Orthosteric Binding Experiments: How to Avoid the Most Common Data Pitfalls

Terry's Desk
7 days ago
5 min read

Binding affinity appears straightforward: add ligand, measure signal, fit a curve. Yet discovery teams routinely lose time and misallocate resources because the underlying biology behaves nothing like the idealized systems we learned in textbooks. GPCRs couple, decouple, isomerize, deplete tracers, and shift apparent affinity depending on stoichiometry and time. The result is a recurring pattern across programs—clean data that is not actually telling the truth.

Orthosteric binding experiments remain a cornerstone of pharmacology, but they demand rigor. Terry Kenakin’s session examines not just how binding works, but why so many datasets mislead even seasoned scientists.

In this session, you’ll gain:

Why saturation and displacement assays fail when protein stoichiometry shifts
How two-stage GPCR binding creates “high” and “low” affinity states
What temporal kinetics quietly change about the affinity you think you measured

Understanding Orthosteric Binding Foundations

Orthosteric binding experiments rely on a measurable event: a tracer binds a receptor, and anything that displaces it alters that signal. But as Dr. Kenakin stresses, the apparent simplicity collapses once biological reality intrudes. Tracers bind not only to receptors but also to surfaces and unwanted proteins; non-specific binding must be corrected, not assumed. Running total and protected curves simultaneously is essential to reveal true receptor binding.

Even the familiar saturation experiment hides traps. Linear-scale plots appear to plateau early, encouraging premature calls of Bmax. But plotting on a logarithmic axis exposes how far the system may actually be from true saturation. The midpoint and maximum—core parameters for downstream modeling—are only meaningful when the assay fully explores the system’s capacity.

Unexpected outcomes typically trace back to a single issue: transferring assumptions from idealized models into messy GPCR systems.

Take home message: interpret orthosteric binding data only after verifying that the biology behaved as expected.

Displacement Curves and the Illusion of Potency

Displacement assays measure affinity when no traceable analog exists. But potency in these curves is not intrinsic affinity—it shifts with tracer concentration. A displacer appears weaker at high tracer occupancy because more ligand must be displaced.

In practice:

The IC50 you measure is a function of tracer levels.
Changing tracer concentration moves the displacement curve.
True affinity requires correcting for occupancy state.

Earlier decades relied on Scatchard, Hanes, and other linear transforms to “clean” nonlinear data. Dr. Kenakin is unequivocal: do not use them. Modern computation handles raw nonlinear data precisely, whereas transforms distort error, compress dynamic range, and violate regression assumptions.

When potency is mistaken for affinity, program decisions drift. Proper orthosteric binding design prevents those errors before they propagate into SAR narratives.

Complex Two-Stage Biology Behind Orthosteric Binding

GPCR pharmacology rarely follows the neat Langmuir adsorption isotherm. Proteins are not inert surfaces, and orthosteric binding reactions often continue beyond the first encounter. After a ligand binds, the receptor may transition further—often via G protein coupling. That second step stabilizes a higher-affinity configuration, explaining classical “high” and “low” affinity states.

Mechanistically:

Ligand binding (A + R → AR) is only step one.
AR can become AR* or ARG, raising apparent affinity.
The measured affinity becomes an operational composite.

Removing coupling partners (e.g., GTPγS) collapses the system to a single low-affinity curve. This is not receptor heterogeneity—it is a collapsed two-stage system. Understanding these transitions is essential for interpreting orthosteric binding data accurately.

Stoichiometry: The Quiet Driver of Curve Shape

Two-stage systems expose how easily stoichiometry distorts outcomes. When G proteins are abundant, curves look clean because every receptor–ligand complex can couple. When receptor levels rise or G proteins become limiting, the system becomes stoichiometrically constrained.

The high-affinity state is undersupported, creating biphasic curves.

This frequently masquerades as:

Two binding sites
Multiple receptor subtypes
Allosteric modulation

But often the explanation is simpler: depletion of a required binding partner. Overexpression systems are particularly vulnerable. High receptor levels also deplete free tracer when tracer concentrations are low, breaking the assumption that added concentration equals free concentration.

Across CRO-generated binding panels, this remains one of the most common sources of erroneous affinity estimates.

Distinguishing Multiple Sites from Two-Stage Orthosteric Binding

Genuine multiple-site binding has its own diagnostic signature. When a tracer binds two sites with different affinities, curve shape reflects the ratio of affinities and abundance. Small differences produce subtle curvature; large differences produce biphasic behavior.

Clues that point to true multiple-site binding rather than two-stage GPCR biology:

Disrupting G protein coupling does not collapse the biphasic curve.
Changing receptor expression or G protein levels does not remove curve heterogeneity.
Curve shifts track site properties, not system stoichiometry.

Dr. Kenakin's message is practical: never assign “two sites” before ruling out two-stage orthosteric binding and stoichiometric imbalance.

Experimental Conditions That Make or Break Orthosteric Binding Data

Dr. Kenakin outlines a pragmatic checklist for producing reliable orthosteric binding measurements:

Cell type and receptor expression: Overexpression can distort stoichiometry and drive artifacts.
Protein concentration: Too much receptor depletes tracer and invalidates mass-action assumptions.
Non-specific binding control: Adsorption to surfaces changes free ligand concentration.
Equilibration time: Many assays stop before the system reaches equilibrium, especially with slow competitors.

Clear curves are not evidence of equilibrium. Dr. Kenakin demonstrates how premature stopping mis-ranks compounds, particularly when tracer and displacer bind at different rates. Ensuring equilibrium is non-negotiable.

Temporal Kinetics and the Hidden Bias in Affinity

Kinetic imbalance is one of the most common—and least recognized—artifacts. If the tracer binds faster than the displacer, early time points exaggerate tracer occupancy and underestimate competitor potency. If the displacer binds faster, the opposite occurs.

Many programs unknowingly compare compounds measured under different kinetic biases.

You may see:

Early stopping → potency distortions
Different stopping times → incomparable datasets
Curve shape → hints about missing equilibrium

Real-time binding systems remove the guesswork. By observing the full onset and offset kinetics, scientists obtain affinity, kinetic rates, and equilibrium confirmation in one experiment—ideal for GPCR-focused discovery teams.

Why Terry’s Corner

Weekly pharmacology sessions with Dr. Terry Kenakin give scientists an uncommon advantage: the ability to interrogate foundational assumptions before they distort program decisions. Through deep-dive lectures, monthly AMAs, and a growing on-demand library, the Corner helps discovery teams refine binding strategy, troubleshoot complex GPCR systems, and understand when orthosteric binding data is lying—and why.

Built for pharmacologists strengthening fundamentals, program teams navigating bottlenecks, and leaders who need credible guidance fast, the Corner brings clarity to the complexities shaping modern GPCR innovation. Those who invest now shape the breakthroughs that follow.

This orthosteric binding lesson closes out the year—marking 30 courses released and 3 live AMAs hosted since launch. As we prepare for the next wave of content in 2026, Premium members receive 67% off Terry’s Corner throughout 2025, unlocking full access to every session already available and all new weekly releases next year.

As a member, you get:

✅ Full access to every course — All 30 lessons released this year, plus new ones launching after the year-end break.

✅ AMA replays + priority Q&A — Rewatch all 3 live AMAs and move your questions to the front of the line.

✅ Deep-dive learning paths — Structured progression from foundational concepts to emerging and expert-level decision making.

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