Impact of pathogenic mutations of the GLUT1 glucose transporter on solute carrier dynamics using ComDYN enhanced sampling [version 2; peer review: 1 approved, 1 approved with reservations]

Background: The solute carrier (SLC) family of membrane proteins is a large class of transporters for many small molecules that are vital for cellular function. Several pathogenic mutations are reported in the glucose transporter subfamily SLC2, causing Glut1-deficiency syndrome (GLUT1DS1, GLUT1DS2), epilepsy (EIG2) and cryohydrocytosis with neurological defects (Dystonia-9). Understanding the link between these mutations and transporter dynamics is crucial to elucidate their role in the dysfunction of the underlying transport mechanism, which we investigate using molecular dynamics simulations. Methods: We studied pathogenic and non-pathogenic mutations, using a newly developed coarse-grained simulation approach ‘ComDYN’, which captures the ‘COMmon constraints DYNamics’ between both states of the solute carrier protein. To guarantee the sampling of large conformational approach, we can explain the pathogenicity of the mutation G91D and some of the effects of other known pathogenic mutations, when we observe the configurations of the transmembrane helices, suggesting that their relative position is crucial for the correct functioning of the GLUT1 protein. To fully understand the impact of other mutations in the future, it is necessary to consider the effect of ligands, e.g., glucose, within the transport mechanism. In the revised version we have clarified the scope of the study, which aims to study the impact of mutations on the first part of the transporter mechanism without considering the impact of glucose or other ligands. We have renamed our method to ComDYN for “COMmon constraints DYNamics“ as retaining only common constraints in the elastic network is the key ingredient for the enhanced conformational sampling. To study the complete pathway and elucidate the effect of all mutations in future studies, the simulations need to be extended towards the outward-open conformation and consider the impact of the ligand upon the transportation mechanism. We would like to thank the reviewers again for providing us with useful comments and suggestions which helped improve our manuscript. further responses from the reviewers found end of the The paper reports a molecular dynamics simulation study of the glucose transporter GLUT1 using all-atom and coarse grained force fields, in combination with a conserved elastic network. Using essential dynamics analysis and comparison of various distances the authors compared the dynamics of the wild type protein to pathogenic variants in the outward occluded and inward open states. This procedure enabled the prediction of the effect of mutations on the dynamics of GLUT1 and GLUT3. This paper describes a computational study of the effect of mutations on GLUT transporter dynamics. The work consists of atomistic and coarse grained simulations, including some using a protocol called ConsDYN which imposes constraints on distances conserved across conformational transitions. The study identifies that several pathogenic and other mutations modify the structural ensemble visited by the protein.


Amendments from Version 1
In the revised version we have clarified the scope of the study, which aims to study the impact of mutations on the first part of the transporter mechanism without considering the impact of glucose or other ligands. We have renamed our method to ComDYN for "COMmon constraints DYNamics" as retaining only common constraints in the elastic network is the key ingredient for the enhanced conformational sampling. To study the complete pathway and elucidate the effect of all mutations in future studies, the simulations need to be extended towards the outward-open conformation and consider the impact of the ligand upon the transportation mechanism.

