5.6: Binding - Conformational Selections and Intrinsically Disordered Proteins

Search Fundamentals of Biochemistry

Learning Goals

• Distinguishing Binding Mechanisms

  • Explain the differences between conformational selection and induced fit models of ligand binding.
  • Compare and contrast how the two models account for ligand binding using the hemoglobin and antibody examples.

• Thermodynamic and Kinetic Foundations

  • Describe the thermodynamic cycles and equilibria that underlie both conformational selection and induced fit, including how preexisting protein conformations influence ligand affinity.
  • Analyze how stop-flow kinetics and other rapid mixing techniques can distinguish between the two models based on ligand concentration dependence of fast and slow phases.

• Interpreting Experimental Evidence

  • Evaluate experimental data (e.g., from hemoglobin, ubiquitin NMR studies, and antibody binding studies) to identify evidence supporting conformational selection over induced fit.
  • Discuss how experiments with rabbit ileal bile acid binding protein (I-BABP) and calmodulin (using AFM) further elucidate the order of binding versus folding events.

• Quantitative Modeling of Binding

  • Derive and interpret the kinetic equations describing the conformational selection and induced fit models, including understanding the roles of rate constants (k₁, k₋₁, k₂, k₋₂).
  • Explain the implications of ligand-dependent versus ligand-independent phases in these models.

• Binding to Intrinsically Disordered Proteins (IDPs) and MoRFs

  • Define Molecular Recognition Features (MoRFs) and explain their role in binding interactions of intrinsically disordered proteins.
  • Compare the structural and surface properties (e.g., hydrophobic versus polar character) of MoRF interfaces with those of ordered protein complexes.
  • Discuss how the flexibility and disorder of IDPs influence binding specificity and the conformational changes that occur upon ligand binding.

• Integrative Energy Landscapes

  • Discuss how a 3D energy landscape can accommodate both conformational selection and induced fit mechanisms, and the potential for a hybrid model where initial binding to a preexisting conformer is followed by further conformational relaxation.

By achieving these learning goals, students will develop a comprehensive understanding of how protein conformations influence ligand binding, how these processes are experimentally measured and modeled, and how intrinsic disorder and flexible binding motifs contribute to molecular recognition in biological systems.

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-existing 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 the cooperative binding of ligands. These two models generally distill down to combinations of two simpler models. The first might be called conformational selection, in which the ligand binds tightly to a preexisting conformation in a "lock and key" manner without inducing subsequent macromolecular conformational change. Alternatively, the ligand might bind loosely and 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 the binding of ligand and subsequent conformational changes in protein structure or conformational changes in protein structure and then binding. Is there additional evidence to support the conformational selection model of ligand binding to a protein that can exist in two conformations without a ligand? The answer is yes.

Antibodies are immune system protein molecules that can bind "foreign" molecules and target them for biological neutralization. Many crystal structures of antibodies have been determined 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. An induced fit model for ligand binding or a lock and key ligand binding model to one of two pre-existing antibody conformations could account for this observation. These different mechanisms could be differentiated experimentally by stop-flow kinetic techniques 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, a different structural ligand might bind to the two main antibody conformations in the preexisting conformational models. 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 conformation binds small aromatic molecules with low affinity (including the small molecule 2,4-dinitrophenol, the immunizing molecule, or hapten). Then it rearranges to produce a high-affinity binding complex in which the DNP is bound in a narrow cavity (reducing the bound ligand's effective off rate (k_off). A second antibody conformation binds a ligand over a broad, flat binding site of the antibody molecule.

Lange (2008) et al., using an NMR technique, residual dipolar coupling, which allows sampling of structures on 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 ligands 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 \(\PageIndex{1}\) shows a cartoon illustrating the differences between conformational selection and induced fit binding (after Boehr and Wright, Science 320, 1429 (2008)).

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

Rea et al. offered an interesting experimental model to distinguish conformational selection versus induced ligand binding. 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 adding 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 the induced-fit model). The data support a two-phase model. One phase did not depend on the 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 k_n, while the reverse ones are 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 protein conformation.

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 concentration. 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 on 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 (k_slow = k_-2 + k₂). The authors state that the data are consistent with a variant of induced fit called the "fly casting model." In this model, the protein first encounters a 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 rearrangement. Wild-type protein binds the ligand 1000x as quickly, suggesting entropic barriers 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). This calcium-binding protein binds amphiphilic helical peptides, leading 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 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 yet to be explored.

