Peer review of: Mapping allosteric communications within individual proteins (2020)

Reviewers' comments: 

 

Reviewer #1 (Remarks to the Author): 

 

The authors propose a structure-based stochastic computational method to map allosteric sites, 
pathways and networks based on the hypothesis that packing heterogeneities serve as a mechanism 
to relay allosteric perturbations. The proposed software was tested using a dataset of 20+ allosteric 
strictures and was experimentally validated through mutations, enzymatic assays and NMR CHESCA 
of CheY. Ohm offers a platform to interpret and design allosteric regulatory switches. Being non-MD 
based, Ohm is considerably faster than alternative computational methods that rely on lengthy MD 
trajectories and given its speed, it is likely that Ohm will find many valuable applications in 
bioinformatics. 

 

Main Suggestions: 

 

- The authors suggest that Ohm is more reliable than prior computational methods, such as that by 
Amor (reference 22). A direct Ohm vs. Amor’s method comparison would be helpful. Some elements 
of such comparison are present in the ms, but a dedicated section would be useful to highlight 
experimental data explained by one method but not the other. 

 

-Please explain why the performance of Ohm decreases when active sites are buried or allosteric 
sites are close to active sites. 

 

-Please provide clearer guidelines on how to find optimal ranges of the alpha parameter in equation 
3 

 

-Specify which structure should be used by Ohm as input. Typically allostery involves transitions 
between at least two structures (e.g. unbound or bound to allosteric effectors or inhibitors, 
phosphorylated or in general with or without PTMs), so it’d be helpful to know which structure is 
required by Ohm. This point is in part touched for CheY. However, offering general guidelines to 
Ohm users in this regard will enhance the impact of this software. 

 

-The authors show that the core elements of the allosteric network predicted by Ohm for CheY are 
largely preserved along a 100 ns MD trajectory. Since multiple structures are available for CheY, how 
do the Ohm results change for different experimental CheY input structures? 

 

-Explain more clearly how the Ohm perturbations used for CheY mimic the perturbations used in 
CHESCA NMR 

 

 

 

Minor suggestions: 

-The abstract can be shortened, especially in the initial part on the relevance of allostery 

 

-Define more specifically ‘heterogeneous media’ in abstract. In fact, this aspect is not fully developed 
in the ms and is more part of the speculative section of the discussion. It’d be better if the abstract 
more closely reflected the actual results of the ms. 

 

-Fig3 PDB code needs to changed to 1F4V. 

 

-Color codes need to be explained in Fig S27 

 

 

 

Reviewer #2 (Remarks to the Author): 

 

General Comments 

 

Wang et al. present a computational methodology to uncover allosteric pathways in proteins. The 
method proposed, Ohm, uses the contact topology of a protein 3D structure to create propagation 
matrices Pij that when evaluated using stochastic perturbations provides insights on allosteric 
relevance of residues. The method relies on the knowledge of active sites to be used as starting 
points in the network perturbation process. The authors analyzed 20 protein systems using Ohm and 

correctly identified residues involved in allosteric communication for 13 of them. For the remaining 
7, the Allosteric Coupling Indices were significant but not necessarily the top ones. The authors also 
used the proposed technique to create residue-residue correlation networks that were compared 
against chemical shift covariance analysis in the CheY response regulator. The authors propose that 
this methodology does not rely in expensive molecular dynamics simulations and might serve as a 
useful and user friendly tool to study allostery, compared to other methods. 

 

 

In general, I think the methodology and the presentation are relevant and the implementation 
seems easy to use, e.g. the Ohm website is user friendly. However, in order to be convinced of 
scientific advantages of this methodology, I would like to see more quantitative analysis and controls 
as well as clear comparison with other methods, for example the Amor et al. paper referred several 
times in the study. This would help the authors make the point that there are other advantages than 
computational complexity or user friendliness. I would like to see a revision that could answer the 
following questions: 

 

 

 

1. Is the dataset of 20 proteins diverse enough to support the generality claims of the study? If so, 
the authors should describe this in the article as opposed to only mention that this set was compiled 
in another study. 

 

2. Could the authors also include some negative controls to show that proteins that are not known 
to be allosteric would not have enriched ACI values? One premise of the article is that any protein 
has the potential for allostery, but we know that the functional relevance of allostery is different 
depending on the system. It would be good to see results showing these negative controls. 

 

3. When looking at the definition of the propagation matrix, it was not clear to me why is it 
important to normalize the number of contacts per atom. In other words, why would a single atom 
with X contacts will give a higher probability than two atoms that form the same amount of 
contacts? To me these two cases are more or less equivalent since ultimately the number of 
interactions is the same. I understand that the idea is that the density of contacts is important, but I 
would expect that in terms of residues but not necessarily in terms of the number of atoms per side 
chain. Could the authors clarify this? 

