Cytokine receptors are transmembrane glycoproteins that bind cytokines on the cell surface and transduce signals across the membrane to the interior of the cell. This, in turn, activates signaling pathways leading to multiple outcomes including induction of immune responses, cell proliferation, altered metabolism, and differentiation (Brooks et al., 2016). Class 1 cytokine receptors constitute a subclass of receptors that transverse the membrane by a single α-helical transmembrane domain (TMD; Brooks et al., 2016), separating a folded extracellular domain (ECD) from a disordered intracellular domain (ICD). The prolactin (PRL) receptor (PRLR) is one of the structurally most simple cytokine receptors (Figure 1). Signaling by the PRLR/PRL complex is implicated in the regulation of more than 300 biological functions in vertebrates (Bole-Feysot et al., 1998), and its function is especially well-known for its essential role in mammary gland development and lactation (Hannan et al., 2023). Apart from this, deregulation of PRLR/PRL signaling is associated with several pathologies in humans of which hyperprolactinemia resulting in reproductive failure is best described (Bachelot and Binart, 2007; Newey et al., 2013). Deregulation of PRLR/PRL signaling is linked to other diseases and, although still debated, suggested to be implicated in the development and progression of prostate (Sackmann-Sala and Goffin, 2015) and breast (Clevenger and Rui, 2022) cancers.

Figure 1

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For cytokine receptors, signal transduction through the membrane is received by an ICD, which is intrinsically disordered and lacks kinase activity (Haxholm et al., 2015). Thus, association of auxiliary kinases is mandatory for signaling, with the Janus kinases (JAK1–3 and TYK2) being the most thoroughly described (Brooks et al., 2016; Morris et al., 2018). A proline-rich region constituting the so-called BOX1 motif close to the membrane, as well as a second hydrophobic motif termed BOX2, are known to be essential for JAK binding (Figure 1; Ferrao et al., 2018; Rowlinson et al., 2008). However, although progress has been made in the molecular understanding of cytokine binding and despite several structures of folded ECDs (Broutin et al., 2010; de Vos et al., 1992), TMDs (Bocharov et al., 2018; Bugge et al., 2016), a complete receptor (Kassem et al., 2021), and a receptor ICD in complex with JAK1 (Glassman et al., 2022) have emerged, it is still not clear how the signal inside the cell is received by the disordered region to elicit and control signal transduction.

A subset of class 1 cytokine receptors form homodimers and trimeric complexes with their ligands, with the main dimerization occurring in the TMDs (Brooks et al., 2014; Brown et al., 2005; Gadd and Clevenger, 2006; Kubatzky et al., 2001; Seubert et al., 2003). This group includes the growth hormone receptor (GHR), the erythropoietin receptor, the thrombopoietin receptor, and the PRLR, which have become well-established paradigmatic models. Recently, signal transduction by the GHR has been suggested to occur via a rotation of the transmembrane helices within the dimer leading to a subsequent separation of the ICDs (Brooks et al., 2014; Brown et al., 2005). A torque is hereby exerted on the associated JAK2s, which is thought to relieve inhibition by the pseudokinase domains, initiating signaling. The ICDs of these receptors have been shown to be highly disordered (Haxholm et al., 2015), a feature which is preserved in models of the PRLR (Bugge et al., 2016) and the full-length GHR in nanodiscs (Kassem et al., 2021). This brings forward the question of how signaling is orchestrated by disorder and how a disordered linker region between the TMD and the region bound to the kinases can communicate and transduce information.

For both the PRLR and the GHR, lipid interaction domains (LID) with affinity for negatively charged lipids have been identified in their ICDs (Haxholm et al., 2015). Common to both receptors is that they contain a LID proximal to the membrane, directly overlapping with the JAK2 interaction sites, BOX1 and BOX2 (Seiffert et al., 2020). Using nuclear magnetic resonance (NMR) spectroscopy, we identified three LIDs in the PRLR-ICD termed LID1, LID2, and LID3 (Figure 1; Haxholm et al., 2015), and using lipid dot-blots, we showed that the PRLR-ICD has variable affinities for different membrane constituents, including for different phosphoinositides (PIs). In particular, PRLR has a distinct affinity for PI-4,5-bisphosphate (PI[4,5]P₂) and lacks affinity for PI(3,4,5)P₃ (Haxholm et al., 2015). PIs are important constituents of the membrane and play key roles in signaling, both as membrane interaction partners that can be specifically modulated by phosphorylation (Carracedo and Pandolfi, 2008) and as secondary messengers (McLaughlin et al., 2002; Suh and Hille, 2005). Indeed, some single-pass receptors contain conserved anionic lipid interaction sites close to the membrane (Hedger et al., 2015), and increasing evidence suggests lipid interaction to take on important regulatory roles (McLaughlin et al., 2005; Metcalf et al., 2010). Recently, the epidermal growth factor receptor (EGFR) was shown to sequester PI(4,5)P₂ by accumulating it around the TMD regulating the dimer/monomer equilibrium and with a possible positive feedback loop through the activation of the phospholipase C (PLC) – diacylglycerol (DAG)-IP₃ pathways (Maeda et al., 2018). This will lead to subsequent conversion of PI(4,5)P₂ to PI(3,4,5)P₃ and hence depletion of PI(4,5)P₂ from the membrane. Similar depletion of PI(4,5)P₂ from the plasma membrane has been noted under hypoxia (Lu et al., 2022). For class 1 cytokine receptors, the role of PIs in signaling is less clear.