Binding to Intrinsically Disordered Proteins and MORFs

As described above, the 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 a conformation rearrangement follows an initial binding event to form a more tightly bound complex. But how does binding to a completely intrinsically disordered protein (which has been documented) occur? These cases are removed from those envisioned in simple induced fit mechanisms. Binding to IDPs might occur through specific Molecular Recognition Features (MoRFs).

MoRFs are typically contiguous but disordered protein sections that first encounter a binding partner (a protein, for example). Mohan et al. conducted a structural study of MoRFs in the Protein Data Bank by selecting short regions (less than 70 amino acids) from mostly disordered proteins bound to proteins greater than 100 amino acids. They chose a sequence size of 70 amino acids and smaller since they would most likely 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 "morph" 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

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

Figure \(\PageIndex{8}\) shows interactive iCn3D models of the types of molecular recognition features in intrinsically disordered proteins

(A) α-MoRF, Proteinase Inhibitor IA3, bound to Proteinase A (1DP5) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...tk45aWAeMrTct7	(B) A β-MoRF, viral protein pVIc, bound to Human Adenovirus 2 Proteinase (1AVP) (Copyright; author via source). Click the image for a popup or use this external link:https://structure.ncbi.nlm.nih.gov/i...EFmanRy2T7zjX6
(C) An ι-MoRF, Amphiphysin, bound to α-adaptin C (1KY7) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...JEBC4VHUA9C718	(D) A complex-MoRF, β-amyloid precursor protein (βAPP), bound to the PTB domain of the neuron specific protein X11 (1X11) (Copyright; author via source). Click the image for a popup or use this external link: https://structure.ncbi.nlm.nih.gov/i...XtQ7retidnksT9

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 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 proteins is enriched in polar amino acids, leading them to adopt various 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, flexible, 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, which also applies to IDPs as a whole. MoRF interfaces also have a larger solvent-exposed surface area, similar to IDPs. This is consistent with the notion that MoRFs are disordered before binding and that a defined structure is impossible with little buried surface area.
• As MoRFs have a significant nonpolar character within an IDP highly enriched in polar amino acids, MoRFs should be highly predictable by search algorithms.

Recently, a new program, FINCHES-online, lets you predict interactions between disordered regions using sequence as input. It uses a new "coarse-grained" force field (such as those used for molecular dynamics simulations) to predict the interactions of the IDR of a protein with target proteins. As surmised from the above discussions, three types of interactions of an IDR binder with a target protein are possible:

1. IDR binder + Folded Domain Target ↔ Folded IDR:Folded Domain Target Complex (i.e., folding on binding)
2. IDR binder + Folded Domain Target ↔ IDR:Folded Domain "Fuzzy Complex" (i.e., the IDR remains unfolded on binding)
3. IDR binder + IDR target ↔ IDR:IDR (i.e., both binding and target remain disordered.

The program is currently best suited for category 3.

Recent Updates:  9/16/25

Designing Binders for IDPs

Machine learning and AI approaches, including RFdiffusion, have been used to design a library of protein binding pockets for IDPs, and more specifically, for IDRs.  The binders were designed to interact with peptide backbones and then refined to maximize interactions with side chains.  The binders often display binding affinity in the nanomolar range (Kd around 1 nM).  The IDRs are induced on binding to form conformations competent to fit the binding pocket.  One example is the IDP and neuropeptide dynorphin A.  Its main physiological target is the kappa-opioid receptor (Kd around 200 nm), but it can also bind other targets as it is an IDP. It is bound in an extended form to the synthesized binder with an even lower Kd (around 1 nM).  Figure \(\PageIndex{9}\) is an interactive iCn3D model showing the interactions of the IDP dynorphin A with the synthetic binder (9CCE).  The binder, an alpha-helical bundle, is shown in magenta, and the dynorphin A peptide backbone is shown in cyan.  The noncovalent side chain interactions between the two are shown in dotted lines between the side chains (in sticks).