 

4. In some cases, allostery is realized not by single monomers but via oligomerization, have the 
authors tried these types of cases? Would they expect that the methodology works in the same 
way? If so, could the authors include one of such cases? This would make the application of the 
methodology much more general. 

 

5. For the Caspase-1 results, the authors mentioned perfect accord with experimental results. I think 
it is important to quantify the agreement, maybe with correlation coefficient or other metrics, as 
opposed to a less objective qualitative metric. 

 

6. For the CheY system, the authors mention that D57 is an allosteric site. My understanding is that 
the phosphorylation site should be considered an “active site” as opposed to an allosteric site. Since 
this is a residue that is modified and then the effect of this modification is propagated to the 
allosteric site. How much would the results change if D57 is used as an active site instead? Would 
the method also identify the allosteric site? 

 

7. This work references the work of Amor et al. in several instances, both for selecting the systems to 
work on as well as to compare specific results. However, it is not clear to me from reading the paper, 
if Ohm is better at predicting allosteric sites and pathways than the methodology proposed by Amor 
et al. which was also validated in other 80 proteins. The authors should give more details in this 
respect because both seem to be graph theoretical approaches, dealing with the same systems but 
there is no general comparison in this work. 

 

8. When comparing inter-residue allosteric correlations from Ohm and CHESCA, it was not really 
clear to me why the need of a low-pass filter. The article references some experimental errors, but 
then, why did the authors also applied the filter to the computational results? Similar to my previous 
comment, it is important to have a more quantitative comparison as opposed to a qualitative 
assessment of the agreement. Particularly if this is supposed to be used as an experimental 
validation. 

 

9. For the comparisons with CHESCA, I am wondering how would this comparison would look like if 
we were to use only the native contacts in the protein. Would these dominate? The authors could 
use this comparison as a test case that their method reflects more than just structural topology, and 
if not, then they should revise their conclusions. Finally, is CHESCA considered a ground truth for 
allosteric correlations? Since the results are fairly affected by conformational transitions, I am not 
sure the authors should state that “…the predicted allostery in CheY protein predicted by Ohm was 
validated by NMR CHESCA studies”. I would re-state that the inter-residue correlations are 
correlated with CHESCA analysis, but I am not sure about being validated. 

 

10. The authors described how the method reaches convergence after 10^4 iterations. The 
propagation matrix seems similar to a transition matrix, although I am not sure if it has all the 
properties. However, if the properties are met, maybe there is no need to run stochastic simulations, 
but just check for stationary vectors. I might be making some wrong assumptions, but it would be 
good if the authors check this possible connection with Markovian properties of a transition matrix. 

 

11. The discussion states that Ohm is a reverse process of direct coupling analysis. I am not sure if 
they are really comparable. On the one side, Ohm uses contacts as input to then create a dynamical 
view of allostery, while DCA identifies direct interactions that are important during evolution. These 
definitions are not necessarily reverse. In fact, one could build a “DCA matrix” and maybe run it 
through the Ohm algorithm and possibly find something related to allostery. Rather than reverse, I 
would suggest that DCA could be used as input instead of the contact map, although I am not sure 
what the results would look like. 

 

12. My understanding is that the algorithm starts in the active site and then it ends at the allosteric 
site. however, for the case of the last repressor, DNA is defined as the “active site“. If you see Figure 
S12, the caption says that the active site is the red circle, i.e. close to DNA, however the pathway in 
Fig. S12D starts near residue A79 which is the black square or allosteric site. It feels to me that there 
might be some confusion here that must be clarified. 

 

13. The agreement with CHESCA depends on the conformational state of the protein. It is important 
to mention this limitation in the discussion section. There might be cases where the conformational 
state is not as clearly defined as in the case of phosphorylation. 

 

 

 

Specific comments 

 

1. Table S1 includes active and allosteric site codes that are not clearly defined , please include the 
definition and positions of these residues. 

 

2. The authors mention that residues with high SASA have larger ACI values and then they propose 
this due to a protective environment for the allosteric pathway. But to me, this is just a consequence 
of the definition of Pij, where the probability for propagation is lower for residues with less contacts 
as is the case of higher SASA residues. I am not completely convinced by the protection hypothesis, 
could the authors elaborate on this or maybe remove the claim? 

 

3. Figure S1 seems to have an error in the caption. The first sentence seems like it should not be 
there. 

 

4. Page 12, “atom-average contacts” sounds a bit awkward, I would rather use “average atom-
contacts” 

 

5. Line 295, replace “residue I“ with “residue i” 

 

6. Line 314, replace “by1” with “by 1” 

 

 

 

 

 

Reviewer #3 (Remarks to the Author): 

 

The manuscript by Wang et al. introduces a network-based approach to identify, (i) allosteric sites in 
a protein, (ii) allosteric pathways which locate critical residues, and (iii) allosteric correlations 
between residues. The network is constructed based-on the number of contacts between residue 
pairs, but the value is normalized by the number of atoms in a residue so that the attributes of the 
links are not symmetrical. Using these values to calculate probability of information transfer 
between contacting nodes, selected perturbations are propagated along the network for a large 
number of cycles whereby convergence is achieved and the most visited sites/paths are determined. 

