From Sequences to Structure

1. From Sequences to Structure Illustrations from: C Branden and J Tooze, Introduction to Protein Structure, 2nd ed. Garland Pub. ISBN 0815302703

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. amidenitrogen carbonylcarbon

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

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

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 bondsbetween C=O ofresidue n, andNH of residuen+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 bitabove 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 regionsof 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 averagedover: • Space (many copies) • Time (of the diffractionexperiment)

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 1 N ALA E 1 22.382 47.782 112.975 1.00 24.09 3APR 213 ATOM 2 CA ALA E 1 22.957 47.648 111.613 1.00 22.40 3APR 214 ATOM 3 C ALA E 1 23.572 46.251 111.545 1.00 21.32 3APR 215 ATOM 4 O ALA E 1 23.948 45.688 112.603 1.00 21.54 3APR 216 ATOM 5 CB ALA E 1 23.932 48.787 111.380 1.00 22.79 3APR 217 ATOM 6 N GLY E 2 23.656 45.723 110.336 1.00 19.17 3APR 218 ATOM 7 CA GLY E 2 24.216 44.393 110.087 1.00 17.35 3APR 219 ATOM 8 C GLY E 2 25.653 44.308 110.579 1.00 16.49 3APR 220 ATOM 9 O GLY E 2 26.258 45.296 110.994 1.00 15.35 3APR 221 ATOM 10 N VAL E 3 26.213 43.110 110.521 1.00 16.21 3APR 222 ATOM 11 CA VAL E 3 27.594 42.879 110.975 1.00 16.02 3APR 223 ATOM 12 C VAL E 3 28.569 43.613 110.055 1.00 15.69 3APR 224 ATOM 13 O VAL E 3 28.429 43.444 108.822 1.00 16.43 3APR 225 ATOM 14 CB VAL E 3 27.834 41.363 110.979 1.00 16.66 3APR 226 ATOM 15 CG1 VAL E 3 29.259 41.013 111.404 1.00 17.35 3APR 227 ATOM 16 CG2 VAL E 3 26.811 40.649 111.850 1.00 17.03 3APR 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, 211-222. • 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: • 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) > 0.000075 • The average P(turn) for the four residues > 100 • P(a) < P(turn) > P(b) for the four residues • Accuracy  60-65%