In a cellular context, signaling-related proteins can be membrane anchored through modifications such as acylation (Patwardhan and Resh, 2010; Rawat et al., 2013; Rawat and Nagaraj, 2010) or via designated lipid-binding domains. This includes the four point-1, ezrin, radixin moesin (FERM) domain of radixin, focal adhesion kinase (FAK) and the protein tyrosine phosphatase L1 (PTPL1; Bompard et al., 2003; Feng et al., 2015; Hamada et al., 2000), the SH2 domains of the Src family kinases (Park et al., 2016; Sheng et al., 2016), and the FERM-SH2 domain of merlin (Mani et al., 2011). Thus, colocalization of receptors and related signaling proteins can occur at the plasma membrane without necessarily being bound within a complex. It is, however, unclear whether such membrane co-localization has functional consequences, such as enhancing signaling speed, and whether the membrane may act as an additional scaffolding platform that enhances binding via restriction in the two-dimensional plane.

Recent work on disorder in membrane proteins and on the interplay between membrane proteins and lipids has revealed the need for strong integrative methods, combining successfully various structural biology techniques, biophysics, and computational biology (Basak et al., 2022; Larsen et al., 2022). These include NMR, small-angle X-ray scattering, crosslinking-mass spectrometry, and single molecule fluorescence combined with molecular dynamics (MD) simulations (Chavent et al., 2018; Goretzki et al., 2023). These efforts have provided important insights into the role of lipids in regulation of membrane proteins. For TRPV4, a member of the TRP vanilloid channel family, it was shown that an autoinhibitory patch of the receptor competed with PI(4,5)P₂ binding at the membrane to attenuate channel activity, and MD simulation showed that lipid binding affected the ensemble dynamics (Goretzki et al., 2023). For EphA2, a receptor tyrosine kinase, an integrative study showed how PIs mediate the interaction between the kinase domains, facilitated by clustering of PIPs via binding to the receptor juxtamembrane domain (Chavent et al., 2018), further promoting conformation specific dimerization (Stefanski et al., 2021). Thus, studying dynamic processes at the membrane interface is an emerging field requiring integrative structural biology approaches for detailed atomic resolution information.

For the PRLR, it is still not clear whether, and if so how, interactions between the ICD and the membrane impact signal transduction and association with JAK2. Nor is it understood how structural disorder can relay and transmit information from the TMD to initiate signaling. To shed light on the molecular details underlying a potential interplay between the receptor, membrane, and kinase, we focused on the human PRLR and its LID1 closest to the membrane, facilitating the first intracellular step in signaling. Using an integrative approach combining NMR spectroscopy, cell biology, and computational modeling, we demonstrate the formation of a co-structure composed of the disordered PRLR-ICD, the membrane constituent PI(4,5)P₂, and the FERM-SH2 domain of the JAK2. Facilitated by this co-structure, the disordered region closest to the membrane forms an extended structure, which we suggest stabilizes the disordered domain, allowing signal relay from the extracellular to the ICD.

The sequence of the human PRLR has been known for more than 35 years (Boutin et al., 1988). Little attention has, however, been given to the role of membrane composition for PRLR signaling, despite it being placed in the plasma membrane where PI levels are highly dynamic and spatially variable and being linked to cancer with lipid deregulation (Dadhich and Kapoor, 2022). Here, we asked if JAK2 and PRLR-ICD share a PI(4,5)P₂ binding site and if and how the binding to PI(4,5)P₂ plays a role in the orientation of these proteins with respect to the membrane. Integrating MD simulations with biophysical and cellular experiments has been critical in this endeavor. Our first goal was to identify the residues of LID1 involved, as well as the structure formed—if any—in the protein–lipid complex. Our results suggest that the residues that form the ICJM and BOX1 regions of the ICD interact with the lipids via non-specific hydrophobic interactions that involve penetration of the bilayer below the headgroups. This in turn enables positively charged residues of the ₂₅₁KIK₂₅₃ motif to establish ionic interactions with PI(4,5)P₂, and in doing so the region folds into an extended structure, similar to structures of other cytokine receptors in complex with either JAK1 or TYK2 (Wallweber et al., 2014; Zhang et al., 2016). In turn, PI(4,5)P₂ lipids accumulate around the TMD and LID1 of PRLR, suggesting a relevant functional role of the interaction.

