Chapter 7: Amino Acid Biosynthesis

Part III — Nitrogen & Amino Acids

7.1 The Shikimate Pathway: 7 Steps to Chorismate

The shikimate pathway is present only in plants, fungi, and bacteria — not in animals. It produces chorismate, the precursor to all three aromatic amino acids (Phe, Tyr, Trp) and numerous secondary metabolites including lignin precursors, flavonoids, and folate. This pathway accounts for ~20% of fixed carbon in plants.

Step	Substrate → Product	Enzyme
1	PEP + Erythrose-4-P → DAHP	DAHP synthase (feedback inhibited by Phe/Tyr/Trp)
2	DAHP → 3-dehydroquinate (DHQ)	DHQ synthase
3	DHQ → 3-dehydroshikimate	DHQ dehydratase
4	3-Dehydroshikimate + NADPH → Shikimate	Shikimate DH
5	Shikimate + ATP → Shikimate-3-P (S3P)	Shikimate kinase
6	S3P + PEP → EPSP + Pᵢ	EPSPS (5-enolpyruvylshikimate-3-P synthase) ← GLYPHOSATE TARGET
7	EPSP → Chorismate	Chorismate synthase

Glyphosate (Roundup): EPSPS Inhibitor

Glyphosate (N-phosphonomethylglycine) is a competitive inhibitor of EPSPS with respect to PEPbut binds as an uncompetitive inhibitor with respect to S3P. It forms a stable ternary complex (EPSPS·S3P·glyphosate) with K_i ~13 nM, blocking aromatic amino acid and hence protein, folate, and secondary metabolite synthesis. GMO crops (Roundup Ready) express a glyphosate-insensitive EPSPS (CP4-EPSPS from Agrobacterium strain CP4).

7.2 Aromatic Amino Acids from Chorismate

Phenylalanine (Phe)

Chorismate → Prephenate → Phenylpyruvate → Phe

Enzyme: Chorismate mutase → prephenate DH → PAT

Precursor for: lignin (via PAL), flavonoids, phytoalexins, coumarins

Tyrosine (Tyr)

Chorismate → Prephenate → 4-OH-phenylpyruvate → Tyr

Or from Phe via Phe hydroxylase (minor in plants)

Precursor for: plastoquinone, tocochromanol (vitamin E), betalains, alkaloids

Tryptophan (Trp)

Chorismate → Anthranilate → Indole-3-glycerol-P → Trp

5 steps; regulated by feedback inhibition of anthranilate synthase by Trp

Precursor for: IAA (auxin), glucosinolates, indole alkaloids, serotonin

7.3 Other Amino Acid Biosynthetic Families

Glutamate Family (α-KG precursor)

Glutamate → Glutamine (GS), Proline (P5CS → P5CR), Arginine (N-acetylglutamate path)

Aspartate Family (OAA precursor)

Aspartate → Asn (AS); Asp-β-semialdehyde → Lys (diaminopimelate pathway in plants/bacteria), Thr (TS), Met (CBS/MS), Ile (via Thr)

Key regulation: Asp kinase is feedback-inhibited by Lys + Thr (concerted)

Branched-Chain (pyruvate precursor)

Pyruvate + pyruvate → α-acetolactate → (ALS) → Val/Leu branch; pyruvate + α-KB → Ile. ALS is target of sulfonylurea herbicides.

Serine Family (3-PGA precursor)

3-PGA → 3-PHP → Ser (phosphoserine pathway); Ser → Gly (SHMT, photorespiration); Ser + acetyl-CoA → Cys (SAT/CS)

Amino Acid Biosynthetic Network

Simulation: EPSPS Kinetics & Glyphosate Inhibition

Michaelis-Menten curves for EPSPS with and without glyphosate concentrations, and amino acid biosynthetic family distribution.

