Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations

Abstract

Nicotinic acetylcholine receptor (nAChR) subtypes are key drug targets, but it is challenging to pharmacologically differentiate between them because of their highly similar sequence identities. Furthermore, α-conotoxins (α-CTXs) are naturally selective and competitive antagonists for nAChRs and hold great potential for treating nAChR disorders. Identifying selectivity-enhancing mutations is the chief aim of most α-CTX mutagenesis studies, although doing so with traditional docking methods is difficult due to the lack of α-CTX/nAChR crystal structures. Here, we use homology modeling to predict the structures of α-CTXs bound to two nearly identical nAChR subtypes, α3β2 and α3β4, and use free-energy perturbation (FEP) to re-predict the relative potency and selectivity of α-CTX mutants at these subtypes. First, we use three available crystal structures of the nAChR homologue, acetylcholine-binding protein (AChBP), and re-predict the relative affinities of twenty point mutations made to the α-CTXs LvIA, LsIA, and GIC, with an overall root mean square error (RMSE) of 1.08 ± 0.15 kcal/mol and an R² of 0.62, equivalent to experimental uncertainty. We then use AChBP as a template for α3β2 and α3β4 nAChR homology models bound to the α-CTX LvIA and re-predict the potencies of eleven point mutations at both subtypes, with an overall RMSE of 0.85 ± 0.08 kcal/mol and an R² of 0.49. This is significantly better than the widely used molecular mechanics-generalized born/surface area (MM-GB/SA) method, which gives an RMSE of 1.96 ± 0.24 kcal/mol and an R² of 0.06 on the same test set. Next, we demonstrate that FEP accurately classifies α3β2 nAChR selective LvIA mutants while MM-GB/SA does not. Finally, we use FEP to perform an exhaustive amino acid mutational scan of LvIA and predict fifty-two mutations of LvIA to have greater than 100X selectivity for the α3β2 nAChR. Our results demonstrate the FEP is well-suited to accurately predict potency- and selectivity-enhancing mutations of α-CTXs for nAChRs and to identify alternative strategies for developing selective α-CTXs.

Keywords: conotoxin; free-energy perturbation; nicotinic acetylcholine receptor; selectivity.

Figure 1

FEP calculation of the relative binding free energy due to a mutation. The peptide being mutated is represented in orange and the receptor to which it is bound is depicted in gray. Water molecules are shown as lines, with oxygens colored red and hydrogens colored white. The λ window is shown in the upper right-hand corner of each frame. In particular, λ = 0 represents the unmutated sidechain (Asn-9, leftmost frame) and λ = 1 represents the fully mutated sidechain (Lys-9, rightmost frame). For clarity, only six λ windows are shown, although significantly more are used in a typical FEP calculation.

Figure 2

Overview of chemical systems: (A) extracellular view of LvIA (orange surface) and AChBP (gray cartoon); (B) transmembrane view of LvIA (orange surface) and AChBP (gray cartoon); (C) binding interface of LvIA and α3β2 nAChR; (D) binding interface of LvIA and α3β4 nAChR. LvIA (orange), AChBP (gray), α3 (green), β2 (pale cyan), and β4 (blue) are depicted in above images. Residues shown in pink differ between β subunits, lie within the binding interface of LvIA, and have a sidechain pointing towards the binding pocket (E) sequences of LvIA, LsIA, and GIC and their respective IC₅₀s for different receptors. An asterisk (*) indicates an amidated C-terminus. Lines connecting cysteines labeled with Roman numerals indicate disulfide bonds.

Figure 3

Quantitative prediction of the relative affinity of α-CTX mutants for AChBP: (A) scatter plot of ΔΔG_FEP vs. ΔΔG_EXP; (B) scatter plot of ΔΔG_MM-GB/SA vs. ΔΔG_EXP with unity (solid, black line), ±1 kcal/mol error bands (solid gray lines), and ±2 kcal/mol error bands (dashed, gray lines) superimposed. The error bars show the standard error of the mean (SEM) from three independent FEP simulations.

Figure 4

Quantitative prediction of the relative potency levels of LvIA mutants at the α3β2 and α3β4 nAChRs: (A) scatter plot of ΔΔG_FEP vs. ΔΔG_EXP; (B) scatter plot of ΔΔG_MM-GB/SA vs. ΔΔG_EXP with unity (solid, black line), ±1 kcal/mol error bands (solid gray lines), and ±2 kcal/mol error bands (dashed, gray lines) superimposed. The error bars show the standard error of the mean (SEM) from three independent FEP simulations.

Figure 5

Classification of potency- and selectivity-enhancing LvIA mutants: (A) ROC plot comparing the ability of FEP and MM-GB/SA to classify mutations to LvIA, GIC, and LsIA that gain affinity for AChBP relative to WT; (B) ROC plot comparing the ability of FEP and MM-GB/SA to classify LvIA mutations that gain potency for the α3β2 or α3β4 nAChR relative to WT; (C) classification of the selectivity of LvIA mutants by FEP; (D) classification of the selectivity of LvIA mutants by MM-GB/SA.

Figure 6

WaterMaps of nAChR subtypes. The apo (A) α3β2 nAChR binding site and (B) α3β4 nAChR binding site are shown with their respective WaterMaps. LvIA is shown as an orange, semi-transparent cartoon for reference but is not present during the WaterMap simulations. (C) Extracellular view of α3β2 nAChR binding site and WaterMap (D) Extracellular view of α3β4 nAChR binding site and WaterMap. A semi-transparent orange surface is shown around LvIA. Medium-energy water sites with predicted ΔG > 1.5 kcal/mol are colored yellow and high-energy water sites with predicted ΔG > 3.5 kcal/mol are colored red. For clarity, only medium-energy or high-energy water sites that overlap with the position of LvIA in the bound state are shown.

Figure 7

In silico exhaustive mutagenesis of LvIA: (A) Fold selectivity levels of LvIA point mutants predicted by FEP (blue points) and measured experimentally (orange points) are plotted by residue, with box plots overlaid. The black dashed line denotes the cutoff for a mutation to be considered selective (100X), while the red dot-dashed line denotes the fold selectivity of the WT LvIA (18X); (B) Pie charts show the compositions of point mutations predicted to be selective by the loop they are on (left panel) and by their predicted ΔΔGs at each nAChR subtype (right panel).