Substrate Transport and Specificity in a Phospholipid Flippase

  1. Yong Wang
  2. Joseph A Lyons
  3. Milena Timcenko
  4. Felix Kümmerer
  5. Bert L. de Groot
  6. Poul Nissen
  7. Vytautas Gapsys
  8. Kresten Lindorff-Larsen

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Type 4 P-type ATPases are lipid flippases which help maintain asymmetric phospholipid distribution in eukaryotic membranes by driving unidirectional translocation of phospholipid substrates. Recent cryo-EM and crystal structures have provided a detailed view of flippases, and we here use molecular dynamics simulations to study the first steps of phospholipid transport and lipid substrate specificity. Our simulations and new cryo-EM structure shows phospholipid binding to a groove and subsequent movement towards the centre of the membrane, and reveal a preference for phosphatidylserine lipids. We find that only the lipid head group stays in the groove while the lipid tails remain in the membrane, thus visualizing how flippases have evolved to transport large substrates. The flippase also induces deformation and thinning of the outer leaflet facilitating lipid recruitment. Our simulations provide insight into substrate binding to flippases and suggest that multiple sites and steps in the functional cycle contribute to substrate selectivity.

Article activity feed

  1. Version published to 10.1101/2020.06.24.169771v2 on bioRxiv
  2. eLife

    ###Reviewer #3:

    This work reports the results from a set of predominantly coarse-grained (CG) simulation of phospholipid interactions with the yeast fippase Drs2p:cdc50p in the outward facing state. Using the popular MARTINI force field, these simulations reveal multiple putative binding sites of lipid molecules and support a likelihood of the "credit-card" model of lipid transport. The authors have also analyzed the possible preference of different lipids at these sites. While these are interesting observations, they are severely limited by the CG nature of the model and lack strong corroborating support from either atomistic simulations or experiment.

    1. While this work includes a substantial set of atomistic simulations, they do not appear to provide much useful information or provide much support to any of the central conclusions of the work.

    2. Instead, virtually all key conclusions are based on MARTINI simulations. While this is indeed an outstanding CG model that has been successfully applied to an increasing number of problems (particularly self-assembly), it is highly questionable that MARTINI is appropriate for predicting binding sites. To the best of my knowledge, this model has not been demonstrated to be reliable for such purposes. It requires great caution and careful validation to establish and support the predicted binding sites.

    Are there any collaborating experimental evidence to support these sites? The authors only made minimal efforts to validate this critical prediction, largely by noting that EM densities suggest multiple binding sites. This needs to be investigated thoroughly, such as by direct comparison of these locations.

    Can one at least test if lipids can stably occupy those sites using atomistic simulations?

    1. Membrane thinning is only observed in CG but not atomistic simulations; this is alarming, as membrane thinning should be able to be captured in atomistic simulations within a few 100 ns. This has been demonstrated clearly in several published simulations of scramblases (e.g., Bethel and Grabe PNAS 2016, among others). This calls the quality of the MARTINI simulations into question for capturing detailed properties of this flippase complex.

    2. Free energy analysis was done with the MARTINI model, which greatly reduces its usefulness. As stated above, the MARTINI model is really not appropriate for such detailed free energy analysis of these putative binding sites.

  3. eLife

    ###Reviewer #2:

    This manuscript "Computational Studies of Substrate Transport and Specificity in a Phospholipid Flippase" presents multiscale simulations to understand the details of a yeast flippase in lipid binding, membrane deformation, and protein hydration. Overall, an examination of the Drs2p-Cdc50p complex was carried out with 500-ns-long all-atom and 100-us-long coarse-grained simulations in different membrane models (pure PS, PE, PC and mixtures). Free-energy simulations were also employed to compare lipid binding free energies. A major finding is the identification of the anionic PS lipid binding to a water-filled substrate binding groove. However, I find the work lacks clarity, novelty, and biological insight.

