Peer review of: Protein–ligand binding with the coarse-grained Martini model (2014)

 
Peer Review File: Reviewers' comments first round: 
 
Reviewer #1 (Remarks to the Author): 
 
Review of NCOMMS-19-28960 
 
This is an interesting study of the use of Martini (version 3) coarse-grained molecular dynamics 
simulations to explore millisec timescale sampling of ligand/drug binding to 3 protein targets. The 
results are very promising with respect to reproducing available experimental data, and suggest 
that this approach may have considerable value in the future in novel approaches to rapid MD 
based screening of ligand/target protein interactions. The authors are experts in the development 
of coarse-grained simulations for proteins and membranes, and the simulations and analysis are 
performed to a high standard. I think perhaps the phrase ‘mark a milestone on the way’ is a bit 
hyped (and some reference to the comparable use of atomistic simulations for fragment screening 
e.g. Arcon et al. (2017) J. Chem. Inf. Model. should be included) but this is an important step 
forward and merits publication following revision. 
 
The approach is illustrated via its application to three test systems: T4 lysozyme, a well 
characterized model system, S-selective aminotransferase (ATA), and the A2a receptor as an 
example of a pharmacologically important model target. Whilst I can understand why just 3 
examples were studied in a proof-of-principle study, I remain unclear as to why ATA was selected 
as one of these rather than selecting a better-characterized pharmacologically important target 
protein. 
 
For T4 lysozyme, 4 structurally similar ligands (benzene, phenol, indole, and n-propylbenzene) – 
were explored, and gave excellent agreement with experiments, both structurally and in terms of 
free energies. This is very encouraging. 
 
For the second example - the S-selective aminotransferase (ATA) – it is not clear that there are 
suitable experimental data against which to evaluate the simulation results. 
 
The third example is a membrane inserted protein, the adenosine A2A receptor, and the 
crystallographic binding poses of adenosine and caffeine are reproduced in the simulations. 
 
So, overall this is very promising, and in addition to predicting binding sites/poses and free 
energies of interaction, details of multiple interaction pathways are predicted. However, to be 
certain of the future value of the method it may be necessary to probe a wider range of chemical 
space (in terms of ligands) and of protein target space, alongside validation against a wider range 
of experimental data. 
 
One more ‘technical’ aspect which requires some amplification is that of the degree of flexibility of 
the protein targets. In the SI it is mentioned (page S6) that, as is standard for Martini protein 
simulations, an elastic network was applied to the protein. It is also noted (page S9) that a shorter 
cutoff was used for the GPCR study. It would be interesting to know to what extent the results of 
the screening simulations were dependent on e.g. the cutoff for the elastic network. In the 
atomistic simulation literature (e.g. Lexa and Carlson, 2010, J. Amer. Chem. Soc.) it has been 
shown that correct representation of protein flexibility is important for hot-spot mapping by MD 
simulations. 
 
 
 
 
 
 
 
 
Reviewer #2 (Remarks to the Author): 
 

The authors are the well-known developers for MARTINI coarse-grained models for various 
biomolecules. The coarse-grained models have been utilized in many biological simulations, such 
as biological membranes, and membrane proteins. In this work, the authors used the updated 
MARTINI model for protein-ligand binding simulations. Considering the reduced computational 
cost, this approach has great possibility in in-silico drug discovery and should be useful for many 
computational scientists. 
 
Overall, I have good impressions about the work, however, some details are now well written. I 
would like to point out my concerns in the work one by one. 
 
1) In the work, the authors described only the positive aspects in the protein-ligand binding. 
However, coarse-graining of the models brings several limitations. So, the authors should describe 
the limitation of the current approach in protein-ligand binding. 
 
For instance, 
the accuracy of the binding mode, i.e. ligand-sidechain interaction accuracy 
kinetics of binding processes 
protein conformational change upon ligand binding, in particular, for larger ligands 
 
2) The authors indicated that this approach is successful to search the ligand binding sites without 
knowing any experimental knowledge. However, the previous simulation studies suggests that in 
long MD trajectories, not only the canonical binding sites but also other weaker binding sites are 
predicted. For instance, JACS 133, 9181-9183 (2011). 
I would like to know how to discern the canonical and other binding sites in the approach. 
 
