On 2023-04-30 15:24:14, user Gul Zerze wrote:
I sincerely thank both Emil Thomasen and Kresten Lindorff-Larsen for their time, careful reading, and comments on the manuscript. Below, I attach my responses to each point with reproduction of the comment. Since these commentary is not capable of pasting modified visuals, added/modified visuals can be seen in the published version of the manuscript (doi: 10.1021/acs.jctc.2c01273)
Comment:<br />
The manuscript by Zerze reports on molecular dynamics simulations of the intrinsically disordered low complexity domain (LCD) of FUS using a beta version of the coarse-grained force field Martini 3. The author performed simulations to study the formation of FUS LCD condensates under varying protein-water interaction strengths (in the Martini force field) and at different NaCl concentrations, and concludes that strengthening protein-water interactions by a factor of 1.03 improves the agreement with experimental transfer free energies between the dilute and dense phases. Additionally, the author concludes that the NaCl concentration affects condensate morphology and protein-protein interactions in the condensate, and that the effect of NaCl concentration on protein-protein interactions in the condensate is sensitive to rescaling of the protein-water interactions. The manuscript provides an interesting and novel benchmark of the (beta) Martini 3 model in predicting phase separation of IDPs, and reveals potential short-comings of the model in predicting protein concentrations in (or volumes of) the condensed and dilute phases. This benchmark will be useful for readers who wish to simulate liquid-liquid phase separation of IDPs with Martini 3, and the work will be interesting to a wider audience interested in the biophysics of IDPs and their condensates.<br />
Below we outline some questions and comments that the author might take into account when revising the manuscript. Our main comment regards a clearer assessment of the convergence of the simulations and correspondingly the lack of error estimates for observables calculated from the simulations. We also suggest a clearer presentation of the experimental data used to validate the simulations. While some of these changes are mostly textual, in other cases we suggest additional simulations. We realize that some of these simulations require substantial resources; if these are beyond what is available, we suggest at least to clarify caveats as per the points below.
The author’s response: I thank the reviewer for their scrutiny and thoughtful comments that greatly helped substantiating the optimization analysis in the revised version of the manuscript.
Comment: We have the following suggestions for revisions to the manuscript:<br />
1)<br />
Fig. 1 and 2: The finding of non-spherical droplets is interesting and intriguing. To examine whether the formation of these shapes in the simulations with higher salt and λ-values represent stable states or perhaps trapped metastable states of the system, we suggest that:<br />
1a) The author runs simulations with the parameters that give rise to non-spherical morphologies (e.g. λ=1.025 and 50 mM NaCl) starting from the structure of the spherical droplet (for example formed with λ=1.0 and no salt) and observe whether the non-spherical morphology is recovered or the droplet remains stable. If the droplet remains stable, then the effect of salt concentration on the inter-chain contacts (Fig. 6) could be assessed without potentially confounding factors from different dense phase morphologies.
The author’s response: Following the reviewer’s suggestion, I have performed an additional set of simulations for all λ values (1, 1.01, 1.02, 1.025, 1.03) at 50 mM salt concentration starting from a preformed spherical droplet. The initial condition with the preformed droplet is obtained from the last saved frame of the λ=1 simulation for 0 mM salt. We ran the simulations for 10 microseconds each. Within the given time frame the droplet remained stable for λ values 1, 1.01, 1.02, and 1.025 without a dilute phase concentration. I now added these findings into the supporting information (Figure S5).<br />
I also modified the main text (Page 9 last paragraph and Page 10 first paragraph) as follows:
“Recent studies from independent groups show that the nonspherical droplet formation might be a kinetic arrest, playing an important role in droplet maturation and aging [51–53]. To test whether the nonspherical morphologies we observed are impacted by the initial conditions, we rerun 50 mM at all λ values starting from a preformed droplet (last saved configuration of 0 mM salt, λ = 1 condition). We simulated each λ for 10 μs and presented the analysis in Figure S5. Within the given simulation time, the initially spherical droplets stayed intact and spherical, except for λ=1.03, which had one copy of the FUS LC protein exchange back and forth between the dense and dilute phases). The enlarged droplet in the case of λ=1.03 also deviated from its initially spherical shape. These findings show that the nonspherical morphology was not reproducible for λ values less than 1.03 when starting from a preformed spherical droplet. We argue that the strength of effective protein-protein interactions at low λ are largely<br />
responsible from the initial spherical droplet staying intact.”
