Agonist efficiency links binding and gating in a nicotinic receptor
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eLife assessment
This valuable work investigates the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of a ligand-gated ion channel. The conclusions are based on analysis of solid data in terms of a mechanistic model, but adequate statistical analysis is lacking and the uniqueness of the proposed model is not discussed. The findings will be interesting to biophysicists working on ligand-gated ion channels and, more generally, to enzymologists focused on allosteric enzyme regulation.
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Abstract
Receptors signal by switching between resting (C) and active (O) shapes (‘gating’) under the influence of agonists. The receptor’s maximum response depends on the difference in agonist binding energy, O minus C. In nicotinic receptors, efficiency (η) represents the fraction of agonist binding energy applied to a local rearrangement (an induced fit) that initiates gating. In this receptor, free energy changes in gating and binding can be interchanged by the conversion factor η. Efficiencies estimated from concentration-response curves (23 agonists, 53 mutations) sort into five discrete classes (%): 0.56 (17), 0.51(32), 0.45(13), 0.41(26), and 0.31(12), implying that there are 5 C versus O binding site structural pairs. Within each class efficacy and affinity are corelated linearly, but multiple classes hide this relationship. η unites agonist binding with receptor gating and calibrates one link in a chain of coupled domain rearrangements that comprises the allosteric transition of the protein.
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Author Response
Reviewer #1 (Public Review):
Specifically, the authors define "efficacy" (eta) of a ligand as the fractional change in binding free energy between the open and the closed states of the channel.
We assume that the word in quotes is a typo; ղ is efficiency, not efficacy (now given the symbol λ). We now emphasize the distinction immediately after Eq. 2.
- One concern regards the clustering of the data sets in Fig. 5 into exactly 5 eta-classes. First, two clusters contain only two data points each. Second, the proposed "catch&hold LFER model" (Fig. 2) does not predict the existence of a discrete number of such eta-classes. How strong is the evidence that there are exactly 5 classes as opposed to a continuum of possible eta values.
Statistical (x means cluster) analysis indicates that the 23 agonists segregate into 5 ղ …
Author Response
Reviewer #1 (Public Review):
Specifically, the authors define "efficacy" (eta) of a ligand as the fractional change in binding free energy between the open and the closed states of the channel.
We assume that the word in quotes is a typo; ղ is efficiency, not efficacy (now given the symbol λ). We now emphasize the distinction immediately after Eq. 2.
- One concern regards the clustering of the data sets in Fig. 5 into exactly 5 eta-classes. First, two clusters contain only two data points each. Second, the proposed "catch&hold LFER model" (Fig. 2) does not predict the existence of a discrete number of such eta-classes. How strong is the evidence that there are exactly 5 classes as opposed to a continuum of possible eta values.
Statistical (x means cluster) analysis indicates that the 23 agonists segregate into 5 ղ classes. Groups with only 2 members (plus the intercept) are less well defined (Fig 4) but are supported by the 5 mutational ղ classes (Fig. 7). (see above)
- The authors do not discuss the uniqueness of the proposed model.
see above. Ln 405 Induced fits are common.
In fact, it seems to me that the existence of eta-classes might be explained just as well by an alternative model which assumes a single gating mechanism for the receptor,
We are not sure what a “single gating mechanism” means. Does non-single refer to i) the2 stage induced fits (catch-hold LFER)? … ղ classes makes this conclusion unavoidable. ii) our conjecture that are there are 5 different C versus O binding site structural pairs…? Energy derives from structure, so we the 5 energy ratios indicate 5 structural pairs. iii) multiple steps inside gating (ϕ)? …So far there have not been any alternative explanations for the organized map of ϕ. iv) catch itself?... Evidence for this induced fit is given in Fig 2 and 7 SI, and on Ln 528-547 we discuss the implications of kon to C versus O. Ln 405 Local ‘Induced fit’ rearrangements in enzymes are common. We think the evidence is strong for the bottom scheme in Fig 2A.
but distinct patterns of ligand-protein interactions for the different agonists.
