Peer review of: DIP/Dpr interactions and the evolutionary design of specificity in protein families (2020)

Reviewers' comments: 

Reviewer #1 (Remarks to the Author): 

Sergeeva et al. report a detailed computational analysis of determinants of interaction specificity in 
Drosophila DIP and Dpr cell-cell recognition proteins. This system is intriguing because 7 
subfamilies of proteins form preferred heterotypic interactions (DIP-to-Dpr protein interactions) 
without extensive cross-talk between subfamilies. The question addressed by the authors is how 
this intricate interaction specificity network is established at the structural level, which has been 
addressed before, but less comprehensively. Here the authors approach this question by building 
and analyzing homology models of numerous complexes and mutant complexes and predicting 
residues that can change binding specificity. They test selected predictions using binding 
experiments. An in-depth study of one pair of subfamilies includes experiments that confirm 
predictions of mutations that disrupt cognate complexes, as well as a nice example that shows 
how swapping appropriate residues into a weakly interacting complex can restore affinity. A fairly 
compelling picture emerges of how sets of distributed interactions are used to disfavor non-
cognate DIP/Dpr complexes. The summary of the negative design strategies in Figure 5 provides a 
good overview. Overall the work is interesting, well done and makes a valuable contribution – by 
suggesting and illustrating general principles – that will shape thinking in the protein interaction 
field. There are some aspects of the paper that require clarification and the manuscript should be 
accompanied by a comprehensive report of the results of the analysis. 
Three points related to Figure 1b. (1) Are these all fly proteins? (2) I couldn’t find an explanation 
of where the binding data in that figure panel come from. Are these values all from reference 11? 
(3) The authors appear to assume that orthologs in the same subfamily conserve their interaction 
specificity, but they don’t discuss why this is valid. This is a key point for the sequence-based 
analysis. 
As written, it is confusing whether the members of a subfamily, when used for a given purpose, 
include only fly proteins vs. all orthologous insect proteins. E.g. when residues were mutated “to 
all residues appearing at the same position in DIPs of other specificity subgroups,” did this 
consider only fly proteins? Lines 183 – 193 were particularly confusing in this regard. I am 
guessing there were two filters required to designate a residue as a negative constraint: (1) if 
analyzing fly subfamily i, the mutation must be predicted as disruptive when modeled in each fly 
protein member of subfamily i (giving DDG > 0 for at least one other, non-i, subfamily). (2) In a 
separate test, “we only defined a constraint as a position that would also be predicted for other 
Insecta species so as to focus on constraints that are evolutionarily conserved.” I gather this 
second prediction was not based on structural modeling, but rather on a qualitative comparison of 
biophysical properties, and applied to a much larger number of proteins (which makes sense; the 
residue equivalence categories used should be reported in the methods). Regardless of whether 
this is the correct interpretation of what was done, the authors should make this section easier to 
follow (e.g. by liberally adding the use of “fly” as a qualifier, in many places in the paper, and 
making clear there were 2 different tests at this stage). 
Then, on line 240 another criterion for a negative constraint is introduced: “In addition to this 
requirement, predictions are then filtered based on the requirement that DDG > 0 for all available 
blue subgroup crystal structures.” Along with the statement on line 220 that “Calculations are 
carried out on every complex for which a crystal structure is available.” A general question: Were 
two different protocols used, one to study all fly DIP/Dpr subfamily pairs (using just one or two 
templates each) and a different one for complexes involving the blue/purple groups (using all 
available crystal structures and adding at least one new filter)? Or was there just one protocol that 
is described slightly differently in different places? If there were two different protocols, did the 
results differ? This was confusing. 
The paper should be accompanied by a list of the predicted negative constraints for all subfamilies, 
according to the described algorithm. Figure 6 highlights the positions that were found, but it 
would be good to include a comprehensive annotation (in the supplement) of which residues are 
predicted to destabilize which subfamily interactions. Or, even better, to make figure 5 for all 
subfamilies (given that the data are available). The paper says “Overall, we identified negative 

