Go Beyond the Dissociation Constant (KD): Why Other Measurements Also Matter 

K_D: The Start of the Journey, Not the Destination

The dissociation constant (K_D) has long been the cornerstone of protein interaction studies. As a measure of binding affinity, it offers a clear, quantitative snapshot of how tightly two molecules interact under equilibrium conditions[1]. It’s simple, interpretable, and comparable, making it a staple in everything from drug discovery to basic biological research [2,3].

As the complexity of biological systems, becomes ever more apparent; however, relying on K_D alone can leave you with an incomplete or even misleading picture [4]. Binding affinity is just one piece of the puzzle; just a single metric, telling you how tightly proteins interact, but not how fast they bind, how long they stay bound, whether their structures change upon interaction, or how they behave in real-world biological environments [4]

To truly understand the nature and significance of a protein-protein interaction (PPI), you need to go beyond the dissociation constant. In this article, we’ll explore why K_D alone doesn’t tell the whole story, what other measurements can add to your understanding, and how to build a more comprehensive assay strategy.

1. The Dissociation Constant (K_D): What It Tells Us, and What It Doesn’t

K_D represents the equilibrium between bound and unbound states of two interacting molecules. A lower K_D indicates a stronger interaction, meaning less of the protein complex dissociates once it’s formed. This makes K_D an essential parameter when ranking binding affinities or comparing potential ligands.

But K_D is calculated under specific assumptions [5]:

• • The system is at equilibrium.
  • Binding is reversible.
  • Often, a 1:1 binding model is applied, even when more complex behaviours may be at play.

So, what doesn’t K_D tell us?

• • Stoichiometry and complexity: K_D assumes a specific binding model, but many biological systems involve multivalent interactions, cooperativity, or complexes with more than two components (See figure 1).

Figure 1. Schematic overview of key binding scenarios. Monovalent (Top-left) – 1:1 interaction described by a single KD. Cooperative binding (Top-right) – in which the first ligand alters the affinity of the second. Homobivalent/multivalent binding (bottom-left) – where two identical sites on one ligand engage two identical receptor pockets, generating avidity. Heterobivalent, multispecific interaction (Bottom-right) – assembles a higher-order complex from distinct ligand and receptor sites. Figure adapted from Erlendsson & Teilum 2021, Front. Mol. Biosci., (CC-BY 4.0.) [6]

• • Structural or functional changes: Interactions can trigger conformational shifts, activation, or inhibition—none of which are captured in a single K_D value.
  • Physiological relevance: K_D is often measured in idealised buffer systems. These don’t account for the crowded, heterogeneous conditions of real biological environments like serum, cell lysates, or membranes.
  • Binding dynamics: K_D gives no information about how quickly the interaction forms (association rate, K_on) or breaks apart (dissociation rate, K_off). Two interactions could have the same K_D but very different biological behaviours if one forms quickly and the other lingers longer.

In short, K_D is valuable—but insufficient on its own. If you want to understand not just whether proteins interact, but how and why that interaction matters, you’ll need to explore other parameters too.

2. The Metrics That Complete the Picture

To fully understand protein interactions, you need to consider more than just how tightly two molecules bind. Complementary measurements provide context that K_D alone cannot, helping you uncover the dynamics, complexity, and physiological relevance of your system [7]. Here are some of the most valuable additional parameters to consider:

• • Stoichiometry: Understanding how many molecules bind together is crucial for deciphering complex formation and function. Is the interaction 1:1, or does it involve multiple subunits. K_D won’t tell you this, but techniques that report on stoichiometry provide critical insight into the architecture of protein complexes.
  • Molecular Size: Changes in hydrodynamic radius can indicate whether an interaction results in structural rearrangement, oligomerisation, or aggregation. Monitoring molecular size helps detect conformational changes that are invisible to K_D-centric methods.
  • Absolute Concentration: Accurately determining the concentration of free and bound species helps validate K_D measurements and ensures meaningful interpretation, especially in non-ideal or unpurified systems.
  • Conformational State: Some interactions only occur when proteins adopt specific conformations. Binding can induce folding, domain exposure, or allosteric changes—none of which are captured by K_D alone but can be detected by size-based or structural methods.
  • Contextual binding in real samples: Many assays require purified proteins in simplified buffers. But biological systems are messy: serum, lysate, or membrane environments can influence binding. Measuring interactions under these native conditions provides more accurate biological relevance.
  • K_on / K_off (Association and Dissociation Rates): These kinetic measurements reveal how fast binding occurs and how long the complex remains intact. Two interactions may have the same K_D but vastly different Kon and Koff values—leading to different biological effects. For drug candidates, slower Koff values can correlate with longer target engagement and improved efficacy.

