Cell Chemistry and Bioenergetics

Introduction

Every process in a living cell is, at its core, a chemical reaction. To understand how cells work, we need to understand the chemistry that underlies them: the special properties of water, the noncovalent forces that shape biological molecules, the small molecules from which macromolecules are built, and the thermodynamic principles that govern which reactions can occur. We also need to understand how cells capture energy from their environment and channel it into the work of biosynthesis, transport, and movement.

This lesson covers the chemical and energetic foundations of life, then introduces the computational tools and databases used to study enzymes, metabolic pathways, and the small molecules of cellular metabolism.

Water: The Solvent of Life

Cells are roughly 70% water by mass, and virtually all cellular chemistry occurs in aqueous solution. Water’s unique properties arise from its ability to form hydrogen bonds — weak electrostatic attractions between the partially positive hydrogen of one water molecule and the partially negative oxygen of another. Each water molecule can participate in up to four hydrogen bonds simultaneously, creating a dynamic, interconnected network.

These hydrogen bonds give water an unusually high heat capacity (it takes a lot of energy to raise its temperature), high heat of vaporization (sweating cools effectively), and high surface tension. Most importantly, water is an exceptional solvent for ions and polar molecules — substances that can form favorable interactions with water molecules are hydrophilic and dissolve readily; those that cannot are hydrophobic and are excluded.

Water also participates directly in acid-base chemistry. Molecules that release protons (H⁺) in solution are acids; those that accept protons are bases. The concentration of H⁺ ions is measured on the pH scale (pH = −log[H⁺]). Cellular pH is tightly regulated near 7.2–7.4 in most compartments, because protein function is exquisitely sensitive to pH changes that alter the ionization state of amino acid side chains.

Noncovalent Interactions

While covalent bonds hold atoms together within molecules, noncovalent interactions hold molecules together and determine their three-dimensional shapes. Four types dominate in cells:

Hydrogen bonds form between a partially positive H (bonded to N or O) and a partially negative O or N on another molecule. They are central to DNA base pairing, protein secondary structure (α-helices and β-sheets), and enzyme-substrate recognition.

Electrostatic (ionic) interactions occur between oppositely charged groups. They contribute to protein folding, the binding of proteins to DNA (positively charged histones interact with the negatively charged DNA backbone), and enzyme-substrate interactions.

Van der Waals forces are weak, short-range attractions that arise from transient fluctuations in electron distribution. Individually negligible, they become significant when two molecular surfaces fit closely together — a principle exploited by molecular recognition.

The hydrophobic effect is the tendency of nonpolar molecules to cluster together in aqueous solution, minimizing their contact with water. This is the primary driving force for protein folding (burying hydrophobic side chains in the interior), membrane assembly (hydrophobic lipid tails facing inward), and many molecular interactions.

Individually, each noncovalent interaction is weak (4–30 kJ/mol, compared to 350–570 kJ/mol for a covalent bond). But when many act in concert — as when a protein folds or an enzyme binds its substrate — they produce highly specific, yet reversible, molecular interactions. This combination of specificity and reversibility is essential for the dynamic chemistry of life.

Let’s compare the molecular properties of a hydrophobic amino acid (leucine) with a charged amino acid (glutamate). Their chemical differences drive protein folding — hydrophobic residues pack into the interior while charged residues face the solvent.

let leucine = "CC(CC)C(N)C(=O)O"
let glutamate = "C(CC(=O)O)C(C(=O)O)N"
let leu_props = Chem.properties(leucine)
let glu_props = Chem.properties(glutamate)
print("Leucine (hydrophobic):")
print(leu_props)
print("Glutamate (charged):")
print(glu_props)

Carbon: The Backbone of Biological Molecules

Life is based on carbon chemistry. Carbon’s ability to form four covalent bonds, to bond stably with itself in chains and rings, and to combine with hydrogen, oxygen, nitrogen, sulfur, and phosphorus produces an almost limitless variety of organic molecules.