ղ classes derive from distinct interactions for different agonists, but what these are and whether the ‘contact number’ idea is useful are uncertain (see above).
The pore opening-associated increase in agonist affinity is typically caused by a tightening of the substrate binding site (often called clamshell closure) …
Ln 379-386 In the Discussion we now relate catch-hold to induced fit
Ln 455, 461-463, 471-474 Fig 2SI and the induced fit to clamshell closure
Reviewer #2 (Public Review):
This is an interesting manuscript with a worthwhile approach to receptor mechanisms. The paper contains an impressive amount of new data. These single molecule concentration response curves have been compiled with care and the authors deserve great credit for obtaining these data.
Ln 233 ղ can be estimated from a CRC built from whole-cell currents…
Ln 150 …or indeed any method that estimates KdC and KdO (for example binding assays, or perhaps in silico simulations of AC and AO structures)
I judge the main result to be that there are different values of the recently-proposed agonist-related quantity "efficiency".
Ln 21, 26-27, 535-547 OK, but to us the most interesting insight is that in AChRs binding IS gating.
These values are clustered into 5 quite closely spaced groups. The authors propose that these groups are the same whether considering mutations in the binding site or different agonists.
see above
It was unclear to me in several places, what new data and what old data are included in each figure. Therefore readers may have difficulty judging the claimed advance. This difficulty is not helped by the discussion, which includes some previous findings as "results".
see above.
A further weakness is that it is unclear how general or how specific these concepts are. The authors assert that they are, by definition, completely universal. However, we do not have reference to previous work or current data on any other receptor than the muscle nicotinic. I could not square the concept that "every receptor works like this" with the evident lack of desire to demonstrate this for any other receptor.
Ln 132-136 There are reasons to think that receptors in general work according to Figure 1A. A thermalized ligand (for instance TriMA, MW 60) has the momentum of only ~3 water molecules. A momentum sensor would have terrible signal/noise.
Reviewer #3 (Public Review):
This work attempts to introduce a new attribute of the receptor- efficiency, a fraction of an agonist binding energy consumed by conformational transition of the receptor from resting to active (open) states. Furthermore, the authors use an impressive set of experimental data (single channel recordings with 23 agonists and 53 mutations) to measure the efficiency for each agonist and mutant receptor. All the estimated efficiencies fall into a few groups and inside each of the efficiency groups there is a strong correlation between agonist affinity and receptor opening efficacy.
The main finding in this study is that estimated efficiencies fall into 5 groups.
see above.
There is no clear description of the method how the efficiencies were allocated into different groups. Most importantly, it is not clear if the method used takes into account the uncertainty of the efficiency estimate. The study does not show any statistical metrics of the efficiency estimates as well as any other calculated variable such as dissociation equilibrium constants to resting or open states. Surely, the uncertainty of the efficiency should matter especially considering how near the efficiency group values are (eg. difference about 10% between 0.51 and 0.56 or 0.41 and 0.45).
see above
All the tested agonists fell into groups according to the efficiency value attributed to them. It is difficult to see why some of the agonists belong to the same group. For example, it is not obvious at all why such agonists as epibatidine, decamethonium and TMP are in the same group. The question, I guess, arises if this grouping based on efficiency has any predictability value. Furthermore, if a series of mutations with the same agonist fall into different groups, the prediction power of this approach is very limited if one attempts to design a new agonist or look for a new mutation.
see above and Ln 548-561 (last para of text). Efficiency is a relatively new idea. This report is one of only a few on the subject. More experiments with different receptors by more labs using other approaches are needed to ascertain whether ղ is general.
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eLife assessment
This valuable work investigates the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of a ligand-gated ion channel. The conclusions are based on analysis of solid data in terms of a mechanistic model, but adequate statistical analysis is lacking and the uniqueness of the proposed model is not discussed. The findings will be interesting to biophysicists working on ligand-gated ion channels and, more generally, to enzymologists focused on allosteric enzyme regulation.