Together, these metrics enable a richer, more nuanced understanding of how proteins interact. By combining affinity with size, stoichiometry, and dynamic behaviour, you can uncover not just whether proteins bind, but how and why those interactions occur, and what they mean in a physiological context.

 3. When K_D Alone Falls Short

While K_D is a useful measure of affinity, it doesn’t capture the full picture of how proteins interact. Several real-world scenarios highlight its limitations:

• • Same K_D, Different Kinetics: Two molecules may share the same K_D but have vastly different dissociation rates (k_off), leading to different biological effects—especially in drug efficacy.
  • Missed Multivalency or Cooperativity: K_D assumes a simple 1:1 model. Complex systems with cooperative or multivalent binding can be misinterpreted or overlooked entirely.
  • Failure in Real-World Conditions: Binding seen in purified buffers may fail in complex media like serum due to competition or nonspecific interactions—contexts where K_D alone is insufficient.
  • Hidden Conformational Changes: Some interactions trigger structural shifts or complex formation, undetectable through K_D but observable via changes in size or stoichiometry.

These examples underscore the need to combine K_D with additional metrics—like kinetics, stoichiometry, and conformational analysis—to truly understand protein interactions.

4. Choosing the Right Tool for the Right Metric

No single assay can measure every aspect of a protein-protein interaction (see figure 2). Different techniques are optimised for different readouts, and choosing the right tool depends on what you need to know, whether that’s affinity, kinetics, size, stoichiometry, or performance in complex samples.

Figure 2. Comparison of commonly used techniques summarised from recent technique reviews and application [8-13]

Key takeaways:

• • SPR and BLI are excellent for capturing real-time kinetics but require surface immobilisation, which can introduce artefacts and restrict assay conditions.
  • ITC offers rich thermodynamic data, including K_D and stoichiometry, but demands high protein purity and large volumes, limiting its throughput.
  • DLS is useful for observing size changes but does not provide binding affinity or stoichiometry.
  • MDS (Microfluidic Diffusional Sizing) bridges critical gaps by offering:

– Accurate K_D measurements

– Simultaneous insight into molecular size and stoichiometry

– Operation entirely in solution, with no need for immobilisation

– Compatibility with complex media like 100% serum

– Low sample volume requirements—just 4 µL per measurement

Selecting the right technique is about aligning your experimental goals with the method’s capabilities. Often, the most powerful insights come from combining orthogonal approaches, using one technique for kinetics, another for stoichiometry, and a third for confirmation in native environments.

 5. The Role of MDS in a Broader Strategy

As the need for richer, more physiologically relevant protein interaction data grows, Microfluidic Diffusional Sizing (MDS) offers a compelling solution. While not a replacement for kinetic techniques like SPR or BLI, MDS excels in areas where many traditional methods struggle, making it an ideal orthogonal and complementary approach.

What makes MDS different?

• • Works in solution: No need for surface immobilisation means interactions occur under more natural conditions, minimising artefacts.
  • Quantifies size changes directly: As molecules bind and form complexes, MDS detects shifts in hydrodynamic radius; giving insight into stoichiometry, oligomerisation, and conformational changes.
  • Accurate K_D measurements: Though equilibrium-based, MDS produces robust affinity data with low noise and excellent reproducibility.
  • Ultra-low sample consumption: Requires just 4 µL per measurement, making it perfect for precious, low-yield proteins.
  • Compatible with complex biological matrices: MDS can be performed in 100% serum, cell lysates, and other native conditions: where many assays fail.
  • Minimal sample preparation: No need for protein purification or labelling beyond a simple fluorescent tag.

These advantages make MDS a powerful tool for:

• • Confirming that interactions observed in vitro also occur in vivo-like conditions.
  • Exploring binding behaviour when kinetics are already known.
  • Studying difficult targets like membrane proteins or proteins that are unstable on surfaces.

By adding MDS to your assay toolkit, you gain a clearer, broader picture of your protein interactions—without compromising on throughput, sample economy, or biological relevance.

Conclusion – K_D Is Just the Beginning

The dissociation constant has served as a valuable benchmark for characterising protein-protein interactions but in isolation, it paints an incomplete picture. To truly understand how proteins behave, bind, and function in biological systems, researchers must look beyond K_D and explore additional metrics like stoichiometry, molecular size, binding dynamics, and performance in real biological environments.

Each parameter offers a unique lens into the complexity of molecular interactions. And while no single technique captures everything, the right combination can unlock deeper, more meaningful insights.

Microfluidic Diffusional Sizing (MDS) stands out as a powerful complement to traditional tools. By providing accurate K_D measurements alongside stoichiometry and size changes, in solution, with minimal volume, and without immobilisation, MDS gives you the ability to assess interactions under more native, realistic conditions. It adds clarity where other techniques leave questions and enables confident decision-making in both basic and applied research.

So don’t stop at K_D. Go further. Measure more. And uncover the full story behind your protein interactions.

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