Since the droplets stayed nearly spherical, I also analyzed the contact formation in these simulations (50 mM added salt, initially starting from a spherical preformed droplet) and presented the findings in Figure S7.
I also discussed these findings in the main text as follows (Page 19, 20, the last paragraph before Conclusions):
“Finally, we also examined the contact formation for the case of 50 mM added salt that starts from a preformed droplet (see Identification of condensate formation subsection for the description). As presented in Figure S5, we found that the initially spherical droplet remains largely spherical within the simulation time (never forms rod-like percolated structures) for this case. Therefore, this case helps us assess the effect of salt concentration on the inter-chain contacts without potentially confounding factors from different dense phase morphologies. Figure S7 shows both the contact propensity (A.) and the effect of salt concentration (B.) on the contact propensity. Figure S7A shows that the contact propensity decreases as the λ parameter increases, similar to the findings in Figure 5. Figure S7B shows, however, that the change in contact fraction with respect to 0 mM salt at λ = 1 is weaker (resembling λ = 1.02 at 50 mM salt in Figure 6A) although the salting out effect at high λ (λ = 1.025 and 1.03) are more prominent and stronger compared to those in Figure 6A.”
Comment: 1b) The author shows time-series or distributions of an observable that reports on the dynamics of the proteins in the non-spherical droplet (e.g. Rg, mean square displacement, residue-residue contacts) and/or of an observable that reports on the dynamics of the droplet shape (e.g. the x-, y-, and z-components of the gyration tensor).
The author’s response: Following the reviewer’s suggestion, we added the analysis of observables that reports on dynamics of shape fluctuations and size and presented them in Figure S4.
We also modified the main text (Page 9, second half of the second paragraph) to discuss these findings: <br />
“We also investigated the time dependence of the size and shape of these morphologies by quantifying the radius of gyration (Rg) and the ratio of the smallest and largest eigenvalues of the gyration tensor (Figure S4). The latter offers a measure of sphericity of droplets. We found that low λ cases (λ = 1, 1.01, 1.02) at 0 mM salt have the most spherical morphologies. Beyond λ = 1.025 at no salt, the cluster formation is not tight (as evident from the Rg) so it also loses its sphericity. The condition that shows percolation (λ = 1 at 50 mM salt) has the largest deviation from the sphericity (it is rod-like instead) combined with a large Rg.”
Comment: 1c) Additionally, independent replicas of droplet formation for each condition and parameter set would be ideal, but we realize that this would be expensive in computational resources and may be infeasible.
The author’s response: We agree with the reviewer that the molecular simulations presented in this work are highly computationally demanding (e.g., a 10-microsecond simulation of one of these simulations at given salt and given λ takes about 25 days in terms of walk-clock time, occupying 28 CPUs and 4 GPUs) While it certainly is computationally demanding to replicate all λ parameters at all salt concentrations, we now rerun 50 mM salt concentration at all λ parameters where we start from a completely different initial condition (preformed droplet) for each. And we found that the morphology was not reproducible within the given simulation time at low λ, highlighting the initial condition dependence at low λ conditions. We now discussed this in the main text (Page 21, Conclusions).
“We also note that we observed an initial condition dependence of the morphology at low λ conditions at 50 mM salt. This finding emphasizes the necessity of future work for exploring condensate morphology with proper advanced sampling techniques.”
Comment: 2)<br />
“As λ increases, the volume of the dense phase increases (and condensed phase concentration decreases accordingly) until the system is not capable of forming a dense phase (λ >1.03)”: From Fig. 1 it seems that the rate of cluster formation decreases as λ increases. Is it not then possible that droplet formation at λ>1.03 is stable at equilibrium, but occurs on time-scales greater than those tested in the simulations? To support the statement that no droplets are stable at λ>1.03, we suggest that the author runs simulations with a higher value of λ starting from the structure of the spherical droplet (formed with λ=1.0 and no salt) to observe whether the droplet is dissolved or remains stable.
