F1000Research F1000Research 2046-1402 F1000 Research Limited London, UK 10.12688/f1000research.18553.2 Research Article Articles Impact of pathogenic mutations of the GLUT1 glucose transporter on solute carrier dynamics using ComDYN enhanced sampling

[version 2; peer review: 2 approved, 1 approved with reservations]

 Mouhib Halima Data Curation Formal Analysis Investigation Visualization Writing – Original Draft Preparation Writing – Review & Editing 1 2 Higuchi Akiko Data Curation Formal Analysis Investigation Visualization Writing – Original Draft Preparation 3 Abeln Sanne Methodology Writing – Review & Editing https://orcid.org/0000-0002-2779-7174 4 Yura Kei Conceptualization Funding Acquisition Resources Supervision 5 6 Feenstra K. Anton Conceptualization Data Curation Formal Analysis Investigation Methodology Software Supervision Visualization Writing – Original Draft Preparation Writing – Review & Editing https://orcid.org/0000-0001-6755-9667 a 4 Laboratoire Modélisation et Simulation Multi Echelle (MSME) - UMR 8208 CNRS, Université Paris-Est, Champs-sur-Marne, France Institut Universitaire de France (IUF), Paris Cedex, 05 75231, France Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan Dept. Computer Science, Integrative Bioinformatics, Vrije Universiteit, Amsterdam, The Netherlands Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan School of Advanced Science and Engineering, Waseda University, Tokyo, Japan k.a.feenstra@vu.nl

No competing interests were disclosed.

 13 6 2022 2019 8 322 24 5 2022 Copyright: © 2022 Mouhib H et al. 2022 This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 changes, we only include common constraints of the elastic network introduced upon coarse-graining, which showed similar reference distances between both conformational states (≤1 Å difference).

Results: ComDYN is computationally efficient and sufficiently sensitive to capture effects of different mutations. Our results clearly indicate that the pathogenic mutation in GLUT1, G91D, situated at the highly conserved RXGRR motif between helices 2 and 3, has a strong impact on transporter function, as it blocks the protein from sampling both conformational states. In comparison, predictions from SIFT and PolyPhen only provided an impression of the impact upon mutation in the highly conserved RXGRR motifs, but yielded no clear differentiation between pathogenic and non-pathogenic mutations.

Conclusions: Using our 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.

 GLUT1 glucose transporter deficiency syndrome Human glucose transporters SLC transporter family transport mechanism molecular dynamics simulation Martini force field coarse-grained simulations enhanced sampling method European Molecular Biology Organization Ministry of Education, Culture, Sports, Science and Technology AH was supported through the Program for Leading Graduate Schools, “Global Leader Program for Social Design and Management,” by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. HM would like to thank the European Molecular Biology Organization for a personal grant (EMBO short-term fellowship). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 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 also 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.

 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 ( Deng et al., 2015): outward open (ligand bound or ligand free), outward occluded (O O, ligand bound), inward open (I O, ligand bound or ligand free), and inward occluded (ligand free), as summarized in 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.

GLUT1 structural overview.

( A) Pipe representation of the inward-open (I O) conformation (PDB-ID: 5EQI; bound inhibitor removed) of GLUT1 situated in the lipid bilayer. Note that the protein structure has a two-fold rotational symmetry and the two conserved RXGRR-motifs are located at the junctions of the transmembrane (TM) helices 2 and 3 and TM8-TM9, around the R333/R334 and G91/R92 mutation sites shown in magenta. The red arrows symbolize the inside and outside distances. Note that we number the helices starting from TM1 at the N-terminus of the transporter (dark blue in the pipe representation). ( B) Schematic cycle of the glucose transport mechanism between the four different open and occluded states as adapted from ( Deng et al., 2015). The bound glucose ligand is indicated as a red sphere. In the open states (inward or outward), glucose may be bound or unbound; this is represented using white dots. This work concerns the first part of the transport mechanism, i.e., the dynamics between the outward-occluded state (O O) and inward-open state (I O) as highlighted with red arrows. Note however, that we do not consider ligand binding within the scope of this work (see Discussion further below). ( C) Pipe representation of the I O conformation (PDB-ID: 5EQI) of GLUT1 viewing on the outward facing part of the transporter inside the periplasm. ( D) Definition of the order parameters to follow the motion of the helices over the ComDYN simulations.

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.

