Manipulate an Enzyme

Play with the sliders!

Manipulate[createGrid[kk],{{kk,1,"\[NumberSign] of sites"},1,3,1},Initialization:>(
draw[n_,sub_]:=Module[{x,y,r,r2},
x=1;
y=n;
r=0.23;
r2=0.15;
Graphics[{{,EdgeForm[],Rectangle[{0,0},{x,y},RoundingRadius->0.2]},{,Disk[{x,#},r],,Circle[{x,#},r,{π/2,3π/2}]}&/@(y((Range@n)/(n+1))),{,EdgeForm[Black],Disk[{x,#},r2]}&/@(y(sub/(n+1)))},ImageSize->{{100},100}]
];
fontSize=12;
write[textNoParen_,text_,num_]:=Switch[num,0,Style["1",1.5*fontSize,FontFamily->"Times"],1,textNoParen,_,Style[Superscript[text,Style[ToString@num,fontSize,FontFamily->"Times"]],ScriptBaselineShifts->{Automatic,2}]];
fontSize2=20;
{x,y}={2,0.5};
{state,weight}=Graphics[{,Rectangle[{0,0},{x,y},RoundingRadius->0.1],,Text[Style[#,fontSize2,FontFamily->"Times"],{x,y}/2]},ImageSize->100]&/@{"State","Weight"};
createGrid[n_]:=(
enzymes={draw[n,n+1-#],write["(([S])/(Subscript[K, M]))Text","\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{"(", 
FractionBox[
RowBox[{"[", "S", "]"}], 
SubscriptBox["K", "M"]], ")"}], TraditionalForm]],
FontSize->24]], "Text",
CellChangeTimes->{{3.644797646757168*^9, 3.6447976545256124`*^9}}]\)",Length@#]}&/@Subsets[Range@n];
Prepend[enzymes,{state,weight}]//Grid
);
)]

Figures

Minor discrepancies in figure output may be due to operating system, Mathematica version, or due to the minor shifts that are incurred when saving output to PDF format.

Figure 1: Michaelis-Menten Enzyme Kinetics

(* Legend *)
leg=LineLegend[ColorData[97][#]&/@Range@2,(timesFont[#,13]&/@{"(Subscript[p, E])","(Subscript[p, ES])"}),Spacings->{0.5,0.4}];

(* Plots *)
plot1=Labeled[relabel@LogLinearPlot[{(1/(1+SKmA)),(SKmA/(1+SKmA))},{SKmA,1/100,100},FrameLabel->label1,Evaluate@opts,PlotLegends->Placed[leg,{Scaled[{0.73,0.4}], Scaled[{0, 0.1}]}],FrameTicks->ticks1],aLabel,{{Top,Left}}];
plot2=Labeled[relabel@LogLinearPlot[(SKmA/(1+SKmA)),{SKmA,1/100,100},FrameLabel->label2,Evaluate@opts,FrameTicks->ticks1,PlotStyle->ColorData[95]],bLabel,{{Top,Left}}];
grid=Grid[{{plot1,plot2}}]

Figure 4: MWC Enzyme Kinetics

With[{(* (Subscript[K, M]^A)/(Subscript[K, M]^I) *)KmAIratio=(10^(-1)),
(* (E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I])))) *)ΔE=(E^(-1)),
(* (Subscript[k, cat]^A)/(Subscript[k, cat]^I) *)kcatAIratio=(10^2)},

(* Legend *)
leg=LineLegend[ColorData[97][#]&/@Range@4,(timesFont/@{"(Subscript[p, (Subscript[E, A])])","(Subscript[p, (Subscript[E, A])S])","(Subscript[p, (Subscript[E, I])])","(Subscript[p, (Subscript[E, I])S])"}),LegendMarkerSize->{15,12},Spacings->{0.3,0.}];
(* Epilog *)
epilog={Black,Dashed,Line[{Log@#[[1]],#[[2]]}&/@{{1,0},{1,0.85}}],Line[{Log@#[[1]],#[[2]]}&/@{{1/KmAIratio,0},{1/KmAIratio,0.85}}],Arrowheads[{-Automatic,Automatic}],Dashing[None],Arrow[{Log@#[[1]],#[[2]]}&/@{{(10^(-2.2)),((ΔE)/(ΔE+1))},{(10^(-2.2)),(1/(ΔE+1))}}],Text[timesFont[#,14]&@"(([(Subscript[E, A])])/([(Subscript[E, I])]))=\!\(\*SuperscriptBox[\(E\), \(\(-β\)\\ \((\*SubscriptBox[\(ϵ\), \(A\)] - \*SubscriptBox[\(ϵ\), \(I\)])\)\)]\)",{Log@((10^(-1.2))),0.48}]};

(* Plots *)
plot1=Labeled[relabel@LogLinearPlot[{((ΔE)/(ΔE(1+SKmA)+(1+SKmA KmAIratio))),((ΔE SKmA)/(ΔE(1+SKmA)+(1+SKmA KmAIratio))),(1/(ΔE(1+SKmA)+(1+SKmA KmAIratio))),((SKmA KmAIratio)/(ΔE(1+SKmA)+(1+SKmA KmAIratio)))},{SKmA,1/((10^2.3)),100},FrameLabel->label3,Evaluate@opts,FrameTicks->ticks1,PlotRange->{Automatic,{Automatic,0.85}},PlotLegends->Placed[leg,{Scaled[{0.77,0.33}], Scaled[{0, 0.1}]}],Epilog->epilog],aLabel,{{Top,Left}}];
plot2=Labeled[relabel@LogLinearPlot[((ΔE SKmA+SKmA KmAIratio/kcatAIratio)/(ΔE(1+SKmA)+(1+SKmA KmAIratio))),{SKmA,1/((10^2.3)),100},FrameLabel->label4,Evaluate@opts,FrameTicks->ticks1,PlotRange->{Automatic,{Automatic,0.85}},PlotStyle->ColorData[95]],bLabel,{{Top,Left}}];
grid=Grid[{{plot1,plot2}}]
]

