BIOSCI 106 Lecture 5

Prions, Folding, and Energetics (quick recap)

• Prions and related diseases (Kuru, Creutzfeldt–Jakob) explored as infectious proteins that can propagate themselves.
  • Prions are indestructible and propagate via a chain reaction of converting normal protein copies.
• Common feature of amyloid-related diseases (prions, Parkinson’s, Alzheimer’s): a structural shift from alpha-helix to beta-sheet in the misfolded protein.
  • Beta-sheets can stack into large fibrils, stabilizing via extensive hydrogen bonding and other weak interactions.
• Protein folding basics:
  • Primary sequence (amino acid order) determines the protein’s three-dimensional folded form and function.
  • Hydrophobic vs. hydrophilic residues strongly influence folding; hydrophobic effects drive burying nonpolar groups away from water.
  • Folding is spontaneous and can be described by Gibbs energy concepts.
• Energy concepts in folding:
  • Energy landscapes depict unfolding/high-energy states at the top and folded/low-energy states at the bottom.
  • Gibbs free energy change for folding: $\Delta G = \Delta H - T\Delta S$
  • If $\Delta G_{folding} < 0$, folding is spontaneous.
  • Some reactions are not spontaneous but can be made to proceed by coupling to other reactions (energetic coupling).
• Three parts of folding (occurring together):
  1) Conformational entropy: many states vs. one state; contributes to higher energy, an entropy cost.
  2) Hydrophobic effect: burial of nonpolar groups lowers energy via water-enabled effects.
  3) Formation of weak interactions (hydrogen bonds, ionic interactions, van der Waals, hydrophobic contacts) that further lower energy.
• Misfolding and disease mechanism (connecting to biology):
  • Misfolded proteins can expose extensive intramolecular H-bonding in beta sheets and form intermolecular H-bonds between many copies, creating a massively stabilizing, low-energy aggregate.
  • This extreme stability makes misfolded proteins hard to degrade and linked to disease.

Molecular Recognition: Glucagon and its Receptor (conceptual framework)

• Core idea: molecular recognition relies on a combination of weak interactions summed across many contact points to achieve specific binding.
• Glucagon and receptor as a model:
  • Glucagon is an alpha-helix with exposed side chains; the glucagon receptor recognizes glucagon specifically and not other molecules.
  • Blood sugar regulation context: pancreas secretes insulin to lower blood glucose and glucagon to raise it; glucagon binding to its receptor signals liver glycogen breakdown to release glucose.
  • Binding strength arises from the collective contributions of all 29 glucagon residues.
• Learning objective (module-wide): be able to infer interaction tendencies by looking at molecule properties and functional groups; apply to glucagon-receptor binding.
• Practical demo analogy (banana demo):
  • Glucagon as a banana-like cylinder; receptor as a grabbing hand.
  • Recognition relies on size and shape (fit) and texture (presence of specific weak interactions) to decide if binding will occur.
  • The demonstration emphasizes that multiple subtle interactions (not just a single feature) determine binding strength and specificity.
• The concept of a binding pocket:
  • Size (length) and diameter are critical; if glucagon (or its side chains) are too long/short or the geometry doesn’t fit, binding is weak or non-existent.
  • The pocket must be complementary to the shape and capable of forming specific weak interactions with side chains.
  • Pocket chemistry includes hydrophobic contacts, hydrogen bonds, ionic interactions, and pi-related interactions (discussed below).
• Textural cue vs. sensory limitation:
  • Proteins do not have eyes or smell; recognition is achieved via contact and interaction patterns (i.e., “texture” corresponds to pattern of contacts).
• The balance of interactions:
  • Together, many moderately strong (not overly strong) interactions yield tight enough binding to activate signaling but can be dissociated when needed.
• Scale and visualization:
  • Demonstrations used real glucagon and receptor structures to illustrate interactions at the atomic level.
  • Scale comparison: a single glucagon receptor is tiny on its own; large multi-protein assemblies (e.g., rat liver vault) can be enormous, illustrating how modular protein interactions occur inside cells.
• Structural biology methods mentioned:
  • X-ray crystallography used to determine structures; raw data resemble a wireframe (blue “chicken wire”) that models fit into to generate a refined 3D structure.
  • The goal is to predict and validate interactions observed in nature, enabling understanding of binding specificity.