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Reviewer #1 (Public Review):
In this work Indurthy and Auerbach investigate the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of the nicotinic acetylcholine receptor (nAChR). The conclusions are based on a very large pool of experimental data that are interpreted with great mechanistic insight.
Specifically, the authors define "efficacy" (eta) of a ligand as the fractional change in binding free energy between the open and the closed states of the channel. They construct a log-log scatter plot of efficacy vs. affinity which represents 23 different agonists acting on the WT receptor, plus a subset of the same agonists acting on various nAChR mutants. They go on to show that these largely scattered dots can be partitioned into 5 distinct clusters …
Reviewer #1 (Public Review):
In this work Indurthy and Auerbach investigate the fundamental concept of how the energy of agonist binding is converted into the energy of the conformational change that opens the pore of the nicotinic acetylcholine receptor (nAChR). The conclusions are based on a very large pool of experimental data that are interpreted with great mechanistic insight.
Specifically, the authors define "efficacy" (eta) of a ligand as the fractional change in binding free energy between the open and the closed states of the channel. They construct a log-log scatter plot of efficacy vs. affinity which represents 23 different agonists acting on the WT receptor, plus a subset of the same agonists acting on various nAChR mutants. They go on to show that these largely scattered dots can be partitioned into 5 distinct clusters ("eta-classes") within which the dots are linearly arranged. They interpret these clusters in terms of a mechanistic gating model (the "catch&hold LFER model"), and suggest that a different model accounts for each different eta-class. Put in simple terms, the interpretation is that 5 different subtypes of gating isomerization exist for the nAChR, the choice among which depends on the agonist used.
These types of study are necessary to advance conceptual understanding in biophysics. I have some reservations regarding the mechanistic interpretation of the data set and the uniqueness of the proposed model.
1. One concern regards the clustering of the data sets in Fig. 5 into exactly 5 eta-classes. First, two clusters contain only two data points each. Second, the proposed "catch&hold LFER model" (Fig. 2) does not predict the existence of a discrete number of such eta-classes. How strong is the evidence that there are exactly 5 classes as opposed to a continuum of possible eta values.
2. The authors do not discuss the uniqueness of the proposed model. In fact, it seems to me that the existence of eta-classes might be explained just as well by an alternative model which assumes a single gating mechanism for the receptor, but distinct patterns of ligand-protein interactions for the different agonists. The pore opening-associated increase in agonist affinity is typically caused by a tightening of the substrate binding site (often called clamshell closure) which brings further protein side chains into the vicinity of the ligand, thereby allowing further ligand-protein interactions to form (or further strengthening interactions that exist also in the closed-pore state). Thus, at a first approximation, the ratio between binding free energies in the open- and closed-pore states reflects the ratio of the numbers (and strengths) of ligand-protein bonds in those two states.
As an illustration, consider the following simplified model for a channel and a given ligand. In the open-pore state the number of ligand-protein interactions is n(o), and all those interactions are comparably strong. Out of those interactions only a subset is formed in the closed-pore state, their number is n(c) (where n(c)
The maximal possible values of n(c) and n(o) are determined by the number and spatial arrangement of protein chemical groups that surround the substrate binding site. On the other hand, depending on the number and arrangement of matching chemical groups on the ligand, different ligands will be able to "exploit" different subsets of these possible ligand-protein interactions, resulting in different values of eta. Furthermore, ligands for which the absolute values of n(o) are different, but the ratio n(c)/n(o) is similar, will form apparent "eta-classes", i.e., will be arranged on a "eta-plot" along a straight line. (See attached image file for a graphical representation of the model.)This model would suggest that there is a single gating mechanism (i.e., the actual protein conformational change is similar regardless of which agonist is bound), but the relative stabilities of the ligand-bound closed and open states are agonist-dependent. Wouldn't such a mechanism equally well explain all the data shown? The authors should either acknowledge this possibility or discuss available structural or functional evidence to exclude it.
