Celbiologie I - Practica
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Practicum 2: DNA: structuur, amplificatie, restrictiedigestie, gelelektroforese¶
Practicum 3: koolhydraten en lipiden¶
Practicum 4: enzym kinetica¶
Theorie¶
- evenwichtsconstante voor $\ce{A -> B}$: $K_{eq} = \dfrac{[B]}{[A]}$
- biologische evenwichtsconstante $K'_{eq}$
- $\ce{[H2O]} = 1$
- $\ce{[H+] = [H3O+]} = 10^{-7} mol/l = 1$
- $pH = 7$
Michaelis-Menten kinetica¶
- omzettingssnelheid $v$ (in mol/min) van substraat $S$ in product $P$ m.b.v. enzym $E$
- $v = \dfrac{d[P]}{dt} = -\dfrac{d[S]}{dt}$
- $\ce{E + S <=> E-S <=> E-P <=> E + P}$
- vereenvoudigd: $\ce{E + S <=>[k_1][k_2] E-S <=>[k_3][k_4] E + P}$
- voorwaarden
- 1 substraat $S$
- $[S] \gg [E]$
- nog niet veel product gevormd, dus $[S]$ stabiel
- steady-state assumptie
- $\ce{[E-S]}$ constant
- dus als aanmaak = afbraak
- $k_1 [E] [S] = k_2 \ce{[E-S]} + k_3 \ce{[E-S]}$
- kan enkel als $[S]$ hoog en $[P]$ laag
- parameters
- $V_{max} = k_3 ([E] + [\ce{E-S}]) = k_3 [E_t]$: maximale omzettingssnelheid (eenheid mol/min)
- $K_m = \dfrac{k_2 + k_3}{k_1} = \dfrac{[E] [S]}{[\ce{E-S}]}$: Michaelis-Menten constante (eenheid mol/l)
- lager ~ reactiever substraat
- $v([S]) = \dfrac{[S]}{K_m + [S]}V_{max}$
- afleiding: zie practicumboek
- hyperbool in variabele $[S]$
- $[S] \ll K_m \implies v([S]) \approx \dfrac{V_{max}}{K_m} [S]$
- lineair verband
- $[S] \gg K_m \implies v([S]) \approx V_{max}$
- $v(K_m) = \frac{1}{2} V_{max}$
- omzettingsgetal $k_{cat} = k_3? = \dfrac{V_{max}}{[E_t]}$
- hoeveel $S$ per seconde kan omgezet worden naar $P$ door 1 $E$
- cat: catalysis
- eenheid: $\dfrac{1}{s}$
Linearisatie¶
- risico: uitvergroting meetfouten door niet-lineaire transformaties
- Lineweaver-Burk
- $\dfrac{1}{v}\left(\dfrac{1}{[S]}\right) = \dfrac{1}{V_{max}} + \dfrac{K_m}{V_{max}} \dfrac{1}{[S]}$
- $\dfrac{1}{v}\left(\dfrac{-1}{K_m}\right) = 0$
- $\dfrac{1}{v}(0) = \dfrac{1}{V_{max}}$
- Eadie-Hofstee
- $v\left(\dfrac{v}{[S]}\right) = V_{max} - K_m \dfrac{v}{[S]} \iff \dfrac{v}{[S]}(v) = \dfrac{V_{max}}{K_m} - \dfrac{v}{K_m}$
- $v(0) = V_{max}$
- $v\left(\dfrac{V_{max}}{K_m}\right) = 0$
- rico: $-K_m$
- Hanes-Woolf
- $\dfrac{[S]}{v}([S]) = \dfrac{K_m}{V_{max}} + \dfrac{[S]}{V_{max}}$
- $\dfrac{[S]}{v}(-K_m) = 0$
- rico: $\dfrac{1}{V_{max}}$
In [103]:
xmax = 5
vmax = 10
Km = .5
In [79]:
s = np.linspace(0.001, xmax)
v = s / (Km + s) * vmax
v2 = s / (2*Km + s) * vmax
v3 = s / (Km + s) * (vmax-5)
plt.plot(s, v, 'k', label=f'$K_m={Km}, V_{{max}}={vmax}$')
plt.plot(s, v2, label=f'$K_m={2*Km}, V_{{max}}={vmax}$')
plt.plot(s, v3, label=f'$K_m={Km}, V_{{max}}={vmax-5}$')
plt.hlines(vmax, 0, xmax, ['k'], ['--'])
plt.hlines(vmax/2, 0, Km, ['k'], ['--'])
plt.vlines(Km, 0, vmax/2, ['k'], ['--'])
plt.xlabel("[S]")
plt.ylabel("v")
plt.legend()
plt.title("Michaelis-Menten");
In [82]:
si = np.linspace(-1/Km, xmax)
vi = Km / vmax * si + 1 / vmax
vi2 = 2*Km / vmax * si + 1 / vmax
vi3 = Km / (vmax-5) * si + 1 / (vmax-5)
vi4 = 2*Km / (vmax-5) * si + 1 / (vmax-5)
plt.plot(si, vi, 'k', label=f'$K_m={Km}, V_{{max}}={vmax}$')
plt.plot(si, vi2, label=f'$K_m={2*Km}, V_{{max}}={vmax}$')
plt.plot(si, vi3, label=f'$K_m={Km}, V_{{max}}={vmax-5}$')
plt.plot(si, vi4, label=f'$K_m={2*Km}, V_{{max}}={vmax-5}$')
plt.xlabel("1/[S]")
plt.ylabel("1/v")
plt.legend()
plt.title("Lineweaver-Burk");
plt.gca().spines['left'].set_position('zero')
plt.gca().spines['bottom'].set_position('zero')
plt.gca().spines['right'].set_color('none')
plt.gca().spines['top'].set_color('none')
Inhibitoren¶
- $I$: inhibitor
- $K_i$: dissociatie constante van $I$, eenheid mol/l?