Figure 6: Allosteric Regulator Theory

With[{(* (Subscript[K, M]^A)/(Subscript[K, M]^I) *)KmAIratio=(10^(-1)),
(* (E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I])))) *)ΔE=(E^(-1)),
(* (Subscript[k, cat]^A)/(Subscript[k, cat]^I) *)kcatAIratio=(10^2),
(* (Subscript[R, D]^A)/(Subscript[R, D]^I) *)RdAIratio=(10^(-2))},

(* Legend *)
Rlist={2,1,0,-1};
legend=LineLegend[ColorData[95][#]&/@Range@Length@Rlist,(timesFont[#,12]&/@(Row[{"[R]/(Subscript[R, D]^A) =",#}]&/@(Superscript["10",#<>If[#=="0","   ",""]]&/@ToString/@Rlist))),LegendMarkerSize->{15,12},Spacings->{0.2,0.4}];

(* Plotting the function (1/([(Subscript[E, tot])](Subscript[k, cat]^A)))((\[DifferentialD][P])/(\[DifferentialD]t))=(((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(([S])/(Subscript[K, M]^A))(1+(([R])/(Subscript[R, D]^A)))+((Subscript[k, cat]^I)/(Subscript[k, cat]^A))(([S])/(Subscript[K, M]^A))((Subscript[K, M]^A)/(Subscript[K, M]^I))(1+(([R])/(Subscript[R, D]^A))((Subscript[R, D]^A)/(Subscript[R, D]^I))))/((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(1+(([S])/(Subscript[K, M]^A)))(1+(([R])/(Subscript[R, D]^A)))+(1+(([S])/(Subscript[K, M]^A))((Subscript[K, M]^A)/(Subscript[K, M]^I)))(1+(([R])/(Subscript[R, D]^A))((Subscript[R, D]^A)/(Subscript[R, D]^I))))) *)
plot=relabel@LogLinearPlot[Evaluate@Table[((ΔE SKmA(1+RRdA)+SKmA KmAIratio/kcatAIratio(1+RRdA RdAIratio))/(ΔE(1+SKmA)(1+RRdA)+(1+SKmA KmAIratio)(1+RRdA RdAIratio)))/.RRdA->(10^RR),{RR,Rlist}],{SKmA,(10^(-2)),(10^2)},FrameLabel->label4,Evaluate@opts,FrameTicks->ticks1,PlotStyle->ColorData[95],PlotLegends->Placed[legend,{Scaled[{0.03,0.45}], Scaled[{0, 0.1}]}],Epilog->{,Thick,Arrowheads[0.05],Arrow[{{Log[(10^0.6)],0.2},{Log[(10^(-0.2))],0.7}}],Text[Column[Style[#,FontFamily->"Times",12,Bold]&/@{"Increasing","[R]/(Subscript[R, D]^A)"},Alignment->Center],{Log[(10^1.2)],0.2}]}]
]

Figure 8: Competitive Inhibitor Theory

With[{(* (Subscript[K, M]^A)/(Subscript[K, M]^I) *)KmAIratio=(10^(-1)),
(* (E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I])))) *)ΔE=(E^(-1)),
(* (Subscript[k, cat]^A)/(Subscript[k, cat]^I) *)kcatAIratio=(10^2),
(* (Subscript[C, D]^A)/(Subscript[C, D]^I) *)CdAIratio=(10^0)},

(* Legend *)
Clist={-1,0,1,2};
legend=LineLegend[ColorData[95][#]&/@Range[1,4],(timesFont[#,12]&/@(Row[{"[C]/(Subscript[C, D]^A) =",#}]&/@(Superscript["10",#]&/@ToString/@(Reverse@Clist)))),LegendMarkerSize->{15,12},Spacings->{0.2,0.4}];

(* Plotting the function (1/([(Subscript[E, tot])](Subscript[k, cat]^A)))((\[DifferentialD][P])/(\[DifferentialD]t))=(((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(([S])/(Subscript[K, M]^A))+((Subscript[k, cat]^I)/(Subscript[k, cat]^A))(([S])/(Subscript[K, M]^A))((Subscript[K, M]^A)/(Subscript[K, M]^I)))/((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(1+(([S])/(Subscript[K, M]^A))+(([C])/(Subscript[C, D]^A)))+(1+(([S])/(Subscript[K, M]^A))((Subscript[K, M]^A)/(Subscript[K, M]^I))+(([C])/(Subscript[C, D]^A))((Subscript[C, D]^A)/(Subscript[C, D]^I))))) *)
plot2=relabel@LogLinearPlot[Evaluate@Table[((ΔE SKmA+SKmA KmAIratio/kcatAIratio)/(ΔE(1+SKmA+CCdA)+(1+SKmA KmAIratio+CCdA CdAIratio)))/.CCdA->(10^Cexp),{Cexp,Clist}],{SKmA,(10^(-3.4)),(10^5.5)},FrameLabel->label4,Evaluate@opts,FrameTicks->ticks2,PlotLegends->Placed[legend,{Scaled[{0.02,0.44}], Scaled[{0, 0.1}]}],PlotStyle->(ColorData[95]/@Range[4,1,-1]),Epilog->{,Thick,Arrowheads[0.05],Arrow[{{Log[(10^(-0.5))],0.33},{Log[(10^3.1)],0.33}}],Text[Column[Style[#,FontFamily->"Times",12,Bold]&/@{"Increasing","[C]/(Subscript[C, D]^A)"},Alignment->Center],{Log[(10^4.4)],0.33}]}]
]

Figure 10: Alpha Amylase (Allosteric Regulator)

