Skip to main content
Biology LibreTexts

5.6: Binding - An Extension

  • Page ID
    21148
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \) \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)\(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\) \(\newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\) \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\) \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\) \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\) \( \newcommand{\Span}{\mathrm{span}}\)

    Conformational Selection

    In our study of hemoglobin structure in the MWC model, we developed the idea that there were two forms of hemoglobin in solution, the taut and relaxed form, which are pre-exisiting and interconvertible even in the absence of dioxygen. Oxygen was presumed to bind preferentially to the relaxed form. In the KNF model we saw that ligand binding can induce conformational changes in adjacent subunits, promoting cooperative binding of ligand. In general these two models distill down to combinations of two simpler models. The first might be called the conformational selection in which ligand binds tightly to a preexisting conformations in a "lock and key manner" without inducing subsequent macromolecular conformational change. Alternatively, the ligand might bind loosely and then alter the macromolecular conformation to produce tighter binding, an example of an induced fit model. For the binding of dioxygen to hemoglobin, thermodynamic cycles could be drawn showing either binding of ligand and subsequent conformational changes in protein structure or conformational changes in protein structure proceeding binding. Is there additional evidence to support the conformational selection model of binding of ligand to a protein that can, in the absence of ligand, exist in two conformations? The answer is yes.

    Antibodies are immune system protein molecules than can bind "foreign" molecules and target them for biological neutralization. Many crystal structures have been determined of antibodies in the presence or absence of a "foreign" ligand molecule. In these cases, the conformation of the bound antibody is different from that of the free. Either an induced fit model for ligand binding or a lock and key model of binding of ligand to one of two different pre-existing conformations of the antibody could account for this observation. These different mechanisms could be differentiated experimentally by stop-flow kinetic technique since both display slow and fast phases that are affected differently by ligand concentration. Theoretically, in the induced fit model, only one ligand type could bind to the antibody which would undergo a conformational rearrangement to produce tighter binding. However, in the two preexisting conformational models, a different structural ligand might bind to each of the two main antibody conformations. James et al. have recently shown through stop flow kinetics techniques (to investigate binding) and x-ray crystallography (to investigate final structures) that one antibody molecule can, through existing in two different preexisting conformations, bind two different ligands (antigens). One antibody conformations binds small aromatic molecules with low affinity (including the small molecule 2,4-dinitrophenol, the immunizing molecule or hapten) and then rearranges to produce a high affinity binding complex in which the DNP is bound in a narrow cavity (reducing the effective off rate of the bound ligand. A second antibody conformation binds a protein ligand over a broad, flat binding site of the antibody molecule.

    Lange (2008) et al, using a NMR technique, residual dipolar coupling, that allows sampling of structures in the microsecond time scale, have shown that the solution structure of ubiquitin (which we modeled in our first lab), in the absence of ligand, exists in an ensemble of conformational states. More importantly, these different conformational states are identical to those found in the 46 crystal structure of ligand complexed to various protein ligands, strongly supporting the concept of conformational selection. In all likelihood, a combination of both induced fit and conformational selection probably occurs within a 3D energy landscape in which an initial binding encounter by either a lock and key fit to the "optimal fit" conformer or to a higher energy conformer in which the bound state relaxes to a lower energy through the induction of shape changes in the binding protein.

    Figure \(\PageIndex{1}\) below shows a cartoon illustrating the differences between conformational selection and induced fit binding (after Boehr and Wright, Science 320, 1429 (2008)).

    ConformSelectInducedFit.svg
    Figure \(\PageIndex{1}\): Conformational Selection vs Induced Fit Binding (after Boehr and Wright, Science 320, 1429 (2008))

    An interesting experimental model to distinguish conformational selection versus induced ligand binding was offered by Rea et al. They studied rabbit ileal bile acid binding protein (I-BABP). The wild type protein has a helix-turn-helix motif at its N terminus. They produced a mutant (Δa-I-BABP) that replaced this motif with a Gly-Gly-Ser-Gly linker, causing the protein to unfold. Next they conducted binding and folding studies on addition of taurochenodeoxycholate (TCDC) using stopped-flow fluorescence to measure the binding behavior. They wished to distinguish between two distinct mechanisms – folding before binding (or conformational selection) and binding before folding (or induced-fit model). The data support a two phase model. One phase did not depend on ligand and one did, suggesting binding followed by a conformational change).