Introduction
The solute carrier (SLC) transporter superfamily is known to play a key role in the transport of small molecules. The superfamily comprises 52 families, and at least 386 different transporter genes have so far been identified in humans (Hediger et al., 2013;Higuchi et al., 2018). This family of membrane proteins is a large class of transporters for many small molecules such as glucose that are vital for the cell, and can be found in all kingdoms of life. Of particular interest are the glucose transporters SLC2A1 from the SLC2 subfamily; GLUT1 mutations are associated with GLUT1 deficiency syndrome (GLUT1DS1 and GLUT1DS2), and some forms of spasticity (Dystonia-9) and epilepsy (EIG2) (Klepper et al., 2016;Mongin et al., 2016). Shedding light on the molecular mechanism of the transport function, enables us to understand the difference between pathogenic and benign mutations that have been observed in human subjects. Glucose transporter GLUT1 is built from 12 transmembrane helices (TMs) and exhibits a two-fold symmetry plane joining the two times six TM helices over a bridging helix on the cytoplasmic side of the membrane (see Figure 1A). The transport mechanism of glucose involves cycling through four states ( Figure 1B. Throughout the SLC transporters, a highly conserved RXGRR-motif is found between TM2 and TM3 and between TM8 and TM9 at the intracellular side of the corresponding loops (Pao et al., 1998;Sato & Mueckler, 1999). Several mutations at these anchor points are known to be disease-related, such as G91D and R92W which are known to cause GLUT1DS1 (Klepper et al., 2001;Klepper & Voit, 2002), whereas R93W is associated with GLUT1DS2 (Joshi et al., 2008). Between TM8 and TM9, R333W is also confirmed to be pathogenic (Klepper et al., 2001;Klepper & Voit, 2002), while the clinical significance of R334Q is unknown, although it is likely to affect the protein function.
Here, we investigate the effect of different mutations on the dynamic of the human GLUT1 protein. As the dynamic response upon mutation may depend on the conformational state, we aimed to simulate the first part of the transport mechanism between the outward-occluded state (O O ) and inward-open state (I O ), as shown in Figure 1B. Hereby, we investigate the intrinsic dynamics of the transporter protein without the ligand. For GLUT1, however, a three-dimensional structure is only available for the I O state; therefore, simulations of GLUT3, which is evolutionarily quite close to GLUT1, in the O O state were analysed. GLUT3 is also suspected to be associated with neurological disorders such as Alzheimer (An et al., 2018;Gu et al., 2018;Simpson et al., 1994;Szablewski, 2017). We expect that the intrinsic dynamics of the ligand-free states of the transporter provides new insight on the effects of the pathogenicity of certain mutations, without explicit consideration of the ligand-bound states. ; these were used as starting points for the simulations, as we focus on the first part of the transport mechanism. The next step, i.e., to elucidate the complete pathway, would be to extend our work to the outwardopen conformation which is also available in the PDB (4ZWC GLUT3 with maltose bound), and to study the effect of ligand binding on the transporter dynamics. However, this is beyond the scope of the present study. Based on the reported pathogenic mutations of GLUT1 in the conserved RXGRR motif region, we searched the corresponding positions of GLUT3 for additional mutations and included them in our study. Due to the high sequence identity (~70%) between the proteins, we intentionally did not build a homology model for one or the other protein. This is justified, as our main aim is to characterize the global opening and closing mechanism rather than to look into atomistic details such as protonation states. Moreover, we now avoid additional uncertainties about details of the structure as would be inevitably introduced during the homology modelling process. Our composite scheme using coarse-grained molecular dynamics with common constraints elastic network 'ComDYN', that keeps part of the unchanged position constraints between the two protein structures, is explained in the approach below and further details are supplied in the supporting methods, available in the deposited code (Feenstra, 2019b).

Molecular dynamics simulations
In this study, we employed molecular dynamics (MD) simulations using the GROMACS 4.0.5 programme package (Hess et al., 2008). For efficiency reason, we investigated the applicability of the MARTINI coarse-grained (CG) force field . This speed-up is obtained at the expense of explicit description of hydrogen bonds in the protein, which necessitates the addition of an elastic network to maintain secondary structure and other tertiary contacts and thus overall protein stability; the elastic network is thus tailed to a particular conformational state of the protein. Here, we propose to modify the elastic network that is used in MARTINI based on our starting conformations, to include only common elastic network constraints that differed less than 1Å between the I O and O O states. Increasing the distance threshold for the common constraints would result in even fewer constraints in the ComDYN, increasing the risk of instability and possibly unfolding of the protein. Therefore, our chosen constraints allow transition between both states, while maintaining protein structure stability. This composite scheme will henceforward be referred to as ComDYN (COMmon constraints DYNamics). The detailed computational set-up is provided in the Supporting Methods (Feenstra, 2019b).