(* Fitting to [S]/A=(((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(1+(([S])/(Subscript[K, M]^A)))(1+(([R])/(Subscript[R, D]^A)))+(1+(([S])/(Subscript[K, M]^I)))(1+(([R])/(Subscript[R, D]^I))))/((Subscript[k, cat]^A)(E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(1/(Subscript[K, M]^A))(1+(([R])/(Subscript[R, D]^A)))+(Subscript[k, cat]^I)(1/(Subscript[K, M]^I))(1+(([R])/(Subscript[R, D]^I))))) *)
fitFunction=((((E^(ΔE))(1+(S/(E^KmALog)))(1+(R/(E^RdALog)))+(1+(S/(E^KmILog)))(1+(R/(E^RdILog))))/((E^kcatALog)(E^(ΔE))(1/(E^KmALog))(1+(R/(E^RdALog)))+(E^kcatILog)(1/(E^KmILog))(1+(R/(E^RdILog))))));
(* Constrained Fitting. (Subscript[K, M]),(Subscript[C, D]),(Subscript[R, D])∈[(10^(-2)),(10^6)]μM; (Subscript[k, cat])∈[(10^(-2)),(10^5)](s^(-1)); ΔE∈[-10,10] Note the units for [S] are mM and for [R] are mM *)
newFit=Quiet@NonlinearModelFit[regulatorData,{fitFunction,-10.<ΔE<10.&&Log[(10^(-5.))]<KmALog<Log[(10^3.)]&&Log[(10^(-5.))]<KmILog<Log[(10^3.)]&&Log[(10^(-5.))]<RdALog<RdILog<Log[(10^3.)]&&Log[(10^(-2.))]<kcatALog<Log[(10^5.)]&&Log[(10^(-2.))]<kcatILog<Log[(10^5.)]},{{ΔE,1.},{KmALog,1.6},{KmILog,0.3},{RdALog,Log[5.9]},{RdILog,-1.},{kcatALog,3.},{kcatILog,0.}},{R,S}];

(* Plot results of the fit *)
Clear[regulatorFitResult]
regulatorFitResult[Rval_]:=fitFunction/.newFit["BestFitParameters"]/.R->Rval;

(* Legend *)
leg=LineLegend[ColorData[95]/@Reverse@{5,6,7},Reverse[timesFont[#,12]&/@{"2mM [R]","5mM [R]","50mM [R]"}],LegendMarkers->Automatic,Spacings->{0.5,0.5}];

(* Plot *)
relabel@Show[ListPlot[Partition[regulatorData[[All,{2,3}]],Length@regulatorData/3],PlotRange->{{-0.2,2.7},{0,15.}},FrameLabel->label5,Evaluate@opts,PlotLegends->Placed[leg,{Scaled[{0.07,0.52}], Scaled[{0, 0.1}]}],AxesOrigin->{-40,0},FrameTicks->ticks3,PlotStyle->({PointSize[0.025],ColorData[95][#]}&/@Range[5,7])],Plot[Evaluate[regulatorFitResult/@{2,5,50}],{S,-2.5,5},PlotStyle->(ColorData[95]/@{5,6,7})]]

Figure 11: Alpha Amylase (Competitive Inhibitor) + Data Collapse

fitFunction2=(((E^(ΔE))(1+(S/(E^KmALog))+(CC/(E^CdALog)))+(1+(S/(E^KmILog))+(CC/(E^CdILog))))/((E^vALog)(E^(ΔE))(S/(E^KmALog))+(E^vILog)(S/(E^KmILog))));
(* Constrained Fitting. (Subscript[K, M]),(Subscript[C, D]),(Subscript[R, D])∈[(10^(-2)),(10^6)]μM; (Subscript[k, cat])∈[(10^(-2)),(10^5)](s^(-1)); ΔE∈[-10,10] Note the units for [S] are mM and for [C] are nM. kcat unconstrained here because [(Subscript[E, tot])] was not specified *)
newFit=Quiet@NonlinearModelFit[inhibitorData,{fitFunction2,-10.<ΔE<10.&&Log[(10^(-5.))]<KmALog<Log[(10^3.)]&&Log[(10^(-5.))]<KmILog<Log[(10^3.)]&&Log[(10^1.)]<CdALog<Log[(10^9.)]&&Log[(10^1.)]<CdILog<Log[(10^9.)](*&&Log[(10^(-2.))]<kcatALog<Log[(10^5.)]&&Log[(10^(-2.))]<kcatILog<Log[(10^5.)]*)},{{ΔE,2.77887201955241`},{KmALog,1.632457745376087`},{KmILog,0.2502653348580043`},{CdALog,3.},{CdILog,5.},{vALog,0.},{vILog,2.}},{CC,S}];

(* ========================================================================= *)
(* ============================= PLOT 1: Fitting =========================== *)
(* ========================================================================= *)
inhibitorFitResult[Sval_]:=fitFunction2/.newFit["BestFitParameters"]/.S->Sval;
labels=timesFont[#,12]&/@{"0.245mM [S]","0.625mM [S]","1.225mM [S]"};
leg=LineLegend[ColorData[95]/@Reverse@{5,6,7},Reverse@labels,LegendMarkers->Automatic,Spacings->{0.5,0.5}];
plot1=Labeled[relabel@Show[ListPlot[Partition[inhibitorData[[All,{1,3}]],Length@inhibitorData/3],PlotRange->{{-20,60},{0,6}},FrameLabel->label6,Evaluate@opts,PlotLegends->Placed[leg,{Scaled[{0.045,0.52}], Scaled[{0, 0.1}]}],AxesOrigin->{-40,0},FrameTicks->ticks4,PlotStyle->({PointSize[0.025],ColorData[95][#]}&/@Range[5,7])],Plot[Evaluate[inhibitorFitResult/@{0.245,0.625,1.225}],{CC,-20,60},PlotStyle->(ColorData[95]/@{5,6,7})]],aLabel,{{Top,Left}}];

(* ========================================================================= *)
(* ========================= PLOT 2: Data Collapse ========================= *)
(* ========================================================================= *)
dataCollapseData=Map[{-Log[((S/(E^KmILog))/((E^(ΔE))(1+(CC/(E^CdALog)))+(1+(CC/(E^CdILog)))))]/.{CC->#[[1]],S->#[[2]]}/.newFit["BestFitParameters"],#[[3]]}&,Partition[inhibitorData,Length@inhibitorData/3],{2}];
(* Plot ranges *)
{Fmin,Fmax}={Min@#,Max@#}&@Flatten[dataCollapseData,1][[All,1]];

plot2=Labeled[relabel@Show[Plot[Evaluate[(((((((E^vALog)(E^(ΔE))((E^KmILog)/(E^KmALog))+(E^vILog))(E^(-f)))/(((E^(ΔE))((E^KmILog)/(E^KmALog))+1)(E^(-f))+1))))^(-1))/.newFit["BestFitParameters"]],{f,-0.5,10},PlotStyle->Black,PlotRange->{{Round[Fmin]-0.1,Round[Fmax]+0.1},{0,6}},AxesOrigin->{-1,0},FrameLabel->label6,Evaluate@opts,FrameTicks->ticks5],ListPlot[dataCollapseData,PlotStyle->({PointSize[0.025],ColorData[95][#]}&/@Range[5,7])]],bLabel,{{Top,Left}}];
grid=Grid[{{plot1,plot2}}]

Figure 23 and 26 (in SI) made similarly.