The key functional groups of biological molecules include hydroxyl (−OH), carboxyl (−COOH), amino (−NH&sub2;), phosphate (−PO&sub4;), carbonyl (C=O), and sulfhydryl (−SH) groups. These groups determine a molecule’s solubility, reactivity, and ability to participate in hydrogen bonds and ionic interactions.

The Four Families of Small Organic Molecules

Cells contain four major families of small organic molecules that serve as building blocks, energy sources, and signaling molecules:

These small molecules are assembled into macromolecules through condensation reactions that release water (dehydration synthesis). The reverse process, hydrolysis, breaks macromolecules back down by adding water. The balance between synthesis and degradation is tightly regulated to maintain cellular homeostasis.

Let’s compare glucose, ATP, and water — three molecules central to cellular chemistry — and see how their molecular properties differ.

let glucose = "OC[C@@H](O1)[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O"
let atp = "c1nc(c2c(n1)n(cn2)[C@@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)OP(=O)(O)OP(=O)(O)O)O)O)N"
let water = "O"
print("Glucose:")
print(Chem.properties(glucose))
print("ATP:")
print(Chem.properties(atp))
print("Water:")
print(Chem.properties(water))

Exercise: Identify the Energy Carrier

Compare the molecular properties of the three molecules below. Which one has the highest molecular weight? That molecule is the cell’s primary energy carrier.

let mol_a = "OC[C@@H](O1)[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O"
let mol_b = "c1nc(c2c(n1)n(cn2)[C@@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)OP(=O)(O)OP(=O)(O)O)O)O)N"
let mol_c = "CC(=O)O"
print("Molecule A:")
print(Chem.properties(mol_a))
print("Molecule B:")
print(Chem.properties(mol_b))
print("Molecule C:")
print(Chem.properties(mol_c))
let answer = "ATP"
print(answer)

Macromolecules and Their Remarkable Properties

The four classes of macromolecules — proteins, nucleic acids, polysaccharides, and lipids — dominate cell chemistry. Each is a polymer built from a specific set of monomers, yet the properties of the polymer far exceed what the monomers alone could achieve.

Proteins, for example, are linear chains of amino acids that fold into precise three-dimensional shapes. This folding is driven primarily by noncovalent interactions — hydrophobic residues pack into the interior, hydrogen bonds stabilize secondary structures, and ionic interactions contribute to surface features. The final shape determines function: a protein’s activity depends on its ability to bind specific partner molecules through the complementary fit of molecular surfaces.

The same principle applies to DNA (where base-pairing rules specify the double helix), to polysaccharides (where branching patterns determine energy storage vs. structural roles), and to lipid assemblies (where amphipathic molecules spontaneously form bilayers). In each case, noncovalent bonds specify both the precise shape of the macromolecule and its binding to other molecules — the molecular basis of biological specificity.

We can analyze the physical properties of a short protein fragment to see how hydrophobicity, charge, and molecular weight vary. The peptide below represents an alpha-helix-forming region with a mix of hydrophobic and polar residues.

let helix_peptide = "AELKVIAGM"
let props = Struct.protein_props(helix_peptide)
print("Alpha-helix peptide properties:")
print(props)

Exercise: Hydrophobic Core vs. Surface

Proteins fold so that hydrophobic residues are buried in the core while polar residues face the solvent. Compare these two peptide fragments and determine which would more likely be found in the protein interior.

let surface_peptide = "EKRDNQST"
let core_peptide = "VILMAAGL"
let surface_props = Struct.protein_props(surface_peptide)
let core_props = Struct.protein_props(core_peptide)
print("Surface candidate:")
print(surface_props)
print("Core candidate:")
print(core_props)
let answer = "VILMAAGL"
print(answer)

Enzymes: Biological Catalysts

The chemical reactions of life rarely occur at useful rates on their own. Enzymes — mostly proteins, though some are RNA molecules (ribozymes) — accelerate reactions by factors of 10⁶ to 10¹⁴ or more.

Enzymes achieve this by stabilizing the transition state of a reaction, lowering the activation energy barrier without altering the thermodynamic equilibrium. The substrate binds to the enzyme’s active site — a precisely shaped pocket that positions the reacting groups optimally and often provides catalytic amino acid side chains that participate directly in the chemistry.