 Methods Starting structures

The crystal structures of both I O and O O states are available in the PDB; 5EQI for GLUT1 I O state ( Deng et al., 2015), and 4ZW9 for GLUT3 O O state ( Kapoor et al., 2016); 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 outward-open 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 ( Arnarez et al., 2015; de Jong et al., 2013; Hsu et al., 2017; Monticelli et al., 2008; Periole et al., 2009), which is about 500-fold faster than the full-atomistic GROMOS ( May et al., 2014). 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 wild-type). 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.

Overview of selected mutations of GLUT1 (PDB-ID: 5EQI) and GLUT3 (PDB-ID: 4ZW9) studied in this work and the impact predictions obtained from SIFT and PolyPhen.

The pathogenic mutations in GLUT1 are underlined.

Protein Mutation Clinical significance * SIFT PolyPhen
GLUT1 G91D ( Klepper et al., 2001; Klepper & Voit, 2002) GLUT1DS1 omim:606777 affect protein function probably damaging
R92Q ( Leen et al., 2010) dbSNP rs779073410 significance unknown affect protein function probably damaging
R92W ( Schneider et al., 2009) GLUT1DS2 omim:612126 affect protein function probably damaging
R93Q dbSNP rs80359815 significance unknown tolerated probably damaging
R93W ( Joshi et al., 2008) GLUT1DS2 omim:612126 affect protein function probably damaging
R333W ( Klepper & Voit, 2002; Klepper et al., 2001) EIG12 omim:614847 affect protein function probably damaging
R334Q ClinVar rs892715050 significance unknown affect protein function probably damaging
GLUT3 G89V dbSNP rs758117298 significance unknown affect protein function probably damaging
R90W not reported in dbSNP ( rs1270428275 R90T ) affect protein function probably damaging
R91C dbSNP rs756172777 significance unknown affect protein function benign
R91H dbSNP rs145936296 significance unknown tolerated benign
R331K dbSNP rs770855736 significance unknown affect protein function probably damaging
R331S dbSNP rs749200071 significance unknown affect protein function probably damaging
R331W not in dbSNP significance unknown affect protein function probably damaging

* References given to literature describing clinical appearance, OMIM entries, and dbSNP entries given, if available.

 Verification of constraining approach

Firstly, we want to verify if our common constraints-based approach for coarse-grained MD simulations (ComDYN) is able to sample the intermediate states between the I O and O O states. Including only elastic network constraints that differed less than 1Å between the I O and O O states in ComDyn, resulted in 1025 constraints (39.9% of the 2568 in the Martini elastic network) for the O O state and 978 (42.2% of 2315) for the I O state. We performed essential dynamics (ED) analysis ( Amadei et al., 1993; Van Aalten et al., 1997) to compare AT, traditional CG MARTINI, and ComDYN simulations, as described in the Methods. 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. The sampling of the different states, inward-open and outward-occluded, in AT simulation hardly converges due to their limited time scales. The regular CG simulations reach much longer timescales, and already sample more intermediate conformations, but there is no overlap. The ComDYN simulations, on the other hand, also sample many states intermediate to the inside-open and outside-open starting states, compared to the other simulations. This shows that improved sampling of large conformational transitions may be attained using this approach of CG and ComDyn simulations.

Two-dimensional essential dynamics plot of the simulations.

In this projection, eigenvector 1 corresponds to changes from O O (left) to I O (right), showing also the overlap and differences between the AT (full atomistic), the CG (coarse-grained), and the ComDYN (common constraints CG) simulations. Note that there is considerable overlap in the sampling, but that the time-scale of the AT simulations only samples conformations around the I O and O O states and the elastic network in the CG simulations also limits the visited conformations, while the ComDYN samples a large number of conformations between both states.; I O, inward open state (PDB-ID: 5EQI); O O, outward occluded state (PDB-ID: 4ZW9). Spheres indicate the respective starting conformations for each method, triangles to show the corresponding crystal structures; black arrows and labels indicate the RMSD between the starting conformations of AT and ComDYN simulations.

 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.

Overview of the changes in inside and outside distances in the CG ComDYN simulations, between transmembrane helix 5 (TM5) and TM11.

