1. Intro to Bioinformatics Computational Approaches to Receptor Structure Prediction Uğur Sezerman Biological Sciences and Bioengineering Program Sabancı University, Istanbul

2. Intro to BioinformaticsProtein Folding2 Determining Protein Structure  There are O(100,000) distinct proteins in the human proteome.  3D structures have been determined for over 60,000 proteins, from all organisms Includes duplicates with different ligands bound, etc. X-ray crystallography or NMR  Coordinates are determined by X-ray crystallography or NMR

3. Intro to BioinformaticsProtein Folding3 X-Ray Crystallography ~0.5mm The crystal is a mosaic of millions of copies of the protein. As much as 70% is solvent (water)! May take months (and a “green” thumb) to grow.

4. Intro to BioinformaticsProtein Folding4 X-Ray diffraction  Image is averaged over: Space (many copies) Time (of the diffraction experiment)

5. Intro to BioinformaticsProtein Folding5 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

6. Intro to BioinformaticsProtein Folding6 The Protein Data Bank 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  http://www.rcsb.org/pdb/

7. Intro to BioinformaticsProtein Folding7 A Peek at Protein Function  Serine proteases – cleave other proteins Catalytic Triad: ASP, HIS, SER

8. Intro to BioinformaticsProtein Folding8 Cleaving the peptide bond

9. Intro to BioinformaticsProtein Folding9 Three Serine Proteases  Chymotrypsin – Cleaves the peptide bond on the carboxyl side of aromatic (ring) residues: Trp, Phe, Tyr; and large hydrophobic residues: Met.  Trypsin – Cleaves after Lys (K) or Arg (R) Positive charge  Elastase – Cleaves after small residues: Gly, Ala, Ser, Cys

10. Intro to BioinformaticsProtein Folding10 Specificity Binding Pocket

11. Intro to BioinformaticsProtein Folding11 Protein Folding – Biological perspective  “Central dogma”: Sequence specifies structure  Denature – to “unfold” a protein back to random coil configuration  -mercaptoethanol – breaks disulfide bonds Urea or guanidine hydrochloride – denaturant Also heat or pH  Anfinsen’s experiments Denatured ribonuclease Spontaneously regained enzymatic activity Evidence that it re-folded to native conformation

12. Intro to BioinformaticsProtein Folding12 PROTEIN FOLDING PROBLEM  STARTING FROM AMINO ACID SEQUENCE FINDING THE STRUCTURE OF PROTEINS IS CALLED THE PROTEIN FOLDING PROBLEM

13. Intro to BioinformaticsProtein Folding13 The Protein Folding Problem Given a particular sequence of amino acid residues (primary structure), what will the tertiary/quaternary structure of the resulting protein be?”  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)

14. Intro to BioinformaticsProtein Folding14 Folding intermediates  Levinthal’s paradox – Consider a 100 residue protein. If each residue can take only 3x3=9 positions, there are 9 100 possible conformations.  Folding must proceed by progressive stabilization of intermediates Molten globules – most secondary structure formed, but much less compact than “native” conformation.

15. Intro to BioinformaticsProtein Folding15 Protein Packing occurs in the cytosol (~60% bulk water, ~40% water of hydration) involves interaction between secondary structure elements and solvent may be promoted by chaperones, membrane proteins tumbles into molten globule states overall entropy loss is small enough so enthalpy determines sign of  E, which decreases (loss in entropy from packing counteracted by gain from desolvation and reorganization of water, i.e. hydrophobic effect) yields tertiary structure

16. Intro to BioinformaticsProtein Folding16 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

17. Intro to BioinformaticsProtein Folding17 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

18. Intro to BioinformaticsProtein Folding18 Secondary Structure  non-linear  3 dimensional  localized to regions of an amino acid chain  formed and stabilized by hydrogen bonding, electrostatic and van der Waals interactions

19. Intro to BioinformaticsProtein Folding19 Common motifs

20. Intro to BioinformaticsProtein Folding20 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

21. Intro to BioinformaticsProtein Folding21 Sickle Cell Anemia Sequestering hydrophobic residues in the protein core protects proteins from hydrophobic agglutination.

22. Intro to BioinformaticsProtein Folding22 Computational Approaches  Ab initio methods  Threading  Comperative Modelling  Fragment Assembly

23. Intro to BioinformaticsProtein Folding23 Why is ab-initio prediction hard?

24. Intro to BioinformaticsProtein Folding24 conformation energy Ab-initio protein structure prediction as an optimization problem 2.Solve the computational problem of finding an optimal structure. 3. 1.Define a function that map protein structures to some quality measure.