Analysis of transporter dynamics
Essential dynamics analysis was performed on the GLUT1 and GLUT3 simulations using the built-in analysis tools of GROMACS. To allow this comparison between these two homologous proteins, and allow for focusing on overall motions of the transporter region, we selected the structurally conserved helical segments, as summarized in Supporting Table S2, available as extended data (Feenstra, 2019a). Then, the covariance and eigenvalue calculation were performed on the ensemble of both wild-type systems, using the coordinates of the C-alpha atoms for the full atomistic (AT) simulations, and the backbone particle at the C-alpha position for the coarse-grained (CG) and ComDYN simulations.
To analyse the transporter dynamics from the ComDYN, we defined several order parameters as previously proposed by Nagarathinam et al. (2018) by measuring distances between adjacent TM helices, at the intracellular (in)-and extracellular (out)sides of the protein (see Figure 1B, C). For each of the ring of six central helices that make up the solute transporter region, TM2, TM1, TM5, TM8, TM7, TM11 (and back to TM2), we defined an 'inside' and 'outside' segment of ten residues (See Figure 1 and extended data, Supporting Figure S3 (Feenstra, 2019a)). Comparing the distances of the mutations to both wild types allows us to capture abnormal behaviour and identify the mutations that have the highest impact on the opening and closing mechanism of the apo-form of the transporter protein (see Results and Discussion).
To compare the distributions of sampled distances during the simulations, two metrics were employed: overlap and shift. 'Overlap' is the fraction of overlap between both distributions, as the integral of the minimum of both functions. 'Shift' quantifies the direction of change, and is obtained by taking the difference in the position of the peak of the two distributions from the ensemble of the simulations (typically, mutation version wildtype). Negative indicates a 'closing' motion, positive is 'opening'. This analysis was performed using the script 'calc_overlap.py', which may be found in the extended data.

Results and discussion
Prediction of mutation impact Table 1 lists the mutants of GLUT1 and GLUT3 that we will consider here, which are situated in the conserved RXGRR-motif distal to the transporter region. The table lists the predicted impact upon mutation obtained from SIFT (Sim et al., 2012) and PolyPhen-2 (Adzhubei et al., 2010). Most mutations are classified as likely pathogenic by both methods, with the exception of GLUT1 R93Q, and GLUT3 R91C and R91H. However, these methods are trained on the dbSNP database which also includes these known mutations, so this should be no surprise. Moreover, these predictions do not allow us to gain any insights into the mechanism by which these mutations may affect transporter function.  Figure 2 shows 2D plots of the first two (largest) ED eigenvectors, representing the extracted correlated motions over the ensemble of our simulations. The first eigenvector (EV1) represents the major conformational changes between the O O and I O states, as also indicated by the RMSD values between starting states. The changes observed along EV2 may also be genuinely part of the state transition, however we cannot exclude the possibility that some of these conformational changes may relate to differences between the forcefields used.