- hoe groter, hoe meer $I$ nodig om reactie te vertragen
- drie soorten
- competitieve inhibitie
- $S$ en $I$ binden op zelfde plaats aan $E$
- $V_{max}^{inh} = V_{max}$
- want bij heel hoge $[S]$ is $[I]$ verwaarloosbaar
- $K_m^{inh} = K_m \left(1 + \dfrac{[I]}{K_i}\right) \geq K_m$
- $K_i \uparrow \implies K_m^{inh} \downarrow \implies$ snellere omzetting
- Lineweaver-Burk: lijnen snijden in $\left(0, \dfrac{1}{V_{max}}\right)$, dus waar $[S] = +\infty$ en $v=V_{max}$
- niet-competitieve inhibitie
- $S$ en $I$ binden op verschillende plaats op $E$
- $V_{max}^{inh} = \dfrac{1}{1 + \frac{[I]}{K_i}} V_{max} \leq V_{max}$
- deel van $E$ zijn inactief
- cf. $V_{max} = k_{cat} [E_t]$
- $K_m^{inh} = K_m$
- de resterende $E$ werken zoals voorheen
- Lineweaver-Burk: lijnen snijden in $\left(\dfrac{-1}{K_m}, 0\right)$
- oncompetitieve inhibitie
- $I$ bindt enkel aan $\ce{E-S}$, zodat $\ce{E-S-I}$ inactief is
- $V_{max}^{inh} = \dfrac{1}{1 + \frac{[I]}{K_i}} V_{max} \leq V_{max}$
- $K_m^{inh} = \dfrac{1}{1 + \frac{[I]}{K_i}} K_m \leq K_m$
- Lineweaver-Burk: lijnen parallel
- competitieve inhibitie
Experimenteel gedeelte¶
- $S$: p-nitrofenylfosfaat (pNPP)
- $P$: nitrofenol
- $E$: alkalisch fosfatase (AF)
- EC 3.1.3.1, hydrolase
- $I$: $\beta$-glycero-fosfaat
Opstellen ijklijn¶
| Proefbuis | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
|---|---|---|---|---|---|---|---|---|---|---|
| tris (ml) | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| nitrofenol (ml) | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 |
| $\ce{H2O}$ (ml) | 1 | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 | 0.4 | 0.3 | 0.2 | 0.1 |
| $\ce{NaOH}$ (ml) | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
| totaal (ml) | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 | 7 |
| nitrofenol (μmol) | 0 | 10 | 20 | 30 | 40 | 50 | 60 | 70 | 80 | 90 |
| absorbantie | 0 | 0.20 | 0.41 | 0.61 | 0.80 | 1.02 | 1.22 | 1.44 | 1.77 | 1.89 |
In [102]:
x = np.arange(0, 100, 10)
y = [0, 0.20, 0.41, 0.61, 0.80, 1.02, 1.22, 1.44, 1.77, 1.89]
m, q = np.polyfit(x, y, 1)
plt.plot(x,y)
plt.plot(x, m*x+q, 'k--', label=f'$y = {m:.4f}x {q:+.4f}$')
plt.xlabel("nitrofenol [μmol]")
plt.ylabel("absorbantie")
plt.legend();
$f(x) = 0.0213x - 0.0240 \iff f^{-1}(y) = \dfrac{y + 0.0240}{0.0213}$
Parameters bepalen¶
- $[S]_1 = \dfrac{100 mM \cdot 0.25 ml}{2 ml} = 12.5 mM$
- $[S]_2 = \dfrac{50 mM \cdot 0.25 ml}{2 ml} = 6.25 mM$
- ...
