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Capturing spontaneous partitioning of peripheral proteins using a biphasic membrane-mimetic model.

Arcario MJ, Ohkubo YZ, Tajkhorshid E - J Phys Chem B (2011)

Bottom Line: Furthermore, in many cases, the nature of the membrane "anchor", i.e., the part of the protein that inserts into the membrane, is also unknown.In addition to efficiently and consistently identifying the "keel" region as the hydrophobic membrane anchor, within a few nanoseconds each configuration simulated showed a convergent height (2.20 ± 1.04 Å) and angle with respect to the interface normal (23.37 ± 12.48°).We demonstrate that the model can produce the same results as those obtained from a full representation of a membrane, in terms of both the depth of penetration and the orientation of the protein in the final membrane-bound form with an order of magnitude decrease in the required computational time compared to previous models, allowing for a more exhaustive search for the correct membrane-bound configuration.

View Article: PubMed Central - PubMed

Affiliation: Center for Biophysics and Computational Biology, Department of Biochemistry, College of Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.

ABSTRACT
Membrane binding of peripheral proteins, mediated by specialized anchoring domains, is a crucial step for their biological function. Computational studies of membrane insertion, however, have proven challenging and largely inaccessible, due to the time scales required for the complete description of the process, mainly caused by the slow diffusion of the lipid molecules composing the membrane. Furthermore, in many cases, the nature of the membrane "anchor", i.e., the part of the protein that inserts into the membrane, is also unknown. Here, we address some of these issues by developing and employing a simplified representation of the membrane by a biphasic solvent model which we demonstrate can be used efficiently to capture and describe the process of hydrophobic insertion of membrane anchoring domains in all-atom molecular dynamics simulations. Applying the model, we have studied the insertion of the anchoring domain of a coagulation protein (the GLA domain of human protein C), starting from multiple initial configurations varying with regard to the initial orientation and height of the protein with respect to the membrane. In addition to efficiently and consistently identifying the "keel" region as the hydrophobic membrane anchor, within a few nanoseconds each configuration simulated showed a convergent height (2.20 ± 1.04 Å) and angle with respect to the interface normal (23.37 ± 12.48°). We demonstrate that the model can produce the same results as those obtained from a full representation of a membrane, in terms of both the depth of penetration and the orientation of the protein in the final membrane-bound form with an order of magnitude decrease in the required computational time compared to previous models, allowing for a more exhaustive search for the correct membrane-bound configuration.

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(Top) The height of the keel Cα atoms above the DCLE/water interface, which was assumed to be the average position of the first layer of DCLE molecules in contact with water. (Bottom) The angle between the GLA domain’s axis, determined by the vector traced from the Ca2+-4 and the Cα of Phe40 (see Figure 1), and the interface normal (z-axis). The colors used in these plots correspond to the outline around each orientation found in Figure 5.
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fig6: (Top) The height of the keel Cα atoms above the DCLE/water interface, which was assumed to be the average position of the first layer of DCLE molecules in contact with water. (Bottom) The angle between the GLA domain’s axis, determined by the vector traced from the Ca2+-4 and the Cα of Phe40 (see Figure 1), and the interface normal (z-axis). The colors used in these plots correspond to the outline around each orientation found in Figure 5.

Mentions: The time evolution of the height of the GLA domain from the interface over the entire 30 ns course of the simulations is displayed in Figure 6. It is clear from the plot that each initial configuration converges to a height of 2.20 ± 1.04 Å (Table 2) within a few nanoseconds and fluctuates slightly around this “equilibrium point” for the remainder of the simulation (Figure 6). The angle with respect to the interface normal also converges at approximately the same rate to 23.37 ± 12.48° (Figure 6); Figure 7 displays the converged structure of hPrC GLA domain (snapshot taken at t = 29 ns). As expected, the 60° and 90° trials were the slowest to insert, since they needed more time in solution to explore and find an optimal orientation toward the interface. However, even in these cases, insertion is completed within a few nanoseconds. This rate of insertion corresponds to at least 1 order of magnitude increase in the speed of insertion over previous studies,21,64 making this method more accessible to MD simulations. In the simulation study of FVIIa anchoring,(21) the GLA domain fluctuates ∼5 Å around its equilibrium height; in this study, we see a similar degree of fluctuation around the equilibrium height. The plot of the domain’s angle to the interface normal, however, shows a greater variability which is likely due to the absence of headgroup specific interactions with the anchoring domain. Ohkubo and Tajkhorshid(21) have demonstrated that the seven bound Ca2+ ions of the hFVII GLA domain interact significantly with the phosphate groups of the lipid headgroups resulting in the stabilization of the membrane-bound complex. This effect is clearly missing from our model, resulting in a larger degree of fluctuation of the domain at the interface. We note that specific contacts between the lipid headgroups and peripheral proteins are of utmost importance in interaction of proteins such as the Ras family64,65 and FERM domain(66) with the membrane. These interactions are not represented by the simple model used in our simulations, and while the model appears to be very efficient in determining mostly hydrophobic interactions, it is not able to describe specific lipid–protein interactions. We are currently working on including the effect of the headgroups in the model.