Each enzyme is highly specific, typically catalyzing one reaction or a closely related set of reactions. This specificity arises from the complementary shape and charge distribution between the active site and the substrate — often described by the induced fit model, where both enzyme and substrate adjust their shapes upon binding.

Enzymes find their substrates with remarkable efficiency despite the crowded interior of the cell. This is possible because of the enormous rapidity of molecular motions: molecules in solution undergo billions of collisions per second, and an enzyme typically encounters its substrate within milliseconds. The rate of enzyme-catalyzed reactions is further enhanced by the organization of enzymes into multienzyme complexes and by the confinement of related enzymes to specific cellular compartments.

Enzyme activity is regulated by multiple mechanisms: allosteric regulation (binding of an effector molecule at a site distant from the active site), covalent modification (e.g., phosphorylation), proteolytic activation (irreversible clipping of an inactive precursor), and changes in gene expression that alter the amount of enzyme present.

Free Energy and Thermodynamics

Whether a chemical reaction can occur spontaneously is determined by the free-energy change, ΔG. A reaction with a negative ΔG releases free energy and can proceed spontaneously (it is exergonic). A reaction with a positive ΔG requires an input of free energy and cannot occur on its own (it is endergonic).

The ΔG of a reaction depends on two factors: the intrinsic energetics of the reaction (measured by the standard free-energy change, ΔG°, defined at 1 M concentration of all reactants and products) and the actual concentrations of reactants and products in the cell. Even a reaction with a positive ΔG° can be driven forward if the cell maintains a high concentration of reactants and a low concentration of products.

The equilibrium constant K_eq is directly related to ΔG° by the equation ΔG° = −RT ln K_eq. A large K_eq (products favored at equilibrium) corresponds to a large negative ΔG°. This relationship allows biochemists to compare the energetics of different reactions on a common scale.

A crucial principle for cellular metabolism is that free-energy changes are additive in sequential reactions. If reaction A → B has ΔG = +20 kJ/mol and reaction B → C has ΔG = −35 kJ/mol, the overall reaction A → C has ΔG = −15 kJ/mol and is thermodynamically favorable. Cells exploit this principle by coupling unfavorable biosynthetic reactions to the hydrolysis of ATP or other favorable reactions, making the overall process exergonic.

Biological order — the highly organized, low-entropy state of a living cell — is made possible because cells are open systems that continuously release heat energy to their surroundings. The increase in entropy of the surroundings more than compensates for the decrease in entropy within the cell, satisfying the second law of thermodynamics.

Oxidation, Reduction, and Energy from Food

Cells obtain most of their energy by oxidizing organic molecules — removing electrons from fuel molecules and transferring them, through a series of carriers, to a final electron acceptor (usually O&sub2; in aerobic organisms). This process releases energy because electrons move from higher-energy states to lower-energy states.

Oxidation is the loss of electrons; reduction is the gain of electrons. The two always occur together (one molecule is oxidized as another is reduced), so these are called redox reactions. The electron carriers NAD⁺ (reduced to NADH) and FAD (reduced to FADH&sub2;) shuttle high-energy electrons from fuel molecules to the electron transport chain in mitochondria.

ATP and Activated Carrier Molecules

ATP (adenosine triphosphate) is the most widely used activated carrier molecule in cells. Hydrolysis of ATP to ADP + Pᵢ releases approximately −30.5 kJ/mol under standard conditions (and even more under cellular conditions where ATP is maintained at much higher concentrations than ADP). This energy is harnessed to drive biosynthetic reactions, active transport, and mechanical work.

ATP works by transferring its terminal phosphate group to other molecules, creating high-energy phosphorylated intermediates that can then undergo otherwise unfavorable reactions. This mechanism is how energy stored in ATP is used to join two molecules together — for example, linking amino acids into proteins or nucleotides into DNA.