Figure 12: Substrate Inhibition + Inhibitor Acceleration Data Curves

(* ========================================================================= *)
(* ===================== PLOT 1: Substrate Inhibition ====================== *)
(* ========================================================================= *)

(* Fitting to activity. For Acetylcholinesterase, which has n=2 active sites, activity=(((Subscript[k, cat]^A)(E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(([S])/(Subscript[K, M]^A))(((1+(([S])/(Subscript[K, M]^A))))^(n-1))+(Subscript[k, cat]^I)(([S])/(Subscript[K, M]^I))(((1+(([S])/(Subscript[K, M]^I))))^(n-1)))/((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(((1+(([S])/(Subscript[K, M]^A))))^n)+(((1+(([S])/(Subscript[K, M]^I))))^n))) *)
n=2; (* Number of active sites *)
fitFunction3=(((E^kcatALog)(E^(ΔE))(S/(E^KmALog))(((1+(S/(E^KmALog))))^(n-1))+(E^kcatILog)(S/(E^KmILog))(((1+(S/(E^KmILog))))^(n-1)))/((E^(ΔE))(((1+(S/(E^KmALog))))^n)+(((1+(S/(E^KmILog))))^n)));
(* Constrained Fitting. (Subscript[K, M]),(Subscript[C, D]),(Subscript[R, D])∈[(10^(-2)),(10^6)]μM; (Subscript[k, cat])∈[(10^(-2)),(10^5)](s^(-1)); ΔE∈[-10,10] Note the units for [S] are mM and for [R] are mM *)
fit1=Quiet@NonlinearModelFit[substrateInhibitionData,{fitFunction3,-10.<ΔE<10.&&Log[(10^(-8.))]<KmALog<Log[(10^0.)]&&Log[(10^(-8.))]<KmILog<Log[(10^0.)]},{{ΔE,2.77887201955241`},{KmALog,9.632457745376087`},{KmILog,0.2502653348580043`},{kcatALog,8.},{kcatILog,2.}},S];

(* To make the typical Michaelis-Menten activity curve for purely illustrative purposes (not real data!) *)
KmExample=(10^(-4.2));
(* Legend *)
legend=LineLegend[ColorData[95][#]&/@{2,1},(timesFont[#,11]&/@{"Michaelis enzyme","Substrate inhibition"}),LegendMarkerSize->{15,12},Spacings->{0.2,0.4}];
plot1=Labeled[relabel@Show[LogLinearPlot[{60((S/KmExample)/(1+(S/KmExample))),Normal@fit1},{S,1.3 (10^(-7)),1.3 (10^(-1))},FrameLabel->label7,FrameTicks->ticks6,Evaluate@opts,PlotLegends->Placed[legend,{Scaled[{0.001,0.7}], Scaled[{0, 0.1}]}],PlotStyle->(ColorData[95]/@{2,1})],ListLogLinearPlot[substrateInhibitionData,PlotStyle->{{ColorData[95][1],PointSize[0.02]}}]],aLabel,{{Top,Left}}];

(* ========================================================================= *)
(* ===================== PLOT 2: Inhibitor Acceleration ==================== *)
(* ========================================================================= *)
(* Fitting to activity. For ATCase, which has n=6 active sites, activity=(((Subscript[k, cat]^A)(E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(([S])/(Subscript[K, M]^A))(((1+(([S])/(Subscript[K, M]^A))+(([C])/(Subscript[C, D]^A))))^(n-1))+(Subscript[k, cat]^I)(([S])/(Subscript[K, M]^I))(((1+(([S])/(Subscript[K, M]^I))+(([C])/(Subscript[C, D]^I))))^(n-1)))/((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(((1+(([S])/(Subscript[K, M]^A))+(([C])/(Subscript[C, D]^A))))^n)+(((1+(([S])/(Subscript[K, M]^I))+(([C])/(Subscript[C, D]^I))))^n))) *)
n=6; (* Number of active sites *)
fitFunction3=(((E^kcatALog)(E^(ΔE))(S/(E^KmALog))(((1+(S/(E^KmALog))+(CC/(E^CdALog))))^(n-1))+(E^kcatILog)(S/(E^KmILog))(((1+(S/(E^KmILog))+(CC/(E^CdILog))))^(n-1)))/((E^(ΔE))(((1+(S/(E^KmALog))+(CC/(E^CdALog))))^n)+(((1+(S/(E^KmILog))+(CC/(E^CdILog))))^n)));
(* Ultimately, we fit relative activity, which equals activity divided by its value at [C]=0 *)
fitFunction4=(fitFunction3/(fitFunction3/.CC->0));
(* Values found by fitting ATCase curves with ATP, with CTP, and with neither nucleotide to make Figure 24 (in SI). Here we forgo this fitting and only show the results *)
ATCaseFitResult=fitFunction4/.{ΔE->-5.312932685686053`,KmALog->0.10254817905274688`,KmILog->0.5653524267671912`,kcatALog->6.`,kcatILog->-4.`,CdALog->-14.968605041464937`,CdILog->-13.218786114589015`,S->5.};

(* Plot *)

(* The following parameters for the illustrative Michaelis-Menten curve (not real data!) *)
Seff=(10^(-6));KmAeff=1.;CdAeff=(10^(-5.5));
(* Legend *)
legend=LineLegend[ColorData[95][#]&/@{2,1},(timesFont[#,11]&/@{"Michaelis enzyme","Inhibitor acceleration"}),LegendMarkerSize->{15,12},Spacings->{0.2,0.4}];
plot2=Labeled[relabel@Show[LogLinearPlot[{((1+(Seff/KmAeff))/(1+(Seff/KmAeff)+(CC/CdAeff))),ATCaseFitResult},{CC,(10^(-8.3)),(10^(-4))},FrameLabel->label8,FrameTicks->ticks7,Evaluate@opts,PlotLegends->Placed[legend,{Scaled[{0.001,0.7}], Scaled[{0, 0.1}]}],PlotStyle->(ColorData[95]/@{2,1})],ListLogLinearPlot[Map[{#[[1]]/(10^6),#[[2]]}&,{inhibitorAccelerationData},{2}],PlotStyle->{{ColorData[95][1],PointSize[0.02]}}]],bLabel,{{Top,Left}}];
grid=Grid[{{plot1,plot2}}]