The results highlight the capacity of the LID1 to establish highly populated and specific interactions with PI(4,5)P₂ via residues essential for its interaction with JAK2. Therefore, we addressed whether LID1 in complex with the FERM-SH2 domain of JAK2 could engage with the lipid bilayer in the absence of the TMD and with or without PI(4,5)P₂ lipids. Indeed, PI(4,5)P₂ was required for binding of the complex to the membrane, as the presence of only POPS in the lower leaflet was not enough to sustain binding despite its negative charge. Remarkably, when PI(4,5)P₂ was present, we observed specific binding orientations that positioned the ICJM region of the LID1 in the same position as when tethered to the TMD. Furthermore, in the complex, the protein–lipid contact profiles were similar to the one observed for LID1 alone suggesting the PI(4,5)P₂ binding pattern to be maintained in complex with JAK2. A detailed study of two of the most populated PRLR-bound states of JAK2 revealed a striking difference in orientation and contact pattern with the lipids that could shed light on functionally relevant states. For example, the most populated state, the Y state, had contacts from the F2 lobe of the JAK2-FERM-SH2 domain and the ICJM of the PRLR, which penetrated the membrane forming hydrophobic interactions with the acyl chains. In this orientation, regions of both JAK2 and the PRLR that have been associated with receptor dimerization and activation for signaling (Ferrao et al., 2018; Wilmes et al., 2020) are exposed to solvent and available for interactions. We note that this orientation has resemblance to that shown in recent cryo electron microscopy (EM) structures of JAK1 bound to IFNAR1 (Glassman et al., 2022). For the Flat state, we observed a drastic change in the protein–lipid contact profiles for both proteins but more markedly for LID1. While the main interaction site remains the F2 lobe of JAK2 and the ICJM of the PRLR, the N-terminal residues of the LID1 now lie sandwiched between the membrane and JAK2-FERM-SH2 domain and recapitulates the binding pattern observed from TMD-ICD_F206-H300 simulations with membranes containing PI(4,5)P₂. Remarkably, in this Flat state, most of the accessible regions in the Y states are now hidden under the membrane. Thus, we speculate that the Y and Flat states may mimic an available and hidden state, respectively, that could be relevant for regulation of dimerization and activation of signaling. Interestingly, one residue that has been suggested to play a major role in JAK2 membrane association, orientation, dimerization, and activation is L224 (Wilmes et al., 2020). This residue anchors into the membrane only in the Y state and not in the Flat state. Similarly, in simulations where L224 was mutated to glutamate, a change in preferred orientation together with loss of dimerization and JAK2 and STAT5 phosphorylation was observed (Wilmes et al., 2020). This further highlight that the orientation of the PRLR-JAK2 complex relative to the membrane has functional relevance. Importantly, the presence of PI(4,5)P₂ in the membrane structurally tightens the path from the ECD, folding the ICJM and BOX1 in an extended structure, making transmission of information of hormone binding possible. Thus, signal relay by disordered linker of the PRLR can now be possible through its complex formation with the membrane and JAK2 (Figure 6).

Figure 6

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Lastly, we tested the functional relevance of our observations by mutating residues that appeared significant for PI(4,5)P₂:PRLR:JAK2 co-structure formation and determining their impact on cellular PRL signaling. Both KxK motifs and the hydrophobic residues connecting them were important for PI(4,5)P₂ interaction, PRLR membrane localization, and cellular JAK2/STAT5 signaling. From the MD simulation, it was however evident that not all residues in these motifs were in direct contact with the membrane, further highlighting that co-structure formation between PRLR, JAK2, and the membrane is essential for optimal signal transduction. Another interesting observation was that even though mutating the CIF motif had the largest impact on PI(4,5)P₂ binding, it had only a limited effect on cellular JAK2/STAT5 signaling. Since the NMR results suggested that the ICJM serves as a primary PI(4,5)P₂ anchoring point facilitating additional contacts along the chain, this could indicate that a cooperative interaction within the co-structure is needed to control signaling and that PI(4,5)P₂ interaction is necessary for proper and substantial co-structure formation.

