Impact of pathogenic mutations of the GLUT1 glucose transporter on channel dynamics using ConsDYN enhanced sampling

Halima Mouhib; Akiko Higuchi; Sanne Abeln; Kei Yura; K. Anton Feenstra

doi:10.12688/f1000research.18553.1

Home Browse Impact of pathogenic mutations of the GLUT1 glucose transporter on...

ALL Metrics

Views

Downloads

Get PDF

Get XML

Export

▬

✚

Research Article

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]

Halima Mouhib¹, Akiko Higuchi², Sanne Abeln³, Kei Yura^4,5, K. Anton Feenstra ³

Halima Mouhib¹, Akiko Higuchi², [...] Sanne Abeln³, Kei Yura^4,5, K. Anton Feenstra ³

PUBLISHED 22 Mar 2019

Author details Author details

¹ Laboratoire Modélisation et Simulation Multi Echelle (MSME) - UMR 8208 CNRS, Université Paris-Est, Champs-sur-Marne, France
² Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
³ Dept. Computer Science, Integrative Bioinformatics, Vrije Universiteit, Amsterdam, The Netherlands
⁴ Graduate School of Humanities and Sciences, Ochanomizu University, Tokyo, Japan
⁵ School of Advanced Science and Engineering, Waseda University, Tokyo, Japan

Halima Mouhib
Roles: Data Curation, Formal Analysis, Investigation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Akiko Higuchi
Roles: Data Curation, Formal Analysis, Investigation, Visualization, Writing – Original Draft Preparation

Sanne Abeln
Roles: Methodology, Writing – Review & Editing

Kei Yura
Roles: Conceptualization, Funding Acquisition, Resources, Supervision

K. Anton Feenstra
Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Software, Supervision, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Cell & Molecular Biology gateway.

This article is included in the Bioinformatics gateway.

Abstract

Background: The solute carrier (SLC) family of membrane proteins is a large class of transporters for many small molecules that are vital for the cell. 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.
Methods: Predictions from SIFT and PolyPhen provided an impression of the impact upon mutation in the highly conserved RXGRR motifs, but no clear differentiation could be made by these methods between pathogenic and non-pathogenic mutations. Therefore, to identify the molecular effects on the transporter function, insight from molecular dynamic simulations is required. We studied a variety of pathogenic and non-pathogenic mutations, using a newly developed coarse-grained simulation approach ‘ConsDYN’, which allows the sampling of both inward-open and outward-occluded states. To guarantee the sampling of large conformational changes, we only include conserved restraints of the elastic network introduced upon coarse-graining, which showed similar reference distances between the two conformational states (≤1 Å difference).
Results: We capture the ‘conserved dynamics’ between both states using ConsDYN. Simultaneously, it allowed us to considerably lower the computational costs of our study. This approach is sufficiently sensitive to capture the effect of different mutations, and 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 channel function, as it blocks the protein from sampling both conformational states.
Conclusions: Using our approach, we can explain the pathogenicity of the mutation G91D when we observe the configurations of the transmembrane helices, suggesting that their relative position is crucial for the correct functioning of the GLUT1 protein.

Keywords

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

Corresponding author: K. Anton Feenstra

Competing interests: No competing interests were disclosed.

Grant information: 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.

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

How to cite: Mouhib H, Higuchi A, Abeln S et al. 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]. F1000Research 2019, 8:322 (https://doi.org/10.12688/f1000research.18553.1) First published: 22 Mar 2019, 8:322 (https://doi.org/10.12688/f1000research.18553.1) Latest published: 13 Jun 2022, 8:322 (https://doi.org/10.12688/f1000research.18553.2)

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 channel transport function, can enable us to understand the difference between pathogenic and benign mutations that have been observed in human subjects. Glucose transporter GLUT1 is built of 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). 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).

Figure 1. GLUT1 structural overview.

(A) Pipe representation of the inward-open (I_O) conformation (PDB-ID: 5EQI) 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. The red arrows symbolize the inside and outside distances. Note that we number the helices starting from TM1 at the beginning of the N-terminus of the transporter (dark blue in the pipe representation). (B) Pipe representation of the I_O conformation (PDB-ID: 5EQI) of GLUT1 viewing on the outward facing part of the channel inside the periplasm. The four main mutation sites, G91, R92, R333, and R334, are highlighted in magenta. (C) Definition of the order parameters to follow the motion of the helices over the ConsDYN 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 both the outward-occluded state (O_O) and inward-open state (I_O). For GLUT1, however, only the I_O state is available; 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).

