Lineweaver-Burk Calculator
Vmax and Km from a double-reciprocal fit.
Work out Lineweaver-Burk. Vmax and Km from a double-reciprocal fit. Free, with no account and nothing to download.
Comma separated. All must be above zero — the plot takes reciprocals.
Must line up one-to-one with the concentrations.
Reciprocal transformation weights the noisiest points most heavily. Use non-linear regression for parameters you intend to publish.
From the double-reciprocal fit
Vmax 99.9975
Km 4.9998 · R² = 1.00000
The double-reciprocal plot turns the Michaelis-Menten hyperbola into a straight line: 1/v against 1/[S], with intercept 1/Vmax and slope Km/Vmax. It is worth knowing that this is a poor way to fit real data. Taking reciprocals compresses the well-measured high-substrate points into a cluster near the origin and stretches the noisy low-substrate ones far out, so the least reliable measurements dominate the regression. A visually excellent line can hide a badly biased Vmax. Use it to visualise inhibition patterns — competitive inhibition shares a y-intercept, non-competitive shares an x-intercept — and use non-linear regression on the untransformed data for the actual numbers.
How the Lineweaver-Burk Calculator works
Paste your substrate concentrations and velocities and this fits the double-reciprocal line, returning Vmax from the intercept and Km from the slope. The page is also explicit that this is a poor way to fit real data, and why.
Also known as: double reciprocal plot calculator · km vmax from lineweaver burk · 1/v vs 1/s plot · enzyme kinetics plot calculator
Frequently asked questions
How does a Lineweaver-Burk plot work?
Plot 1/v against 1/[S]. The y-intercept is 1/Vmax and the slope is Km/Vmax, so both parameters fall out of a straight-line fit.
Why is it considered a bad method?
Because taking reciprocals distorts the error structure. Well-measured high-substrate points cluster near the origin while noisy low-substrate points stretch far out, so the least reliable data dominate the regression. Non-linear regression on untransformed data is correct.
Why is it still taught?
Because it makes inhibition patterns visible at a glance. Competitive inhibitors share a y-intercept, non-competitive share an x-intercept, and uncompetitive give parallel lines — that diagnostic value is real even when the numbers are not.
What are the alternatives?
Eadie-Hofstee and Hanes-Woolf both distort the errors less, and direct non-linear fitting of the Michaelis-Menten equation distorts them not at all. Use the plot to look, and the fit to measure.
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