Figure 23 (in SI) made similarly

Figure 13: Substrate Inhibition (Theory Peaks)

With[{KmAIratio=10},
(* Activity for an MWC enzyme with 2 catalytic sites (and (Subscript[k, cat]^I)=0) is given by activity=((2(Subscript[k, cat]^A)(E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(([S])/(Subscript[K, M]^A))(1+(([S])/(Subscript[K, M]^A))))/((E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I]))))(((1+(([S])/(Subscript[K, M]^A))))^2)+(((1+(([S])/(Subscript[K, M]^I))))^2))) *)
activityTheory=((2(E^(ΔE))SKmA(1+SKmA))/((E^(ΔE))(((1+SKmA))^2)+(((1+SKmA KmAIratio))^2)));
(* Values of (E^(-β((Subscript[ϵ, A])-(Subscript[ϵ, I])))) to draw *)
Elist=Log[{0.025,0.05,0.1,0.2}];
(* Legend *)
leg1=LineLegend[(ColorData[95][#]&/@Range[4,1,-1]),(timesFont[#,11]&/@(Row[{"\!\(\*SuperscriptBox["E", 
RowBox[{
RowBox[{"-", "β"}], 
RowBox[{"(", 
RowBox[{SubscriptBox["ϵ", 
StyleBox["A",
FontSlant->"Italic"]], "-", SubscriptBox["ϵ", 
StyleBox["I",
FontSlant->"Italic"]]}], ")"}]}]]\) =  ",#}]&/@(Reverse[(ToString/@(E^Elist))]))),Spacings->{0.2,0.1},LegendMarkerSize->{15,13}];

(* Plot 1 *)
plot1=Labeled[relabel@LogLinearPlot[Evaluate@Table[activityTheory,{ΔE,Elist}],{SKmA,(10^(-5.2)),(10^2)},FrameLabel->label9,Evaluate@opts,FrameTicks->ticks8,PlotLegends->Placed[leg1,{Scaled[{0.015,0.4}], {0, 0}}],PlotStyle->ColorData[95]],aLabel,{{Top,Left}}];

(* Plot 2 *)
Elist=Log[{20,40,60,80}];
leg2=LineLegend[(ColorData[95][#]&/@Range[8,5,-1]),(timesFont[#,11]&/@(Row[{"\!\(\*SuperscriptBox["E", 
RowBox[{
RowBox[{"-", "β"}], 
RowBox[{"(", 
RowBox[{SubscriptBox["ϵ", 
StyleBox["A",
FontSlant->"Italic"]], "-", SubscriptBox["ϵ", 
StyleBox["I",
FontSlant->"Italic"]]}], ")"}]}]]\) =  ",#}]&/@(Reverse[ToString/@(E^Elist)]))),Spacings->{0.2,0.2},LegendMarkerSize->{15,12}];

plot2=Labeled[relabel@LogLinearPlot[Evaluate@Table[activityTheory,{ΔE,Elist}],{SKmA,(10^(-3)),(10^2)},FrameLabel->label9,Evaluate@opts,FrameTicks->ticks9,PlotRange->{Automatic,{0,1.02}},PlotLegends->Placed[leg2,{Scaled[{0.03,0.4}], {0, 0}}],PlotStyle->(ColorData[95][#]&/@Range[5,8])],bLabel,{{Top,Left}}];
grid=Grid[{{plot1,plot2}}]
]

Initialization

Manipulate an Enzyme

draw[n_,sub_]:=Module[{x,y,r,r2},
x=1;
y=n;
r=0.23;
r2=0.15;
Graphics[{{,EdgeForm[],Rectangle[{0,0},{x,y},RoundingRadius->0.2]},{,Disk[{x,#},r],,Circle[{x,#},r,{π/2,3π/2}]}&/@(y((Range@n)/(n+1))),{,EdgeForm[Black],Disk[{x,#},r2]}&/@(y(sub/(n+1)))},ImageSize->{{100},100}]
];
fontSize=12;
write[textNoParen_,text_,num_]:=Switch[num,0,Style["1",1.5*fontSize,FontFamily->"Times"],1,textNoParen,_,Style[Superscript[text,Style[ToString@num,fontSize,FontFamily->"Times"]],ScriptBaselineShifts->{Automatic,2}]];
fontSize2=20;
{x,y}={2,0.5};
{state,weight}=Graphics[{,Rectangle[{0,0},{x,y},RoundingRadius->0.1],,Text[Style[#,fontSize2,FontFamily->"Times"],{x,y}/2]},ImageSize->100]&/@{"State","Weight"};
createGrid[n_]:=(
enzymes={draw[n,n+1-#],write["(([S])/(Subscript[K, M]))Text","\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{"(", 
FractionBox[
RowBox[{"[", "S", "]"}], 
SubscriptBox["K", "M"]], ")"}], TraditionalForm]],
FontSize->24]], "Text",
CellChangeTimes->{{3.644797646757168*^9, 3.6447976545256124`*^9}}]\)",Length@#]}&/@Subsets[Range@n];
Prepend[enzymes,{state,weight}]//Grid
);

Figure Options

(* 'power10format' is a helper function for 'relabel' which makes all log ticks take the form (10^x) *)
power10format=Replace[#,n_?NumericQ|NumberForm[n_,_]:>Superscript[10,Round@Log10@n]]&;
relabel=#/.CST_Charting`ScaledTicks:>(MapAt[power10format,CST[##],{All,2}]&)&;