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. Based on the reported pathogenic mutations of GLUT1 in the conserved RXGRR motif region, we searched for additional mutations at the corresponding positions of GLUT3 and included them in our study. The selected mutations are situated in the conserved RXGRR-motif distal to the channel. An overview of all mutations for GLUT1 and GLUT3 studied in this work is provided in Table 1. Due to the high sequence identify (~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 building process. Our composite scheme using coarse-grained molecular dynamics with a conserved elastic network ConsDYN is explained in the approach below and further details are supplied in the supporting methods, available in the deposited code (Feenstra, 2019b).

Table 1. 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.

Molecular dynamics simulations

In this study, we employ 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). In addition, we modified the elastic network that is used in MARTINI based on our starting conformations, including only elastic network constraints that differed less than 1Å between the I_O and O_O states. This allows transition between both states, while maintaining protein structure stability. This composite scheme will henceforward be referred to as ConsDYN (CONServed DYNamics). The detailed computational set-up is provided in the Supporting Methods (Feenstra, 2019b).

Analysis of channel dynamics

Essential dynamics analysis was performed on the GLUT1 and GLUT3 simulations using built-in analysis tools on GROMACS. To allow this comparison between these two homologous proteins, and allow for focusing on overall motions of the channel 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 was performed on the ensemble of both wild-type systems, using the full atomistic (AT), coarse-grained (CG) and ConsDYN simulations.

To analyse the channel dynamics from the ConsDYN, 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 channel, 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 (see Results and Discussion).

Results and discussion

Prediction of mutation impact

For each of the mutants of GLUT1 and GLUT3 considered here, Table 1 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.

Verification of constraining approach

Firstly, we want to verify if our conservation-based constraining approach for coarse-grained MD simulations (ConsDYN) is able to sample both I_O and O_O states. We performed essential dynamics (ED) analysis (Amadei et al., 1993; Van Aalten et al., 1997) to compare AT, traditional CG MARTINI, and ConsDYN 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 sampling of the different states, inward-open and outward-occluded, in AT simulation hardly converges. The regular CG simulations already sample more intermediate conformations, but there is no overlap. The ConsDYN simulations, on the other hand, sample many states intermediate to the inside-open and outside-open starting states, compared to other simulations. This means our goal of improved sampling of large conformational transitions has been attained.

Figure 2. Two-dimensional essential dynamics plot of the simulations.

Note that the time-scale of the full-atomistic simulations only samples conformations around the I_O and O_O states, while the ConsDYN samples a large number of conformations between both states. AT, full-atomistic; CG, coarse-grained; ConsDYN, conserved CG; I_O, inward open state (PDB-ID: 5EQI); O_O, outward occluded state (PDB-ID: 4ZW9).

Probing conformational changes

To probe for the degree of the conformational changes during the 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 ConsDYN simulations, resemble those reported by Nagarathinam et al. (2018), providing an independent validation that our ConsDYN 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 is not surprising when comparing human glucose transporters with a bacterial multidrug transporter (see also extended data, Figure S4 and Table S3 (Feenstra, 2019a)).

Therefore, in addition to the TM5-TM8 distances, we extended the analysis to other helices along and across the channel rim that make up for the entire SLC channel architecture, allowing us to monitor changes in their position (see Figure 1B, C). For each of these order parameters, we calculate 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 between the observed distribution 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 direction of change is quantified by the difference in the position (‘shift’) of the maximum of the distribution; negative being a ‘closing’ motion, and positive ‘opening’ (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 ConsDYN simulations, calculated as a function of the inner and outer distances between TM5 and TM7 are given in Figure 3.

Table 2. Overview of the changes in inside and outside distances between transmembrane helix 5 (TM5) and TM11, as quantified using the overlap in distributions of the wild type and mutant (small value is large change), and the shift of the peak location (including the direction; positive is to larger distances).

The pathogenic mutations are underlined. All the large shifts (above 0.5) and small overlaps (below 0.6) are set to bold. For a visual aid, the shifts are set in italic.