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 R333W R334Q R92Q R92W R93Q R93W
Inside Overlap 4% 24% 47% 60% 64% 46% 62%
Shift 0.84 0.34 -0.05 -0.02 -0.02 0.28 -0.03
Outside Overlap 20% 69% 70% 70% 48% 70% 61%
Shift 0.25> -0.13 -0.13 0.02 -0.23 -0.12 -0.18
GLUT3 G89V R331K R331S R331W R90W R91C R91H
Inside Overlap 25% 67% 9% 7% 6% 56% 12%
Shift 0.27 -0.06 -0.28 -0.35 -0.32 -0.12 0.30
Outside Overlap 28% 57% 50% 53% 50% 62% 15%
Shift 0.37 0.11 0.14 0.21 0.12 0.33 0.58
Distance plots of the inner and outer distances along the order parameter TM5-TM11 in nm over the complete time span of the CG ComDYN simulations (see <xref ref-type="fig" rid="f1">Figure 1D</xref>).

Colour code: wild types I O in purple (PDB-ID: 5EQI), O O in green (PDB-ID: 4ZW9), mutants in orange. Pathogenic mutants are highlighted in red. It should be noted that in contrast to the benign R93Q mutant, the pathogenic mutants do not sample the I O and O O states during the simulation, which strongly indicates that the mutation blocks the proper opening and closing mechanism. 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.

 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 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.

 Data availability Underlying data

Crystal structures for GLUT1 I O state ( Deng et al., 2015) and for GLUT3 O O state ( Kapoor et al., 2016) were obtained from the Protein Data Bank, under accession numbers 5EQI and 4ZW9, respectively.

 Extended data

Open Science Framework: ComDYN. https://doi.org/10.17605/OSF.IO/F82H5 ( Feenstra, 2019a).

The following extended data are available:

Data.tgz. data files accompanying analyses performed in this study.

Table S1. Summary of the molecular composition of simulated systems.

Table S2. Structurally conserved helical segments between 4ZW9 and 5EQI.

Table S3. Wild-type and Mutant simulations compared by Overlap and shift between TM helix distance distributions.

Figure S1. Sequence alignment between E. coli multi-drug transporter MDFA, and human glucose transporters GLUT1 and GLUT3.

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).

 Software availability

Scripts used to setup and analyze the ComDYN simulations available from:

https://github.com/ibivu/ComDYN.

Archived source code at time of publication: https://doi.org/10.5281/zenodo.2591477 ( Feenstra, 2019b).

License: GNU General Public License 3.0.

 Acknowledgments

This research (in part) used computational resources of COMA and HA-PACS provided by Multidisciplinary Cooperative Research Program in Center for Computational Sciences, University of Tsukuba, Japan.