(* Options for plotting *)
opts={Frame->{True,True,False,False},ImageSize->{280,280/GoldenRatio},PlotRangePadding->{{Automatic,0},{0,Automatic}},
Prolog->{,Rectangle[Scaled[{0,0}],Scaled[{1,1}]]},FrameTicksStyle->Directive[,FontColor->],FrameStyle->Directive[,FontColor->]};

(* Font size *)
timesFont[expr_,size_:12]:=Style[expr,FontFamily->"Times",size];

(* Use Times New Roman font *)
fontSize=12;
(* To increase size of plain text *)
plainText=Style[#,FontFamily->"Times",fontSize]&;
(* To increase size of typset text *)
typsetText:=ReleaseHold@MakeExpression@StringReplace[ToBoxes@#,"FontSize->12"|"FontSize->10"->"FontSize->"<>ToString[fontSize]]&;
increaseSize:=plainText@typsetText@#&;

(* Labels for multiple plots *)
aLabel="AText";
bLabel="BText";
cLabel="CText";
dLabel="DText";

(* Frame labels for plots *)
label1=increaseSize/@{"\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
RowBox[{"[", "S", "]"}], "/", 
SubscriptBox["K", "M"]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336505480169225`*^9, 3.6336505504380608`*^9}}]\)","probability"};
label2=increaseSize/@{"\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
RowBox[{"[", "S", "]"}], "/", 
SubscriptBox["K", "M"]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336505480169225`*^9, 3.6336505504380608`*^9}}]\)","\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
RowBox[{
RowBox[{"\[DifferentialD]", 
RowBox[{"[", "P", "]"}]}], "/", 
RowBox[{"\[DifferentialD]", "t"}]}], " ", 
SuperscriptBox[
RowBox[{"(", 
RowBox[{
SubscriptBox["k", 
StyleBox["cat",
FontSlant->"Italic"]], "[", 
SubscriptBox["E", 
StyleBox["tot",
FontSlant->"Italic"]], "]"}], ")"}], 
RowBox[{"-", "1"}]]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336506574151797`*^9, 3.6336506685798187`*^9}}]\)"};
label3=increaseSize/@{"\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
StyleBox[
RowBox[{
RowBox[{"[", "S", "]"}], "/", 
SubsuperscriptBox["K", "M", "A"]}],
FontSize->12], TraditionalForm]],
FontSize->10]], "Text",
CellChangeTimes->{{3.6336505480169225`*^9, 3.6336505504380608`*^9}}]\)","probability"};
label4=increaseSize/@{"\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
RowBox[{"[", "S", "]"}], "/", 
SubsuperscriptBox["K", "M", "A"]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336505480169225`*^9, 3.6336505504380608`*^9}}]\)","\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
RowBox[{
RowBox[{"\[DifferentialD]", 
RowBox[{"[", "P", "]"}]}], "/", 
RowBox[{"\[DifferentialD]", "t"}]}], " ", 
SuperscriptBox[
RowBox[{"(", 
RowBox[{
SubsuperscriptBox["k", 
StyleBox["cat",
FontSlant->"Italic"], "A"], "[", 
SubscriptBox["E", 
StyleBox["tot",
FontSlant->"Italic"]], "]"}], ")"}], 
RowBox[{"-", "1"}]]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336506574151797`*^9, 3.6336506685798187`*^9}}]\)"};
label5=increaseSize/@{"[S] (mM)","\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
RowBox[{
RowBox[{"[", 
StyleBox["S",
FontSlant->"Italic"], "]"}], "/", "A"}], " ", 
RowBox[{"(", 
RowBox[{
SuperscriptBox["10", 
RowBox[{"-", "6"}]], " ", 
RowBox[{
StyleBox["M",
FontSlant->"Plain"], "·", 
StyleBox["s",
FontSlant->"Plain"]}]}], ")"}]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336506574151797`*^9, 3.6336506685798187`*^9}}]\)"};
label6=increaseSize/@{"[C] (nM)","(\[DifferentialD][P]/\[DifferentialD]t)^(-1) (arb. units)"};
label7=increaseSize/@{"\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
StyleBox[
RowBox[{
RowBox[{"[", "S", "]"}], " ", 
RowBox[{"(", 
StyleBox["M",
FontSlant->"Plain"], ")"}]}],
FontSize->12], TraditionalForm]],
FontSize->10]], "Text",
CellChangeTimes->{{3.6336505480169225`*^9, 3.6336505504380608`*^9}}]\)","activityText"};
label8=increaseSize/@{"\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
RowBox[{"[", "C", "]"}], " ", 
RowBox[{"(", 
StyleBox["M",
FontSlant->"Plain"], ")"}]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336505480169225`*^9, 3.6336505504380608`*^9}}]\)","relative activityText"};
label9=increaseSize/@{"\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{
StyleBox[
RowBox[{"[", "S", "]"}],
FontSize->12], 
StyleBox["/",
FontSize->12], 
SubsuperscriptBox[
StyleBox["K",
FontSize->12], "M", "A"]}], TraditionalForm]],
FontSize->10]], "Text",
CellChangeTimes->{{3.6336505480169225`*^9, 3.6336505504380608`*^9}}]\)","\!\(\*Cell[TextData[Cell[BoxData[
FormBox[
RowBox[{"activity", " ", 
SuperscriptBox[
RowBox[{"(", 
SubsuperscriptBox["k", 
StyleBox["cat",
FontSlant->"Italic"], "A"], ")"}], 
RowBox[{"-", "1"}]]}], TraditionalForm]],
FontSize->12]], "Text",
CellChangeTimes->{{3.6336506574151797`*^9, 3.6336506685798187`*^9}}]\)"};