The author’s response: Following the reviewer’s suggestion, we have performed a simulation for λ=1.04 at no salt condition starting from the preformed spherical droplet (last saved configuration of λ=1.0 at 0mM salt) and we found that the droplet quickly dissolves for λ=1.04. This finding is now presented in Figure S3.
The main text is also modified as follows (Page 9, end of the first paragraph):
“To further verify that no droplets are stable beyond λ = 1.03, we also ran λ = 1.04 simulations<br />
at no salt conditions starting from a preformed spherical droplet (last saved configuration of<br />
λ = 1 at 0 mM salt). We then analyzed the cluster formation as a function of time (Figure<br />
S3) and found that the initial droplet dissolves quickly (at a timescale shorter than that of<br />
the formation of the droplets).”
Comment: 3)<br />
Figure 3: The use of the radial distribution does not seem ideal for the droplets that have a non-spherical morphology, as certain distances will report on an average over the dense and dilute phases. This should at a minimum be discussed.
The author’s response: Following the reviewer’s suggestion, we have added further discussion related radial density distribution to the main text (Page 12, first paragraph):
“This approach works reasonably well for droplets that have spherical/ellipsoidal shapes. However, since the condensates for the conditions with finite salt concentrations significantly deviate from a sphere (they do not show a clear plateau as the center is approached), we used a surface reconstruction method [54] to estimate the volume and concentration instead of fitting the radial density profiles/using the limiting values.”
Comment: 4)<br />
Table 1: It seems that the discrepancy between the sigmoidal fit approach and the surface reconstruction approach increases with λ, possibly due to sensitivity to the shape of the droplets, illustrating that there might be significant uncertainty associated with the reported dense phase volumes. We think it would be useful to have an error estimate for the reported dense phase volumes (e.g. an error over volume calculation approaches and/or over different probe sizes).
The author’s response: The volume obtained by surface reconstruction is definitely highly sensitive to the probe size. To justify the size of the probe that I used, I directly compared the sigmoidal fit protein concentrations and the surface construction protein concentration calculated by different probe sizes (Figure S3 in the old SI, Figure S6 in the revised SI). Based on that comparison, probe radius 10 A was the size that minimized the differences considering all lambda values. That’s how I justified the probe size I used. For the uncertainty/error estimates, I performed block averaging analyses (please also see the response to the point 7).
Comment: 5)<br />
Table 2 and Fig. 4: We suggest that the author more explicitly states which experimental data was used for comparison with the simulations in Fig. 4. We also suggest a more direct comparison with experimental data points where possible (e.g. by showing the experimental values of csat as a function of NaCl concentration).
The author’s response: We used two experimental papers to extract the experimental data, one is reference 36. In reference 36, the authors state: “Using incubation on ice to increase the driving force for droplet formation followed by centrifugation to fuse the droplets due to their higher density, our 15 ml samples of 1 mM FUS LC phase-separated to form an ∼400 μl viscous, protein-dense phase stable for weeks at room temperature. FUS LC concentration in the phase is approximately 7 mM (120 mg/ml FUS LC) as determined by spectrophotometry.“
We note that the salt concentration is not specified in this case (or the authors obtained approximately the same protein concentration in the dense phase regardless of the salt concentration). Also, the thermodynamic conditions defined here does not exactly correspond to those in our simulations. That’s partly the reason why we looked for multiple sources of experimental data. The other experimental work that we used is reference 39. In reference 39, the authors state that “The relative intensity of the glutamine side chain residue NMR resonances in the condensed phase compared to a standard concentration (100 μM) dispersed phase FUS LC suggests a concentration of 27.8 mM = 477 mg/ml in the condensed phase.”
The salt concentration in the corresponding NMR experiments were carried out at 25 °C in 50 mM MES, 150 mM NaCl pH 5.5. The conditions do not exactly correspond to our thermodynamic conditions, either. Since an exact match is not available in the conditions, we did not prefer to present a direct comparison of dense phase concentrations, instead, we preferred to show a range in Figure 4. We now modified the main text (Page 15, right above the Contact Maps subsection) to more explicitly state the source of the data:
“The experimental data range is referenced from the work by Fawzi and coworkers; [36,39] where reference [36] measures the FUS LC concentration in the dense phase as approximately 120 mg/mL (spectroscopically) and in reference [39], a 477 mg/mL FUS LC concentration is deduced from the relative intensity of the glutamine side chain residue NMR resonances in the condensed phase (compared to a standard protein concentration in the dispersed phase, which is given as 100 μM, or 1.71 mg/mL). 477 mg/mL FUS LC dense phase has been obtained from 15 ml samples of 1 mM FUS LC solutions [36] (from which we calculated the dilute phase concentration as approximately 14.3 mg/mL). We used these dense phase and their respective dilute phase concentrations to calculate the experimental range of transfer free energy (gray-shaded areas in Figure 4).”