    1. My primary concern is that three different phospholipids were selected in this work: PS, PE, and PC, but only the PS lipid is anionic. First of all, it is quite obvious that the PS lipid is preferred in this limited set, due to the formal charge difference. The higher affinity of anionic lipids to transmembrane proteins has been extensively studied (too many to list, but here are a few recent examples PNAS 2020 117, 7803-7813; Structure, 2019, 27, 392-403.e3; Sci Rep. 2018, 8, 4456; Sci Rep. 2016, 6, 29502)

    Second, according to prior experiments (Appl Environ Microbiol. 2014, 80, 2966-2972), the major phospholipids in yeast are phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidic acid (PA), with minor amounts of cytidinediphosphate-diacylglycerol (CDP-DAG). There are also glycosphingolipids, ergosterol, and proteins. None of the membrane models simulated in this work is an approximate to the realistic yeast cellular membrane. Because the lipid composition has important physiological impacts, I found a lack of justification of why key anionic lipids (like PI and PA) and ergosterol were not included.

    1. In addition, it was claimed "As our atomistic simulations were limited to 0.5-1.0 𝜇𝑠 due to their high computational cost". I cannot agree with the authors, given the system size of ~340,000 atoms. It is not rare to see microsecond or multiple-microsecond all-atom simulations (of this size or larger) in current studies of membrane proteins. Further, longer simulations might be more likely to sample lipid exchange and competition within the groove, as well as relevant protein conformational changes (which cannot be captured in CG simulations).

    2. Moreover, while I found the results presented in Fig. 5 quite interesting, the related paragraphs seem to lack the in-depth analysis and clarity to support "a 'credit-card'-like model" First, it is not clear to me how this lipid in Fig. 5 was selected. How did this lipid look in the outer leaflet vs. in the deep state of the groove? Second, there is no analysis of the event at ~21-23 us when the lipid starts to transition. What was the trigger of the event? Were there any specific interactions? Last but not the least, as the authors said "X-ray diffraction and Cryo-EM experiments on ATP8A1 and ATP11C show density for PL head groups", it is possible to compare the simulation results (lipid density) to the experimental density. It would greatly strengthen this paper if such analysis is included.

    3. The "water-filled cavities" results overall may need more clarification and probably even experimental support. First of all, how were the AA simulations compared with CG simulations, in terms of the cavities? Given the ENM constraints, there were little conformational changes of the cavities (of the protein) in response to PS moving the groove. There might be some induced fit effect and the cavities may adopt different shapes when such effect is fully considered in the AA modeling. Second, is there any experimental evidence to support this observation from MD simulations? For example, mutation of the key residue Ile508, suggested by the authors to separate the two cavities.

  4. eLife

    ###Reviewer #1:

    This is an outstanding paper. MD simulations at two resolutions are employed to provide convincing predictions regarding the lipid-binding to flippases in terms of mechanism of binding and specificity. The topic is of fundamental biology interest and the results provide deeper insights than are possible with experimental structural biology methods alone.

    The simulations are certainly state-of-the-art in terms of methodology and are well ahead of the field in terms of simulation length.

    The paper is written and presented clearly. The results are explained in detail and have the necessary statistical treatment to provide confidence in them. The discussion is based on the results and contextualised appropriately- there is no claim that cannot be supported by the results.

    A number of important observations are reported including those concerning lipid tail orientations, water-filled cavities, and lipid binding affinity.

    Overall the authors should be commended on a thorough computational study.

  5. eLife

    ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

    ###Summary:

    In general, all reviewers agreed that the problem is of importance and the simulations have been well conceived and thoroughly conducted at the coarse-grained level. However, there is the concern that while MARTINI is able to capture many collective properties of lipid membranes, it is not sufficiently reliable for dissecting molecular recognition processes governed by subtle free energy differences, especially when electrostatics (difference in charge state) and protein conformational rearrangements are expected to play major roles. In absence of direct supporting experimental verification, this concern undermines the central conclusions of the study.

  6. Version published to 10.1101/2020.06.24.169771v1 on bioRxiv