Figure Ticks

(* Plot ticks, made using Mark Caprio's CustomTicks package *)
ticks1={{{0.01`,"(10^(-2))",{0.02`,0},{}},{0.1`,"(10^(-1))",{0.02`,0},{}},{1.`,"(10^0)",{0.02`,0},{}},{10.`,"(10^1)",{0.02`,0},{}},{100.`,"(10^2)",{0.02`,0},{}},{0.020000000000000004`,"",{0.01`,0},{}},{0.029999999999999995`,"",{0.01`,0},{}},{0.03999999999999999`,"",{0.01`,0},{}},{0.049999999999999996`,"",{0.01`,0},{}},{0.05999999999999997`,"",{0.01`,0},{}},{0.06999999999999998`,"",{0.01`,0},{}},{0.07999999999999995`,"",{0.01`,0},{}},{0.08999999999999998`,"",{0.01`,0},{}},{0.19999999999999998`,"",{0.01`,0},{}},{0.29999999999999993`,"",{0.01`,0},{}},{0.3999999999999999`,"",{0.01`,0},{}},{0.4999999999999998`,"",{0.01`,0},{}},{0.5999999999999999`,"",{0.01`,0},{}},{0.6999999999999998`,"",{0.01`,0},{}},{0.7999999999999995`,"",{0.01`,0},{}},{0.8999999999999998`,"",{0.01`,0},{}},{1.9999999999999998`,"",{0.01`,0},{}},{2.9999999999999996`,"",{0.01`,0},{}},{3.999999999999999`,"",{0.01`,0},{}},{4.999999999999998`,"",{0.01`,0},{}},{5.999999999999998`,"",{0.01`,0},{}},{6.999999999999998`,"",{0.01`,0},{}},{7.999999999999995`,"",{0.01`,0},{}},{8.999999999999998`,"",{0.01`,0},{}},{20.000000000000004`,"",{0.01`,0},{}},{29.999999999999996`,"",{0.01`,0},{}},{39.99999999999999`,"",{0.01`,0},{}},{49.99999999999999`,"",{0.01`,0},{}},{59.99999999999997`,"",{0.01`,0},{}},{69.99999999999999`,"",{0.01`,0},{}},{79.99999999999996`,"",{0.01`,0},{}},{89.99999999999997`,"",{0.01`,0},{}}},{{0.`,"0.0",{0.02`,0},{}},{0.2`,"0.2",{0.02`,0},{}},{0.4`,"0.4",{0.02`,0},{}},{0.6000000000000001`,"0.6",{0.02`,0},{}},{0.8`,"0.8",{0.02`,0},{}},{1.`,"1.0",{0.02`,0},{}},{0.05`,"",{0.01`,0},{}},{0.1`,"",{0.01`,0},{}},{0.15000000000000002`,"",{0.01`,0},{}},{0.25`,"",{0.01`,0},{}},{0.30000000000000004`,"",{0.01`,0},{}},{0.35000000000000003`,"",{0.01`,0},{}},{0.45`,"",{0.01`,0},{}},{0.5`,"",{0.01`,0},{}},{0.55`,"",{0.01`,0},{}},{0.6500000000000001`,"",{0.01`,0},{}},{0.7000000000000001`,"",{0.01`,0},{}},{0.7500000000000001`,"",{0.01`,0},{}},{0.8500000000000001`,"",{0.01`,0},{}},{0.9`,"",{0.01`,0},{}},{0.9500000000000001`,"",{0.01`,0},{}}}};
ticks2={{{0.001`,"(10^(-3))",{0.02`,0},{}},{0.01`,"",{0.02`,0},{}},{0.1`,"(10^(-1))",{0.02`,0},{}},{1.`,"",{0.02`,0},{}},{10.`,"(10^1)",{0.02`,0},{}},{100.`,"",{0.02`,0},{}},{1000.`,"(10^3)",{0.02`,0},{}},{10000.`,"",{0.02`,0},{}},{100000.`,"(10^5)",{0.02`,0},{}},{1.`*^6,"",{0.02`,0},{}},{0.0020000000000000005`,"",{0.01`,0},{}},{0.003000000000000001`,"",{0.01`,0},{}},{0.004000000000000001`,"",{0.01`,0},{}},{0.004999999999999999`,"",{0.01`,0},{}},{0.005999999999999997`,"",{0.01`,0},{}},{0.007000000000000002`,"",{0.01`,0},{}},{0.007999999999999995`,"",{0.01`,0},{}},{0.008999999999999998`,"",{0.01`,0},{}},{0.020000000000000004`,"",{0.01`,0},{}},{0.029999999999999995`,"",{0.01`,0},{}},{0.03999999999999999`,"",{0.01`,0},{}},{0.049999999999999996`,"",{0.01`,0},{}},{0.05999999999999997`,"",{0.01`,0},{}},{0.06999999999999998`,"",{0.01`,0},{}},{0.07999999999999995`,"",{0.01`,0},{}},{0.08999999999999998`,"",{0.01`,0},{}},{0.19999999999999998`,"",{0.01`,0},{}},{0.29999999999999993`,"",{0.01`,0},{}},{0.3999999999999999`,"",{0.01`,0},{}},{0.4999999999999998`,"",{0.01`,0},{}},{0.5999999999999999`,"",{0.01`,0},{}},{0.6999999999999998`,"",{0.01`,0},{}},{0.7999999999999995`,"",{0.01`,0},{}},{0.8999999999999998`,"",{0.01`,0},{}},{1.9999999999999998`,"",{0.01`,0},{}},{2.9999999999999996`,"",{0.01`,0},{}},{3.999999999999999`,"",{0.01`,0},{}},{4.999999999999998`,"",{0.01`,0},{}},{5.999999999999998`,"",{0.01`,0},{}},{6.999999999999998`,"",{0.01`,0},{}},{7.999999999999995`,"",{0.01`,0},{}},{8.999999999999998`,"",{0.01`,0},{}},{20.000000000000004`,"",{0.01`,0},{}},{29.999999999999996`,"",{0.01`,0},{}},{39.99999999999999`,"",{0.01`,0},{}},{49.99999999999999`,"",{0.01`,0},{}},{59.99999999999997`,"",{0.01`,0},{}},{69.99999999999999`,"",{0.01`,0},{}},{79.99999999999996`,"",{0.01`,0},{}},{89.99999999999997`,"",{0.01`,0},{}},{200.00000000000003`,"",{0.01`,0},{}},{300.0000000000001`,"",{0.01`,0},{}},{400.0000000000001`,"",{0.01`,0},{}},{499.99999999999994`,"",{0.01`,0},{}},{599.9999999999997`,"",{0.01`,0},{}},{700.0000000000001`,"",{0.01`,0},{}},{799.9999999999995`,"",{0.01`,0},{}},{899.9999999999998`,"",{0.01`,0},{}},{2000.0000000000002`,"",{0.01`,0},{}},{3000.000000000001`,"",{0.01`,0},{}},{4000.000000000001`,"",{0.01`,0},{}},{4999.999999999999`,"",{0.01`,0},{}},{5999.999999999997`,"",{0.01`,0},{}},{7000.000000000002`,"",{0.01`,0},{}},{7999.999999999995`,"",{0.01`,0},{}},{8999.999999999998`,"",{0.01`,0},{}},{20000.000000000004`,"",{0.01`,0},{}},{30000.00000000001`,"",{0.01`,0},{}},{40000.00000000001`,"",{0.01`,0},{}},{49999.99999999999`,"",{0.01`,0},{}},{60000.00000000003`,"",{0.01`,0},{}},{70000.00000000001`,"",{0.01`,0},{}},{80000.00000000003`,"",{0.01`,0},{}},{90000.00000000007`,"",{0.01`,0},{}},{200000.00000000003`,"",{0.01`,0},{}},{300000.0000000001`,"",{0.01`,0},{}},{400000.0000000001`,"",{0.01`,0},{}},{499999.99999999994`,"",{0.01`,0},{}},{600000.0000000003`,"",{0.01`,0},{}},{700000.0000000001`,"",{0.01`,0},{}},{800000.0000000003`,"",{0.01`,0},{}},{900000.0000000007`,"",{0.01`,0},{}}},{{0.`,"0.0",{0.02`,0},{}},{0.2`,"0.2",{0.02`,0},{}},{0.4`,"0.4",{0.02`,0},{}},{0.6000000000000001`,"0.6",{0.02`,0},{}},{0.8`,"0.8",{0.02`,0},{}},{1.`,"1.0",{0.02`,0},{}},{0.05`,"",{0.01`,0},{}},{0.1`,"",{0.01`,0},{}},{0.15000000000000002`,"",{0.01`,0},{}},{0.25`,"",{0.01`,0},{}},{0.30000000000000004`,"",{0.01`,0},{}},{0.35000000000000003`,"",{0.01`,0},{}},{0.45`,"",{0.01`,0},{}},{0.5`,"",{0.01`,0},{}},{0.55`,"",{0.01`,0},{}},{0.6500000000000001`,"",{0.01`,0},{}},{0.7000000000000001`,"",{0.01`,0},{}},{0.7500000000000001`,"",{0.01`,0},{}},{0.8500000000000001`,"",{0.01`,0},{}},{0.9`,"",{0.01`,0},{}},{0.9500000000000001`,"",{0.01`,0},{}}}};
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Raw Data

