8′, and α5 throughout 300 ns simulations in Å.
Table 1. Average backbone atoms RMSD of loop α1′-α, loop β4-α4, loop β5-α5, α8′, and α5 throughout 300 ns simulations in Å.
RegionsDPEase + FUDDPEase + FUD + GO
50 °C (Model 1)60 °C (Model 2)50 °C (Model 3)60 °C (Model 4)
loop α1′-α10.64 ± 0.171.89 ± 0.291.34 ± 0.241.47 ± 0.18
loop β4-α40.85 ± 0.351.55 ± 0.331.66 ± 0.341.59 ± 0.24
loop β5-α50.50 ± 0.0770.52 ± 0.090.59 ± 0.090.60 ± 0.08
α8′0.52 ± 0.221.71 ± 0.551.64 ± 0.531.85 ± 0.58
α50.74 ± 0.170.88 ± 0.301.01 ± 0.200.84 ± 0.17
Table
Table 2. Hydrogen bond occupancies between protein (DPEase) and ligand (FUD) for four systems during 300 ns MD simulations.
Table 2. Hydrogen bond occupancies between protein (DPEase) and ligand (FUD) for four systems during 300 ns MD simulations.
SystemDonorReceptorOccupancy (%)DonorReceptorOccupancy (%)
DPEase + FUD (50 °C)FUD:O1E150:OE267.06FUD:O6E34:OE224.35
FUD:O4E244:OE264.52R215:NH2FUD:O119.13
FUD:O1E156:OE246.00FUD:O6E34:OE154.90
FUD:O1E156:OE145.16H186:NE2FUD:O134.88
FUD:O5E244:OE233.98FUD:O3E244:OE214.76
DPEase + FUD (60 °C)FUD:O6E34:OE140.77FUD:O3E244:OE232.21
E34:OE1FUD:O640.77Y7:OHFUD:O630.88
FUD:O6E34:OE239.91H186:NE2FUD:O129.28
FUD:O1E150:OE239.34FUD:O4E244:OE124.12
FUD:O4E244:OE239.14FUD:O1E156:OE210.12
DPEase + FUD + GO (50 °C)FUD:O4E244:OE260.99FUD:O1E156:CD10.83
FUD:O3E244:OE262.43FUD:O5Y6:OH11.41
FUD:O6E34:OE252.02FUD:O6E34:OE129.27
FUD:O1E156:OE117.54R215:NH2FUD:O16.12
Y6:OHFUD:O611.29R215:NH1FUD:O12.55
DPEase + FUD + GO (60 °C)FUD:O4E244:OE285.88FUD:O1E156:OE133.61
FUD:O3E244:OE261.38Y6:OHFUD:O321.65
FUD:O6E34:OE247.20R215:NH2FUD:O121.47
FUD:O1E156:OE238.68Y7:OHFUD:O614.27
FUD:O6E34:OE137.99H186:NE2FUD:O215.46
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Li, C.; Lu, Z.; Wang, M.; Chen, S.; Han, L.; Han, W. A Possible Mechanism of Graphene Oxide to Enhance Thermostability of D-Psicose 3-Epimerase Revealed by Molecular Dynamics Simulations. Int. J. Mol. Sci. 2021, 22, 10813. https://doi.org/10.3390/ijms221910813

AMA Style

Li C, Lu Z, Wang M, Chen S, Han L, Han W. A Possible Mechanism of Graphene Oxide to Enhance Thermostability of D-Psicose 3-Epimerase Revealed by Molecular Dynamics Simulations. International Journal of Molecular Sciences. 2021; 22(19):10813. https://doi.org/10.3390/ijms221910813

Chicago/Turabian Style

Li, Congcong, Zhongkui Lu, Min Wang, Siao Chen, Lu Han, and Weiwei Han. 2021. "A Possible Mechanism of Graphene Oxide to Enhance Thermostability of D-Psicose 3-Epimerase Revealed by Molecular Dynamics Simulations" International Journal of Molecular Sciences 22, no. 19: 10813. https://doi.org/10.3390/ijms221910813

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop