1. From Sequences to Structure
Illustrations from: C Branden and J Tooze, Introduction to Protein Structure, 2nd ed. Garland Pub. ISBN

2. Protein Functions Mechanoenzymes: myosin, actin
Rhodopsin: allows vision
Globins: transport oxygen
Antibodies: immune system
Enzymes: pepsin, renin, carboxypeptidase A
Receptors: transmembrane signaling
Vitelogenin: molecular velcro
And hundreds of thousands more…

3. Proteins are Chains of Amino Acids
Polymer – a molecule composed of repeating units

4. The Peptide Bond Dehydration synthesis
Repeating backbone: N–C –C –N–C –C
Convention – start at amino terminus and proceed to carboxy terminus O O

5. Peptidyl polymers
A few amino acids in a chain are called a polypeptide. A protein is usually composed of 50 to 400+ amino acids.
Since part of the amino acid is lost during dehydration synthesis, we call the units of a protein amino acid residues.
amide nitrogen
carbonyl carbon

6. Side Chain Properties
Recall that the electronegativity of carbon is at about the middle of the scale for light elements
Carbon does not make hydrogen bonds with water easily – hydrophobic
O and N are generally more likely than C to h-bond to water – hydrophilic
We group the amino acids into three general groups:
Hydrophobic
Charged (positive/basic & negative/acidic)
Polar

7. The Hydrophobic Amino Acids
Proline severely
limits allowable
conformations!

8. The Charged Amino Acids

9. The Polar Amino Acids

10. More Polar Amino Acids
And then there’s…

11. Planarity of the Peptide Bond

12. OCCBIO 2006 – Fundamental Bioinformatics
Phi and psi
 =  = 180° is extended conformation
 : C to N–H
 : C=O to C
OCCBIO 2006 – Fundamental Bioinformatics

13. The Ramachandran Plot
Observed
(non-glycine)
Observed
(glycine)
Calculated
G. N. Ramachandran – first calculations of sterically allowed regions of phi and psi
Note the structural importance of glycine

14. Primary and Secondary Structure
Primary structure = the linear sequence of amino acids comprising a protein: AGVGTVPMTAYGNDIQYYGQVT…
Secondary structure
Regular patterns of hydrogen bonding in proteins result in two patterns that emerge in nearly every protein structure known: the -helix and the -sheet
The location of direction of these periodic, repeating structures is known as the secondary structure of the protein

15. The alpha Helix
    60°

16. Properties of the alpha helix
    60°
Hydrogen bonds between C=O of residue n, and NH of residue n+4
3.6 residues/turn
1.5 Å/residue rise
100°/residue turn

17. Properties of -helices
4 – 40+ residues in length
Often amphipathic or “dual-natured”
Half hydrophobic and half hydrophilic
Mostly when surface-exposed
If we examine many -helices, we find trends…
Helix formers: Ala, Glu, Leu, Met
Helix breakers: Pro, Gly, Tyr, Ser

18. The beta Strand (and Sheet)
   135°   +135°

19. Properties of beta sheets
Formed of stretches of 5-10 residues in extended conformation
Pleated – each C a bit above or below the previous
Parallel/aniparallel, contiguous/non-contiguous
OCCBIO 2006 – Fundamental Bioinformatics

20. Parallel and anti-parallel -sheets
Anti-parallel is slightly energetically favored
Anti-parallel
Parallel

21. Turns and Loops
Secondary structure elements are connected by regions of turns and loops
Turns – short regions of non-, non- conformation
Loops – larger stretches with no secondary structure. Often disordered.
“Random coil”
Sequences vary much more than secondary structure regions

22. Levels of Protein Structure
Secondary structure elements combine to form tertiary structure
Quaternary structure occurs in multienzyme complexes
Many proteins are active only as homodimers, homotetramers, etc.

23. Disulfide Bonds
Two cyteines in close proximity will form a covalent bond
Disulfide bond, disulfide bridge, or dicysteine bond.
Significantly stabilizes tertiary structure.

24. Protein Structure Examples

25. Determining Protein Structure
There are ~ 100,000 distinct proteins in the human proteome.
3D structures have been determined for 14,000 proteins, from all organisms
Includes duplicates with different ligands bound, etc.
Coordinates are determined by X-ray crystallography

26. X-Ray diffraction Image is averaged over: Space (many copies)
Time (of the diffraction experiment)

27. Electron Density Maps
Resolution is dependent on the quality/regularity of the crystal
R-factor is a measure of “leftover” electron density
Solvent fitting
Refinement

28. The Protein Data Bank http://www.rcsb.org/pdb/
ATOM N ALA E APR 213
ATOM CA ALA E APR 214
ATOM C ALA E APR 215
ATOM O ALA E APR 216
ATOM CB ALA E APR 217
ATOM N GLY E APR 218
ATOM CA GLY E APR 219
ATOM C GLY E APR 220
ATOM O GLY E APR 221
ATOM N VAL E APR 222
ATOM CA VAL E APR 223
ATOM C VAL E APR 224
ATOM O VAL E APR 225
ATOM CB VAL E APR 226
ATOM CG1 VAL E APR 227
ATOM CG2 VAL E APR 228

29. Views of a Protein
Wireframe
Ball and stick

30. Views of a Protein Spacefill Cartoon CPK colors Carbon = green, black
Nitrogen = blue
Oxygen = red
Sulfur = yellow
Hydrogen = white

31. The Protein Folding Problem
Central question of molecular biology: “Given a particular sequence of amino acid residues (primary structure), what will the tertiary/quaternary structure of the resulting protein be?”
Input: AAVIKYGCAL… Output: 11, 22… = backbone conformation: (no side chains yet)