(* From Feller et al. (1966) "Structural and Functional Aspects of Chloride Binding to Alteromonas haloplanctis α-Amylase". Data is in the form {regulator concentration (mM), substrate concentration (mM), [S]/activity}. Data obtained by digitizing Figure 2 (plot on right) of the paper *)
regulatorData={{2.`,0.12`,1.418`},{2.`,0.25`,2.082`},{2.`,0.49`,3.363`},{2.`,0.99`,5.765`},{2.`,1.484`,8.304`},{2.`,1.979`,10.843`},{2.`,2.473`,13.131`},{5.`,0.125`,0.755`},{5.`,0.245`,1.144},{5.`,0.5`,1.876},{5.`,0.999`,3.248`},{5.`,1.494`,4.621`},{5.`,1.993`,5.856`},{5.`,2.477`,7.114`},{50.`,0.119,0.389`},{50.`,0.244`,0.549`},{50.`,0.494`,0.846`},{50.`,0.988`,1.533},{50.`,1.487`,2.196`},{50.`,1.981`,2.768},{50.`,2.475`,3.431`}};

(* From Li et al. (2005) "Acarbose Rearrangement Mechanism Implied by the Kinetic and Structural Analysis
of Human Pancreatic α-Amylase in Complex with Analogues and Their Elongated
Counterparts". Data is in the form {competitive inhibitor concentration (nM), substrate concentration (mM), (((((\[DifferentialD][P])/(\[DifferentialD]t))))^(-1))}. Data obtained by digitizing Figure 4A of the paper *)
inhibitorData={{0.029`,0.245`,1.274`},{10.029`,0.245`,1.937`},{19.954`,0.245`,2.483`},{29.861`,0.245`,3.501`},{39.843`,0.245`,4.618`},{49.859`,0.245`,4.874`},{0.05`,0.625`,0.745`},{9.989`,0.625`,0.935`},{20.005`,0.625`,1.217`},{29.941`,0.625`,1.481`},{39.95`,0.625`,1.92`},{49.884`,0.625`,2.259`},{0.06`,1.225`,0.488`},{10.005`,1.225`,0.546`},{20.026`,1.225`,0.679`},{29.964,1.225`,0.902`},{39.986,1.225`,1.026`},{49.928,1.225`,1.15}};

(* From Changeux (1966) "Responses of Acetylcholinesterase from
Torpedo marmorata to Salts and Curarizing Drugs". Data is in the form {substrate concentration (M), activity}. Data obtained by digitizing Figures 3 and 4 (the control curve) of the paper *)
substrateInhibitionData={{0.00010,16.025`},{0.00015,28.722`},{0.00020,28.891},{0.00025,29.604`},{0.00050,38.402`},{0.001`,39.553`},{0.002`,46.399`},{0.005`,42.263`},{0.014`,37.032},{0.023`,37.153`},{0.045`,38.856`},{0.068`,42.263`},{0.091`,43.966`}};

(* From Wales et al. (1999)"Divergent Allosteric Patterns Verify the Regulatory Paradigm for Aspartate Transcarbamylase". Data is in the form {competitive inhibitor concentration (μM), relative activity}. Data obtained by digitizing Figure 7A, 7B, and 7C of the paper *)
inhibitorAccelerationData={{0.`,1.009},{0.478,2.787`},{0.809`,3.185`},{1.029`,3.849`},{2.022,4.831`},{3.015`,5.68`},{4.007`,5.108},{5.993`,4.404`},{7.022`,5.054`},{8.015`,3.845`},{9.044`,4.947`},{12.022`,3.272},{15.037`,2.381},{20.037`,1.688`},{25.`,1.898}};