Key Residue Interactions in Glucagon–Receptor Binding (positions 18, 6, 9, 15)

• Overview: Glucagon has 29 residues; specific residues form pockets and interact via various weak forces that sum to strong overall binding.

• Position 18: Arginine (Arg, positive charge)

  • End group: Arg side chain with a positive charge (NH2, NH2).
  • Interactions observed/expected:
  • Hydrogen bonding with a nearby carbonyl group (backbone) at about ~2.8 Å; described as a charged dipole or hydrogen bond depending on naming convention.
  • Proximity to a tryptophan (aromatic) side chain enabling cation-π interactions; Arg cation above an aromatic π-system.
  • Concept learned: cation-π stacking is a named weak interaction where a positively charged group interacts with π orbitals of an aromatic ring.
  • Note on terminology: interactions can be labeled in multiple valid ways (e.g., hydrogen bond vs. charge–dipole). The important point is that such interactions contribute to binding energy.

• Position 6: Phenylalanine (Phe, hydrophobic/aromatic)

  • Local pocket: hydrophobic pocket formed by nearby residues (e.g., Tyr and other Phe/Leu residues) that complement the hydrophobic surface.
  • Interactions observed:
  • Hydrophobic contacts with surrounding nonpolar side chains (Leu, another Phe, Tyr).
  • Pi–Pi stacking between adjacent aromatic rings (Phe–Phe), analogous to DNA base stacking.
  • Van der Waals (mutually induced dipoles) interactions in the hydrophobic core.
  • Visualization: the pocket depth/shape matches the phenylalanine side-chain geometry, enabling a snug fit.

• Position 9: Glutamate (Glu) – negatively charged

  • Interaction partner: Arginine (Arg) with a positive charge.
  • Interactions observed/expected:
  • Strong electrostatic/ionic interaction (charge–charge) between Glu carboxylate and Arg side chain.
  • Also forms a hydrogen bond geometry via backbone carbonyl–NH or side-chain features; discussion highlighted potential hydrogen-bond character depending on geometry.
  • Concept learned: the strongest single interactions in this context are often full charge interactions (ionic) and associated hydrogen-bonding geometries when aligned properly.
  • Sum effect: contributes significantly to overall binding affinity at this site.

• Position 15: Aspartate (Asp) – negatively charged

  • Interaction partner: backbone –NH groups (not just side chain carboxylates) and nearby positive features.
  • Interactions observed/expected:
  • Charge–dipole interaction with the backbone carbonyl or nearby positive charges; can be described as a charge–dipole or hydrogen bond depending on geometry.
  • Also serves in the context of an oxyanion site concept where negatively charged oxygens are stabilized by adjacent amide NH groups that can donate hydrogen bonds or partial positive charges.
  • Concept learned: backbone interactions can contribute significantly, and oxyanion pockets are common in enzymes and binding sites, facilitating stabilization of negative charges.

• Oxyanion pocket concept (relevant to enzymes too):

  • A site where a negatively charged oxygen (oxyanion) is stabilized by multiple nearby NH groups bearing partial positive charges.
  • Relevance to enzymes: similar pocket features are often used in catalytic sites for stabilization and orientation of substrates.