GLUT1		G91D	R333W	R334Q	R92Q	R92W	R93Q	R93W
Inside	Overlap	0.04	0.24	0.47	0.60	0.64	0.46	0.62
	Shift	0.84	0.34	-0.05	-0.02	-0.02	0.28	-0.03
Outside	Overlap	0.20	0.69	0.70	0.70	0.48	0.70	0.61
	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	0.25	0.67	0.09	0.07	0.06	0.56	0.12
	Shift	0.27	-0.06	-0.28	-0.35	-0.32	-0.12	0.30
Outside	Overlap	0.28	0.57	0.50	0.53	0.50	0.62	0.15
	Shift	0.37	0.11	0.14	0.21	0.12	0.33	0.58

Figure 3. Distance plots of the inner and outer distances along the order parameter TM5-TM11 over the complete time span of the simulation (see Figure 1C).

Colour code: wild types I_O in purple (PDB-ID: 5EQI), O_Oin 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.

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.

Conclusion

Using extensive ConsDYN 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), the distances between TM5 and TM11, across the rim of the transporter channel structure, are affected the strongest and can be used as order parameters to elucidate abnormal behaviour in the dynamics of the transporter opening and closing mechanism. Comparing atomistic (AT), coarse-grained MARTINI (CG), and ConsDYN simulations, our work shows that our CG ConsDyn simulations are sufficiently accurate to sample between the conformational states and capture the effect of the mutations on the dynamic and function of these transporter proteins.

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: ConsDYN. 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 channel.
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 ConsDYN simulations available from:

https://github.com/ibivu/ConsDYN.

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

License: GNU General Public License 3.0.

Grant information

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.

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.