constraints for 42 combinations of non-interacting DIP and Dpr subgroups (see supplemental excel 
file for all FoldX data),” but I don’t see the list of negative constraints that passed the filters, nor 
was there an excel file among the materials I saw. 
Line 133 states “We tested the ability of a number of sequence-based methods to identify 
specificity determinants in the seven specificity subgroups.” And line 153 says “Below we compare 
the predictions of these sequence-based methods to an energy-based analysis of DIP and Dpr 
structures.” To understand the result of that test and comparison, it should be easier for the 
reader to see which of these sequence-based predictions overlap with the structure-based 
predictions and which were experimentally verified in this work (or in other work). This could be 
added to Figure 6. 
The paper states: “Similarly, we predict several residues in the hydrophobic core as potential 
negative constraints (12, 13, 16 in Dprs and 5, 13, 16 in DIPs), whereas sequence-based methods 
predict only one of these 344 positions (Figure 2).” But these were not among the mutations that 
were experimentally confirmed to disrupt binding (I don’t think), which is worth mentioning here, 
since this may not be borne out. 
Experimental error estimates are lacking. I did not find descriptions of experimental error or fitting 
error, other than for 2 replicates of the AUC measurements. Were the SPR experiments performed 
more than once? (The checklist indicates replicates, but the paper does not, as far as I saw). For 
Kd values, in Figure 3C and elsewhere, is the value in parentheses the error? E.g. 17.61(7) 
kcal/mol. What kind of error? In what units? How many replicates? Is this meant to indicate std. 
dev. of 0.07 kcal/mol (which seems way too low of an error for true replicates)? Related to this, 
many of the binding curves do not saturate. How was the 100% bound signal estimated, and is 
this well justified? The authors could report more conservative estimates of their measurement 
accuracies that would still substantiate their claims. 
Minor points: 
It would be helpful to clarify why there is a stochastic element of the FoldX modeling procedure. 
E.g. to explain why 10 runs gave different values. 
Several different and contradictory values for the dissociation constant of DIP-g/Dpr11 are 
reported (7.1, 7.9, 8.5) in the text and figures. 
Evaluating mutational energies by comparing mutant and WT structures with the same rotamers in 
each structure is an unusual procedure (though I can imagine it may cancel errors). Can the 
authors explain/justify this? And explain how the structure that was used was selected? I.e., from 
the methods: “This requires initial sampling of different rotamers for a mutated residue and 
subsequent synchronous movement of neighboring residues in a WT complex and a MT complex to 
find the optimal position for the mutated residue.” But what is “optimal” in this context, given 
there are two different structures to consider? 
I didn’t understand this expression: 33*[3(10+19) + 1(10+17) + 177 5(8+16) + 1(10+20) + 
1(9+18) + 1(10+20) + 1(9+16)] = 11418 mutations. First, is the “3” in “3(10+19)” meant to be 
“2” (for “two conformations of Dpr10/DIP”)? Next, if the numbers like 10, 17, 8, 16, etc. are the 
numbers of proteins in non-i subgroups, this seems like an overestimate of the number of 
mutations to be made, given that many proteins share the same interface residue at a given site. 
It would be good to clarify the source of the terms in this expression. 

Reviewer #2 (Remarks to the Author): 

This is a very nice paper that explores the basis of binding specificity using an example of a 
physical interaction between proteins from DIP and Dpr families. The authors performed in silico 
mutational screening and found that some amino acid substitutions serve as negative constraints 
and prevent certain undesired interactions between members of these families. Moreover, the 
authors went further and performed experimental binding affinity measurements to confirm their 
computational findings. I have several suggestions which can potentially improve the paper: 
1. It might be worth discussing that stabilizing mutations can also represent negative constraints 
since the studied interaction is rather weak and transient. 

2. In a reverse set of experiments a triple Dpr11 mutant binds to DIP-a much weaker than wild-
type Dpr10 – is it because of possible conformational changes? These residues are located in a 
flexible loop, could it be a source of this discrepancy? In general, I wonder how computational 
predictions of ddG for reverse mutations would agree with the reverse set of experiments. 
3. Negative constraints in the form of insertions and deletions (so called enabling or disabling 
loops) were studied previously, it might be a good idea to discuss different scenario when 
substitutions or insertions/deletions are used by evolution as negative constraints - does it depend 
on the type of interface and its properties? 
4. “strengthens cognate binding to DIP-h by 0.3 kcal/mol” – such small changes are not 
significant. 
5. The authors found that two thirds of interface utilize negative constraints – this fraction is 
similar to the fraction of destabilizing mutations in a protein or protein interface. Can the negative 
constraints reflect the fact that most interfacial mutations are destabilizing (unless there is some 
sort of enrichment), might be worth discussing it. 

Reviewer #3 (Remarks to the Author): 

The work by Sergeeva et al. addresses an important problem that arises continually in evolution: 
how do multi-protein families generate specific protein-protein interactions using the same overall 
tertiary structure. The authors identify 'negative constraints' that guide the evolution of specificity 
in the DIP/Dpr neuronal targeting proteins and test them in SPR experiments to validate the 
constraints they identified. The authors relied on protein structures and homology models to test 
the effect of mutations on the complex stability using a number of online servers for predicting 
mutational effects, eventually settling on FoldX, a physics-based force field approach, to make 
predictions. Then, they found by mutating a small number of important interface positions, they 
could change specificity of DIP/Dpr subfamilies. The study is sound and technically solid, but there 
are a few major concerns I have that have prevented me from giving the manuscript strong 
support. In addition, the study's breadth is narrow and it is unclear whether this would be of 
interest to the general scientific community. 