    Conformational Selection

    Equation\(\PageIndex{1}\) below describes the equilibria involved in the conformation selection model. The forward rate constants are shown as kn while the reverse ones are shown as k-n.

    \begin{equation}
    P \underset{k-1}{\stackrel{k_{1}}{\leftrightarrow}} P^{*}+L \underset{k_{-2}}{\stackrel{k_{2}}{\leftrightarrow}} P^{*} L
    \end{equation}

    P* in the conformational selection model represents a high affinity, pre-existing conformation of the protein.

    Induced Fit

    Equation\(\PageIndex{2}\) below describes the equilibria involved in the induced fit model.

    \begin{equation}
    P+L \underset{k-1}{\stackrel{k_{1}}{\leftrightarrow}} P L \underset{k_{-2}}{\stackrel{k_{2}}{\leftrightarrow}} P^{*} L
    \end{equation}

    P* in the induced fit models results when high ligand shifts the equilibrium to the right.

    One way to differentiate these models is to look at the dependency of the different kinetic phases on ligand. In the conformation selection model, the slow step is the formation of the high affinity form of the protein, P*. The first slow step has a nonlinear dependence in L while the fast second step has a linear dependence. The data did not fit this model well.

    \begin{equation}
    \begin{aligned}
    &k_{\text {slow }}=k_{-2}+\frac{k_{2}}{1+\frac{L}{\left(\frac{k_{-1}}{k_{1}}\right)}} \\
    &k_{\text {fast }}=k_{-1}+k_{1} L
    \end{aligned}
    \end{equation}

    In the induced fit model, the ligand binds to a low affinity and perhaps unfolded form of the protein, which subsequently collapses to the bound form in a slow step.

    \begin{equation}
    \begin{aligned}
    &k_{\text {slow }}=k_{-2}+\frac{k_{2} L}{\left(L+\frac{k_{-1}}{k_{1}}\right)} \\
    &k_{\text {fast }}=k_{-1}+k_{1}[L]
    \end{aligned}
    \end{equation}

    Both ligand dependent and independent phases are evident in the equation for the slow step for the induced fit mechanism. At high ligand concentration (when L >> k-1/k1) , the slow step in the induced fit would be independent of ligand (kslow = k-2 + k2). The authors state the data is consistent with a variant of induced fit called the "fly casting model". In this model the protein first encounters ligand and forms a hydrophobic collapse intermediate (PL) in a fast step characterized by a linear dependence on ligand concentration. Then the intermediate slowly interconverts into a wild type like complex through conformational re-arrangement. Wild-type protein binds the ligand 1000x as quickly, suggesting entropic barrier to binding of the ligand to the unfolded state and rearrangement of the protein thereafter.

    Junker et al used atomic force microscopy (AFM) to observe the effects of ligand binding on the folding/unfolding fluctuations of a single molecule of calmodulin (CaM), a calcium-binding protein that binds amphiphilic helicals peptides which leads to a large conformation change in the protein. To do this, they sandwiched a single CaM molecule between filamins that serve as attachment points for the AFM tip and a surface. A slow pulling force was then applied to the molecule, and the length gain was measured as the protein unfolded. The rapid fluctuations between folded and unfolded states were quantified and used to derive a complete energy landscape for the folding of CaM. They conducted these experiments in the presence of two ligands, Ca2+ and mastoparan (Mas), a wasp venom peptide. They found that Mas does not affect the folding rate of CaM, although it does stabilize the already folded form. This suggests that Mas does not bind to the transition state or the unfolded protein, but rather selects a particular conformation from an ensemble of possible choices. Ca2+ however, increases the folding rate, which suggests that it stabilizes both the transition state and the folded state. AFM offers a considerable degree of precision in drawing energy landscapes of protein folding and unfolding, and it has several applications that are yet to be explored.