The PI(4,5)P₂-specific interactions observed point toward a possible regulatory role of PI(4,5)P₂ in PRLR signaling. Our simulations showed that the membrane embedded TMD-ICD_F206-H300 was associated with an accumulation of PI(4,5)P₂ around the TMD. One of the suggested roles of PI(4,5)P₂ as a regulatory lipid is indeed to form microdomains around proteins and reduce their lateral movement (Trimble and Grinstein, 2015; van den Bogaart et al., 2011). Another possible role can be inferred from previous studies on the EGFR. Evidence suggests a positive feedback loop where inhibition is released upon activation because PI(4,5)P₂ is hydrolyzed to DAG and IP₃ by PLC_y (Maeda et al., 2018; McLaughlin et al., 2005). Specifically, we have previously shown that PRLR does not interact with PI(3,4,5)P₃ (Haxholm et al., 2015). As PI(4,5)P₂ is phosphorylated by the PI3-kinase to PI(3,4,5)P₃ during PRLR signaling (Aksamitiene et al., 2011; Yamauchi et al., 1998), this could indicate a way of attenuating signaling. Whether hydrolysis of PI(4,5)P₂ by PLC_y is relevant for PRLR signaling is not known.

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. The MD data and models together with the scripts used in the trajectory analysis are available on Github at https://github.com/Niels-Bohr-Institute-XNS-StructBiophys/PRLRmodel (copy archived at Niels Bohr Institute-Structural Biophysics group, 2022). NMR chemical shifts for the GHR-ICD-LID1 are deposited in the BioMagResBank under the accession number 51695.

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Decision letter after peer review:

Thank you for submitting your article "The prolactin receptor scaffolds Janus kinase 2 via co-structure formation with phosphoinositide-4,5-bisphosphate" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Amy Andreotti as the Senior Editor. The following individual involved in the review of your submission has agreed to reveal their identity: Frauke Gräter (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) The introduction needs to be updated to include the motivation for focusing specifically on LID1. In addition, more detailed descriptions of the methodologies used need to be included, specifically how these have been previously successfully applied by researchers to study equivalent membrane systems.

2) Please provide a better connection between the JAK2-PRLR complex conformational states and their functional relevance. This is an important point, as the majority of the simulation part of the paper centers on suggesting different states of the PRLR-JAK2 complex, and their hypothesized functional relevance is presented as a major result, yet not verified by experiments.

3) The connection between simulations and mutational study is not very direct. It is not clear that the mutants can distinguish between the effects of PRLR-PIP2 interaction or PRLR-JAK2 interaction, yet this conclusion is still drawn from the data.

4) Please provide some evidence (from experiments or simulations) regarding the role of PIP3 interactions. Currently (very strong) experimental evidence is provided only for PIP2, showing it to be an important regulator, while no results are provided for PIP3, despite being included in the final model.

5) The conclusions drawn from the mutagenesis study (lines 547-555) are not directly supported by data. There is only a partial correlation between PRLR membrane localisation and STAT5 activation, and this is insufficient to attribute the unexplained part of the STAT5 activation to PRLR-JAK2 interactions without further studies.

6) Based on the method section, the JAK2-FERM-SH2 CG-MD simulations are based on Martini 2.2 forcefield. If it is the case, the results of the orientation of the protein towards the membrane may be affected, as there can be some underestimations of the aromatics-choline interactions (https://pubs.acs.org/doi/full/10.1021/acs.jctc.9b01194). This issue seems to be corrected in the new Martini3 version. Would it be possible to run a few control simulations using Martini3 to compare with Martini2.2 results and see if the protein orientation is affected? Otherwise, this limitation should at least be mentioned in the Discussion section.

7) Relatively large chemical shift changes are detected in PRLR res 285-290 upon lipid binding, these need to be discussed.

8) You might want to move some of the main figures to the supplementary data and further emphasize some of the major conclusions (for example Figure 3H and 3I).

Reviewer #1 (Recommendations for the authors):

The data of Figure 5A are explained as changes in peak intensities are the smallest for φ4G mutant. This is not true as K4G shows similar changes, and K4E shows even fewer changes.

Also, the explanation for the increase in intensity with titration to be due to weak binding is also not convincing. Could binding be measured by ITC for example to show indeed that one mutant binds weaker than the other?

Reviewer #2 (Recommendations for the authors):

The study would benefit from a clearer distinction of mutation effects on PRLR-PIP2 interaction or PRLR-JAK2 interaction by designing mutants that only affect one and not the other, if possible. The same applies to the JAK5 activation: experimental evidence is currently lacking.

Also, the connection between simulations and mutant experiments could be more direct:

– CIF motif identified to be involved in PIP2 interaction using NMR and simulation.

– KxK motif 1 (residue ~252) identified in simulation to interact with PIP2.

– KxK motif 2 (residue ~262) not identified in simulation or NMR as PIP2 contact but as a contact point with JAK2 in simulation (Figure 4 figure supplement 1).

– Parts of φ4G contact point with JAK2 in simulation but L252 is part of KxK motif 1.

Finally, the discussion of different conformational states needs to be revisited and refined: which state should be functionally rather switched off, which state switched on, and how is function inferred from the conformations?

Also, experiments/simulations on PIP3 or at least a discussion on how PIP3 interactions can be inferred from the results on PIP2 would strengthen the study (point 5 above).