 Adzhubei IA Schmidt S Peshkin L : A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–249. 20354512 10.1038/nmeth0410-248 2855889 Amadei A Linssen AB Berendsen HJ : Essential dynamics of proteins. Proteins. 1993;17(4):412–425. 8108382 10.1002/prot.340170408 An Y Varma VR Varma S : Evidence for brain glucose dysregulation in Alzheimer’s disease. Alzheimers Dement. 2018;14(3):318–329. 29055815 10.1016/j.jalz.2017.09.011 5866736 Arnarez C Uusitalo JJ Masman MF : Dry Martini, a coarse-grained force field for lipid membrane simulations with implicit solvent. J Chem Theory Comput. 2015;11(1):260–275. 26574224 10.1021/ct500477k de Jong DH Singh G Bennett WF : Improved Parameters for the Martini Coarse-Grained Protein Force Field. J Chem Theory Comput. 2013;9(1):687–697. 26589065 10.1021/ct300646g Delemotte L : Peer Review Report For: Impact of pathogenic mutations of the GLUT1 glucose transporter on channel dynamics using ConsDYN enhanced sampling [version 1; peer review: 1 approved with reservations, 1 not approved]. F1000Res. 2019;8:322. 10.5256/f1000research.20307.r46135 Deng D Sun P Yan C : Molecular basis of ligand recognition and transport by glucose transporters. Nature. 2015;526(7573):391–396. 26176916 10.1038/nature14655 Feenstra KA : ComDYN.2019a. http://www.doi.org/10.17605/OSF.IO/F82H5 Feenstra KA : ibivu/ConsDYN v1.1b (Version v1.1b). Zenodo. 2019b. http://www.doi.org/10.5281/zenodo.2591477 Gu J Jin N Ma D : Calpain I Activation Causes GLUT3 Proteolysis and Downregulation of O-GlcNAcylation in Alzheimer’s Disease Brain. J Alzheimers Dis. 2018;62(4):1737–1746. 29614685 10.3233/JAD-171047 Hediger MA Clémençon B Burrier RE : The ABCs of membrane transporters in health and disease (SLC series): introduction. Mol Aspects Med. 2013;34(2–3):95–107. 236860 10.1016/j.mam.2012.12.009 3853582 Hess B Kutzner C van der Spoel D : GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput. 2008;4(3):435–447. 26620784 10.1021/ct700301q Higuchi A Nonaka N Yura K : iMusta4SLC: Database for the structural property and variations of solute carrier transporters. Biophys Physicobiol. 2018;15:94–103. 29892515 10.2142/biophysico.15.0_94 5992856 Hsu PC Bruininks BMH Jefferies D : CHARMM-GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides. J Comput Chem. 2017;38(27):2354–2363. 28776689 10.1002/jcc.24895 5939954 Joshi C Greenberg CR De Vivo D : GLUT1 deficiency without epilepsy: yet another case. J Child Neurol. 2008;23(7):832–834. 18403583 10.1177/0883073808314896 Kapoor K Finer-Moore JS Pedersen BP : Mechanism of inhibition of human glucose transporter GLUT1 is conserved between cytochalasin B and phenylalanine amides. Proc Natl Acad Sci U S A. 2016;113(17):4711–4716. 27078104 10.1073/pnas.1603735113 4855560 Klepper J Leiendecker B Eltze C : Paroxysmal Nonepileptic Events in Glut1 Deficiency. Mov Disord Clin Pract. 2016;3(6):607–610. 28042592 10.1002/mdc3.12387 5157724 Klepper J Voit T : Facilitated glucose transporter protein type 1 (GLUT1) deficiency syndrome: impaired glucose transport into brain-- a review. Eur J Pediatr. 2002;161(6):295–304. 12029447 10.1007/s00431-002-0939-3 Klepper J Willemsen M Verrips A : Autosomal dominant transmission of GLUT1 deficiency. Hum Mol Genet. 2001;10(1):63–68. 11136715 10.1093/hmg/10.1.63 Leen WG Klepper J Verbeek MM : Glucose transporter-1 deficiency syndrome: the expanding clinical and genetic spectrum of a treatable disorder. Brain. 2010;133(Pt 3):655–70. 20129935 10.1093/brain/awp336 May A Pool R van Dijk E : Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins. Bioinformatics. 2014;30(3):326–334. 24273239 10.1093/bioinformatics/btt675 Mongin M Mezouar N Dodet P : Paroxysmal Exercise-induced Dyskinesias Caused by GLUT1 Deficiency Syndrome. Tremor Other Hyperkinet Mov (N Y). 2016;6:371. 27351150 10.7916/D89W0F96 4790204 Monticelli L Kandasamy SK Periole X : The MARTINI Coarse-Grained Force Field: Extension to Proteins. J Chem Theory Comput. 2008;4(5):819–834. 26621095 10.1021/ct700324x Nagarathinam K Nakada-Nakura Y Parthier C : Outward open conformation of a Major Facilitator Superfamily multidrug/H + antiporter provides insights into switching mechanism. Nat Commun. 2018;9(1):4005. 30275448 10.1038/s41467-018-06306-x 6167325 Pao SS Paulsen IT Saier MH Jr : Major facilitator superfamily. Microbiol Mol Biol Rev. 1998;62(1):1–34. 9529885 98904 Periole X Cavalli M Marrink SJ : Combining an Elastic Network With a Coarse-Grained Molecular Force Field: Structure, Dynamics, and Intermolecular Recognition. J Chem Theory Comput. 2009;5(9):2531–2543. 26616630 10.1021/ct9002114 Sato M Mueckler M : A conserved amino acid motif (R-X-G-R-R) in the Glut1 glucose transporter is an important determinant of membrane topology. J Biol Chem. 1999;274(35):24721–24725. 10455140 10.1074/jbc.274.35.24721 Schneider SA Paisan-Ruiz C Garcia-Gorostiaga I : GLUT1 gene mutations cause sporadic paroxysmal exercise-induced dyskinesias. Mov Disord. 2009;24(11):1684–1688. 19630075 10.1002/mds.22507 Sim NL Kumar P Hu J : SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012;40(Web Server issue):W452–7. 22689647 10.1093/nar/gks539 3394338 Simpson IA Chundu KR Davies-Hill T : Decreased concentrations of GLUT1 and GLUT3 glucose transporters in the brains of patients with Alzheimer’s disease. Ann Neurol. 1994;35(5):546–551. 8179300 10.1002/ana.410350507 Szablewski L : Glucose Transporters in Brain: In Health and in Alzheimer’s Disease. J Alzheimers Dis. 2017;55(4):1307–1320. 27858715 10.3233/JAD-160841 Van Aalten DMF De Groot BL Findlay JBC : A comparison of techniques for calculating protein essential dynamics. J Comput Chem. 1997;18(2):169–181. 10.1002/(SICI)1096-987X(19970130)18:2<169::AID-JCC3>3.0.CO;2-T 10.5256/f1000research.133928.r358305 Reviewer response for version 2 Sobrinho Lívia Maria Ferreira 1 Referee https://orcid.org/0000-0003-4816-3899 Federal University of Minas Gerais, Minas Gerais, Brazil