Faculty Opinions recommended

References

Adzhubei IA, Schmidt S, Peshkin L, et al.: A method and server for predicting damaging missense mutations. Nat Methods. 2010; 7(4): 248–249. PubMed Abstract | Publisher Full Text | Free Full Text
Amadei A, Linssen AB, Berendsen HJ: Essential dynamics of proteins. Proteins. 1993; 17(4): 412–425. PubMed Abstract | Publisher Full Text
An Y, Varma VR, Varma S, et al.: Evidence for brain glucose dysregulation in Alzheimer’s disease. Alzheimers Dement. 2018; 14(3): 318–329. PubMed Abstract | Publisher Full Text | Free Full Text
Arnarez C, Uusitalo JJ, Masman MF, et al.: Dry Martini, a coarse-grained force field for lipid membrane simulations with implicit solvent. J Chem Theory Comput. 2015; 11(1): 260–275. PubMed Abstract | Publisher Full Text
de Jong DH, Singh G, Bennett WF, et al.: Improved Parameters for the Martini Coarse-Grained Protein Force Field. J Chem Theory Comput. 2013; 9(1): 687–697. PubMed Abstract | Publisher Full Text
Deng D, Sun P, Yan C, et al.: Molecular basis of ligand recognition and transport by glucose transporters. Nature. 2015; 526(7573): 391–396. PubMed Abstract | Publisher Full Text
Feenstra KA: ConsDYN. 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, et al.: Calpain I Activation Causes GLUT3 Proteolysis and Downregulation of O-GlcNAcylation in Alzheimer’s Disease Brain. J Alzheimers Dis. 2018; 62(4): 1737–1746. PubMed Abstract | Publisher Full Text
Hediger MA, Clémençon B, Burrier RE, et al.: The ABCs of membrane transporters in health and disease (SLC series): introduction. Mol Aspects Med. 2013; 34(2–3): 95–107. PubMed Abstract | Publisher Full Text | Free Full Text
Hess B, Kutzner C, van der Spoel D, et al.: GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput. 2008; 4(3): 435–447. PubMed Abstract | Publisher Full Text
Higuchi A, Nonaka N, Yura K: iMusta4SLC: Database for the structural property and variations of solute carrier transporters. Biophys Physicobiol. 2018; 15: 94–103. PubMed Abstract | Publisher Full Text | Free Full Text
Hsu PC, Bruininks BMH, Jefferies D, et al.: CHARMM-GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides. J Comput Chem. 2017; 38(27): 2354–2363. PubMed Abstract | Publisher Full Text | Free Full Text
Joshi C, Greenberg CR, De Vivo D, et al.: GLUT1 deficiency without epilepsy: yet another case. J Child Neurol. 2008; 23(7): 832–834. PubMed Abstract | Publisher Full Text
Kapoor K, Finer-Moore JS, Pedersen BP, et al.: 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. PubMed Abstract | Publisher Full Text | Free Full Text
Klepper J, Leiendecker B, Eltze C, et al.: Paroxysmal Nonepileptic Events in Glut1 Deficiency. Mov Disord Clin Pract. 2016; 3(6): 607–610. PubMed Abstract | Publisher Full Text | Free Full Text
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. PubMed Abstract | Publisher Full Text
Klepper J, Willemsen M, Verrips A, et al.: Autosomal dominant transmission of GLUT1 deficiency. Hum Mol Genet. 2001; 10(1): 63–68. PubMed Abstract | Publisher Full Text
Leen WG, Klepper J, Verbeek MM, et al.: Glucose transporter-1 deficiency syndrome: the expanding clinical and genetic spectrum of a treatable disorder. Brain. 2010; 133(Pt 3): 655–70. PubMed Abstract | Publisher Full Text
May A, Pool R, van Dijk E, et al.: Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins. Bioinformatics. 2014; 30(3): 326–334. PubMed Abstract | Publisher Full Text
Mongin M, Mezouar N, Dodet P, et al.: Paroxysmal Exercise-induced Dyskinesias Caused by GLUT1 Deficiency Syndrome. Tremor Other Hyperkinet Mov (N Y). 2016; 6: 371. PubMed Abstract | Publisher Full Text | Free Full Text
Monticelli L, Kandasamy SK, Periole X, et al.: The MARTINI Coarse-Grained Force Field: Extension to Proteins. J Chem Theory Comput. 2008; 4(5): 819–834. PubMed Abstract | Publisher Full Text
Nagarathinam K, Nakada-Nakura Y, Parthier C, et al.: Outward open conformation of a Major Facilitator Superfamily multidrug/H⁺ antiporter provides insights into switching mechanism. Nat Commun. 2018; 9(1): 4005. PubMed Abstract | Publisher Full Text | Free Full Text
Pao SS, Paulsen IT, Saier MH Jr: Major facilitator superfamily. Microbiol Mol Biol Rev. 1998; 62(1): 1–34. PubMed Abstract | Free Full Text
Periole X, Cavalli M, Marrink SJ, et al.: 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. PubMed Abstract | Publisher Full Text
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. PubMed Abstract | Publisher Full Text
Schneider SA, Paisan-Ruiz C, Garcia-Gorostiaga I, et al.: GLUT1 gene mutations cause sporadic paroxysmal exercise-induced dyskinesias. Mov Disord. 2009; 24(11): 1684–1688. PubMed Abstract | Publisher Full Text
Sim NL, Kumar P, Hu J, et al.: SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012; 40(Web Server issue): W452–7. PubMed Abstract | Publisher Full Text | Free Full Text
Simpson IA, Chundu KR, Davies-Hill T, et al.: Decreased concentrations of GLUT1 and GLUT3 glucose transporters in the brains of patients with Alzheimer’s disease. Ann Neurol. 1994; 35(5): 546–551. PubMed Abstract | Publisher Full Text
Szablewski L: Glucose Transporters in Brain: In Health and in Alzheimer’s Disease. J Alzheimers Dis. 2017; 55(4): 1307–1320. PubMed Abstract | Publisher Full Text
Van Aalten DMF, De Groot BL, Findlay JBC, et al.: A comparison of techniques for calculating protein essential dynamics. J Comput Chem. 1997; 18(2): 169–181. Publisher Full Text

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 22 Mar 2019

Author details Author details

Halima Mouhib
Roles: Data Curation, Formal Analysis, Investigation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Akiko Higuchi
Roles: Data Curation, Formal Analysis, Investigation, Visualization, Writing – Original Draft Preparation

Sanne Abeln
Roles: Methodology, Writing – Review & Editing

Kei Yura
Roles: Conceptualization, Funding Acquisition, Resources, Supervision

Competing interests

No competing interests were disclosed.

