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Protein Folding & Biospectroscopy Lecture 4

Protein Folding & Biospectroscopy Lecture 4. F14PFB David Robinson. Protein Folding. Introduction Protein Structure Interactions Protein Folding Models Biomolecular Modelling Bioinformatics. Protein Folding.

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Protein Folding & Biospectroscopy Lecture 4

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  1. Protein Folding & BiospectroscopyLecture 4 F14PFB David Robinson

  2. Protein Folding • Introduction • Protein Structure • Interactions • Protein Folding Models • Biomolecular Modelling • Bioinformatics

  3. Protein Folding • Protein folding considers the question of how the process of protein folding occurs, i. e. unfolded  native state. • This very challenging problem has been described as the second half of the genetic code, and as the three-dimensional code, as opposed to the one-dimensional code involved in nucleotide/amino acid sequence. • Importance: • Predict 3D structure from primary sequence • Avoid misfolding related to human diseases • Design proteins with novel functions

  4. Anfinsen Experiment • Denaturation of ribonuclease A (4 disulfide bonds), with 8 M Urea containing b-mercaptoethanol, leads to random coil and no activity

  5. Anfinsen Experiment • After renaturation, the refolded protein has native activity, despite 105 ways to renature the protein. • Conclusion: All the information necessary for folding into its native structure is contained in the amino acid sequence of the protein.

  6. Anfinsen Experiment • Remove b-mercaptoethanol only, oxidation of the sulfhydryl group, then remove urea → scrambled protein, no activity • Further addition of trace amounts of b-mercaptoethanol converts the scrambled form into native form. • Conclusion: The native form of a protein has the thermodynamic-ally most stable structure.

  7. The Levinthal paradox • Many proteins fold in seconds or less: how is this possible? • Cyrus Levinthal tried to estimate how long it would take a protein to do a random search of conformational space for the native fold. • Imagine a 100-residue protein with three possible conformations per residue. Thus, the number of possible folds = 3100 = 5 x 1047. • Let us assume that protein can explore new conformations at the same rate that bonds can reorient (1013 structures/second). • Thus, the time to explore all of conformational space = 5 x 1047/1013 = 5 x 1034 seconds = 1.6 x 1027 years >> age of universe • This is known as the Levinthal paradox.

  8. Models of Protein Folding

  9. C N Framework model of protein folding Supported by experimental observation of rapid formation of secondary structure during protein folding process

  10. C N Framework model of protein folding Formation of individual secondary structure elements

  11. N C Framework model of protein folding Coalescence and rearrangement of individual secondary structure elements

  12. C N Nuclear condensation model Supported by protein engineering studies and various theoretical calculations

  13. C N Nuclear condensation model Formation of a nucleus of hydrophobic residues

  14. N C Nuclear condensation model Expansion of nucleus

  15. Framework and Condensation models are extremes on a continuum Framework (or diffusion-collision): 2º structure forms independently and “dock” to form 3º structure Nucleation condensation: Concerted consolidation of 2º and 3º structure as nucleus expands Condensation when 2º structure inherently unstable in absence of 3º Structure. Framework becomes more probable as 2º elements become more stable

  16. Molten Globule State • Collapsed, with native-like 2º structure (far UV CD) • Weak or transient side-chain interactions (near UV CD) • Binds hydrophobic dyes • Many proteins form molten globules at low pH • Model for early stages of protein folding (hydrophobic collapse)

  17. The Folding Funnel • A new view of protein folding suggested that there is no single route, but a large ensemble of structures follow a many dimensional funnel to its native structure. • Progress from the top to the bottom of the funnel is accompanied by an increase in the native-like structure as folding proceeds.

  18. Levinthal & Landscapes • Structure space 3100 conformations • Sequence space 20100 sequences Figure from Englander & co-workers, Proc Natl Acad Sci98 19104 (2001)

  19. Boltzmann distribution

  20. Boltzmann distribution Distribution of conformations over the available energy levels. Which is the most probable? Boltzmann distribution is the outcome of blind chance occupation of energy levels, subject to the requirement that the total energy has a particular value Occupancy (Ni) of level i q = partition function; N total number

  21. Partition function q is the sum of Boltzmann factors Reflects the number of thermally accessible states at the temperature of interest

  22. Toy protein model Red – Hydrophobic (H) Black – Polar (P) HP model 1 conformation: E = -e 4 conformations: E = 0 All possible random walks

  23. Partition function – toy model Q = 4 exp(-E0/kT) + exp(-E1/kT) Let E0 = 0 and E1 = -e, then Q = 4 + exp(e/kT) Prob (Native state), P = qNative/Q P = exp(e/kT)/{4 + exp(e/kT)} EHH = -e

  24. DG = -kT {Prob(Nat)/Prob(Unf)} 1 Prob (Nat) UNFOLDED 0 Tm T

  25. Flat landscape (Levinthal paradox) Tunnel landscape (discrete pathways) Realistic landscape (“folding funnel”) Folding landscapes and the Levinthal paradox

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