NeuralBlitz Biochemistry
What I Do
I provide comprehensive biochemistry tools including enzyme kinetics, metabolic pathways, protein structure analysis, nucleic acid biochemistry, lipid chemistry, and molecular biology calculations for life science applications.
When to Use Me
- Enzyme kinetics analysis
- Metabolic pathway modeling
- Protein structure prediction
- DNA/RNA calculations
- Ligand binding analysis
- Metabolic engineering
Core Concepts
- Enzyme Kinetics: Michaelis-Menten, Lineweaver-Burk
- Metabolic Pathways: Glycolysis, TCA, oxidative phosphorylation
- Protein Structure: Primary, secondary, tertiary, quaternary
- Nucleic Acids: DNA, RNA, transcription, translation
- Thermodynamics: Gibbs free energy in biological systems
- Ligand Binding: KD, IC50, Hill equation
- Coenzymes: NAD+, ATP, Coenzyme A
- Membrane Biology: Lipid bilayers, transport
Code Examples
Enzyme Kinetics
import numpy as np
from scipy.optimize import curve_fit
def michaelis_menten(S, Vmax, Km):
return Vmax * S / (Km + S)
def lineweaver_burk_linear(S, v):
return 1/v, 1/S
def inhibition_types(Km_app, Vmax_app, type_name):
types = {
'competitive': {'Km': 'increased', 'Vmax': 'unchanged'},
'noncompetitive': {'Km': 'unchanged', 'Vmax': 'decreased'},
'uncompetitive': {'Km': 'decreased', 'Vmax': 'decreased'},
'mixed': {'Km': 'varied', 'Vmax': 'decreased'}
}
return types.get(type_name, {'Km': 'unknown', 'Vmax': 'unknown'})
S_data = np.array([0.5, 1.0, 2.5, 5.0, 10.0])
v_data = np.array([12, 20, 30, 38, 42])
Vmax, Km = curve_fit(michaelis_menten, S_data, v_data, p0=[50, 2])[0]
print(f"Vmax: {Vmax:.2f} μmol/min, Km: {Km:.2f} mM")
kcat = Vmax / 0.001 # if [E] = 1 μM
print(f"kcat: {kcat:.2f} s⁻¹")
Protein Calculations
AMINO_ACID_MASS = {
'A': 89, 'R': 174, 'N': 132, 'D': 133, 'C': 121,
'E': 147, 'Q': 146, 'G': 75, 'H': 155, 'I': 131,
'L': 131, 'K': 146, 'M': 149, 'F': 165, 'P': 115,
'S': 105, 'T': 119, 'W': 204, 'Y': 181, 'V': 117
}
def protein_molecular_weight(sequence):
water_loss = 18.015 * (len(sequence) - 1)
mass = sum(AMINO_ACID_MID.get(aa, 0) for aa in sequence) + 1.008
return mass - water_loss / 1000
def calculate_extinction_coefficient(sequence, wavelength=280):
W = sequence.count('W')
Y = sequence.count('Y')
C = sequence.count('C') // 2
return 1490 * W + 550 * Y + 125 * C
def predict_isoelectric_point(sequence):
pKa = {'D': 3.9, 'E': 4.3, 'C': 8.3, 'Y': 10.1,
'H': 6.0, 'K': 10.5, 'R': 12.5}
charges = []
for aa in sequence:
if aa in ['D', 'E']:
pH = pKa.get(aa, 4.0)
elif aa in ['K', 'R']:
pH = pKa.get(aa, 11.0)
elif aa == 'H':
pH = pKa.get(aa, 6.0)
else:
pH = 7.0
charges.append(pH)
return np.median(charges) if charges else 7.0
sequence = "MSEQKQUENCE"
print(f"MW: {protein_molecular_weight(sequence):.2f} kDa")
print(f"ε280: {calculate_extinction_coefficient(sequence)} M⁻¹cm⁻¹")
DNA/RNA Calculations
from collections import Counter
DNA_COMPLEMENT = {'A': 'T', 'T': 'A', 'G': 'C', 'C': 'G'}
RNA_COMPLEMENT = {'A': 'U', 'U': 'A', 'G': 'C', 'C': 'G'}
def gc_content(sequence):
gc = sequence.count('G') + sequence.count('C')
return gc / len(sequence) * 100
def melting_temperature(sequence):
if len(sequence) < 14:
return 2 * (sequence.count('A') + sequence.count('T')) + 4 * (sequence.count('G') + sequence.count('C'))
return 64.9 + 41 * (gc_content(sequence) - 16.4) / len(sequence)
def reverse_complement(sequence, dna=True):
complement = DNA_COMPLEMENT if dna else RNA_COMPLEMENT
return ''.join(complement.get(base, base) for base in reversed(sequence))
dna = "ATGCGCTA"
print(f"GC content: {gc_content(dna):.1f}%")
print(f"Tm: {melting_temperature(dna):.1f}°C")
print(f"Reverse complement: {reverse_complement(dna)}")
Metabolic Calculations
ATP_YIELD = {
'glycolysis': 2,
'pyruvate_to_acetyl_CoA': 0,
'citric_acid_cycle': 2,
'oxidative_phosphorylation': 26
}
def calculate_atp_yield(glucose=1):
yield_dict = ATP_YIELD.copy()
yield_dict['oxidative_phosphorylation'] *= 2.5 * glucose
yield_dict['total'] = sum(yield_dict.values())
return yield_dict
def nadh_to_atp_conversion(nadh, proton_pump_efficiency=3):
return nadh * 2.5 * proton_pump_efficiency
def calculate_gibbs_free_energy(deltaG0, Q, T=298):
R = 8.314
return deltaG0 + R * T * np.log(Q)
print(f"ATP from glucose: {calculate_atp_yield()['total']:.0f}")
Ligand Binding Analysis
def hill_equation(L, KD, n, Bmax):
return Bmax * L**n / (KD + L**n)
def ic50_from_kd(KD, inhibitor_conc, Ki):
return Ki * (1 + inhibitor_conc / KD)
def schild_receptor_agonist(EC50, antagonist_conc, dose_ratio):
return antagonist_conc / (dose_ratio - 1)
def binding_free_energy(KD):
R = 8.314
T = 298
return -R * T * np.log(KD * 1e-6) / 1000 # kcal/mol
KD = 1e-9 # 1 nM
L = np.logspace(-12, -6, 100)
B = hill_equation(L, KD, 1, 100)
print(f"Binding free energy: {binding_free_energy(KD):.2f} kcal/mol")
Best Practices
- Enzyme Assays: Measure initial velocities
- Protein Purity: Use multiple methods for confirmation
- Buffer Conditions: Consider pH and ionic strength
- Temperature Control: Biological systems are temperature sensitive
- Controls: Always include appropriate controls
Common Patterns
# Bradford protein assay
def bradford_concentration(od595, intercept=0.045, slope=0.62):
return (od595 - intercept) / slope
# Circular dichroism secondary structure
def estimate_alpha_helix(cd_signal_222, reference=-33000):
return cd_signal_222 / reference * 100
# Restriction digest
def predict_digest_pattern(sequence, enzyme_sites):
sites = []
for site in enzyme_sites:
sites.extend([m.start() for m in re.finditer(site, sequence)])
return sorted(set([0] + [s + len(site) for s in sites for site in enzyme_sites if site in sequence]))
Core Competencies
- Enzyme kinetics and inhibition
- Protein structure and properties
- Nucleic acid calculations
- Metabolic pathway analysis
- Ligand binding thermodynamics