Competing interests: No competing interests were disclosed.

 29 1 2025 Copyright: © 2025 Sobrinho LMF 2025 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

General Summary of the Article

This study presents an innovative exploration of conformational dynamics in the GLUT1 glucose transporter, utilizing coarse-grained simulations and the ComDYN technique. The research highlights the impact of specific mutations on the structure and dynamics of the transporter, with implications for clinical conditions such as GLUT1 deficiency syndrome. The study significantly contributes to understanding the dynamic behavior of transporters and proposes a promising methodology for future investigations.

General Assessment

Strengths:

Scientific Relevance: The topic is highly pertinent, considering the importance of GLUT1 in physiological processes and its association with genetic disorders.

Innovative Methodology: The introduction of the ComDYN technique demonstrates an efficient approach to exploring conformational transitions in transport proteins, providing a robust model for dynamic analysis.

Clarity and Organization: The article is well-structured, with figures and tables that enhance the understanding of the results, enabling readers to follow the scientific rationale effortlessly.

Methodological Contribution: The combination of coarse-grained simulations and dynamic analyses provides detailed insights into conformational transitions.

Significant Results: The analyses highlight the impact of specific mutations, such as G91D, providing clear evidence of dynamic alterations associated with pathogenicity.

Constructive Discussion: The discussion acknowledges methodological limitations and proposes clear directions for future studies, such as the inclusion of ligand-bound states and atomistic simulations.

Minor Limitations:

Restricted Scope of Conformational States: While the study focuses on the "outward occluded" and "inward open" states, including additional states in future research could broaden insights into the conformational cycle.

Absence of Ligands: The choice to work with ligand-free states is valid for exploring intrinsic dynamics but integrating ligands could provide a more comprehensive perspective.

 Detailed Comments

1. Introduction

Strengths:

The introduction offers a comprehensive overview of the importance of GLUT1 and the impact of pathogenic mutations. The explanation of the transporter's role in the conformational cycle is clear and well-founded.

Suggestion: Briefly expand the introduction to discuss how the methodology could be applied to other SLC-type transporters.

2.  Methodology

Strengths:

The use of the MARTINI forcefield is appropriate and enables efficient exploration of large-scale dynamics.

The justification for the 1 Å contact cutoff is consistent with the limitations of the coarse-grained model.

The choice of ligand-free simulations allows detailed analysis of the intrinsic dynamics of the transporter.

Minor Limitation:

Briefly discussing how alternative contact cutoffs could complement the results would be a valuable addition, though not essential.

 3. Results

Strengths:

The graphs and tables are clear and well-presented, providing visual support that facilitates data interpretation.

The analysis of TM5-TM11 distances is robust and demonstrates how mutations like G91D impact conformational dynamics.

The figures effectively describe transitions, and the captions link the results to the initial hypotheses.

 4. Discussion and Conclusion

Strengths:

The discussion effectively connects the results to the overall study objective, highlighting the impact of the analyzed mutations.

The conclusion is clear and acknowledges methodological limitations without undermining the scientific contribution of the work.

The proposal to expand the study to include ligand-bound states demonstrates a forward-looking approach to continuing the research.

 Suggestions for Improvement

Future Additions: Explore additional conformational states and include ligands in future studies to complement the presented analyses.

Methodological Details: Provide a brief justification regarding the limitations of the contact cutoff to reinforce the methodological choices.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Not applicable

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Yes

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Clinical genetics; Inborn errors of metabolism; GLUT1 deficiency syndrome

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.5256/f1000research.133928.r140632 Reviewer response for version 2 Delemotte Lucie 1 Referee https://orcid.org/0000-0002-0828-3899 Department of Applied Physics, Science for Life Laboratory (SciLifeLab), KTH Royal Institute of Technology, Stockholm, Sweden

Competing interests: No competing interests were disclosed.