Major concerns: 

1) The study makes use of homology models and a host of online servers for the prediction of 
specificity-determining residues at the interface and their contributions to complex stability. There 
is no innovation in these techniques, so it is unclear whether this study reflects an advancement in 
our ability to uncover the determinants of specificity in a protein-protein interaction family. 

2) The experimental work presented in this paper, while impressive, is restricted to two of the 
seven subfamilies. It would be useful to know the accuracy of the predictions made by FoldX would 
apply outside of these two subfamilies. 

General notes: 

It is unclear from the manuscript why the DIP/Dpr interaction has the same constraints on 
specificity that the Dscams/clustered protocadherins do? What is the biological consequence of 
interaction promiscuity? 

"Negative constraints" is not well defined in the manuscript. My interpretation is that a negative 
constraint is a mutation or set of mutations that decrease the affinity of a subfamily to another 
(ancestral) family. Some definition should be included early on in the manuscript. 

I don't quite understand the need for 42 distinct negative constraints in order for specificity. Take 
an example of 4 uniquely specific pairs. Given your calculation, there would be 16 possible pairs, 
with 4 that bind strongly, leading to your conclusion that there would be 12 sets of negative 
constraints. However, if one negative constraint differentiates the set into a group of two and two, 
and then two other negative constraints separate them into four groups of one, then only 3 sets of 
negative constraints are needed for the four subfamilies. Extending that to seven families, only 6 
sets of negative constraints are needed. This can be thought of as a decision tree where each leaf 
should be isolated. At a minimum, 6 decisions are needed to isolate 7 subfamilies. 

Are the homology models (or crystal structures) stable in simulations? How do the structures differ 
from the original models after equilibration/refinement in FoldX? 

How well do changes in affinity by SPR correlate with FoldX calculations? 

The authors make a comparison of the DIP/Dpr family to protocadherins, but do not cite their own 
work and others on understanding the specificity determinants of these interactions. The 
comparison to these approaches (and overall conclusions) and the one presented here would be 
greatly appreciated. 

Specific notes: 

Line 73. I'm a little confused about the description of identities: "average identity between 
individual DIPs and Dprs is about 30%." Is this a comparison of identity between a DIP and a Dpr? 
What is the conservation of DIP and Dpr orthologs? 

Figure 1B. It looks as though the DIP-alpha and DIP-beta/DIP-gamma subfamilies should be 
swapped such that there are fewer Kd measurement lines that go through subfamilies. Is there a 
reason for the order as is? 

Line 119. How many contacts are shared between subfamilies? 

Line 123. "Throught" -> "throughout" 

Line 125. A reference to figure should show up in this sentence instead of the next one. 

Line 129. Stylistic - "of course" should not be placed between "cannot" and "be" 

Line 151. In what ways were the number of sites expanded? Which ones were missed in your 
earlier studies and why based on your new analysis? 

Line 192. What do these look like? It would be useful to look this over. 

Line 204. Remove "It is clear from the table that" 

Line 205. Where is the figure reference for "shown in green"? 

Line 245. Instead of "qualitative" I think "biophysical" could be used to indicate that you are 
looking at biophysical properties of amino acid interactions. In fact, you use "physical" later in the 
manuscript. 

Figure 3. From FoldX, it was found that position 10 would be more important to binding than 
position 29 and 31. I'm curious whether this is true experimentally. If you introduce mutations to 
position 10 and position 29 or 31 only, how does the affinity change for the parent species. 
Basically, how much can we trust the magnitude of the predictions? 

Figure 4A. Why was position 6 identified as a negative constraint? It is cationic in both DIP-alpha 
and DIP-gamma. Is this negative constraint across all families or are they just between the 
subfamilies being tested? 

General question for Figure 3 and 4. How did you select the positions to test? In some cases it 
looks like the highest negative constraint was not tested. 

Line 306. Remove "It is evident from the figure that" 

Line 330. Do you have an idea of the delta-delta G needed to ensure specificity based on this 
observation? 

Figure 6. It is unclear to me how one would use this figure to determine the negative constraints 
between any two given subfamilies, i.e. what constitutes the 42 negative constraints that were 
described. 

Line 385. This parenthesis is not closed. 

Line 386. Change "constraints" to "negative constraints" for consistency throughout the 
manuscript. 

Line 391. Can't several "tricks" be used in combinations, in some cases being important and in 
others, not? 

Line 435. "dihedtral" -> "dihedral"