The alpha-helical bundle is the most common type of fold for membrane proteins. Their diverse functions include transport, signalling, and catalysis. While structure determination is much more difficult for membrane proteins than it is for soluble proteins, it is accelerating and there are now 586 unique proteins in the database of Membrane Proteins of Known 3D Structure. However, we still have quite a poor understanding of how membrane proteins fold. There is increasing evidence that it is more complicated than the two-stage model proposed in 1990 by Popot and Engelman.

The machinery that inserts most alpha-helical membrane proteins is the Sec apparatus. In prokaryotes, it is located in the plasma membrane, while eukaryotic Sec is found in the ER. Sec itself is an alpha-helical bundle in the shape of a pore, and its structure is able both to allow peptides to pass fully across the membrane, and also to open laterally to insert transmembrane helices into the membrane. In both cases, this occurs co-translationally, with translation halted by the signal recognition particle until the ribosome is associated with the Sec complex.

Voorhees, R. M. et al. (2014) Cell, 157(7), 1632–43

If helices are inserted during the process of translation, does folding only begin after translation is finished? On what timescale are these folding processes occuring? There is evidence that a hairpin of two transmembrane helices forms on a timescale of miliseconds in vitro. Are helices already interacting during translation to form components of the native structure? It has also been suggested that helices may insert into the membrane in pairs, via the Sec apparatus.

There are still many aspects of the insertion process which are not fully understood, and even the topology of an alpha-helical membrane protein can be affected by the last part of the protein to be translated. I am starting to investigate some of these questions by using computational tools to learn more about the membrane proteins whose structures have already been solved.

Many methods are available for prediction of topology of transmembrane helices, this being one of the success stories of protein structure prediction with accuracies over 90%. However, there are still areas where there is disagreement in some areas about the partitioning between the states of dissolved in water and positioned across a lipid bilayer. Complications arise because there are so many methods of measuring the thermodynamics of this transition – experimental and theoretical, in vivo and in vitro. It is uncertain what difference the translocon makes to the energetics of insertion – is the topology and conformation of a membrane protein the global thermodynamic minimum or just a kinetic product?

This paper uses three approaches to measure partitioning to test the agreement between different methods. The authors aim to reconcile differences calculated so far for insertion of an arginine residue into the membrane (ranging from +2 to +15 kcal/mol). This is an important question, because many transmembrane helices are only marginally hydrophobic and it is not known how and when they insert in the folding process. Arginine is chosen here because the pKa of 12.5 of the side chain is very high so it will not deprotonate in the centre of a bilayer and complications of protonation and deprotonation do not need to be considered. The same peptide is used for each method, of the form L_nRL_n, and the ratio between the interface and transmembrane states is used to calculate estimates of ΔG. In order to make sure that there were helices with a ΔG close to zero for accurate estimates, they used a range of values of n from 5-8.

The first method was an insertion assay using reconstituted microsomes, where this helix was inserted into the luminal domain of LepB. A glycosylation site was added at each end of the helix, but glycosylation takes place only on sites inside microsomes. Helices inserted into the membrane are only glycosylated once, whereas secreted helices are glycosylated twice and those which did not go through the translocon are not glycosylated. SDS-PAGE can separate these states by mass, and the ratio between single and double glycosylation gives the partitioning between inserted and interface helices out of those which entered the translocon. As expected, the trend is for longer helices with more leucine to favour the transmembrane state.

Adapted from Figure 4a: The helix, H, either passes through the translocon into the lumen (“S”) resulting in two glycosylations (green pentagons), or is inserted (TM) resulting in one glycosylation.

The second method was also experimental: oriented synchrotron radiation circular dichroism (ORSCD). Here they used just the peptide with one glycine at each end, as this would be able to equilibrate between the two states quickly. Theoretical spectra can be calculated for a helix , and therefore the ratio in which they must be combined to give the measured spectrum for a given peptide gives the ratio of transmembrane and interface states present.

Figure 2b: TM and IP are the theoretical spectra for the transmembrane and interface states, and the peptides fall somewhere in between.

Finally, the authors present 4 μs molecular dynamics simulations of the same peptides at 140°C, so that equilibration between the two states would be fast. The extended peptide at the start of the simulation quickly associates with the membrane and adopts a helical conformation. An important observation to note is that the transmembrane state is in fact at around 30° to the membrane normal, to allow the charged guanidinium group of the arginine to “snorkel” up to interact with charged phosphate groups of the lipids. Therefore this state is defined as transmembrane, in contrast to the OSRCD experiments where the theoretical TM spectrum was calculated for a perpendicular helix. This may be a source of some inaccuracy in the propensities calculated from OSRCD.

Figure 2c: Equilibration in the simulation for the L₇RL₇ peptide. Transmembrane and interface states are seen in the partitioning and equilibration phases after the helix has formed.