 22 7 2022 Copyright: © 2022 Delemotte L 2022 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve

The manuscript is improved and describes accurately the insights made possible by the newly developed ComDYN method.

I noticed two small issues that should be resoved:

In the abstract, a reference is made to "both states of the protein". However, it should be clarified which two states given that this transporter cycles through more than 2 states during its conformational cycle.

In the conclusion, "human glutamate transporter proteins" should be replaced by "human glucose transporter proteins".

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Membrane proteins, ion channels, molecular dynamics simulations, enhanced sampling

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

 10.5256/f1000research.20307.r47253 Reviewer response for version 1 Vreede Jocelyne 1 Referee https://orcid.org/0000-0002-6977-6603 Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands

Competing interests: No competing interests were disclosed.

 8 5 2019 Copyright: © 2019 Vreede J 2019 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation approve-with-reservations

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. 

The manuscript reports results on mutations at positions 92, 93, 333 and 334, which are highlighted in Fig. 1. Only mutations 92 and 93 are discussed in the introduction. For more context, the mutations at 333 and 334 should be discussed in the introduction as well. 

What motivates the cut-off of 1 angstrom for including constraints in the elastic network? Would 1.5 angstrom or 2 angstrom work as well? 

As essential dynamics analysis is performed on both all-atom and coarse grained simulations, I assume only C-alpha positions are included. Is this assumption correct? 

What do the two eigenvectors shown in Fig. 2 mean? My interpretation is that EV1 is the transition from the O o to the I o state, and that EV2 is the transition from the all-atom to coarse grained-constrained description. If this interpretation is correct, would the conclusion be correct that the dynamics sampled in the different force fields overlap? 

The O o and I o states as sampled with the consdyn and the AT approach seem quite different in Fig. 2. What could be the explanation for this difference? 

Snapshots of the conformations at the maxima of the probability histograms would help my understanding of the differences as introduced by the mutations. 

I do not understand how the overlap in the distributions in Table 2 is computed. 

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

Is the work clearly and accurately presented and does it cite the current literature?

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

Is the study design appropriate and is the work technically sound?

Yes

Are the conclusions drawn adequately supported by the results?

Partly

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Molecular simulation of proteins and DNA

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

Feenstra K. Anton VU University Amsterdam, The Netherlands

Competing interests: No competing interests were disclosed.

 30 5 2022

We thank the reviewer for the attention and time spent on our work, and respond to each of their comments in detail below:

Reply: We have added a short explanation of the basics of the SLC glucose transport and how the process cycles through the various states in the Introduction, and now also emphasize the main changes arising from the pathogenic mutations in the Conclusion.

Reply: We have added a discussion on the mutations at the R333 and R334 positions in the Introduction.

Reply: The 1Å cutoff retains 40% of the constraints, which is already relatively little. Shorter cut-offs would yield more restricted sampling. A wider cut-off would not necessarily provide better sampling, but would very likely lead to too large conformational changes or even unfolding of the transporter protein. This is now explicitly mentioned in the methods section under Molecular Dynamics.

Reply: Indeed, the ED analysis uses C-alpha for AT and backbone particles at C-alpha 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.

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”.

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. 

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.

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

10.5256/f1000research.20307.r46135 Reviewer response for version 1 Delemotte Lucie 1 Referee https://orcid.org/0000-0002-0828-3899 Department of Applied Physics, Science for Life Laboratory (SciLifeLab), KTH Royal Institute of Technology, Stockholm, Sweden

Competing interests: No competing interests were disclosed.

 15 4 2019 Copyright: © 2019 Delemotte L 2019 This is an open access peer review report distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. recommendation reject

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.

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.

Why was the analysis limited to outward occluded and inward open states, when high-resolution structures of other states are available? If only two states should be considered, why not consider the inward open and outward open since they are the two end-points of the transport cycle?

The ConsDYN method can be an interesting way to promote conversion between states 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”.

Minor comments: 

The authors refer to a “channel” in the title and later in the manuscript. Do they mean the transporter lumen? In an alternating access mechanism, a channel is never observed.

p5: The difference between Nagarathinam et al. (2018) and this work is ascribed to differences in the transporter protein studied (bacterial vs human) but the authors cannot rule out that the differences can come from differences in the sampling protocol.