Comment: 6)<br />
“We used the “tiny” bead type (TQ1) both for Na+ and Cl- ions”: The author should clarify the reason for and possible effects of choosing the TQ1 bead type, as TQ5 is, we think, the standard bead type for Na+ and Cl- ions in Martini 3.
The author’s response: We would like to clarify that tiny refers to the bead type being Txx. We then also would like to clarify that TQ5 type was not available in the MARTINI version that we used. Ion topology file in the version that we used only had TQ1 types as the ion type. We are pasting the contents of “martini_v3.0_ions.itp” file below:
;;; IONS<br />
;
;;;;;; SODIUM ION
[moleculetype]<br />
; molname nrexcl<br />
TNA 1
[atoms]<br />
;id type resnr residu atom cgnr charge<br />
1 TQ1 1 ION NA 1 1.0
;;;;;; CHLORIDE ION
[moleculetype]<br />
; molname nrexcl<br />
TCL 1
[atoms]<br />
;id type resnr residu atom cgnr charge<br />
1 TQ1 1 ION CL 1 -1.0
;;;;;; CHOLINE ION
[moleculetype]<br />
; molname nrexcl<br />
NC3 1
[atoms]<br />
;id type resnr residu atom cgnr charge<br />
1 Q0 1 ION NC3 1 1.0
;;;;;; CALCIUM ION
[moleculetype]<br />
; molname nrexcl<br />
SCA 1
[atoms]<br />
;id type resnr residu atom cgnr charge<br />
1 SQ2 1 ION CA 1 2.0
Since we understand that this is causing a confusion, we modified the sentence as below (Page 6, right above the Simulation Details section):
“We used the relevant TQ bead types for Na+ and Cl- ions and kept the ion-water and ion-protein interactions unmodified.”
For further details of the parameters (e.g., epsilon-sigma), we made our topology and run parameter files publicly available (please see the response to the point 10).
Comment: 7)<br />
We suggest that the author, where possible, reports error estimates for the various observables, for example from block error analysis and/or repeated simulations.
The author’s response: We performed block averaging analysis (using two block) for volume estimation (accordingly, the protein concentration in the dense phase) and included the error estimates in Table 1 (Page 12). We note that for most ???? parameters, the error was less than 1%. But we now added the errors larger than 1% in Figure 4. We modified the Table 1 caption as:<br />
“…. Statistical errors calculated by block averaging of the data (dividing the equilibrated data into two equal blocks) are less than 1% at low ???? conditions. Errors larger than 1% are reported.”
Comment: 8)<br />
It would be useful to include a discussion of the effects of simulation convergence and simulation starting configurations on the reported results.
The author’s response: We added a discussion of the reproducibility issue and the initial condition dependence both to the Results and Discussion section and the Conclusions section (please also see the responses to the point 1a and 1c).
Comment: 9)<br />
A discussion of the potential differences in the effect of non-bonded cut-offs in the dilute and dense phase would also be useful.