Probing conformational changes
To probe the degree of the conformational changes during ComDyn simulations of the wild types and the mutants in more detail than done with the ED analysis, two distances were used to describe the opening and closing of the periplasmic and cytoplasmic sides of the transporter. Nagarathinam et al. (2018) studied a bacterial homolog of GLUT1 and GLUT3, and analysed the movement between TM5 and TM8. In the extended data, Figure S4 (Feenstra, 2019a), we can see that the distributions obtained from our ComDYN simulations, resemble those reported by Nagarathinam et al. (2018), providing an independent validation that our ComDYN approach is able to sample biologically relevant conformational states for large scale motions, such as those involved in the glucose transporter mechanism. Nevertheless, there are differences between the distance distributions in our work and that of Nagarathinam et al. (2018), which could, apart from obvious differences comparing human glucose transporters with a bacterial multidrug transporter (see also extended data, Figure S4 and Table S3 (Feenstra, 2019a)), also arise from differences in the sampling protocols applied in the two studies.
Therefore, in addition to the TM5-TM8 distances, we extended the analysis to other helices along and across the rim that make up for the entire SLC transporter architecture, allowing us to monitor changes in their position (see Figure 1C, D). For each of these order parameters, we calculated the distance at the inside and outside of the protein with respect to the membrane. Using this analysis, we can immediately observe the changes occurring between the inward-open and outward-occluded states. We see several distances changing significantly during this process: TM1/TM2(in), TM1/TM5(out), TM1/TM7(out), TM1/ TM8(in), and TM5/TM11(in) are all closing, while TM1/TM2(out), TM1/TM5(in), TM1/TM7(in), TM2/TM11(in), TM2/TM8(out), and TM5/TM11(out) are opening (see extended data, Table S3 and Figure S4 for more details (Feenstra, 2019a)); these motions are also schematically summarised in extended data, Figure S3 (Feenstra, 2019a). Table 2 summarises the overlap and shift between the sampled distributions of distances between the wild type and each of the mutants for TM5 and TM11 that exhibited the strongest effects and conformational changes during the simulations (the complete table of the distributions for all order parameters are available in the extended data, Table S3 (Feenstra, 2019a)). The corresponding conformational distributions from the ComDYN simulations, calculated as a function of the inner and outer distances between TM5 and TM11 are given in Figure 3.

Impact of mutations on dynamics
Not all mutations have a high impact on the overall dynamics (extended data, Table S3 and Figure S4 (Feenstra, 2019a)). However, in GLUT1, the reported pathogenic mutation G91D has a profound effect on the dynamics of the protein (i.e., a low overlap and large shift, see Table 2). Also when we consider the distance of TM5-TM11, as shown in Figure 3, the strongest effect is observed for the pathogenic G91D mutant: its distribution varies strongest from the wild types, and hardly visits the inward-open and outward-occluded states. Furthermore, Figure 3 Figure 3 shows the corresponding distance plots for some of the mutants. These are quantified using the fraction of overlap between both distributions in sampling distributions of sampled conformations, calculated as the integral of the minimum of both functions, i.e., the volume (normalized to a maximum of one), given in %, of the wild type and mutant (small value is large change), and the shift of the peak location (in nm including the direction; positive is to larger distances). The pathogenic mutations are underlined. All the large shifts (absolute above 0.15 nm) and small overlaps (below 0.6 nm) are set to bold. For a visual aid, the shifts are set in italic.

GLUT1
G91D  Corresponding plots for all mutations are in the extended data, Figure S4. The corresponding quantification of these plots provided as shift and overlap are given in Table 2.
shows that for the pathogenic R92W and R333W mutations, only one state or small parts from both can be accessed. For the benign mutant R93Q, in contrast, it can be seen that both states, inward-open and outward-occluded, are sampled thoroughly during the simulations. Assuming that the relative distance between the two helices is crucial for the correct functioning, this strongly suggests that the pathogenic mutations directly affect the opening and closing mechanism of the GLUT1 transporter.
Mutations in GLUT3 show similar behaviour in TM dynamics compared to those in GLUT1. Here, two mutants with strong abnormal behaviour can be identified: G89V and R91H (Table 2; extended data Figure S4 (Feenstra, 2019a) shows the corresponding distance distribution plots). Additionally, similar to the observations for pathogenic mutations on GLUT1, these mutations no longer sample intermediate states associated with the transport function, unlike the wild type and many of the other mutations. This strongly suggests that the corresponding mutations between GLUT1 and GLUT3 also have the same direct blocking effects on the opening and closing mechanism of the GLUT3 transporter. However, it should be noted that we cannot make any conclusions about the clinical significance of these GLUT3 mutants, as none have been reported to be pathogenic. A next step to elucidate the complete pathway would be to extend the analysis to the outward-open conformation, and include ligand-bound states (Delemotte, 2019), which may shed further light on some of the currently still unexplained pathogenic mutations.