 

At the outset, I wish to point out that the web-based interface that displays the results of these 
analyses is well-thought out and is very user friendly if the results are biologically relevant. This is 
where I question the methodology and the significance of the findings. 

Network-based models to study path and community analyses in relation to allostery have long been 
in use in the protein structure community. Of course, there are different ways to construct the 
network on which the communication pathway between binding site and allosteric site is to be 
studied; yet, the main features of the network construction approach outlined in this work has to be 

put into perspective in relation to the existing methodologies. In particular, early pioneering work by 
Vishveshwara et al. has been overlooked in this manuscript. 

 

Once the network construction approach has been into perspective for the potential user, the 
authors should also rigorously compare their findings to existing methods. 

 

For example, Chennubhotla and Bahar used the normalized adjacency matrix element as the 
probability that a random walker uses to jump between connected nodes and to define communities 
(please see Mol. Syst. Biol. 2006). In fact, once the transition probabilities are in place, one can in 
principal calculate the converging values of the random walk, without actually doing the 10000 
rounds of simulations (in fact, convergence is displayed in figure S22). Why would the approach here 
be superior to that methodology? I note that, there is some comparison to Amor’s method for the 
CheY example, but the origin of the differences observed is not discussed. Finally, the guarantee of 
convergence issue also makes me question the “perturbation propagation” label of the methodology 
as all the pathways are pre-determined by the probabilities on the links. 

 

On the other hand, in relation to finding pathways connecting allosteric to the active site, the results 
outlined in the manuscript resemble the findings from a simple optimal path analysis where the 
network is constructed via proximity of the residues, and the link weights are selected from contact 
potentials (please see Atilgan, Turgut, Atilgan, Biophys. J. 2007). One would definitely like to see a 
comparison of the findings here, with those from such a simplistic analysis (e.g., on page 5, R286 and 
E390 are found as the most critical residues, but they also reside on the shortest path connecting the 
two end points). Note that, that approach also automatically takes into account the problem of 
including (i,i+1) contacts mentioned on page 9 and figure S24. 

 

One also wonders if the critical residues residing along the pathways found in this work have high 
betweenness centrality, another graph theoretical parameter that has been found to be useful in 
identifying hot spots in proteins. I was able to locate the mention of this quantity to the prediction of 
hot spots in proteins in a paper that appeared as early as in 2005 (del Sol et al, Bioinformatics) as 
well as many others that appeared later on. The authors acknowledge in the Discussion (page 10) 
that the perturbation likely propagates through higher density regions, which is conceptually similar. 

 

In terms of response of folded protein structures to perturbations, there is at least one network-
based perturbation-response analysis method that is widely used to determine effector and sensor 
residues for external perturbations as well as important connections for signaling within a protein 
that is not mentioned in the manuscript (please see http://prody.csb.pitt.edu/prs/). 

 

I think all these approaches will provide similar results, for the basic reason that the folded protein 
structure and residue cross-correlations dominate the phenomena investigated in this line of 
research. Case-in-point: The CHESCA results displayed in figure 5A,B are mainly controlled by residue 
interactions, manifested in the adjacency matrix (which, I presume will resemble the perturbation 
propagation probability matrix derived here). This is why, the results in Figure 5D,E are similar to, 
e.g. Anisotropic Network Model (ANM) findings (I have used the online tool ANM 2.1 to roughly 
compare the highlighted regions). Again, the authors need to persuade the readers why a more 
complex analysis is necessary to find results whose main features are captured by the neighbor 
information. Since the central example protein here, CheY, is relatively small, it may not be a good 
system to demonstrate the subtleties of the current approach; its surface to volume ratio is high and 
there are only a few densely packed residues which can reside on allosteric pathways. 

 

Minor points: 

 

Page 2: Sentence beginning, “It follows that if …” needs rewording. 

Page 5: The sentence, “These low-ACI areas serve to protect high-ACI areas from the influences of 
perturbation from the surrounding environments,” is speculative. 

Page 9: On the impact of mutations on the results, how different are the structures of the mutants 
from the wild type? Do the results vary significantly more than those obtained at different time 
points in the MD simulation and presented in figure S21? 

Page 10: The term “thermodynamically linked” is vague. 

Page 13: Subsection explaining allosteric hotspots identification is extremely hard to follow. 

Figure 2C and all similar figures; please also specify the ID of the start/end points. 

Figure 3, caption: Please correct the PDB ID. 

Figure 5, caption: Please reword “columns in are caused…” 

Figure S23: Please use the alpha symbol as in the main manuscript. Also, what appears in the three 
columns of this figure need to be explained in the caption. 

Figure S27: This figure also needs explanation. What is displayed in not self-evident.