Grant information

Article Versions (2)

version 2

Revised

Published: 13 Jun 2022, 8:322

https://doi.org/10.12688/f1000research.18553.2

version 1

Published: 22 Mar 2019, 8:322

https://doi.org/10.12688/f1000research.18553.1

© 2019 Mouhib H et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Download

Export To

metrics

	Views	Downloads
F1000Research	-	-
PubMed Central Data from PMC are received and updated monthly.	-	-

Citations

SEE MORE DETAILS

CITE

how to cite this article

Mouhib H, Higuchi A, Abeln S et al. 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]. F1000Research 2019, 8:322 (https://doi.org/10.12688/f1000research.18553.1)

NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article.

track

receive updates on this article

Track an article to receive email alerts on any updates to this article.

Open Peer Review

Version 1

VERSION 1

PUBLISHED 22 Mar 2019

Views

Reviewer Report 08 May 2019

Jocelyne Vreede, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands

Approved with Reservations

https://doi.org/10.5256/f1000research.20307.r47253

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
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

Competing Interests: No competing interests were disclosed.

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.

CITE

Report a concern

Author Response 13 Jun 2022

K. Anton Feenstra, VU University Amsterdam, The Netherlands

13 Jun 2022

Author Response
We thank the reviewer for the attention and time spent on our work, and respond to each of their comments in detail below:
1. Reply: We have added a
... Continue reading
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.
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.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 13 Jun 2022

K. Anton Feenstra, VU University Amsterdam, The Netherlands

13 Jun 2022

Author Response
We thank the reviewer for the attention and time spent on our work, and respond to each of their comments in detail below:
1. Reply: We have added a
... Continue reading
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.
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.
Competing Interests: No competing interests were disclosed. Close
Report a concern

Views

Reviewer Report 15 Apr 2019

Lucie Delemotte, Department of Applied Physics, Science for Life Laboratory (SciLifeLab), KTH Royal Institute of Technology, Stockholm, Sweden

Not Approved

https://doi.org/10.5256/f1000research.20307.r46135

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

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

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
Are the conclusions drawn adequately supported by the results?

No

Competing Interests: No competing interests were disclosed.

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.

CITE

Report a concern

Author Response 13 Jun 2022

K. Anton Feenstra, VU University Amsterdam, The Netherlands

13 Jun 2022

Author Response
We thank the reviewer for the attention and time spent on our work, and respond to each of their comments in detail below:
1. Reply: All the simulations were
... Continue reading
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.
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.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 13 Jun 2022

K. Anton Feenstra, VU University Amsterdam, The Netherlands

13 Jun 2022

Author Response
We thank the reviewer for the attention and time spent on our work, and respond to each of their comments in detail below:
1. Reply: All the simulations were
... Continue reading
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.
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.
Competing Interests: No competing interests were disclosed. Close
Report a concern

Comments on this article Comments (0)

Version 2

VERSION 2 PUBLISHED 22 Mar 2019

Open Peer Review

Reviewer Status

Reviewer Reports

	Invited Reviewers
	1	2	3
Version 2 (revision) 13 Jun 22	read		read
Version 1 22 Mar 19	read	read

Lucie Delemotte, KTH Royal Institute of Technology, Stockholm, Sweden
Jocelyne Vreede, University of Amsterdam, Amsterdam, The Netherlands
Lívia Maria Ferreira Sobrinho, Federal University of Minas Gerais, Minas Gerais, Brazil

Comments on this article

All Comments(0)

Add a comment

Browse by related subjects

Back to all reports

Reviewer Report

8 Views

29 Jan 2025 | for Version 2

Lívia Maria Ferreira Sobrinho, Federal University of Minas Gerais, Minas Gerais, Brazil

8 Views Cite this report Responses(0)

Approved

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
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?

Not applicable
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Yes

Competing Interests

No competing interests were disclosed.

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.

Respond to this report

Responses (0)

Back to all reports

Reviewer Report

8 Views

22 Jul 2022 | for Version 2

Lucie Delemotte, Department of Applied Physics, Science for Life Laboratory (SciLifeLab), KTH Royal Institute of Technology, Stockholm, Sweden

8 Views Cite this report Responses(0)

Approved

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".

Competing Interests

No competing interests were disclosed.

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.

Respond to this report

Responses (0)

Back to all reports

Reviewer Report

25 Views

08 May 2019 | for Version 1

Jocelyne Vreede, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, The Netherlands

25 Views Cite this report Responses(1)

Approved With Reservations

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
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

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Molecular simulation of proteins and DNA

Respond to this report

Responses (1)

Author Response

13 Jun 2022

K. Anton Feenstra, VU University Amsterdam, The Netherlands

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.