Figure \(\PageIndex{9}\): Interactions of the IDP dynorphin A with the synthetic binder (9CCE).  (Copyright; author via source). Click the image for a popup or use this external link: https://www.ncbi.nlm.nih.gov/Structu...1be4e15ad2f3ec

The binding pocket is an elongated groove to which the IDP binds in an extended conformation.  On binding, the IDP is not induced to form secondary structure. Rather, it is the IDP backbone that is "read" by the binding pocket.  This binding mode is then ideal for IDRs that don't have a specific secondary structure.  Note in the iCn3D model that many of the hydrogen bonds between dynorphin A and the binder are between peptide bonds in the IDR to side chains in the binder.  The hydrogen bonds are often "bidentate," involving hydrogen bonds between the amide H and the carbonyl O of the peptide.  Additional specificity arises from interactions of the side chain in the IDR with the binder.

Binders can be designed and synthesized that can fit many different types of sequences of varying sequence and polarity.  There are many more disordered states for the IDR than highly ordered ones (such as all alpha helical). The binder forces the IDR peptide into an extended state, so there is an induced-fit aspect to binding.  The IDR can be bound in an optimal extended fit to provide a complementary match to the binding groove in the binder.  Hence, a binder could be made that specifically recognizes a given IDR.  Designed binders might eventually be used as drugs similar to antibodies and also as probes for the normal functions of IDFs.  For example, biomolecular condensates are often enriched in proteins with IDRs.

Summary

This chapter examines the dynamic nature of protein–ligand interactions, contrasting two fundamental binding mechanisms—conformational selection and induced fit—and exploring how these models apply to structured and intrinsically disordered proteins (IDPs).

1. Foundations of Conformational Selection and Induced Fit
   The chapter begins by revisiting classic models of cooperative ligand binding using hemoglobin as a prime example. In the Monod-Wyman-Changeux (MWC) model, proteins exist in multiple preexisting conformations (e.g., taut and relaxed forms), with ligands preferentially binding to the high-affinity state. This concept—conformational selection—suggests that ligand binding stabilizes one of several accessible protein conformers without necessarily inducing significant structural changes. In contrast, the Koshland-Némethy-Filmer (KNF) model describes induced fit, where ligand binding itself triggers a conformational change in the protein, leading to tighter binding.

2. Experimental Evidence and Kinetic Differentiation
   Detailed experimental studies, including stop-flow kinetics and NMR residual dipolar coupling measurements, support conformational selection. For instance, antibodies have been shown to exist in multiple conformations that selectively bind different ligands, while studies on ubiquitin reveal that its solution ensemble mirrors the conformations observed in ligand-bound crystal structures. Kinetic analyses distinguish the models by revealing different dependencies on ligand concentration for fast and slow binding phases. Experiments with rabbit ileal bile acid binding protein (I-BABP) and atomic force microscopy (AFM) on calmodulin further illustrate how binding events can be either preceded or followed by conformational changes.

3. Hybrid Models and Energy Landscapes
   The discussion integrates the two mechanisms by suggesting that protein–ligand interactions often occur on a complex 3D energy landscape where both conformational selection and induced fit play roles. In some cases, an initial encounter with a preexisting conformation (lock-and-key) is followed by further conformational relaxation (induced fit) that optimizes binding.

4. Binding in Intrinsically Disordered Proteins (IDPs) and MoRFs
   The chapter expands these concepts to intrinsically disordered proteins, which lack fixed three-dimensional structures until they bind their partners. Here, short segments known as Molecular Recognition Features (MoRFs) mediate binding. MoRFs are flexible regions that adopt defined secondary structures—such as α-helices, β-strands, or irregular conformations—upon interacting with a binding partner. Structural analyses of MoRF interfaces reveal that, despite being part of overall polar IDPs, these regions are enriched in hydrophobic residues, facilitating effective molecular recognition and often leading to large interaction surfaces comparable to those seen in ordered protein complexes.

5. Implications for Biological Function
   Understanding these binding mechanisms has profound implications for the regulation of biological processes. The ability of proteins to switch conformations underlies the specificity and efficiency of signaling pathways, enzymatic reactions, and immune responses. Moreover, the hybrid model of conformational selection followed by induced fit provides a versatile framework to explain how proteins can be both highly specific and adaptable in dynamic cellular environments.

In summary, this chapter provides a comprehensive overview of the mechanisms underlying protein–ligand binding. It illustrates how both conformational selection and induced fit contribute to the dynamic nature of protein interactions, with experimental evidence from diverse systems reinforcing the concept. The discussion is extended to include the unique binding properties of intrinsically disordered proteins, highlighting the critical role of MoRFs in mediating these interactions and offering insights into the complexity of molecular recognition in biological systems.