32. Forces Driving Protein Folding
It is believed that hydrophobic collapse is a key driving force for protein folding
Hydrophobic core
Polar surface interacting with solvent
Minimum volume (no cavities)
Disulfide bond formation stabilizes
Hydrogen bonds
Polar and electrostatic interactions

33. Folding Help Proteins are, in fact, only marginally stable
Native state is typically only 5 to 10 kcal/mole more stable than the unfolded form
Many proteins help in folding
Protein disulfide isomerase – catalyzes shuffling of disulfide bonds
Chaperones – break up aggregates and (in theory) unfold misfolded proteins

34. The Hydrophobic Core
Hemoglobin A is the protein in red blood cells (erythrocytes) responsible for binding oxygen.
The mutation E6V in the  chain places a hydrophobic Val on the surface of hemoglobin
The resulting “sticky patch” causes hemoglobin S to agglutinate (stick together) and form fibers which deform the red blood cell and do not carry oxygen efficiently
Sickle cell anemia was the first identified molecular disease

35. Sickle Cell Anemia
Sequestering hydrophobic residues in the protein core protects proteins from hydrophobic agglutination.

36. Computational Problems in Protein Folding
Two key questions:
Evaluation – how can we tell a correctly-folded protein from an incorrectly folded protein?
H-bonds, electrostatics, hydrophobic effect, etc.
Derive a function, see how well it does on “real” proteins
Optimization – once we get an evaluation function, can we optimize it?
Simulated annealing/monte carlo EC
Heuristics

37. Fold Optimization Simple lattice models (HP-models)
Two types of residues: hydrophobic and polar
2-D or 3-D lattice
The only force is hydrophobic collapse
Score = number of HH contacts

38. Scoring Lattice Models
H/P model scoring: count noncovalent hydrophobic interactions.
Sometimes:
Penalize for buried polar or surface hydrophobic residues

39. What can we do with lattice models?
For smaller polypeptides, exhaustive search can be used
Looking at the “best” fold, even in such a simple model, can teach us interesting things about the protein folding process
For larger chains, other optimization and search methods must be used
Greedy, branch and bound
Evolutionary computing, simulated annealing
Graph theoretical methods

40. Learning from Lattice Models
The “hydrophobic zipper” effect:
Ken Dill ~ 1997

41. Representing a lattice model
Absolute directions
UURRDLDRRU
Relative directions
LFRFRRLLFFL
Advantage, we can’t have UD or RL in absolute
Only three directions: LRF
What about bumps? LFRRR
Bad score
Use a better representation

42. Preference-order representation
Each position has two “preferences”
If it can’t have either of the two, it will take the “least favorite” path if possible
Example: {LR},{FL},{RL}, {FR},{RL},{RL},{FR},{RF}
Can still cause bumps: {LF},{FR},{RL},{FL}, {RL},{FL},{RF},{RL}, {FL}

43. More Realistic Models Higher resolution lattices (45° lattice, etc.)
Off-lattice models
Local moves
Optimization/search methods and / representations
Greedy search
Branch and bound
EC, Monte Carlo, simulated annealing, etc.

44. The Other Half of the Picture
Now that we have a more realistic off-lattice model, we need a better energy function to evaluate a conformation (fold).
Theoretical force field:
G = Gvan der Waals + Gh-bonds + Gsolvent + Gcoulomb
Empirical force fields
Start with a database
Look at neighboring residues – similar to known protein folds?

45. Threading: Fold recognition
Given:
Sequence: IVACIVSTEYDVMKAAR…
A database of molecular coordinates
Map the sequence onto each fold
Evaluate
Objective 1: improve scoring function
Objective 2: folding

46. Secondary Structure Prediction
AGVGTVPMTAYGNDIQYYGQVT…
A-VGIVPM-AYGQDIQY-GQVT…
AG-GIIP--AYGNELQ--GQVT…
AGVCTVPMTA---ELQYYG--T…
AGVGTVPMTAYGNDIQYYGQVT…
----hhhHHHHHHhhh--eeEE…

47. Secondary Structure Prediction
Easier than folding
Current algorithms can prediction secondary structure with 70-80% accuracy
Chou, P.Y. & Fasman, G.D. (1974). Biochemistry, 13,
Based on frequencies of occurrence of residues in helices and sheets
PhD – Neural network based
Uses a multiple sequence alignment
Rost & Sander, Proteins, 1994 , 19, 55-72

48. Chou-Fasman Parameters

49. Chou-Fasman Algorithm
Identify -helices
4 out of 6 contiguous amino acids that have P(a) > 100
Extend the region until 4 amino acids with P(a) < 100 found
Compute P(a) and P(b); If the region is >5 residues and P(a) > P(b) identify as a helix
Repeat for -sheets [use P(b)]
If an  and a  region overlap, the overlapping region is predicted according to P(a) and P(b)

50. Chou-Fasman, cont’d Identify hairpin turns: Accuracy  60-65%
P(t) = f(i) of the residue  f(i+1) of the next residue  f(i+2) of the following residue  f(i+3) of the residue at position (i+3)
Predict a hairpin turn starting at positions where:
P(t) >
The average P(turn) for the four residues > 100
P(a) < P(turn) > P(b) for the four residues
Accuracy  60-65%

51. Chou-Fasman Example CAENKLDHVRGPTCILFMTWYNDGP
CAENKL – Potential helix (!C and !N)
Residues with P(a) < 100: RNCGPSTY
Extend: When we reach RGPT, we must stop
CAENKLDHV: P(a) = 972, P(b) = 843
Declare alpha helix
Identifying a hairpin turn
VRGP: P(t) =
Average P(turn) =
Avg P(a) = 79.5, Avg P(b) = 98.25

52. Lots More to Come Microarray analysis Mass Spectrometry
Interactions/ Knockouts
Synthetic Lethality
RPPA
.....