View more View less

Competing Interests

No competing interests were disclosed.

Back to all reports

Reviewer Report

42 Views

15 Apr 2019 | for Version 1

Lucie Delemotte, Department of Applied Physics, Science for Life Laboratory (SciLifeLab), KTH Royal Institute of Technology, Stockholm, Sweden

42 Views Cite this report Responses(1)

Not Approved

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

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

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
Are the conclusions drawn adequately supported by the results?

No

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

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

Respond to this report

Responses (1)

Author Response

13 Jun 2022

K. Anton Feenstra, VU University Amsterdam, The Netherlands

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.

View more View less

Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

[1] Adzhubei IA, Schmidt S, Peshkin L, et al.: A method and server for predicting damaging missense mutations. Nat Methods. 2010; 7(4): 248–249. PubMed Abstract | Publisher Full Text | Free Full Text

[2] Amadei A, Linssen AB, Berendsen HJ: Essential dynamics of proteins. Proteins. 1993; 17(4): 412–425. PubMed Abstract | Publisher Full Text

[3] An Y, Varma VR, Varma S, et al.: Evidence for brain glucose dysregulation in Alzheimer’s disease. Alzheimers Dement. 2018; 14(3): 318–329. PubMed Abstract | Publisher Full Text | Free Full Text

[4] Arnarez C, Uusitalo JJ, Masman MF, et al.: Dry Martini, a coarse-grained force field for lipid membrane simulations with implicit solvent. J Chem Theory Comput. 2015; 11(1): 260–275. PubMed Abstract | Publisher Full Text

[5] de Jong DH, Singh G, Bennett WF, et al.: Improved Parameters for the Martini Coarse-Grained Protein Force Field. J Chem Theory Comput. 2013; 9(1): 687–697. PubMed Abstract | Publisher Full Text

[6] Deng D, Sun P, Yan C, et al.: Molecular basis of ligand recognition and transport by glucose transporters. Nature. 2015; 526(7573): 391–396. PubMed Abstract | Publisher Full Text

[7] Feenstra KA: ConsDYN. 2019a. http://www.doi.org/10.17605/OSF.IO/F82H5

[8] Feenstra KA: ibivu/ConsDYN v1.1b (Version v1.1b). Zenodo. 2019b. http://www.doi.org/10.5281/zenodo.2591477

[9] Gu J, Jin N, Ma D, et al.: Calpain I Activation Causes GLUT3 Proteolysis and Downregulation of O-GlcNAcylation in Alzheimer’s Disease Brain. J Alzheimers Dis. 2018; 62(4): 1737–1746. PubMed Abstract | Publisher Full Text

[10] Hediger MA, Clémençon B, Burrier RE, et al.: The ABCs of membrane transporters in health and disease (SLC series): introduction. Mol Aspects Med. 2013; 34(2–3): 95–107. PubMed Abstract | Publisher Full Text | Free Full Text

[11] Hess B, Kutzner C, van der Spoel D, et al.: GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput. 2008; 4(3): 435–447. PubMed Abstract | Publisher Full Text

[12] Higuchi A, Nonaka N, Yura K: iMusta4SLC: Database for the structural property and variations of solute carrier transporters. Biophys Physicobiol. 2018; 15: 94–103. PubMed Abstract | Publisher Full Text | Free Full Text

[13] Hsu PC, Bruininks BMH, Jefferies D, et al.: CHARMM-GUI Martini Maker for modeling and simulation of complex bacterial membranes with lipopolysaccharides. J Comput Chem. 2017; 38(27): 2354–2363. PubMed Abstract | Publisher Full Text | Free Full Text

[14] Joshi C, Greenberg CR, De Vivo D, et al.: GLUT1 deficiency without epilepsy: yet another case. J Child Neurol. 2008; 23(7): 832–834. PubMed Abstract | Publisher Full Text

[15] Kapoor K, Finer-Moore JS, Pedersen BP, et al.: 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. PubMed Abstract | Publisher Full Text | Free Full Text

