MACS Matchmaker

Heterogeneous Analyte — parallel reactions

When independent sample characterization suggests a stable mixture, the parallel-reaction model can represent two candidate analyte components — for example monomer and aggregate, or two conformers — with different kinetic phases. This simulator mirrors the model used by the evaluation: the fitted response is the sum of two effective Langmuir 1:1 components.

This simulator explores analyte mixtures. Determine Kinetics provides one Heterogeneous binding — two components fitting model, which uses the same input concentration for both components and does not estimate mixture fractions or competition.

How to interpret the model

Both response components use the nominal injected concentration. Each fitted Rmax,i is therefore an effective amplitude that can absorb component abundance, molecular-mass response, activity, and accessible capacity. It is not a direct measurement of the molar fraction of that component.

The two terms do not explicitly compete for a finite pool of free ligand. Treat the model as a low-occupancy or empirical approximation. At high surface occupancy, shared-site competition and exchange require a coupled competitive-binding model and the fitted rate constants can become biased.

Parallel reactions
A1A2++LLA1·LA2·LL denotes the same ligand class in both parallel terms
R(t)=R1(t)+R2(t),dRidt=kon,i C (Rmax,i−Ri)−koff,i RiR(t) = R_1(t) + R_2(t),\quad \frac{dR_i}{dt} = k_{on,i}\, C\, (R_{max,i} - R_i) - k_{off,i}\, R_i

Component 1 can represent a candidate dominant population and component 2 a candidate minor aggregate, oligomer, or conformer. Their effective responses add, so a small slow-dissociating term can dominate the late tail.

Assumptions
  • Sample composition stays constant across the dilution series.
  • Each component follows a reversible, pseudo-first-order Langmuir 1:1 response term.
  • Effective component amplitudes absorb abundance and response weighting.
  • Shared-site competition, exchange, mass transport, and rebinding are not modelled explicitly.
Parameters
Component 1 (dominant population)

kon,1 (M⁻¹ s⁻¹)

koff,1 (s⁻¹)

Component 2 (minor population)

kon,2 (M⁻¹ s⁻¹)

koff,2 (s⁻¹)

Nominal concentration series (nM)
Kinetic mode
Single-cycle: concentrations are injected sequentially on the same surface, no regeneration.

Noise σ (normalized, dimensionless)

Derived

KD,1 = 10 nM

KD,2 = 2 nM

Req,1 (highest C) = 0.79 (dimensionless)

Req,2 (highest C) = 0.2 (dimensionless)

Diagnostic cues
  • A minor component with a slow koff can create a pronounced late dissociation tail.
  • Set Rmax,2 to zero to recover the Langmuir 1:1 model.
  • This fit cannot by itself prove analyte heterogeneity: surface heterogeneity, avidity, rebinding, and aggregation can produce similar traces.
  • Compare untreated and size-exclusion-purified sample, or repeat at lower surface density, before assigning the two components.
Download traces (CSV)