Integrated AlphaFold2 and DEER investigation of the conformational dynamics of a pH-dependent APC antiporter

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The Amino Acid-Polyamine-Organocation transporter GadC contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular GABA. Its structure, determined exclusively in an inward-facing conformation at alkaline pH, consists of the canonical LeuT-fold of a conserved five-helix inverted repeat, thereby resembling functionally divergent transporters such as the serotonin reuptake transporter SERT and the glucose-sodium symporter transporter SGLT1. However, despite this structural similarity, it is unclear if the conformational dynamics of antiporters such as GadC follows the blueprint of these or other well-studied LeuT-fold transporters. Here, we used double electron-electron resonance (DEER) spectroscopy to monitor the conformational dynamics of GadC in lipid bilayers in response to acidification and substrate binding. To guide experimental design and facilitate the interpretation of the DEER data, we generated an ensemble of structural models in multiple conformations using a recently introduced AlphaFold2 methodology. Our experimental results reveal acid-induced conformational changes that dislodge the C-terminus from the permeation pathway coupled with rearrangement of helices that enable isomerization between both inward- and outward-facing states. The substrate glutamate, but not GABA, modulates the dynamics of an extracellular thin gate without shifting the equilibrium between inward- and outward-facing conformations. In addition to introducing an integrated methodology for probing transporter conformational dynamics, the congruence of the DEER data with patterns of structural rearrangements deduced from ensembles of AlphaFold2 models illuminate the conformational cycle of GadC underpinning transport and exposes yet another example of the divergence between the dynamics of different functional families in the LeuT-fold.


The transporter GadC contributes to acid resistance in bacterial pathogens by exchanging two substrates, glutamate and GABA, using a mechanism termed alternating access. In this study, the conformational dynamics underlying alternating access was studied using a combination of spectroscopy and computational modeling. A conformationally diverse ensemble of models, generated using AlphaFold2, guided the design and interpretation of double electron-electron resonance spectroscopy experiments. We found that whereas GadC was inactive and conformationally homogeneous at neutral pH, low pH induced isomerization between two conformations. From our integrated computational/experimental investigation emerges a transport model that may be relevant to eukaryotic homologs that are involved in other cellular processes.

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  1. Consolidated peer review report (22 June 2022)


    GadC is a LeuT-fold antiporter that contributes to the survival of pathogenic bacteria under extreme acid stress by exchanging extracellular glutamate for intracellular GABA. The current study by del Alamo et al. uses DEER spectroscopy, in combination with AlphaFold2 (AF2), to investigate the conformational dynamics of GadC invoked by pH and substrates. AlphaFold2 was used to generate structural models, which likely represent different steps within the antiport conformational cycle of GadC, and use them to identify reporter residue pairs that undergo large inter-residue distance changes between the different conformations. By labeling these residue pairs with spin labels, the authors were able to use DEER spectroscopy to measure the distributions of inter-residue distances in the transporter under different conditions, namely high and low pH and the presence or absence of glutamate or GABA. They found many differences between high and low pH, but few differences triggered by the presence of substrates. They conclude that: (i) low pH triggers release of the intracellular C-terminal plug, unlocking the transporter and populating multiple conformational states; (ii) the bundle helices (TMs 1,2,6,7) do not behave as a rigid body; (iii) Alternating access in GadC does not involve large-scale movements of the intracellular and extracellular loops IL1 and EC4, as is believed to occur in other LeuT-fold transporters – instead, an extracellular “thin gate” in TM10 may be involved in permeation and substrate discrimination. They arrive at a model in which the substrates primarily affect the kinetics of conformational changes rather than the conformational equilibrium. This work also demonstrates the value of using AlphaFold2 to generate plausible structures tied to functional protein states to facilitate experimental design and data interpretation.

    Overall, the work is very interesting and provides significant new insights into the transport mechanism of GadC. The manuscript is clearly written, well organized, and the conclusions are reasonable. The DEER and CW EPR data are of high quality and the analysis of the experimental data is appropriate. We have several recommendations that we feel would improve the presentation and interpretation of the experiments.