In [177]:
concentraties = np.array([100, 50, 25, 12.5, 6, 3, 1.5])
concentraties_proefbuis = concentraties * 0.25 / 2
df_norm = pd.DataFrame({
'proefbuis': np.arange(1, 8),
'[S]': concentraties_proefbuis,
'Absorbantie': [2.00, 1.59, 1.39, 1.23, 1.01, 0.77, 0.57]
})
df_norm
Out[177]:
In [178]:
df_inh = pd.DataFrame({
'proefbuis': np.arange(8, 15),
'[S]': concentraties_proefbuis,
'Absorbantie': [1.28, 0.91, 0.69, 0.47, 0.30, 0.18, 0.10]
})
df_inh
Out[178]:
In [193]:
df_blanco = pd.DataFrame({
'proefbuis': np.arange(15, 22),
'[S]': concentraties_proefbuis,
'Absorbantie': [0.134, 0.074, 0.043, 0.028, 0.021, 0.015, 0.013]
})
df_blanco
Out[193]:
In [238]:
df_norm['Absorbantie - blanco'] = df_norm['Absorbantie'] - df_blanco['Absorbantie']
df_inh['Absorbantie - blanco'] = df_inh['Absorbantie'] - df_blanco['Absorbantie']
df = pd.concat([df_norm, df_inh], keys=['normaal', 'inhibitor'])
df['nitrofenol'] = (df['Absorbantie - blanco'] + 0.0240) / 0.0213
df['v'] = df['nitrofenol'] / 15
df['1/[S]'] = 1 / df['[S]']
df['1/v'] = 1 / df['v']
df.plot(x='[S]', y='v', kind='scatter', title='Michaelis-Menten', color=7*['C0']+7*['C1'])
df.plot(x='1/[S]', y='1/v', kind='scatter', title='Lineweaver-Burk', color=7*['C0']+7*['C1'])
plt.gca().spines['left'].set_position('zero')
plt.gca().spines['bottom'].set_position('zero')
plt.gca().spines['right'].set_color('none')
plt.gca().spines['top'].set_color('none')
x = df.loc['normaal', '1/[S]']
m, q = np.polyfit(x, df.loc['normaal', '1/v'], 1)
x2 = np.append(x, [-3]) # trek verder negatief door om nulpunt te vinden
plt.plot(x2, m*x2+q, 'C0--', label=f'$\dfrac{{1}}{{v_{{norm}}}} = {m:.4f}\dfrac{{1}}{{[S]}} {q:+.4f}$')
x = df.loc['inhibitor', '1/[S]']
m, q = np.polyfit(x, df.loc['inhibitor', '1/v'], 1)
x2 = np.append(x, [-1]) # trek verder negatief door om nulpunt te vinden
plt.plot(x2, m*x2+q, 'C1--', label=f'$\dfrac{{1}}{{v_{{inh}}}} = {m:.4f}\dfrac{{1}}{{[S]}} {q:+.4f}$')
plt.legend()
df
Out[238]:
- normaal: $\dfrac{1}{v} = \dfrac{1}{V_{max}} + \dfrac{K_m}{V_{max}} \dfrac{1}{[S]} = 0.2036 + 0.0684 \dfrac{1}{[S]}$
- $\dfrac{1}{V_{max}} = 0.2036 \iff V_{max} = 4.9116$ μmol/min
- $\dfrac{K_m}{V_{max}} = 0.0684 \iff K_m = 0.0684 V_{max} = 0.3360$ mM
- alternatief: $0.2036 + 0.0684 \dfrac{-1}{K_m} = 0 \iff \dfrac{-1}{K_m} = \dfrac{-0.2036}{0.0684} = -2.9766 \iff K_m = 0.3360$
- met inhibitor: $\dfrac{1}{v} = \dfrac{1}{V_{max}^{inh}} + \dfrac{K_m^{inh}}{V_{max}^{inh}} \dfrac{1}{[S]} = 0.3265 + 0.4884 \dfrac{1}{[S]}$
- $\dfrac{1}{V_{max}^{inh}} = 0.3265 \iff V_{max}^{inh} = 3.0628$ μmol/min
- $\dfrac{K_m^{inh}}{V_{max}^{inh}} = 0.4884 \iff K_m^{inh} = 0.4884 V_{max}^{inh} = 1.4959$ mM
- soort inhibitor
- $K_m$ liggen ver uit elkaar maar $V_{max}$ liggen dicht(er) bij elkaar
- LB grafiek: snijpunt bij $x \approx 0$
- wijst op competitieve inhibitor
- dus $K_m^{inh} = K_m \left(1 + \dfrac{[I]}{K_i}\right)$
- $\iff K_i = \dfrac{K_m [I]}{K_m^{inh} - K_m}$
- $[I] = \dfrac{100 mM \cdot 0.25 ml}{2 ml} = 12.5 mM$
- $K_i = \dfrac{0.3360 mM \cdot 12.5 mM}{1.4959 mM - 0.3360 mM} = 3.621 mM$