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For example, if the line crosses the x-axis at −0.1 (µM⁻¹), then Km = 10 µM. Note that the x-axis represents 1/",{"type":26,"tag":68,"props":902,"children":903},{},[904],{"type":32,"value":72},{"type":32,"value":906},", so the left half of the plot (negative x values) corresponds to negative concentrations and is a mathematical extrapolation — the physical data all appears at positive 1/",{"type":26,"tag":68,"props":908,"children":909},{},[910],{"type":32,"value":72},{"type":32,"value":117},{"type":26,"tag":35,"props":913,"children":914},{},[915,920,922,927,929,933,935,939,941,952],{"type":26,"tag":41,"props":916,"children":917},{},[918],{"type":32,"value":919},"How do I determine the inhibition constant Ki from a Lineweaver-Burk plot?",{"type":32,"value":921},"\nExtract the apparent Km from the inhibited lines: for competitive inhibition, the apparent Km at inhibitor concentration ",{"type":26,"tag":68,"props":923,"children":924},{},[925],{"type":32,"value":926},"I",{"type":32,"value":928}," is Km(app) = Km × (1 + ",{"type":26,"tag":68,"props":930,"children":931},{},[932],{"type":32,"value":926},{"type":32,"value":934},"/Ki). Plot Km(app) vs ",{"type":26,"tag":68,"props":936,"children":937},{},[938],{"type":32,"value":926},{"type":32,"value":940}," — the x-intercept of that secondary plot is −Ki. Ask the AI to ",{"type":26,"tag":232,"props":942,"children":943},{},[944,946,950],{"type":32,"value":945},"\"extract apparent Km from each inhibitor concentration and plot Km(app) vs ",{"type":26,"tag":68,"props":947,"children":948},{},[949],{"type":32,"value":926},{"type":32,"value":951}," to read Ki\"",{"type":32,"value":117},{"type":26,"tag":35,"props":954,"children":955},{},[956,961],{"type":26,"tag":41,"props":957,"children":958},{},[959],{"type":32,"value":960},"Why do my Lineweaver-Burk lines not converge exactly at the axis?",{"type":32,"value":962},"\nReal data has measurement noise, so lines from different inhibitor concentrations never intersect at a perfect geometric point. Fit a linear regression to each condition's reciprocal data separately, then compute the mathematical intersection of the best-fit lines. If the intersection is near (but not exactly on) the y-axis, it is still consistent with competitive inhibition — the deviation is experimental noise, not a different mechanism.",{"type":26,"tag":35,"props":964,"children":965},{},[966,971],{"type":26,"tag":41,"props":967,"children":968},{},[969],{"type":32,"value":970},"Can I use the Lineweaver-Burk plot for non-enzyme data?",{"type":32,"value":972},"\nYes — any relationship that follows Michaelis-Menten-like hyperbolic saturation can be plotted in double reciprocal form: transporter kinetics (uptake rate vs. substrate for membrane transporters), receptor binding curves, or any Langmuir adsorption isotherm. The intercept and slope interpretations remain the same.",{"title":7,"searchDepth":974,"depth":974,"links":975},2,[976,977,978,979,980,981,982,983],{"id":29,"depth":974,"text":33},{"id":175,"depth":974,"text":178},{"id":266,"depth":974,"text":269},{"id":449,"depth":974,"text":452},{"id":611,"depth":974,"text":614},{"id":724,"depth":974,"text":727},{"id":810,"depth":974,"text":813},{"id":851,"depth":974,"text":854},"markdown","content:tools:037.lineweaver-burk-plot.md","content","tools/037.lineweaver-burk-plot.md","tools/037.lineweaver-burk-plot","md",{"loc":4},1775502468197]