Reviewer #3 (Recommendations for the authors):

There are a few points that need to be clarified:

1. In the introduction. While the biological context of this work is well explained, the methodological context is not really detailed. It gives the impression that technically speaking, this work is completely new, which is not the case. There are numerous published studies (both in terms of NMR and modelling) studying PIP2 lipids, IDPs, and membrane receptors. Thus, it may be interesting for the reader to see that other research has already been successfully applied to study equivalent membrane systems showing that the authors' strategy is indeed robust.

2. Figure 3 and Figure 3 supplement1: an RSMF analysis of the peptide LID1 would be useful to evaluate its degree of flexibility for each membrane system. It may help better understand how PIP2 lipids may partly structure the peptide.

3. From the method section, it is not clear where the PIP2 CG model is coming from. How this model can be compared with the recent parametrization of PIP2 (https://pubs.acs.org/doi/abs/10.1021/acs.jctc.1c00615)? If the two models are different how this can affect the modelling results?

4. My main concern is related to the JAK2-FERM-SH2 CG-MD simulations (Figure 4). Based on the method section these simulations seem to be based on Martini 2.2 forcefield. If it is the case, the results of the orientation of the protein towards the membrane may be affected as there can be some underestimations of the aromatics-choline interactions (https://pubs.acs.org/doi/full/10.1021/acs.jctc.9b01194). This issue seems to be corrected in the new Martini3 version. Would it be possible to run a few control simulations using Martini3 to compare with Martini2.2 results and see if the protein orientation is affected? Or, at least, mention this limitation in the Discussion section.

Typos and format:

p.6, figure2-A: MD simulations box: K335 needs to be changed to K235.

Essential revisions:

1) The introduction needs to be updated to include the motivation for focusing specifically on LID1.

We have now provided our rationale for selectively focusing on the LID1 region in the PRLR. The selection was done to address how structural disorder in the juxtamembrane regions can transmit information on extracellular hormone binding to the intracellularly bound JAK2. This constitutes the first step in signaling on the intracellular side and—given the distance to the other two LIDs (LID2 and LID3) and their disconnect from the TMD by long disordered regions—LID1 was the focus. We have emphasized this choice in the introduction (p. 5) and in more detail in the result section (p. 5-6).

In addition, more detailed descriptions of the methodologies used need to be included, specifically how these have been previously successfully applied by researchers to study equivalent membrane systems.

We have included a paragraph in the introduction on previous work, which have successfully applied the used methodologies to study receptors and their interaction with lipids using integrative structural biology. We refer to various membrane systems of relevance and explain the rationale for choice of methodology for the study (p. 5).

2) Please provide a better connection between the JAK2-PRLR complex conformational states and their functional relevance. This is an important point, as the majority of the simulation part of the paper centers on suggesting different states of the PRLR-JAK2 complex, and their hypothesized functional relevance is presented as a major result, yet not verified by experiments.

In the original manuscript we already provided a detailed analysis of the different states, highlighting accessible residues and lipid interacting residues and comparing these across the states. From our experiments, including the cellular assays, we cannot with certainty link the two major states to active and/or inactive states, and therefore do not claim this in the manuscript. What we do put forward as a major result, is the presence of more than one major state as also stated in the abstract and in the conclusion of the result section as follows (p xx):

“Another key observation is the existence of different states in which different regions of both JAK2 FERM-SH2 domain and LID1 of PRLR are exposed to the solvent or hidden below the bilayer.”

In the discussion we do speculate as to which state may be the active and/or inactive dimer/monomer but we do not make conclusions. We have now made the finding of more states clearer in the text, and further compare the two major states, the Y and the Flat state, to the recent cryo-EM structures of JAK1 bound to IFNAR1, which lends some support to our speculations. The abstract now reads:

“We find that the co-structure exists in different states which we speculate could be relevant for turning signalling on and off.”

To discern the functional relevance of these state, if possible, will require experiments also in cells that by themselves would be a new study. We have to the best of our ability clarified that the functional relevance of the states has not been elucidated by the current work.

3) The connection between simulations and mutational study is not very direct. It is not clear that the mutants can distinguish between the effects of PRLR-PIP2 interaction or PRLR-JAK2 interaction, yet this conclusion is still drawn from the data.

This is a very important point, and we thank the reviewer for pointing out that we did not sufficiently clearly describe our choices and the connection between the experimental data, the simulations, and the mutational study. As highlighted by Reviewer 2, the experimental data have provided a guideline for the selection, however, the arguments for choosing the mutations were unfortunately omitted from the text.

We have now explained these points in more detail, both regarding the choices (p. 16,17) and the data analysis and conclusions (p. 19,20). Furthermore, we have emphasized that in the case of PRLR, where a co-structure is formed, separating functions by mutation may be complicated by the intimate interplay of the structural preferences among the three components.