Figure 3c: As the simulations run, the proportion of helices in the transmembrane state (P_TM) converges to a different value for each peptide.

Overall, the ΔG calculated experimental and molecular dynamics (MD) simulations agree very well. In fact, they agree better than those from previous studies of a similar format looking at polyleucine helices, where there was a consistent offset of 2 kcal/mol between the experiment and simulation derived values. The authors are unable to explain why the agreement for this study is better, but they indicate that it is unlikely to be related to any stabilisation by dimerisation in the experimental results, as a 4 μs MD simulation of two helices did not show them forming stable interactions. The calculated difference in insertion energy (ΔΔG) on replacing a leucine with argnine is therefore calculated to be +2.4-4.3 kcal/mol by experiment and +5.4-6.8 by simulation, depending on the length of the peptide (it is a more costly substitution for longer peptides as the charge is buried deeper). The difference between the experimental and simulation results is accounted for by their disagreement in the polyleucine study.

We thought this paper was a great example of experimental design, where the system was carefully chosen so that different experimental and theoretical approaches would be directly comparable. The outcome is good agreement between the methods, demonstrating that the vastly different values recorded previously seem to be because very different questions were being asked.

G-protein coupled receptors (GPCRs) are the target of 50-60% of drugs, including many of those involved in the treatment of cancer and cardiovascular disease. Over 100 GPCR crystal structures are now available, but these are for only around 30 different receptors, and there are still hundreds more receptors for which no structure exists. There is huge diversity in the ligands which bind to GPCRs, so it may often be difficult to predict the shape of a binding pocket for a specific receptor of interest, especially if no close relatives have a structure solved.

Helix kinks (see previous blog posts) are a structural feature of GPCRs which are thought to be important for function. An ability to predict their presence and the magnitude of helix direction change is important for obtaining an accurate structure. A kink prediction method has already been used in the context of GPCR structure prediction, which scored the overall structures after replacing kink segments with others from a database. This made it possible to predict the change in a kink angle based on the stability of the whole GPCR structure.

To better inform this kind of modelling, we wanted to investigate specifically how much variation there is in kink angles between GPCRs. To do this we used the tool Kink Finder to measure angles in all of the transmembrane helices of the GPCRs in the GPCRDB, and estimate a confidence interval on those angles. Then we could state whether the variation that we see in GPCR kink angles is greater than what we would expect from measurement error alone.

Each helix appears to show different behaviour. Some helices were very well conserved, but others showed a huge amount of variation. For these helices with very variable angles, it would be interesting to know if this is a change related to sequence differences, or conformational flexibility between more than one preferred conformation. We found an example where significantly different angles were found even in the same receptor. In this case, the kink angle size is related to whether the structure has an agonist or an antagonist bound, so we propose that this is a functionally relevant and flexible kink.

We also carried out the same analysis on helices from other families of membrane and soluble proteins, and found many more highly variable kinks (one example shown below). This shows that they should be a very important consideration when carrying out homology modelling, and that their conformational flexibility could also be important for function in many other contexts.

Much work has been done to study the conformational changes taking place in antibodies, particularly during the event of binding to an antigen. This has been done through comparison of crystal structures, circular dichroism, and recently with high resolution single particle electron microscopy. The ability to resolve domains within an antibody from single particles without any averaging  made it possible to show distributions of properties such as the shape of a Fab domain, measured by the ratio of width to length. Some of the variation in structure seen involves very large scale motions, but it is not known how conformational changes may be transmitted from the antigen binding region to the Fc, and therefore influence effector function. Molecular dynamics simulations have been performed on some large antibody systems, however none have been possible on a time scale which would be able to provide information on the converged distributions of large scale properties such as the angle between the Fab and Fc fragments.

In my short project with Peter Minary, I used MOSAICS to investigate the dynamics of an antibody Fab fragment, using the coarse-grained natural move Monte Carlo approach described by Sam a few weeks ago. This makes it possible to split a structure into units which are believed to move in a correlated way, and propose moves for the components of each region together. The rate of sampling is accelerated in degrees of freedom which may have functional significance, for example the movement of the domains in a Fab fragment relative to one another (separate regions shown in the diagram below). I used ABangle to analyse the output of each sampling trajectory and observe any changes in the relative orientations of The VH and VL domains.

Fab region definitions for MOSAICS

Of particular interest would be any correlations between conformational changes in the variable and constant parts of the Fab fragment, as these could be involved in transmitting conformational changes between remote parts of the antibody. We also hoped to see in our model some effect of including the antigen in the simulation, bound to the antibody fragment as seen in the crystal structure. In the time available for the project, we was able to  set up a model representing the Fab fragment and run some relatively short simulations to explore favoured conformational states and see how the set up of regions affects distributions seen. In order to draw conclusions about the meaning of the results, a much greater number of simulations will need to be run to ensure sampling of the whole conformational space.