The author’s response: We used a fairly large cutoff distance (1.1 nm) for short-range treatment of vdW and electrostatics but a potential nonbonded cutoff effect that I can think of is the long-range treatment of electrostatics. While vdW interactions are large power of r in denominator (therefore, negligible contribution to the potential at large r), we may argue that the long-range treatment of electrostatics might be a concern in general. It is well known that the simple cutoff of electrostatic interactions introduces artifacts on phase behavior of anomalous liquids that has two distinct phases [e.g., J. Chem. Phys. 131, 104508 (2009)]. Here, we applied the reaction field method for long-range treatment of electrostatics. In this method, a given particle is assumed to be surrounded by a spherical cavity of finite radius within which the electrostatic interactions are calculated explicitly. Outside the cavity, the system is treated as a dielectric continuum. Any net dipole within the cavity induces a polarization in the dielectric, which in turn interacts with the given molecule. The reaction field method allows the replacement of the infinite Coulomb sum by a finite sum plus the reaction field. One caveat of this approach might be the nonuniform distribution of the particles within the system (i.e., one protein-dense phase and one protein-dilute phase), which may jeopardize the assumption that outside the cavity is a uniform continuum dielectric. While this caveat may make the Ewald summation (or particle mesh Ewald, faster version of Ewald sum) look more preferable, we note that Ewald sum and reaction field techniques yield nearly identical phase behavior for liquid crystals (also nonuniform in nature) (see, Molecular Physics 92(4), 723-734 (1997)). We discussed some of these points in the main text as follows (Page 6, third from the last sentence):
“Long-range electrostatic interactions were calculated using a generalized reaction field method [45]. We note that a long-range treatment of electrostatic interactions is essential to obtain accurate phase behavior [46].”
Comment: 10)<br />
It would be very useful if the inputs/settings (including starting configurations) used for simulation and code for analysis were available.
The author’s response: Following the reviewer’s suggestion, we uploaded the initial configurations and run files for all lambda values for 0 mM salt and 100 mM to GitHub and made it publicly available. We now noted in the availability of the data in the main text by modifying the last paragraph of Modeling subsection as follows:
“Equilibrated initial conditions, topology files, and run parameter files for all λ values of 0 mM and 100 mM salt are publicly available on GitHub (https://github.com/gzerze/m...
Comment: We also have the following suggestions for minor revisions to the manuscript:<br />
1)<br />
“We kept the protein-protein interactions unmodified (and no additional elastic backbone constraints were applied)”: The author should clarify whether this includes assignment of secondary structure and/or side chain angle and dihedral restraints (ss and scfix in Martinize).
The author’s response: Yes, this would apply for any restraints (i.e., they would remain unmodified). This particular protein, FUS LC, is left fully flexible, without any backbone/side chain structure. We clarified this in the main text by modifying the relevant part in the Modeling subsection:
“No elastic backbone (or side chain) constraints were applied (i.e., FUS LC is kept fully flexible). We kept the protein-protein interactions unmodified but systematically tested a range of scaled protein-water interactions.”
Comment: 2)<br />
“All simulations were performed using GROMACS MD engine (version 2016.3).”: Error in references.
The author’s response: The references are fixed.
Comment: 3)<br />
In the Cluster Formation Analysis section: We suggest that the author cites the specific package used (e.g. SciPy).
The author’s response: Following the reviewer’s suggestion, we added the name of the routine related references by modifying the relevant part in Cluster Formation Analysis subsection as follows:
“Any two protein molecules are considered to be in the same cluster if any two beads of the molecules are within 0.5 nm (or less) distance from each other. Based on this criterion, we built adjacency matrices and then found the connected components by using the compressed sparse graph routines of public Python libraries [50]”
Comment: 4)<br />
Fig. 2: There are small red dots on the droplets, which should either be explained in the figure text or removed.
The author’s response: Following the reviewer’s suggestion, we remade the Figure 2 by removing the red dots.
Comment: 5)<br />
Fig. 3: It would be useful for the reader if the NaCl concentration was labelled at the top of each column. Additionally, the radial distribution of the ion concentration is shown as two separate rows, which we assume corresponds to Na+ and Cl- ions. This should be clearly labelled.
The author’s response: Following the reviewer’s suggestion, we updated Figure 3 with proper labels.
Comment: 6)<br />
“We found the largest water fraction For the ionic species…”: Typo?
The author’s response: We removed that incomplete sentence now.
Comment: 7)<br />
Fig. 4: Depending on how the plot is updated with more details on the experiments, perhaps the range shown on the y-axis could be made smaller.
The author’s response: Figure 4 is updated as presented above (please see the response to point 7 above).
Comment: 8)<br />
Fig. 5: May be clearer with a colourmap with three colours, as in figure 6.
The author’s response: Figure 5 uses a color scale that changes the colors uniformly from black to white. For contact maps (like Figure 5), since the range of change is sequential growth of fraction, we thought a perceptually uniform sequential color scale fits better as opposed to a divergent color scale (e.g. the color scale in Figure 6).