    Revisions essential for endorsement:

    1. The authors assert that distance changes observed by DEER correlate well with in silico predictions of distance changes between the inward-open crystal structure and the putatively outward-open model generated by AF2 (c.f. Figure S15). However, in many cases, the peak in the distribution assigned to a particular state does not correspond to the major peak, and sometimes corresponds to a low amplitude broad peak (maybe not surprising for a cycling transporter). This raises the issue of whether interpretation of the experimental results could be biased by computational predictions. The authors should acknowledge the difficulty inferring functional states from DEER distributions and be very clear about which distances in the calculated distributions are being interpreted as functional states. Also, the site pairs that deviate from the trend in Fig. S15, such as 87/155 and 117/181, should also be included in the figure for transparency, even if the discrepancies are justified in the text.

    2. It would be important to report the distances, widths, amplitudes, and number of Gaussians used to fit each DEER trace.

    3. The authors should describe in the methods section how they determine transport rates. They mentioned that they use a Michaelis-Menten kinetic model, but they should then also describe how they use their data to calculate the initial rates for MM kinetics.

    4. The descriptions of the transport assay data in several of the figure legends are missing important experimental details: which substrate was incorporated in the liposomes, external pH of the liposomes, concentration of substrate. (Different details are missing in different figure legends.)

    Additional suggestions for the authors to consider:

    1. Do the authors know whether the GadC protein has a preferred orientation in liposomes? It seems surprising that they see very little transport unless they have high external pH. Given the low internal pH (pH 5.5) and with a 50%-50% random orientation of proteins, one might expect inside-in transporters to be active.

    2. The authors should show an example of a size exclusion chromatography trace to support their statement that they can separate empty nanodiscs from protein-containing nanodiscs. This seems surprising considering the size and shape of GadC – perhaps they instead separate nanodiscs from free protein?

    3. What are the criteria used for choosing the amino acids to substitute the intrinsic Cys?

    4. Because nanodiscs are known to be unstable at acidic pH, could it be that the observed changes at low pH are partially due to pH-dependent changes of the nanodiscs?

    5. Page 4 mentions that no experimental evidence indicates formation of a quaternary assembly (dimer) in the lipid nanodiscs. Is this based on DEER measurements or other data? The authors should consider supporting their statement with data.

    6. How similar are the predicted distributions for different AF2 models in the same presumed functional state? Was there a reason for choosing just one? Is there some way of combining distributions of all the models in a given functional state?

    7. The authors assert that the AF2 models more accurately fit the DEER data than the Rosetta models. What is the basis for this claim? Is there a way to add a supplemental figure showing a comparison of the distributions of each model relative to the DEER data in the same graph (maybe at just low pH)?

    8. The pH-driven association/dissociation of the C-terminus of GadC shown here is reminiscent of the well-studied “N-type” inactivation of voltage-gated potassium channels. Indeed, the authors even use the term “inactivation” here to describe the lack of transport at alkaline pH. A brief discussion of the similarity to K+-channel inactivation might be interesting to many readers.

    9. A brief description of the method used to model spin labels onto structural models to predict DEER distance distributions would be helpful.

    10. The authors may want to clarify what they mean by a “thin gate”.


    Reviewed by:

    Eric G. Evans, Postdoctoral Researcher, University of Washington, USA: EPR, DEER, Rosetta

    Rachelle Gaudet, Professor, Harvard University, USA: structure and mechanisms of LeuT-fold transporters

    Yun Huang, Postdoctoral researcher, Weill Cornell Medical College: structural and dynamic mechanism of glutamate transporters

    William N. Zagotta, Professor, University of Washington, USA: membrane protein dynamics

    Curated by:

    William N. Zagotta, Professor, University of Washington, USA

    (This consolidated report is a result of peer review conducted by Biophysics Colab on version 1 of this preprint. Minor corrections and presentational issues have been omitted for brevity.)