[16] Klepper J, Leiendecker B, Eltze C, et al.: Paroxysmal Nonepileptic Events in Glut1 Deficiency. Mov Disord Clin Pract. 2016; 3(6): 607–610. PubMed Abstract | Publisher Full Text | Free Full Text

[17] 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. PubMed Abstract | Publisher Full Text

[18] Klepper J, Willemsen M, Verrips A, et al.: Autosomal dominant transmission of GLUT1 deficiency. Hum Mol Genet. 2001; 10(1): 63–68. PubMed Abstract | Publisher Full Text

[19] Leen WG, Klepper J, Verbeek MM, et al.: Glucose transporter-1 deficiency syndrome: the expanding clinical and genetic spectrum of a treatable disorder. Brain. 2010; 133(Pt 3): 655–70. PubMed Abstract | Publisher Full Text

[20] May A, Pool R, van Dijk E, et al.: Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins. Bioinformatics. 2014; 30(3): 326–334. PubMed Abstract | Publisher Full Text

[21] Mongin M, Mezouar N, Dodet P, et al.: Paroxysmal Exercise-induced Dyskinesias Caused by GLUT1 Deficiency Syndrome. Tremor Other Hyperkinet Mov (N Y). 2016; 6: 371. PubMed Abstract | Publisher Full Text | Free Full Text

[22] Monticelli L, Kandasamy SK, Periole X, et al.: The MARTINI Coarse-Grained Force Field: Extension to Proteins. J Chem Theory Comput. 2008; 4(5): 819–834. PubMed Abstract | Publisher Full Text

[23] Nagarathinam K, Nakada-Nakura Y, Parthier C, et al.: Outward open conformation of a Major Facilitator Superfamily multidrug/H⁺ antiporter provides insights into switching mechanism. Nat Commun. 2018; 9(1): 4005. PubMed Abstract | Publisher Full Text | Free Full Text

[24] Pao SS, Paulsen IT, Saier MH Jr: Major facilitator superfamily. Microbiol Mol Biol Rev. 1998; 62(1): 1–34. PubMed Abstract | Free Full Text

[25] Periole X, Cavalli M, Marrink SJ, et al.: 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. PubMed Abstract | Publisher Full Text

[26] 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. PubMed Abstract | Publisher Full Text

[27] Schneider SA, Paisan-Ruiz C, Garcia-Gorostiaga I, et al.: GLUT1 gene mutations cause sporadic paroxysmal exercise-induced dyskinesias. Mov Disord. 2009; 24(11): 1684–1688. PubMed Abstract | Publisher Full Text

[28] Sim NL, Kumar P, Hu J, et al.: SIFT web server: predicting effects of amino acid substitutions on proteins. Nucleic Acids Res. 2012; 40(Web Server issue): W452–7. PubMed Abstract | Publisher Full Text | Free Full Text

[29] Simpson IA, Chundu KR, Davies-Hill T, et al.: Decreased concentrations of GLUT1 and GLUT3 glucose transporters in the brains of patients with Alzheimer’s disease. Ann Neurol. 1994; 35(5): 546–551. PubMed Abstract | Publisher Full Text

[30] Szablewski L: Glucose Transporters in Brain: In Health and in Alzheimer’s Disease. J Alzheimers Dis. 2017; 55(4): 1307–1320. PubMed Abstract | Publisher Full Text

[31] Van Aalten DMF, De Groot BL, Findlay JBC, et al.: A comparison of techniques for calculating protein essential dynamics. J Comput Chem. 1997; 18(2): 169–181. Publisher Full Text

Impact of pathogenic mutations of the GLUT1 glucose transporter on channel dynamics using ConsDYN enhanced sampling

Abstract

Keywords

Introduction

Figure 1. GLUT1 structural overview.

Methods

Starting structures

Table 1. 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.

Molecular dynamics simulations

Analysis of channel dynamics

Results and discussion

Prediction of mutation impact

Verification of constraining approach

Figure 2. Two-dimensional essential dynamics plot of the simulations.

Probing conformational changes

Figure 3. Distance plots of the inner and outer distances along the order parameter TM5-TM11 over the complete time span of the simulation (see Figure 1C).

Impact of mutations on dynamics

Conclusion

Data availability

Underlying data

Extended data

Software availability

Grant information

Acknowledgments

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

Browse by related subjects

Competing Interests Policy

Stay Updated