4) Please provide some evidence (from experiments or simulations) regarding the role of PIP3 interactions. Currently (very strong) experimental evidence is provided only for PIP2, showing it to be an important regulator, while no results are provided for PIP3, despite being included in the final model.

In our previous work addressing the affinity for different phosphoinositides using lipid dot blots we observed a preference for certain species, including PI(4,5)P2 (Haxholm et al., BJ, 2015). In that study, we also observed that there was no detectable affinity for PI(3,4,5)P3. We have now more explicitly described these data, both in the introduction (p.4), in the result section (p. 8) and later in the discussion (p. 21). We thank the reviewer for bringing this up.

5) The conclusions drawn from the mutagenesis study (lines 547-555) are not directly supported by data. There is only a partial correlation between PRLR membrane localisation and STAT5 activation, and this is insufficient to attribute the unexplained part of the STAT5 activation to PRLR-JAK2 interactions without further studies.

We agree and have toned down the conclusion. In conjunction with the more in-depth clarification of the results in this section, the conclusions should now more clearly reflect the data.

6) Based on the method section, the JAK2-FERM-SH2 CG-MD simulations are based on Martini 2.2 forcefield. If it is the case, the results of the orientation of the protein towards the membrane may be affected, as there can be some underestimations of the aromatics-choline interactions (https://pubs.acs.org/doi/full/10.1021/acs.jctc.9b01194). This issue seems to be corrected in the new Martini3 version. Would it be possible to run a few control simulations using Martini3 to compare with Martini2.2 results and see if the protein orientation is affected? Otherwise, this limitation should at least be mentioned in the Discussion section.

We thank the reviewer for the insightful comment and acknowledge the potential limitation of using the Martini2.2 forcefield in our simulations. As the reviewer mentions, the Martini2.2 forcefield may underestimate the aromatic-choline interactions, which could affect the orientation of the protein towards the membrane. While we did not directly address this issue in our study, we note that the main protein – lipid interactions we observe are driven by electrostatic interactions between positively charged residues (K and R) and the PI(4,5)P₂ headgroups (and POPS in the systems without PI(4,5)P₂) which could mitigate the impact of the underestimation of cation-π interactions. As suggested by the reviewer, we have included a discussion of these limitations in the revised manuscript (p. 14).

7) Relatively large chemical shift changes are detected in PRLR res 285-290 upon lipid binding, these need to be discussed.

The region referred to contributes to binding but is on the edge of the main binding site and where the local affinities are weaker. Therefore, the exchange rate is high and allows for following the chemical shift changes. In support of this, we see an almost inverse correlation between the CSPs and the changes in intensities. For the main binding site, the exchange rate between bound and free states is slower because the affinity is stronger. Therefore, we cannot follow the chemical shifts to extract the CSPs to the bound state, as the peaks disappear. We have commented on this in the main text (p.8) as follows:

“In the region from D285-E292 we observed an almost inverse correlation between the CSPs and the intensities. This suggests that in contrast to the preceding region, a faster local exchange rate allows us to follow the resonances from the bound state in this region, giving rise to the large CSPs.”

8) You might want to move some of the main figures to the supplementary data and further emphasize some of the major conclusions (for example Figure 3H and 3I).

A new version of Figure 3 has been generated to consider the reviewers’ comments and suggestions. This figure has been restructured to further emphasize some of the major conclusions obtained from the simulations. We have moved the former Figure 3 A, B, C and D to the supplemental information to increase the focus. Thus, the former Figure 3H is now Figure 3C and it has been significantly enlarged and include some corrections and edits: There was an error in the script we used to process the volumetric data containing the density (from VMD’s Volmap tool). Thus, the -150% issue was solved. Also, we noticed that since Volmap is not aware of the periodic boundary conditions (PBC) of the system, the edges of the box will contain low density values due to fluctuations in the number of lipids in this zone. That leads to average values near 0 which result in an abnormal depletion % at the edges observed in the previous figure. This has been solved by filtering out those values and showing the density only in the regions further inside the simulation box.

The former Figure 3G (now Figure 3C) now also includes residues up to H300 as suggested. We thank the reviewers for suggesting these improvements.

Reviewer #1 (Recommendations for the authors):

The data of Figure 5A are explained as changes in peak intensities are the smallest for φ4G mutant. This is not true as K4G shows similar changes, and K4E shows even fewer changes.

Also, the explanation for the increase in intensity with titration to be due to weak binding is also not convincing. Could binding be measured by ITC for example to show indeed that one mutant binds weaker than the other?

We thank the reviewer for bringing this mistake to our attention. In our analyses, we compared both the intensity changes observed for the variant (internal comparison) and with those observed for the wild-type (across variants) and these were mixed up in the final text. We have now corrected the text accordingly and fully agree with the reviewer’s analyses.