3) It is helpful to show MARTINI model for each ligand and the most successful binding poses (with 
sidechain of the target protein). This information is necessary for readers to understand 'their' 
successful ligand binding prediction. 
 
4) Related to the third point, if the atomistic binding poses cannot be simulated by this coarse-
grained model, is it possible to refine the binding pose by using atomistic force fields ? Or this is 
out of scope in this work ? 
 
I believe that this is not straightforward, since the binding pocket is narrow and ligand may not be 
able to do rotational and translational motions in the narrow binding pocket. I would like to hear a 
practical strategy for converting the coarse-grained model to atomistic model, if the prediction is 
not perfectly fine. 
 
5) In Figure 1, T4 lysozyme structure is shown. It is helpful to add notation of helices (F, G, H, etc) 
for defining the binding pathways. The notation of helices in T4 lysozyme is established in previous 
works and should be common in the work. Also, conformational fluctuation of T4 lysozyme (and 
other two proteins) should be shown in supporting information. In T4 lysozyme, the excited 
conformation was found by NMR spectroscopy (Nature 2011, 477, 111-114). In the simulation, 
was the conformation sampled or not ? 
 
6) In Figure 2, I believe that Fig. 1A should be smaller and the comparison between the binding 
pose prediction and experimental structure should be add. Fig. 2B is not clear for the purpose. 
 
7) I have interest on the conformational changes of the receptor, with or without ligand binding. 
How can the ligand go into the deep inside of membrane is not a simple question. I believe that 
more extensive analysis and data are necessary for the conformational changes, ligand-binding 
pathways, and competition with water. Again, I would like to know not only the canonical binding 
sites but also other weak binding sites, if they found. 
 
8) Terada et al. previously published protein-ligand binding simulation with the MARTINI models. 
The authors should cite the paper and discuss the difference between the current and their works. 
J Comput Chem. 2014 Sep 30;35(25):1835-45. doi: 10.1002/jcc.23693. Epub 2014 Jul 21 
 
9) It is helpful if the authors discuss which update in MARTINI v.3 is the most important for 

improving the prediction accuracy. 
 
 
Reviewer #3 (Remarks to the Author): 
 
Presented are calculations using Course Grained (CG) Simulations of 
the binding of a simple ligands to three different receptors. The 
method is simply to use MD to allow the ligands to diffuse to and 
interact with their binding pockets. While this may be the first 
example of using CG for such calculations, the results are as expected 
and no insights into ligand-protein interactions are presented. 
Furthermore, indicating that this method will be of utility for drug 
design is certainly not supported by the present results. 
 
The calculations use ligands with simple ring system with only one or 
two (if any) rotatable bonds amongst all the ligands as "drugs." These 
are certainly not drug-like molecules and, given that they are 
relatively hydrophobic in nature it can be expected with high 
probability that they will find the hydrophobic sites on the protein 
to which they are known to bind. More importantly, there is 
absolutely no evidence that the course grained method has the 
structural resolution to differentiate the subtle changes in 
drug-protein interactions that lead to changes in drug affinity and 
specifity, including changes in off rates, associated with 
modification of the ligand structures. Accordingly, the present 
results show nothing more than the course grained method can identify 
binding pockets on proteins for very simple molecules using brute 
force simulations; the work certainly does not show that the method 
will be of any utility to drug design. This would require validation 
against significant numbers of ligands for a number of different 
proteins for which experimental affinities and binding orientations 
are known. There a many such examples in the literature that the 
authors can follow when designing such a study. 
 
Following are some issues they authors may considering expanding on in 
the study. 
 
On line 185, the authors discuss on rates for ligands. This is 
largely a diffusion controlled event, with the rate of diffusion in 
the present study being enhanced by the use of small ligands. Can the 
authors make a quantititave estimate of the on rates from the 
simulations and discuss those rate with respect to the diffusion 
controlled limit. It would be interesting to understand how different 
the on rate is with T4 lysozyme given the need for the binding pocket 
to open to become allow for ligand binding. 
 
While the authors have only studied a few ligands, it would be 
interesting if they made a quantitative comparison of the CG results 
and experimental binding data for the compounds. Are they getting 
equilibrium values close to the experimental estimates? Are the 
relative affinities for the few ligands that are studied in agreement 
with experiment?