Specific Interaction Names and Concepts Introduced

• Cation–π stacking: positive charge interacts with π-electron cloud of an aromatic ring (e.g., Arg near a Trp ring).
• Anion–π stacking: negative charge interacting with π orbitals; possible in other contexts.
• Pi–Pi stacking: interactions between aromatic rings (aromatic–aromatic), common in protein cores and nucleic acid bases.
• Van der Waals (VdW) interactions: nonpolar, distance-dependent forces contributing to tight packing.
• Hydrogen bonds: directional interactions between donors (e.g., NH) and acceptors (e.g., carbonyl O); can be described as charge–dipole or pure hydrogen bonds depending on the context.
• Charge–dipole interactions: between a full/partial charge and a dipole moment (e.g., backbone carbonyl partial negative and nearby charged side-chain).

Experimental and Conceptual Takeaways for Drug Design and Biochemistry

• Drug-like molecules binding to protein pockets rely on cumulative weak interactions; the strongest binding often requires a good fit in both shape/size and interaction potential.
• Practical caution: in modeling, one should consider that different interaction naming conventions may describe the same physical contact; the key is the interaction energy and geometry.
• The glucagon–receptor example demonstrates how specific residues create a binding pocket tuned to glucagon’s geometry and side-chain chemistry, enabling selective binding.
• The demonstration also underscores the role of noncovalent interactions in signaling—binding triggers conformational changes that propagate through the transmembrane receptor to initiate intracellular signaling.

Structural and Scale Context (visualization and methods)

• Structural model basics:
  • Glucagon: alpha helix; receptor recognizes the helical shape and surface chemistry.
  • Receptor: transmembrane protein with an extracellular binding pocket and intracellular signaling machinery; binding causes intracellular conformational changes.
• Scale comparison:
  • Receptor on the cell surface vs. large complexes like the rat liver vault (roughly 72 copies of the same protein forming a mega-assembly) to illustrate scale differences in molecular biology.
  • Rat liver vault demonstration used to convey how proteins assemble into large complexes and relate to transport through cellular structures (e.g., nuclear pore).
• Experimental workflow:
  • X-ray crystallography: raw data visualized as a blue wireframe (chicken wire) which is refined into a detailed molecular model.
  • The process is iterative: combine known protein chemistry with experimental data to fit models into electron density maps and refine structures.

Additional Practical Notes from the Lecture

• The instructor’s engaging demonstration included audience participation and a banana analogy to illustrate binding concepts quickly and memorably.
• The lecture included on-the-fly drawing exercises to reinforce residue–pocket interactions and the role of shape/size in recognition.
• Acknowledgement of the importance of scenarios where binding must be strong enough to trigger signaling but not so strong that it cannot be turned off; this balance is achieved by distributing many moderate interactions rather than relying on a single strong one.

Quick Glossary (Key Terms to Know)

• Alpha helix, beta sheet, amyloid, prion, Q:
  • Alpha helix/beta sheet: regular secondary structures; misfolding to beta-sheet forms amyloid/fibrils.
• Hydrophobic effect: drive folding by burying nonpolar residues away from water.
• Conformational entropy: loss of conformational freedom upon folding; a factor in the overall energy balance.
• Gibbs free energy, $\Delta G = \Delta H - T\Delta S$: criterion for spontaneity of folding/binding.
• Pi orbitals, pi stacking, cation–pi stacking, anion–pi stacking: interactions involving aromatic rings and charges.
• Oxyanion hole/site: pocket stabilizing negative charges via multiple NH donors.
• Van der Waals interactions: noncovalent, distance-dependent attractive forces.
• Binding pocket: the physical site on a receptor that accommodates a ligand; size, shape, and chemistry determine affinity and specificity.

Summary Takeaways

• Protein folding and misfolding are governed by a balance between entropy and multiple weak, noncovalent interactions that cumulatively stabilize a folded state.
• Glucagon–receptor binding is an exemplary case of molecular recognition built from many small contributions across 29 residues, not a single key interaction.
• Understanding the types and geometry of weak interactions (hydrogen bonds, ionic, hydrophobic, van der Waals, pi interactions) is crucial for predicting binding and for drug design.
• Real-world techniques (e.g., X-ray crystallography) and conceptual models (binding pockets, shape/size complementarity) underpin how biochemists visualize and predict biomolecular interactions.