25. Intro to BioinformaticsProtein Folding25 A dream function Has a clear minimum in the native structure. Has a clear path towards the minimum. Global optimization algorithm should find the native structure. Chen Keasar BGU

26. Intro to BioinformaticsProtein Folding26 An approximate function Easier to design and compute.  Native structure not always the global minimum.  Global optimization methods do not converge. Many alternative models (decoys) should be generated. Chen Keasar BGU

27. Intro to BioinformaticsProtein Folding27 An approximate function Easier to design and compute.  Native structure not always the global minimum.  Global optimization methods do not converge. Many alternative models (decoys) should be generated.  No clear way of choosing among them. Decoy set Chen Keasar BGU

28. Intro to BioinformaticsProtein Folding28 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

29. Intro to BioinformaticsProtein Folding29  H/P model scoring: count noncovalent hydrophobic interactions.  Sometimes: Penalize for buried polar or surface hydrophobic residues Scoring Lattice Models

30. Intro to BioinformaticsProtein Folding30 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

31. Intro to BioinformaticsProtein Folding31  The “hydrophobic zipper” effect: Learning from Lattice Models Ken Dill ~ 1997

32. Intro to BioinformaticsProtein Folding32 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

33. Intro to BioinformaticsProtein Folding33 Protein Fold Families  CATH website www.cathdb.info

34. Intro to BioinformaticsProtein Folding34 Secondary Structure Prediction AGVGTVPMTAYGNDIQYYGQVT… A-VGIVPM-AYGQDIQY-GQVT… AG-GIIP--AYGNELQ--GQVT… AGVCTVPMTA---ELQYYG--T… AGVGTVPMTAYGNDIQYYGQVT… ----hhhHHHHHHhhh--eeEE…

35. Intro to BioinformaticsProtein Folding35 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

36. Intro to BioinformaticsProtein Folding36 Chou-Fasman Parameters

37. Intro to BioinformaticsProtein Folding37 HOMOLOGY MODELLING  Using database search algorithms find the sequence with known structure that best matches the query sequence  Assign the structure of the core regions obtained from the structure database to the query sequence  Find the structure of the intervening loops using loop closure algorithms

38. Intro to BioinformaticsProtein Folding38 Homology Modeling: How it works oFind template oAlign target sequence with template oGenerate model: - add loops - add sidechains oRefine model

39. Intro to BioinformaticsProtein Folding39 Prediction of Protein Structures  Examples – a few good examples actualpredicted actual predicted

40. Intro to BioinformaticsProtein Folding40 Prediction of Protein Structures  Not so good example

41. Intro to BioinformaticsProtein Folding41 1esr

42. Intro to BioinformaticsProtein Folding42

43. Intro to BioinformaticsProtein Folding43

44. Intro to BioinformaticsProtein Folding44 How can we predict protein structures? Are we lucky? yes A V C W K A G K C AC WKA VGKC C + A V C W K A G K C C homology no ab initio a bit fold recognition

45. Intro to BioinformaticsProtein Folding45 HOMOLOGY MODELLING  Using database search algorithms find the sequence with known structure that best matches the query sequence  Assign the structure of the core regions obtained from the structure database to the query sequence  Find the structure of the intervening loops using loop closure algorithms

46. Intro to BioinformaticsProtein Folding46 Homology Modeling: How it works oFind template oAlign target sequence with template oGenerate model: - add loops - add sidechains oRefine model

47. Intro to BioinformaticsProtein Folding47 Prediction of Protein Structures  Examples – a few good examples actualpredicted actual predicted

48. Intro to BioinformaticsProtein Folding48 Prediction of Protein Structures  Not so good example

49. Intro to BioinformaticsProtein Folding49 1esr

50. Intro to BioinformaticsProtein Folding50

51. Intro to BioinformaticsProtein Folding51

52. Intro to BioinformaticsProtein Folding52 G-protein coupled receptors (GPCRs) G-protein coupled receptors (GPCRs)  Vital protein bundles with versatile functions.  Play a key role in cellular signaling, regulation of basic physiological processes by interacting with more than 50% of prescription drugs.  Therefore excellent potential therapeutic target for drug design and the focus of current pharmaceutical research.

53. Intro to BioinformaticsProtein Folding53 GPCR Functional Classification Problem  Although thousands of GPCR sequences are known, the crystal structure solved only for one GPCR sequence at medium resolution to date.  For many of them, the activating ligand is unknown.  Functional classification methods for automated characterization of such GPCRs is imperative.  Not suitable for homology modelling but hybrid methods may work. A Rayan J. Mol. Modelling (2010) p 183-191

54. Intro to BioinformaticsProtein Folding54 Schematic overview of the MHC-I antigen processing and presentation pathway

55. Intro to BioinformaticsProtein Folding55 Pathway and MHC Molecule  Cytotoxic T-cells recognize antigen peptides (8-10 residues) bound to a MHC class I molecule on the cell surface.

56. Intro to BioinformaticsProtein Folding56 MHC-I bound epitope is scanned by T-cell receptor