Cells also use several other activated carriers:

The formation of each activated carrier is coupled to an energetically favorable reaction, and the carrier is “spent” when it donates its group or electrons to a biosynthetic process. This coupling is the fundamental mechanism by which cells channel energy from catabolic reactions into anabolic work.

The synthesis of biological polymers (proteins, nucleic acids, polysaccharides) is driven by ATP hydrolysis. For each monomer added to a growing chain, one or more high-energy phosphate bonds are consumed, making polymerization thermodynamically favorable.

Glycolysis and Fermentation

Glycolysis (Greek: “sugar splitting”) is the central ATP-producing pathway, conserved across virtually all organisms. It converts one molecule of glucose (6 carbons) into two molecules of pyruvate (3 carbons each), producing a net gain of 2 ATP and 2 NADH per glucose. The 10 enzymatic steps of glycolysis occur in the cytoplasm and do not require oxygen.

Glycolysis illustrates a key principle: enzymes couple oxidation to energy storage. At one critical step, the enzyme glyceraldehyde-3-phosphate dehydrogenase simultaneously oxidizes its substrate (transferring electrons to NAD⁺) and attaches a high-energy phosphate group, which is then transferred to ADP to generate ATP.

In the absence of oxygen, cells regenerate NAD⁺ through fermentation. Lactic acid fermentation (in muscle cells and some bacteria) converts pyruvate to lactate. Alcoholic fermentation (in yeast) converts pyruvate to ethanol and CO&sub2;. Neither pathway produces additional ATP beyond the 2 from glycolysis, but both regenerate the NAD⁺ needed to keep glycolysis running.

The Citric Acid Cycle and Oxidative Phosphorylation

In aerobic organisms, pyruvate from glycolysis enters the mitochondria and is converted to acetyl CoA, which feeds into the citric acid cycle (TCA cycle, Krebs cycle). This cycle oxidizes the acetyl group to CO&sub2;, generating 3 NADH, 1 FADH&sub2;, and 1 GTP per turn. Since each glucose yields two acetyl CoA molecules, the citric acid cycle runs twice per glucose.

Between meals, most animal cells derive their energy from fatty acids rather than glucose. Fatty acids are broken down by β-oxidation into acetyl CoA, which enters the same citric acid cycle. Fat is an exceptionally efficient fuel: 1 gram of fat yields more than twice the energy of 1 gram of carbohydrate.

The NADH and FADH&sub2; generated by glycolysis, the citric acid cycle, and fatty acid oxidation carry their high-energy electrons to the electron transport chain in the inner mitochondrial membrane. As electrons pass through a series of protein complexes (I, II, III, IV), the energy released is used to pump protons (H⁺) across the membrane, creating an electrochemical gradient. Protons flow back through ATP synthase, a molecular turbine that converts this gradient into ATP. This process, oxidative phosphorylation, produces approximately 30–32 ATP per glucose — the vast majority of a cell’s ATP.

Organisms store energy-rich molecules in special reservoirs. Animals store glucose as glycogen (in liver and muscle) and excess energy as triglycerides (in adipose tissue). Plants store glucose as starch and energy as oils in seeds.

The Nitrogen Cycle and Metabolic Regulation

Amino acids and nucleotides contain nitrogen, linking their metabolism to the nitrogen cycle. Nitrogen enters biological systems through nitrogen fixation (N&sub2; → NH&sub3;) and is incorporated into amino acids and nucleotides. When these molecules are degraded, the amino groups are removed, converted to urea (in mammals) or ammonia, and excreted. The nitrogen is eventually returned to the atmosphere by denitrifying bacteria, completing the cycle.

The thousands of reactions in a cell are organized into metabolic pathways — sequential chains of enzyme-catalyzed steps. These pathways are tightly regulated by feedback inhibition (the end product of a pathway inhibits an early enzyme), by allosteric regulation, and by hormonal signals that coordinate metabolism across tissues. This regulation ensures that cells produce exactly what they need, when they need it, without wasteful overproduction.