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Reviewer #2 (Public Review):
This is an interesting manuscript with a worthwhile approach to receptor mechanisms. The paper contains an impressive amount of new data. These single molecule concentration response curves have been compiled with care and the authors deserve great credit for obtaining these data. I judge the main result to be that there are different values of the recently-proposed agonist-related quantity "efficiency". These values are clustered into 5 quite closely spaced groups. The authors propose that these groups are the same whether considering mutations in the binding site or different agonists.
It was unclear to me in several places, what new data and what old data are included in each figure. Therefore readers may have difficulty judging the claimed advance. This difficulty is not helped by the discussion, which …
Reviewer #2 (Public Review):
This is an interesting manuscript with a worthwhile approach to receptor mechanisms. The paper contains an impressive amount of new data. These single molecule concentration response curves have been compiled with care and the authors deserve great credit for obtaining these data. I judge the main result to be that there are different values of the recently-proposed agonist-related quantity "efficiency". These values are clustered into 5 quite closely spaced groups. The authors propose that these groups are the same whether considering mutations in the binding site or different agonists.
It was unclear to me in several places, what new data and what old data are included in each figure. Therefore readers may have difficulty judging the claimed advance. This difficulty is not helped by the discussion, which includes some previous findings as "results".
A further weakness is that it is unclear how general or how specific these concepts are. The authors assert that they are, by definition, completely universal. However, we do not have reference to previous work or current data on any other receptor than the muscle nicotinic. I could not square the concept that "every receptor works like this" with the evident lack of desire to demonstrate this for any other receptor.
On one hand, if the framework can be extended, this can be a very important concept, and in some sense, could be the missing link to understanding concentration response curves. On the other, if it proves not to be general, or not to be generally applicable because of circumstances.
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Reviewer #3 (Public Review):
This work attempts to introduce a new attribute of the receptor- efficiency, a fraction of an agonist binding energy consumed by conformational transition of the receptor from resting to active (open) states. Furthermore, the authors use an impressive set of experimental data (single channel recordings with 23 agonists and 53 mutations) to measure the efficiency for each agonist and mutant receptor. All the estimated efficiencies fall into a few groups and inside each of the efficiency groups there is a strong correlation between agonist affinity and receptor opening efficacy.
The main finding in this study is that estimated efficiencies fall into 5 groups. There is no clear description of the method how the efficiencies were allocated into different groups. Most importantly, it is not clear if the method …
Reviewer #3 (Public Review):
This work attempts to introduce a new attribute of the receptor- efficiency, a fraction of an agonist binding energy consumed by conformational transition of the receptor from resting to active (open) states. Furthermore, the authors use an impressive set of experimental data (single channel recordings with 23 agonists and 53 mutations) to measure the efficiency for each agonist and mutant receptor. All the estimated efficiencies fall into a few groups and inside each of the efficiency groups there is a strong correlation between agonist affinity and receptor opening efficacy.
The main finding in this study is that estimated efficiencies fall into 5 groups. There is no clear description of the method how the efficiencies were allocated into different groups. Most importantly, it is not clear if the method used takes into account the uncertainty of the efficiency estimate. The study does not show any statistical metrics of the efficiency estimates as well as any other calculated variable such as dissociation equilibrium constants to resting or open states. Surely, the uncertainty of the efficiency should matter especially considering how near the efficiency group values are (eg. difference about 10% between 0.51 and 0.56 or 0.41 and 0.45).
All the tested agonists fell into groups according to the efficiency value attributed to them. It is difficult to see why some of the agonists belong to the same group. For example, it is not obvious at all why such agonists as epibatidine, decamethonium and TMP are in the same group. The question, I guess, arises if this grouping based on efficiency has any predictability value. Furthermore, if a series of mutations with the same agonist fall into different groups, the prediction power of this approach is very limited if one attempts to design a new agonist or look for a new mutation.
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