    Binding to Intrinsically Disorder Protein and MORFs

    As described above, binding of a protein to a ligand (including another protein) could occur by a lock and key mechanism, possibly through a conformational selection process, or through an induced fit when an initial binding event is followed by a conformation rearrangement to form a more tightly bound complex. But how does binding to completely intrinsically disorder protein (which has been documented) occur? These cases are quite removed from those envisioned in simple induced fit mechanisms. To accommodate binding to IDPs, a new idea has emerged, that of Molecular Recognition Features (MoRFs).

    MoRFs are typically contiguous but disordered sections of a protein that first encounter a binding partner (a protein for example). Using protein complexes in the Protein Data Bank, Mohan et al conducted a structural study of MoRFs by selecting short regions (less than 70 amino acids) from mostly disordered proteins that were bound to proteins of greater than 100 amino acids. They chose a sequence size of 70 amino acids and smaller since they would be most likely to display conformational flexibility before binding to a target. 2512 proteins fit their criteria. For comparison, they created a similar database of ordered monomeric proteins. The analysis showed that after they encounter a binding surface on another protein, the MoRF would adopt or "morp" into several types of new conformations, including alpha-helices (a-MoRFs), beta-strands (b-MoRFs), irregular strands (i-MoRFs) and combined secondary structure (complex-MoRFs), as shown in the figure below.

    Figure: Types of Molecular Recognition Features in Intrinsically Disordered Proteins

    MorphsbindIDPs.svg
    Figure \(\PageIndex{2}\): Types of Molecular Recognition Features in Intrinsically Disordered Proteins. (A) α-MoRF, Proteinase Inhibitor IA3, bound to Proteinase A (PDB entry 1DP5). (B) A β-MoRF, viral protein pVIc, bound to Human Adenovirus 2 Proteinase (PDB entry 1AVP). (C) An ι-MoRF, Amphiphysin, bound to α-adaptin C (PDB entry 1KY7). (D) A complex-MoRF, β-amyloid precursor protein (βAPP), bound to the PTB domain of the neuron specific protein X11 (PDB entry 1X11). Partner interfaces (gray surface) are also indicated. Vacic, V. et al. Journal of Proteome Research 6, 2351 (2007). Permission from Copyright Clearance Center's Rightslink /American Chemical Society

    Vacic et al have further characterized the binding surfaces between MoRFs and their binding partners using structural data from PDB files. Interfaces were studied by determining the differences in accessible surface area between MoRFs and their binding partners, and the protein in unbound states. These were compared to ordered protein complexes, including homodimers and antibody-protein antigen interactions that were not characterized by disordered interactions. Their findings are summarized below.

    • MoRF interfaces have more hydrophobic groups and fewer polar groups compared to the surface of monomers. This is true even as the overall amino acid composition of intrinsically disordered protein shows them to be enriched in polar amino acids, which leads them to adopt a variety of unfixed solution conformations.
    • a-MoRFs have few prolines, which is expected as prolines are helix breakers.
    • Methionine is enriched in both MoRFs and in their binding partner interface. Methionine is unbranched, flexibile, and contains sulfur, which is large and polarizable, making it an ideal side chain to be involved in London forces in a hydrophobic environment.
    • Even though MoRFs have few residues, their binding interfaces were of similar or larger size than other protein binding interfaces, a result which also applies to IDPs as a whole. MoRFs interfaces also have a larger solvent-exposed surface area, similar to IDPs. This is consistent with the notion MoRFs are disordered before binding and that a defined structure is not possible with little buried surface area.
    • As MoRFs have significant nonpolar character within a IDP that is highly enriched in polar amino acids, MoRFs should be highly predictable by search algorithms.

    5.6: Binding - An Extension is shared under a not declared license and was authored, remixed, and/or curated by Henry Jakubowski and Patricia Flatt.