Lineweaver-Burk Plot Calculator

Compute Michaelis-Menten kinetic constants (Km and Vmax) from substrate-velocity data points using a double-reciprocal linear regression model.

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Experimental Data Pairs

Input substrate concentration [S] and initial velocity v for 5 data points.

Data Points ([S] vs v)
#1
#2
#3
#4
#5

Maximum Velocity (V_max)

100.0

Michaelis Constant (K_m)

5.0

Linear Fit Quality (R²)

0.9998
Double-Reciprocal Linear Equation: 1/v = 0.0500(1/[S]) + 0.0100
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Enzyme Inhibition Diagnostics

Competitive inhibitors increase slope (Km increases, Vmax unchanged). Non-competitive inhibitors change y-intercept (Vmax decreases, Km unchanged).

Enzyme Inhibition Effects on Lineweaver-Burk Parameters Reference

Inhibition Mode Effect on Vmax Effect on Km Lineweaver-Burk Graph Pattern
Competitive Inhibition Unchanged Increases (Km' > Km) Lines intersect at Y-axis (1/Vmax)
Pure Non-Competitive Inhibition Decreases (Vmax' < Vmax) Unchanged Lines intersect at X-axis (-1/Km)
Uncompetitive Inhibition Decreases (Vmax' < Vmax) Decreases (Km' < Km) Parallel lines (slope m = Km/Vmax constant)

Calculation Methodology & Equations

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Double-Reciprocal Transformation

1/v = (K_m / V_max) × (1/[S]) + 1/V_max

Linear regression ($y = mx + c$) is applied to $y = 1/v$ and $x = 1/[S]$.

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Solving Km and Vmax

V_max = 1 / c ; K_m = m × V_max

$c$ represents the Y-intercept ($1/V_$) and $m$ represents the line slope ($K_m / V_$).

Frequently Asked Questions

What is a Lineweaver-Burk Plot?

A Lineweaver-Burk plot (or double-reciprocal plot) graphs 1/v on the y-axis versus 1/[S] on the x-axis. It transforms the hyperbolic Michaelis-Menten curve into a straight line ($y = mx + c$).

How do you extt Km and Vmax from the plot slope and intercepts?

1/Vmax is equal to the y-intercept ($c$), so $V_{} = 1/c$. The x-intercept is $-1/K_m$, and the slope ($m$) is equal to $K_m / V_{}$, so $K_m = m times V_{}$.

What are the limitations of a Lineweaver-Burk plot?

Double-reciprocal transformations distort error structures: small experimental errors at low substrate concentrations (high 1/[S] values) are disproportionately magnified in linear regression.

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