Conclusion
Using extensive ComDYN simulations of GLUT1 and GLUT3 wild type and several clinically relevant mutations, we provide an effective way to study dynamic effects of mutations on the molecular mechanism of human glutamate transporter proteins. Without using full-atomistic details, we were able to get insight into the opening and closing mechanisms, which may account for the (dys)function of the SLC family caused by pathogenic mutations around the conserved RXGRR-motif. Through these mutations (especially G91D, R92W and R333W in GLUT1), we observe that the distances between TM5 and TM11 across the rim of the solute carrier structure are affected the strongest. For this reason, we chose them as our order parameter to explain the abnormal behaviour in the dynamics of the transporter opening and closing mechanism for some of the observed mutants. It should be mentioned that this does not provide an ultimate order parameter to explain all the effects of the pathogenic mutations, but allows us to better understand some of the effects caused by these pathogenic mutations. Comparing atomistic (AT), coarse-grained MARTINI (CG), and ComDYN simulations, our work shows that our CG ComDyn simulations are sufficiently accurate to sample the intermediate states between the conformational states and capture some of the effect of the mutations on the dynamic and function of these transporter proteins. This helps to elucidate the effects of pathogenic mutations on the structure and dynamics of the GLUT1 and GLUT3 transporters which were previously not understood. The following extended data are available:

Data availability
• Data.tgz. data files accompanying analyses performed in this study.
• Table S1. Summary of the molecular composition of simulated systems.
• Table S3. Wild-type and Mutant simulations compared by Overlap and shift between TM helix distance distributions.
• Figure S2. Pipe representation of the inward-open conformation of the transporter.
• Figure S3. Schematic view of the observed pore mechanism going from the inward-open state to the outward-occluded state.
• Figure S4. Distribution of inside and outside helix distances for all examined mutants in GLUT1 and GLUT3.
Extended data are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

Jocelyne Vreede
Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands The paper reports a molecular dynamics simulation study of the glucose transporter GLUT1 using all-atom and coarse grained force fields, in combination with a conserved elastic network. Using essential dynamics analysis and comparison of various distances the authors compared the dynamics of the wild type protein to pathogenic variants in the outward occluded and inward open states. This procedure enabled the prediction of the effect of mutations on the dynamics of GLUT1 and GLUT3.
The manuscript requires a few clarifications to improve my understanding: As I am not very familiar with the mechanism of glucose transporters, I would like a few sentences describing how these proteins work, thus giving more context to the different conformational states I o and O o . Also in the conclusion, a bit more context as to how the states interconvert and the impact of the mutations on these transitions would aid my understanding tremendously.  Table 2 is computed.

○
What is the unit of the shift in Table 2 and of the distances in Fig. 3?

Is the study design appropriate and is the work technically sound? Yes
Are sufficient details of methods and analysis provided to allow replication by others? Yes

If applicable, is the statistical analysis and its interpretation appropriate? Partly
Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly position for CG. We have now clarified this in the Analysis of transporter dynamics section in the Methods.
Reply: Indeed, transitions between Oo and Io are visible on EV1 in Figure 2; we have now added this explicitly in the figure caption. The sampled states do overlap between the different forcefield approaches. Moreover, as we also discuss in our reply to question 3 of Reviewer 2, the CG sampling partly overlaps the AT sampling on both sides of the Oo to Io transition, and partly with the ComDYN sampling more to the middle of the transition. Furthermore, the Oo ComDYN sampling overlaps considerably with the Io ComDYN sampling. We have also added the starting conformations in Fig 2 as an aid to the reader to better navigate this projected sampling space. We cannot exclude that EV2 may represent conformational changes due to differences between the forcefields, however as we are also sampling far longer timescales in the CG and ComDYN simulations, these differences may also genuinely be part of the transition between the Oo and Io states.

