Protein Folding & Biospectroscopy Lecture 4 F14PFB David Robinson

Protein Folding 1.Introduction 2.Protein Structure 3.Interactions 4.Protein Folding Models 5.Biomolecular Modelling 6.Bioinformatics

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

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

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.

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

The Levinthal paradox  Cyrus Levinthal tried to estimate how long it would take a protein to do a random search of conformational space for the native fold.  Many proteins fold in seconds or less: how is this possible?  Imagine a 100-residue protein with three possible conformations per residue. Thus, the number of possible folds = = 5 x  Let us assume that protein can explore new conformations at the same rate that bonds can reorient (10 13 structures/second).  Thus, the time to explore all of conformational space = 5 x /10 13 = 5 x seconds = 1.6 x years >> age of universe  This is known as the Levinthal paradox.

Models of Protein Folding

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

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

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

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

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

Nuclear condensation model N C Expansion of nucleus

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

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)

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.

Levinthal & Landscapes Structure space conformations Sequence space sequences Figure from Englander & co-workers, Proc Natl Acad Sci (2001)

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 (N i ) of level i q = partition function; N total number

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

Toy protein model Red – Hydrophobic (H) Black – Polar (P) HP model 1 conformation: E = -  4 conformations: E = 0

Partition function – toy model Q = 4 exp(-E 0 /kT) + exp(-E 1 /kT) Let E 0 = 0 and E 1 = - , then Q = 4 + exp(  /kT) Prob (Native state), P = q Native /Q P = exp(  /kT)/{4 + exp(  /kT)} E HH = - 

TmTm 0 T 1 Prob (Nat) UNFOLDED  G = -kT {Prob(Nat)/Prob(Unf)}

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