Were the CG or AA simulations of I o and O o analyzed in Figure 3, Table 2 and SI figures?

p6: “outer distances between TM5 and TM7 are given in Figure 3” should be replaced by “outer distances between TM5 and TM11 are given in Figure 3”.

Is the work clearly and accurately presented and does it cite the current literature?

Yes

If applicable, is the statistical analysis and its interpretation appropriate?

Yes

Are all the source data underlying the results available to ensure full reproducibility?

Yes

Is the study design appropriate and is the work technically sound?

Partly

Are the conclusions drawn adequately supported by the results?

No

Are sufficient details of methods and analysis provided to allow replication by others?

Yes

Reviewer Expertise:

Membrane proteins, ion channels, molecular dynamics simulations, enhanced sampling

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

Feenstra K. Anton VU University Amsterdam, The Netherlands

Competing interests: No competing interests were disclosed.

 30 5 2022

We thank the reviewer for the attention and time spent on our work, and respond to each of their comments in detail below:

Reply: All the simulations were carried out without glucose or any other ligand, we only consider the ligand-free states of the transporters. This implies that the pathogenicity of a mutant might already be stated through the intrinsic dynamics of the transporter without explicitly considering the effect of the mutation in the ligand-bound state. This does indeed not exclude that pathogenicity linked to a specific mutant might arise from a dysfunction of the binding mechanism. In this study we focus on the first part of the transport mechanism of SLC transporters, and introduce the ComDYN method, which is designed to capture conformational changes between different states. We have now added this point to the manuscript and explicitly mention that, to guarantee the study of all the relevant effects, simulations including the ligand-bound states might yield additional insight on the structure and dynamic of different mutants. However, this would only be feasible using full-atomistic simulations, since sugars are not well parametrized in the Martini force field. As this would pose strong limits on the time scales we could practically attain in our simulations, it is out of the scope of the current study.

Reply: We want to focus on the first part of the transport mechanism: moving from the inward open to the outward occluded state. The next step to elucidate the complete pathway will be to extend this to the outward-open conformation which is available in the PDB. For the present work, we focus on the impact of some mutations on the intrinsic dynamics of the protein (without considering the impact of any ligand) and show in this case study that ComDYN is able to sample these large conformational changes between states. We have now clarified this point in the manuscript.

Reply: We mostly agree, we have rephrased “sample both IO and OO states” to “sample the intermediate states between IO and OO states” throughout the manuscript.

Reply: A lower force constant would indeed lead to more sampling, just like a wider cut-off which we discuss in our response to question 3 of Reviewer 1, but it would not necessarily lead to a better sampling or better exploration of the landscape. At low force constants for the constraints, in the MARTINI forcefield it becomes increasingly likely that too large conformational changes become permissible, even up to the point of unfolding of the transporter proteins.

Reply: The elastic network used in the MARTINI forcefield captures all residue contacts in the tertiary structure. In the case of SLC about 40% of contacts are conserved between both states, in our definition of less than 1 Å difference in the elastic network distance. Except in the case of (complete) unfolding, the vast majority of these contacts will exist in multiple conformational states of a solute carrier protein. However, it should be noted that we used the word ‘conserved’ in two different meanings: the specific evolutionary conservation, and the more general meaning of preservation, in this case of the contacts between two conformational states. We now clarified this throughout the text by referring to ‘common constraints’, and also changed the name ConsDYN to ComDYN accordingly.

Reply: Indeed, from the dynamics observed we cannot predict pathogenicity. However, we can ‘elucidate’ the known pathogenicity, in the sense of clarifying where previously effects were not understood. We have made the limitations of the method more explicit in the manuscript. In particular, this sentence now reads “For this reason, we chose them as our order parameter to explain the abnormal behavior in the dynamics of the transporter opening and closing mechanism for some of the observed mutants.” In future approaches, sugar binding will certainly need to be considered in order to fully understand the altered dynamics and behavior of the pathogenic mutants.

 Minor comments:

Reply: Indeed, we have now changed the title and appropriate sections in the text to refer to ‘transporter’ in stead of ‘channel’.

Reply: Indeed we cannot. We have clarified this in the text.

Reply: The CG ComDYN simulations are analyzed in Figure 3, Table 2 and SI figures. We now mention this explicitly in the captions of Fig 3 and Table 2 and Figure S3.

Reply: We thank the reviewer for pointing out this mistake, which we have now corrected.