5.
Reply: Both ComDYN simulations start at or near the conformations from the AT simulations, but this is not quite visible in the plot in Fig 2. We have now added spheres to indicate the respective starting conformations, as well as triangles to show the corresponding crystal structures. The CG simulations sample much longer timescales, so allow conformational transition that cannot be reached during the sampling time of the AT simulations. We have now clarified this in results, Section "Verification of constraining approach".

6.
Reply: The outcome of such a visualization would not be informative for a protein of this size. The differences in conformation (namely the distances between the transmembrane helices) are small and therefore difficult to visualize. Superpositioning snapshot does therefore not help to understand the differences occurring through the mutations. To capture molecular motions during our simulations, we use the defined inter-helix distances (cf. Figure 1D.) as order parameters, which are directly interpretable.

7.
Reply: 'Overlap' is the fraction of overlap between both distributions of sampled conformations, calculated as the integral of the minimum of both functions, i.e., the volume (normalized to a maximum of one) that represents the amount of sampling between two distributions. We have now clarified this in the section Analysis of transporter dynamics in Methods, with a reference to the relevant python script and now provide the 'overlap' in percentage to make this explicit.

8.
Reply: They are all in nanometers (nm), we have added this now explicitly to the table and figure captions.

9.
Competing Interests: No competing interests were disclosed. While the topic is important and computational methods are well-suited to answer the question, I have reservations about the study design and the conclusions reached: A pathogenic mutation modifies the function of the protein such that cellular and organism function are altered. Glucose transporters carry out their function, i.e. importing sugars, via an alternating access cycle in which the transporter transits between outward-open and inward-open states via occluded states; whereas sugar binding from the extracellular medium promotes a transition to the inward facing state, sugar release to the inside intracellular medium promotes a return to the outward facing state. Other than the intrinsic dynamics of interconversion between states, the fact that the sugar modifies the stability of states and the kinetics of interconversion is key for function. Thus pathogenicity of a mutation could be due to many factors: among others, sugar binding, unbinding, (de)stabilization of one or more states on the functional cycle, modification of the rate of conversion between states, in the presence and/or absence of sugar. In this paper, the authors investigate the effect of mutations on the dynamics of interconversion between states. I believe the assumption should be spelled out more clearly, and the omission of all other possible effects on the sugar transport cycle should be explained.
Relatedly, were the simulations performed in the presence or absence of sugar? Comparing both cases could lead to increased insights. The ConsDYN method can be an interesting way to promote conversion between states 3. using coarse grained simulations. However, whereas the stated aim on p5 is "to check that ConsDYN is able to sample both I o and O o states", Figure 2 reveals that ConsDYN simulations only sample intermediate states, instead of bridging between states. I would thus disagree with the conclusion according to which the method allows to capture the "conserved dynamics" and to "sample between the conformational states".
Would lowering the force constant of the constraints imposed lead to further exploration of the landscape?
The method also seems to have a serious conceptual drawback, in that it assumes that when switching from a state to another, common contacts are conserved, and additional ones are formed in either states. This does not seem to be a general feature of conformational changes in biological molecules and should be discussed. Table 2 reports changes in distance between helices in the presence and absence of mutations, including pathogenic ones. Whereas the pathogenic G91D seems to cause major changes to the dynamics of the transporter, the other pathogenic mutations only alter some of the distance distributions. It does not appear that applying this methodology and measuring the difference in distance distributions as is done in Table 2 allows to predict pathogenicity. I thus disagree with the conclusions: "the distances between TM5 and TM11 can be used as order parameters to elucidate abnormal behavior in the dynamics of the transporter" and "ConsDYN simulations capture the effect of the mutations on the dynamic and function of the transporter proteins".