Capturing spontaneous partitioning of peripheral proteins using a biphasic membrane-mimetic model.

Arcario MJ, Ohkubo YZ, Tajkhorshid E - J Phys Chem B (2011)

(Top) The height of the keel Cα atoms above the DCLE/water interface, which was assumed to be the average position of the first layer of DCLE molecules in contact with water. (Bottom) The angle between the GLA domain’s axis, determined by the vector traced from the Ca2+-4 and the Cα of Phe40 (see Figure 1), and the interface normal (z-axis). The colors used in these plots correspond to the outline around each orientation found in Figure 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3102442&req=5

fig6: (Top) The height of the keel Cα atoms above the DCLE/water interface, which was assumed to be the average position of the first layer of DCLE molecules in contact with water. (Bottom) The angle between the GLA domain’s axis, determined by the vector traced from the Ca2+-4 and the Cα of Phe40 (see Figure 1), and the interface normal (z-axis). The colors used in these plots correspond to the outline around each orientation found in Figure 5.
Mentions: The time evolution of the height of the GLA domain from the interface over the entire 30 ns course of the simulations is displayed in Figure 6. It is clear from the plot that each initial configuration converges to a height of 2.20 ± 1.04 Å (Table 2) within a few nanoseconds and fluctuates slightly around this “equilibrium point” for the remainder of the simulation (Figure 6). The angle with respect to the interface normal also converges at approximately the same rate to 23.37 ± 12.48° (Figure 6); Figure 7 displays the converged structure of hPrC GLA domain (snapshot taken at t = 29 ns). As expected, the 60° and 90° trials were the slowest to insert, since they needed more time in solution to explore and find an optimal orientation toward the interface. However, even in these cases, insertion is completed within a few nanoseconds. This rate of insertion corresponds to at least 1 order of magnitude increase in the speed of insertion over previous studies,21,64 making this method more accessible to MD simulations. In the simulation study of FVIIa anchoring,(21) the GLA domain fluctuates ∼5 Å around its equilibrium height; in this study, we see a similar degree of fluctuation around the equilibrium height. The plot of the domain’s angle to the interface normal, however, shows a greater variability which is likely due to the absence of headgroup specific interactions with the anchoring domain. Ohkubo and Tajkhorshid(21) have demonstrated that the seven bound Ca2+ ions of the hFVII GLA domain interact significantly with the phosphate groups of the lipid headgroups resulting in the stabilization of the membrane-bound complex. This effect is clearly missing from our model, resulting in a larger degree of fluctuation of the domain at the interface. We note that specific contacts between the lipid headgroups and peripheral proteins are of utmost importance in interaction of proteins such as the Ras family64,65 and FERM domain(66) with the membrane. These interactions are not represented by the simple model used in our simulations, and while the model appears to be very efficient in determining mostly hydrophobic interactions, it is not able to describe specific lipid–protein interactions. We are currently working on including the effect of the headgroups in the model.

Bottom Line: Furthermore, in many cases, the nature of the membrane "anchor", i.e., the part of the protein that inserts into the membrane, is also unknown.In addition to efficiently and consistently identifying the "keel" region as the hydrophobic membrane anchor, within a few nanoseconds each configuration simulated showed a convergent height (2.20 ± 1.04 Å) and angle with respect to the interface normal (23.37 ± 12.48°).We demonstrate that the model can produce the same results as those obtained from a full representation of a membrane, in terms of both the depth of penetration and the orientation of the protein in the final membrane-bound form with an order of magnitude decrease in the required computational time compared to previous models, allowing for a more exhaustive search for the correct membrane-bound configuration.

View Article: PubMed Central - PubMed

Affiliation: Center for Biophysics and Computational Biology, Department of Biochemistry, College of Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801, United States.

ABSTRACT
Membrane binding of peripheral proteins, mediated by specialized anchoring domains, is a crucial step for their biological function. Computational studies of membrane insertion, however, have proven challenging and largely inaccessible, due to the time scales required for the complete description of the process, mainly caused by the slow diffusion of the lipid molecules composing the membrane. Furthermore, in many cases, the nature of the membrane "anchor", i.e., the part of the protein that inserts into the membrane, is also unknown. Here, we address some of these issues by developing and employing a simplified representation of the membrane by a biphasic solvent model which we demonstrate can be used efficiently to capture and describe the process of hydrophobic insertion of membrane anchoring domains in all-atom molecular dynamics simulations. Applying the model, we have studied the insertion of the anchoring domain of a coagulation protein (the GLA domain of human protein C), starting from multiple initial configurations varying with regard to the initial orientation and height of the protein with respect to the membrane. In addition to efficiently and consistently identifying the "keel" region as the hydrophobic membrane anchor, within a few nanoseconds each configuration simulated showed a convergent height (2.20 ± 1.04 Å) and angle with respect to the interface normal (23.37 ± 12.48°). We demonstrate that the model can produce the same results as those obtained from a full representation of a membrane, in terms of both the depth of penetration and the orientation of the protein in the final membrane-bound form with an order of magnitude decrease in the required computational time compared to previous models, allowing for a more exhaustive search for the correct membrane-bound configuration.

Show MeSH