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, 2 (5), e468

Conformational Proofreading: The Impact of Conformational Changes on the Specificity of Molecular Recognition

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Conformational Proofreading: The Impact of Conformational Changes on the Specificity of Molecular Recognition

Yonatan Savir et al. PLoS One.

Abstract

To perform recognition, molecules must locate and specifically bind their targets within a noisy biochemical environment with many look-alikes. Molecular recognition processes, especially the induced-fit mechanism, are known to involve conformational changes. This raises a basic question: Does molecular recognition gain any advantage by such conformational changes? By introducing a simple statistical-mechanics approach, we study the effect of conformation and flexibility on the quality of recognition processes. Our model relates specificity to the conformation of the participant molecules and thus suggests a possible answer: Optimal specificity is achieved when the ligand is slightly off target; that is, a conformational mismatch between the ligand and its main target improves the selectivity of the process. This indicates that deformations upon binding serve as a conformational proofreading mechanism, which may be selected for via evolution.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Models of molecular recognition.
(A) Lock and key. No conformational changes occur upon binding. The ligand (white) and the target (green) have complementary structures. (B) Induced fit. The target changes its conformation due to the interaction with the ligand. (C) Pre-existing equilibrium model. The native state is actually an ensemble of conformations, that is deformations may occur even before binding. The ligand selectively binds the matching target within this ensemble of fluctuating conformations.
Figure 2
Figure 2. Competition between two rigid targets.
The ligand (white) is interconverting within an ensemble of conformations while interacting with two rigid competitors, A and B (green and orange), characterized by different structures. Non-specific binding energy may lead to the formation of functional complexes in which the target and the ligand are not exactly matched. The unmatched complexes may also be functional but their product formation rates, νum, may differ from these of the matched complexes, νm. The specificity of the ligand, that is its ability to discriminate between A and B, depends on the ligand flexibility, the structural mismatch between its native state and the correct target and on the structural difference between the competing targets.
Figure 3
Figure 3. General molecular recognition scheme.
Both the ligand (white) and the target (green) are interconverting between an ensemble of conformations denoted by indices, ai and Ai, respectively. All the different conformations may interact and as a result, a variety of complexes is formed. In some of them the target and the ligand are perfectly matched, for example aiAi and ajAj, and in some there is only partial fit, for example aiAj and ajAi. The rate of product formation depends on the concentrations of the complexes, which depend on Kij, and on the functionality of each complex, which depends on the turnover numbers, υij. In a similar fashion, the different ligand conformations, ai, may interact with competing target conformations Bi and thus catalyze incorrect product.
Figure 4
Figure 4. Lowest elastic mode model.
Conformational changes occur upon binding of a ligand (white) and a target (green). In the native state of the ligand, the binding sites are equally spaced and positioned at xi 0 (i = 0,1,2…N−1) and the total length of the binding domain is l0. The ligand is interacting with a rigid target on which N complementary binding sites are equally spaced and positioned at yi  = y0+i·s/(N−1) where s is the length of the target binding domain. The ligand may undergo a conformational change to fit the target. Since we consider only the lowest mode motion, the ligand may only stretch or expand uniformly. Thus, its binding sites are displaced to xi = x0+i·l/(N−1) and the total length changes from l0 to l.
Figure 5
Figure 5. The dependence of specificity on mismatch.
Each column is for a specific difference between the competing targets, Δ = sAsB, given in Å. The rate production of the correct product RA is a sum of a Gaussian centered at d = 0, which arises from the specific binding, and a uniform contribution due to non-specific binding (top row, blue). Similarly, the incorrect rate production, RB, is composed of a Gaussian centered at d = Δ and a uniform non-specific contribution (top row, red). The specificity, ξ, is the ratio between the correct and incorrect production rates and therefore depends on the location and width of the recognition windows (bottom row). (A) If the competing targets differ in structure, Δ = 3 Å, the windows of recognition partly overlap and the resulting specificity is optimal at a nonzero mismatch. (B) For Δ = 0, both RA and RB are centered around zero mismatch and the resulting specificity is approximately a rectangular window of width, d1/2k−1/2((N−m)ε−f−log(αr)). (C) If the competing targets differ much, Δ≫d1/2, the recognition windows do not overlap and the specificity is again optimal for zero mismatch. The parameters of the plot are N = 15, m = 2, ε = 2 k B T, r = 0.1, g = 1 Å, k = 1 k B T2, f = 15 k B T.
Figure 6
Figure 6. Specificity ξ as a function of mismatch d and flexibility, k.
Colors denote various values of rigidity k (in units of k B T2, legend). (A) For targets that differ by Δ = 3 Å the specificity is optimal at a nonzero mismatch. As the ligand becomes more rigid the optimal mismatch tends to zero as d0k−1/2. (B) For competing targets with similar structure, Δ = 0, the specificity resembles a rectangular window centered on zero mismatch. The width of this window also decreases as k−1/2. (C) The specificity when only matched complexes are functional, r = 0, increases exponentially with the mismatch as ξ∼exp(k·Δ·d). The parameters of the plot are N = 15, m = 2, ε = 2 k B T, r = 0.1, g = 1 Å, f = 15 k B T.
Figure 7
Figure 7. Specificity as a function of a 2D mismatch in the presence of multiple competitors.
Color bar shows log of specificity. In two dimensions, the ligand structure may stretch or shrink along x and y. The mismatches along these axes are dx and dy. The gray circle denotes zero mismatch. In the presence of multiple competitors (green crosses), the optimal mismatch (black X) is nonzero and depends on the structure of the various competitors. Competitors that slightly differ from the correct target have a “window of recognition” which overlaps with correct target recognition window. As a result, the specificity is maximal for a non-zero mismatch. The parameters of the plot are N = 15, m = 2, ε = 2 k B T, r = 0.1, g = 1 Å, k = 1 k B T/Å2, f = 15 k B T.
Figure 8
Figure 8. Specificity ξ in the presence of two noisy targets as a function of mismatch d.
Colors denote various values of rigidity k (in units of k B T2, legend). The lengths of the target binding domain, sA and sB are fluctuating according to a Gaussian noise with variances σA and σB. (A, B) For competing targets of different structure, Δ = 3 Å, similarly to noiseless targets, the specificity is optimal at a nonzero mismatch. (C, D) Competing targets of similar structure, Δ = 0. The specificity has an extremum at d = 0, but whether this point is maximum or minimum depends on the noise. For a noisier correct target, σAB, an optimal ligand has a nonzero mismatch. The parameters of the plot are: N = 15, m = 2, ε = 2 k B T, r = 0.1, g = 1 Å, σA,B = 0.5, 0.6 Å, f = 15 k B T.

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