Chemical File Formats and Molecular Databases

Just as biologists represent sequences as strings, chemists represent small molecules in standardized digital formats:

• SMILES (Simplified Molecular Input Line Entry System) — a compact text notation for molecular structure. For example, glucose is OC[C@@H](O1)[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O
• SDF/MOL — file formats that store 2D or 3D molecular coordinates along with properties
• PDB — the Protein Data Bank format, used for 3D structures of proteins and nucleic acids solved by X-ray crystallography, NMR, or cryo-EM

Major molecular databases include:

• PubChem (NCBI) — the world’s largest collection of chemical structures, with over 110 million compounds and their biological activities
• ChEMBL (EBI) — a curated database of bioactive molecules with drug-like properties, linking chemistry to pharmacology
• ChEBI (Chemical Entities of Biological Interest) — an ontology and database of small molecules relevant to biology

Molecular descriptors and molecular fingerprints are numerical representations of molecular structure used for computational analysis. Descriptors capture properties like molecular weight, logP (hydrophobicity), and polar surface area. Fingerprints encode structural features as bit vectors, enabling rapid similarity searching: given a molecule of interest, one can quickly find structurally similar compounds in a database of millions.

Enzyme Classification and Pathway Databases

Every known enzyme is assigned an EC number (Enzyme Commission number) that classifies it by the reaction it catalyzes. The system has four levels: EC 2.7.1.1 identifies hexokinase (class 2 = transferase, subclass 7 = phosphotransferase, sub-subclass 1 = with an alcohol group as acceptor, entry 1 = hexokinase).

Key enzyme and pathway databases include:

• BRENDA — comprehensive enzyme information: kinetic parameters, substrates, inhibitors, tissue distribution
• KEGG (Kyoto Encyclopedia of Genes and Genomes) — maps genes to metabolic pathways, with beautiful graphical pathway maps linking genomics to metabolism
• MetaCyc and BioCyc — curated collections of metabolic pathways and enzymes across thousands of organisms
• Reactome — a curated database of biological pathways in human and model organisms, covering not just metabolism but also signaling and gene regulation

Pathway enrichment analysis is a widely used computational technique: given a list of genes (e.g., those upregulated in a disease), it tests whether any metabolic or signaling pathways are statistically overrepresented, pointing to the biological processes most affected.

Flux balance analysis (FBA) and other constraint-based modeling approaches use genome-scale metabolic models to predict metabolic fluxes — the rates of individual reactions in a metabolic network — under different conditions. These models can predict growth rates, identify essential genes, and guide metabolic engineering for industrial biotechnology. Enzyme kinetics simulation allows researchers to model the time-dependent behavior of enzyme-catalyzed reactions using Michaelis-Menten and other kinetic frameworks.

Metabolomics and Systems Modeling

Metabolomics measures the complete set of small molecules (metabolites) in a cell, tissue, or organism, providing a snapshot of metabolic state. Major analytical platforms include:

• LC-MS (liquid chromatography-mass spectrometry) — versatile, high-sensitivity detection of polar and nonpolar metabolites
• GC-MS (gas chromatography-mass spectrometry) — excellent for volatile and derivatized metabolites
• NMR (nuclear magnetic resonance) spectroscopy — non-destructive, quantitative, but lower sensitivity

Metabolite identification relies on matching experimental spectra to databases like HMDB (Human Metabolome Database) and METLIN.

At the systems level, genome-scale metabolic models (GEMs) reconstruct the complete metabolic network of an organism from its genome sequence. By mapping genes to enzymes to reactions, GEMs predict which metabolic capabilities an organism possesses. The integration of transcriptomics data with metabolic models allows researchers to predict how metabolism responds to genetic perturbations, environmental changes, or disease states — connecting the genome to cellular phenotype.