Regarding a more quantitative measure of the affinities, ITC would require an affinity at least in the μM range, and since the complex is stabilized further with the membrane surface as well as by binding JAK2, it would not be straightforward.

Reviewer #2 (Recommendations for the authors):

The study would benefit from a clearer distinction of mutation effects on PRLR-PIP2 interaction or PRLR-JAK2 interaction by designing mutants that only affect one and not the other, if possible. The same applies to the JAK5 activation: experimental evidence is currently lacking.

Also, the connection between simulations and mutant experiments could be more direct:

– CIF motif identified to be involved in PIP2 interaction using NMR and simulation.

– KxK motif 1 (residue ~252) identified in simulation to interact with PIP2.

– KxK motif 2 (residue ~262) not identified in simulation or NMR as PIP2 contact but as a contact point with JAK2 in simulation (Figure 4 figure supplement 1).

– Parts of φ4G contact point with JAK2 in simulation but L252 is part of KxK motif 1.

This is a very important point, and we are grateful for the reviewer to point out that we did not sufficiently clearly describe the background for our choices. As highlighted by Reviewer 2, and inserted above, the experimental data have provided a guideline for the selection, however, this was completely omitted from the text. Combined with the confusion on the evaluation on parts of the variant data, mentioned by reviewer 1, this made the conclusion from these data non-transparent. We have explained in much more detail, both on the choices (p. 16,17) and on the data analysis and conclusions (p. 19,20). Furthermore, we have emphasized that the separation of function by mutation may be complicated by the intimate interplay of the structural preferences among the three components of the co-structure.

Finally, the discussion of different conformational states needs to be revisited and refined: which state should be functionally rather switched off, which state switched on, and how is function inferred from the conformations?

Also, experiments/simulations on PIP3 or at least a discussion on how PIP3 interactions can be inferred from the results on PIP2 would strengthen the study (point 5 above).

In the original manuscript we already provided a detailed analysis of the different states, highlighting accessible residues and lipid interacting residues and compare these across the states. From our experiment, including those performed in cellular assay, we cannot with certainty link the two major state to active and/or inactive states. We have therefore no intention or support from the data to claim this. However, what we do put forward as a major result, in the presence of more than one major state as also stated in the abstract and in the conclusion of the result section:

“Another key observation is the existence of different states in which different regions of both JAK2 FERM-SH2 domain and LID1 of PRLR are exposed to the solvent or hidden below the bilayer.”

In the discussion we do speculate as to which state may be the active and/or inactive dimer/monomer but make no firm claims. We have now made the major find of more states clearer in the text, and further compare the two major states, the Y and the Flat state, to the recent cryo-EM structures of JAK1 bound to IFNAR1, which lend some support to our speculations. The abstract now reads:

“We find that the co-structure exists in different states which we speculate could be relevant for turning signalling on and off.”

To discern the functional relevance of these state, if possible, will require experiments also in cells that by themselves would be a new study. We have to the be best of our ability clarified that the functional relevance of the states has not been clarified by the current work.

Reviewer #3 (Recommendations for the authors):

There are a few points that need to be clarified:

1. In the introduction. While the biological context of this work is well explained, the methodological context is not really detailed. It gives the impression that technically speaking, this work is completely new, which is not the case. There are numerous published studies (both in terms of NMR and modelling) studying PIP2 lipids, IDPs, and membrane receptors. Thus, it may be interesting for the reader to see that other research has already been successfully applied to study equivalent membrane systems showing that the authors' strategy is indeed robust.

We have included a paragraph in the introduction on previous work which have successfully applied the used methodologies to study receptors and their interaction with lipids using integrative structural biology. We refer here to various membrane systems of relevance and explain the methodological background for the study (p. 5).

2. Figure 3 and Figure 3 supplement1: an RSMF analysis of the peptide LID1 would be useful to evaluate its degree of flexibility for each membrane system. It may help better understand how PIP2 lipids may partly structure the peptide.

PRLR-TMD-LID1 residues simulated with PI(4,5)P2 and POPC lipids on the lower leaflet to evaluate if PI(4,5)P2 affects the flexibility of the region of the protein that interact with it (Figure 3 – supplemental figure 1C). What we found is that apparently the interactions (with penetration) of the hydrophobic residues of the ICJM and BOX1 stabilize and reduce the flexibility in this region, but it is not necessarily PIP2 and its interaction with the positively charged residues that causes this, as the same effect is observed wit POPS. Thus, this is strengthening our suggestion that the

“The stabilization of the structure provided by the hydrophobic residues from the ICJM and BOX1 is also reflected on their decrease in flexibility, observed as a shoulder on the RMSF-BB plot, for the residues that comprise the ICJM and BOX1 of PRLR-LID1. Very similar profiles of the RMSF-BB plot was obtained for the systems with respectively 5 and 10% PI(4,5)P₂ in POPC:POPS, suggesting that the loss in flexibility is coupled to the buried hydrophobic residues rather than to specific PI(4,5)P₂ interaction (Figure 3 —figure supplement 2C”).