Python

script.py76 lines

import numpy as np
import matplotlib.pyplot as plt
# output saved as output.png

fig, axes = plt.subplots(1, 2, figsize=(14, 7), facecolor='#0a0f0a')
fig.subplots_adjust(wspace=0.38)

# Panel 1: EPSPS Michaelis-Menten with/without glyphosate
ax1 = axes[0]
ax1.set_facecolor('#0d1a0d')
PEP = np.linspace(0, 5, 300)  # mM
Vmax = 10.0  # nmol/min/mg
Km_PEP = 0.3  # mM
# Without inhibitor
v_no_inh = Vmax * PEP / (Km_PEP + PEP)
# Competitive inhibition by glyphosate: apparent Km = Km * (1 + [I]/Ki)
Ki_glyph = 0.02  # mM (very potent)
conc_glyph = [0.05, 0.2, 1.0]  # mM glyphosate
colors = ['#4ade80', '#fbbf24', '#f87171']

ax1.plot(PEP, v_no_inh, color='#a3e635', lw=2.5, label='No inhibitor')
for conc, col in zip(conc_glyph, colors):
    Km_app = Km_PEP * (1 + conc / Ki_glyph)
    v_inh = Vmax * PEP / (Km_app + PEP)
    ax1.plot(PEP, v_inh, color=col, lw=2, ls='--', label=f'Glyphosate {conc} mM')

ax1.axhline(Vmax/2, color='gray', lw=0.8, ls=':')
ax1.set_xlabel('[PEP] (mM)', color='#a3e635', fontsize=9)
ax1.set_ylabel('EPSPS activity (nmol/min/mg)', color='#a3e635', fontsize=9)
ax1.set_title('EPSPS Inhibition by Glyphosate
(Competitive vs PEP)', color='#d9f99d', fontsize=10)
ax1.legend(fontsize=8.5, labelcolor='#d9f99d', facecolor='#0d1a0d', edgecolor='#4ade80')
ax1.tick_params(colors='#a3e635', labelsize=8)
ax1.grid(True, alpha=0.2, color='#4ade80')
ax1.text(0.05, 0.55, f'Ki (glyphosate) = {Ki_glyph} mM', transform=ax1.transAxes, color='#f87171', fontsize=8)
for sp in ax1.spines.values(): sp.set_edgecolor('#4ade80')

# Panel 2: Amino acid biosynthetic families from precursors
ax2 = axes[1]
ax2.set_facecolor('#0d1a0d')
families = {
    'Glutamate family
(Glu, Gln, Pro, Arg)': 4,
    'Aspartate family
(Asp, Asn, Lys, Thr, Met, Ile)': 6,
    'Pyruvate family
(Ala, Val, Leu)': 3,
    'Shikimate pathway
(Phe, Tyr, Trp)': 3,
    'Serine family
(Ser, Gly, Cys)': 3,
    'Histidine
(from ATP+PRPP)': 1,
}
colors_fam = ['#4ade80', '#86efac', '#fbbf24', '#f87171', '#60a5fa', '#c084fc']
labels_fam = list(families.keys())
sizes_fam  = list(families.values())
wedges, texts, autotexts = ax2.pie(sizes_fam, labels=None, colors=colors_fam,
    autopct='%1.0f%%', startangle=140,
    pctdistance=0.78, wedgeprops=dict(edgecolor='#0d1a0d', linewidth=1.5))
for at in autotexts:
    at.set_color('#0a0f0a')
    at.set_fontsize(8)
    at.set_fontweight('bold')
ax2.legend(wedges, [f'{l} ({s} AAs)' for l, s in zip(labels_fam, sizes_fam)],
    loc='lower left', fontsize=7.5, labelcolor='#d9f99d', facecolor='#0d1a0d', edgecolor='#4ade80',
    bbox_to_anchor=(-0.15, -0.3))
ax2.set_title('Amino Acid Biosynthetic Families', color='#d9f99d', fontsize=10)

fig.suptitle('Amino Acid Biosynthesis in Plants', color='#d9f99d', fontsize=13, fontweight='bold')

plt.savefig('output.png', bbox_inches='tight', facecolor=fig.get_facecolor(), dpi=120)
plt.close()
print('Simulation complete.')

Click Run to execute the Python code

Code will be executed with Python 3 on the server

← Part III Index Ch 8: Nucleotide Metabolism →