Molecular fingerprints allow us to quantify how structurally similar two molecules are. The Tanimoto coefficient ranges from 0 (completely different) to 1 (identical). Let’s compare key metabolites: pyruvate (end product of glycolysis), acetyl CoA (entry point to the citric acid cycle), and lactate (fermentation product).

let pyruvate = "CC(=O)C(=O)O"
let lactate = "CC(O)C(=O)O"
let acetyl_coa = "CC(=O)SCCNC(=O)CCNC(=O)C(O)C(C)(C)COP(=O)(O)O"
print("Pyruvate vs Lactate:")
print(Chem.tanimoto(pyruvate, lactate))
print("Pyruvate vs Acetyl CoA:")
print(Chem.tanimoto(pyruvate, acetyl_coa))
print("Pyruvate properties:")
print(Chem.properties(pyruvate))

Metabolic enzymes are among the most highly conserved proteins across life. Their gene sequences, protein structures, and catalytic mechanisms have been preserved over billions of years of evolution, reflecting the fundamental importance of metabolism.

Exercise: Molecular Similarity of Metabolic Intermediates

Pyruvate can be converted to either lactate (via fermentation) or acetyl CoA (via aerobic metabolism). Use the Tanimoto coefficient to determine which product is more structurally similar to pyruvate. Report the name of the more similar molecule.

let pyruvate = "CC(=O)C(=O)O"
let lactate = "CC(O)C(=O)O"
let acetyl_coa = "CC(=O)SCCNC(=O)CCNC(=O)C(O)C(C)(C)COP(=O)(O)O"
let sim_lactate = Chem.tanimoto(pyruvate, lactate)
let sim_acoa = Chem.tanimoto(pyruvate, acetyl_coa)
print("Pyruvate vs Lactate: " + sim_lactate)
print("Pyruvate vs Acetyl CoA: " + sim_acoa)
let answer = "Lactate"
print(answer)

Exercise: Identify a Molecule by Its Properties

One of the molecules below is ethanol (the product of alcoholic fermentation) and the other is acetic acid (vinegar). Use their molecular properties to determine which is which — acetic acid has a carboxyl group, so it should have more hydrogen bond acceptors.

let mol_a = "CCO"
let mol_b = "CC(=O)O"
let props_a = Chem.properties(mol_a)
let props_b = Chem.properties(mol_b)
print("Molecule A:")
print(props_a)
print("Molecule B:")
print(props_b)
let answer = "B"
print(answer)

Knowledge Check

Summary

In this lesson you covered the chemical and energetic foundations of cellular life:

• Water is the solvent of life — hydrogen bonding gives it exceptional properties; cellular pH is tightly regulated near 7.2–7.4
• Four noncovalent interactions (hydrogen bonds, electrostatic, van der Waals, hydrophobic effect) shape macromolecules and mediate molecular recognition
• Carbon compounds form the backbone of biological molecules, with four families of small molecules (sugars, fatty acids, amino acids, nucleotides) serving as building blocks
• Macromolecules (proteins, nucleic acids, polysaccharides, lipids) derive their properties from noncovalent interactions that specify shape and binding
• Enzymes lower activation energy barriers with extraordinary specificity, accelerating reactions by up to 10¹⁴-fold
• Free energy (ΔG) determines reaction spontaneity; cells couple unfavorable reactions to favorable ones, and free-energy changes are additive in sequential reactions
• ATP is the universal energy currency; other activated carriers (NADH, NADPH, FADH&sub2;, Acetyl CoA, SAM) transfer electrons and chemical groups
• Glycolysis produces 2 ATP per glucose in the cytoplasm; fermentation regenerates NAD⁺ without oxygen
• The citric acid cycle and oxidative phosphorylation generate ~30–32 ATP per glucose in mitochondria
• Energy is stored as glycogen and fat; amino acid and nucleotide metabolism links to the nitrogen cycle
• Metabolic pathways are organized and regulated by feedback inhibition and hormonal signals
• Chemical file formats (SMILES, SDF, PDB) and databases (PubChem, ChEMBL, ChEBI) represent and catalog molecular data
• Enzyme classification (EC numbers) and pathway databases (KEGG, MetaCyc, Reactome) organize metabolic knowledge; FBA models predict metabolic fluxes
• Metabolomics (LC-MS, GC-MS, NMR) measures cellular metabolites; genome-scale metabolic models connect genomes to metabolic phenotypes