We discuss these data on p. 10.

3. From the method section, it is not clear where the PIP2 CG model is coming from. How this model can be compared with the recent parametrization of PIP2 (https://pubs.acs.org/doi/abs/10.1021/acs.jctc.1c00615)? If the two models are different how this can affect the modelling results?

We thank the reviewer for pointing this out. Indeed, we did not clearly mention the origin of our Martini 3 parameters for PIP2. At the time when we performed the simulations the only available version of M3 was m3.b3.2 and there were no PIP2 parameters available yet. So, we adapted the PIP2 parameters from the martini2.2 forcefield. These parameters are available on our github and zeonodo repositories for this project. With respect to the new martini 3 parameters for PIP2 referred by the reviewer, it is difficult to compare with our parameters as the reported improvements revolve around self-interaction of phosphoinositide and their aggregation in the presence of ca²⁺. With respect to protein-PIP2 interactions, the authors only show that the Martini 3 PI(4,5)P2 model is correctly interacting with proteins and replicating experimental findings, but no direct comparison with Martini2 is made.

On the other hand, it is known that the martini2 PIP2 model was able to correctly predict the location of PIP2 binding sites on Kir channels (https://doi.org/10.1021/bi9013193) among other successful examples (10.1039/C3CS60093A). Thus, we don’t expect that the use of the parameters derived from the Martini 2 model affect our results, however, further studies of this systems should take advantage of the recently published new models. A clarification regarding the precedence of the M3 PIP2 parameters was included in the methods section.

4. My main concern is related to the JAK2-FERM-SH2 CG-MD simulations (Figure 4). Based on the method section these simulations seem to be based on Martini 2.2 forcefield. If it is the case, the results of the orientation of the protein towards the membrane may be affected as there can be some underestimations of the aromatics-choline interactions (https://pubs.acs.org/doi/full/10.1021/acs.jctc.9b01194). This issue seems to be corrected in the new Martini3 version. Would it be possible to run a few control simulations using Martini3 to compare with Martini2.2 results and see if the protein orientation is affected? Or, at least, mention this limitation in the Discussion section.

We thank the reviewer for the insightful comment and acknowledge the potential limitation of using the Martini2.2 forcefield in our simulations. As the reviewer mentions, the Martini2.2 forcefield may underestimate the aromatic-choline interactions, which could affect the orientation of the protein towards the membrane. While we did not directly address this issue in our study, we do observe that the main protein – lipid interactions in these simulations are driven by electrostatic interactions between positively charged residues (K and R) and the PI(4,5)P₂ headgroups (and POPS in the systems without PI(4,5)P₂) which could mitigate the impact of the underestimation of cation-π interactions. We have included a discussion of this limitation in the revised manuscript (p. 14).

Typos and format:

p.6, figure2-A: MD simulations box: K335 needs to be changed to K235.

This has been corrected.

Author details

1. Raul Araya-Secchi

   1. Structural Biophysics, Section for Neutron and X-ray Science, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
   2. Facultad de Ingenieria Arquitectura y Diseño, Universidad San Sebastian, Santiago, Chile

   Contribution

   Formal analysis, Validation, Investigation, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4872-3553

2. Katrine Bugge

   Structural Biology and NMR Laboratory (SBiNLab), Department of Biology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – review and editing

   Contributed equally with

   Pernille Seiffert

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6286-6243

3. Pernille Seiffert

   Structural Biology and NMR Laboratory (SBiNLab), Department of Biology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Formal analysis, Investigation, Visualization, Methodology, Writing - original draft

   Contributed equally with

   Katrine Bugge

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4213-5336

4. Amalie Petry

   Section for Cell Biology and Physiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Gitte W Haxholm

   Structural Biology and NMR Laboratory (SBiNLab), Department of Biology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Kresten Lindorff-Larsen

   Structural Biology and NMR Laboratory (SBiNLab), Department of Biology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Supervision, Funding acquisition, Validation, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4750-6039

7. Stine Falsig Pedersen

   Section for Cell Biology and Physiology, Department of Biology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Writing – review and editing

   For correspondence

   sfpedersen@bio.ku.dk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3044-7714

8. Lise Arleth

   Structural Biophysics, Section for Neutron and X-ray Science, Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Writing – review and editing

   For correspondence

   arleth@nbi.ku.dk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4694-4299

9. Birthe B Kragelund

   Structural Biology and NMR Laboratory (SBiNLab), Department of Biology, University of Copenhagen, Copenhagen, Denmark

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Writing - original draft, Project administration, Writing – review and editing

   For correspondence

   bbk@bio.ku.dk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7454-1761

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