Impact of pathogenic mutations of the GLUT1 glucose transporter on solute carrier dynamics using ComDYN enhanced sampling

Abstract

Background: The solute carrier (SLC) family of membrane proteins is a large class of transporters for many small molecules that are vital for cellular function. Several pathogenic mutations are reported in the glucose transporter subfamily SLC2, causing Glut1-deficiency syndrome (GLUT1DS1, GLUT1DS2), epilepsy (EIG2) and cryohydrocytosis with neurological defects (Dystonia-9). Understanding the link between these mutations and transporter dynamics is crucial to elucidate their role in the dysfunction of the underlying transport mechanism, which we investigate using molecular dynamics simulations. Methods: We studied pathogenic and non-pathogenic mutations, using a newly developed coarse-grained simulation approach 'ComDYN', which captures the 'COMmon constraints DYNamics' between both states of the solute carrier protein. To guarantee the sampling of large conformational changes, we only include common constraints of the elastic network introduced upon coarse-graining, which showed similar reference distances between both conformational states (≤1 Å difference). Results: ComDYN is computationally efficient and sufficiently sensitive to capture effects of different mutations. Our results clearly indicate that the pathogenic mutation in GLUT1, G91D, situated at the highly conserved RXGRR motif between helices 2 and 3, has a strong impact on transporter function, as it blocks the protein from sampling both conformational states. In comparison, predictions from SIFT and PolyPhen only provided an impression of the impact upon mutation in the highly conserved RXGRR motifs, but yielded no clear differentiation between pathogenic and non-pathogenic mutations. Conclusions: Using our approach, we can explain the pathogenicity of the mutation G91D and some of the effects of other known pathogenic mutations, when we observe the configurations of the transmembrane helices, suggesting that their relative position is crucial for the correct functioning of the GLUT1 protein. To fully understand the impact of other mutations in the future, it is necessary to consider the effect of ligands, e.g., glucose, within the transport mechanism.

Keywords: GLUT1 glucose transporter deficiency syndrome; Human glucose transporters; Martini force field; SLC transporter family; coarse-grained simulations; enhanced sampling method; molecular dynamics simulation; transport mechanism.

Figure 1.. GLUT1 structural overview.

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

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

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

Figure 3.. Distance plots of the inner and outer distances along the order parameter TM5-TM11 in nm over the complete time span of the CG ComDYN simulations (see Figure 1D).

Colour code: wild types I _O in purple (PDB-ID: 5EQI), O _O in green (PDB-ID: 4ZW9), mutants in orange. Pathogenic mutants are highlighted in red. It should be noted that in contrast to the benign R93Q mutant, the pathogenic mutants do not sample the I _O and O _O states during the simulation, which strongly indicates that the mutation blocks the proper opening and closing mechanism. Corresponding plots for all mutations are in the extended data, Figure S4. The corresponding quantification of these plots provided as shift and overlap are given in Table 2.