1. CS790 – BioinformaticsProtein Structure and Function1 Disulfide Bonds  Two cyteines in close proximity will form a covalent bond  Disulfide bond, disulfide bridge, or dicysteine bond.  Significantly stabilizes tertiary structure.

2. CS790 – BioinformaticsProtein Structure and Function2 Determining Protein Structure  There are O(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. X-ray crystallography  Coordinates are determined by X-ray crystallography

3. CS790 – BioinformaticsProtein Structure and Function3 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. CS790 – BioinformaticsProtein Structure and Function4 X-Ray diffraction  Image is averaged over: Space (many copies) Time (of the diffraction experiment)

5. CS790 – BioinformaticsProtein Structure and Function5 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. CS790 – BioinformaticsProtein Structure and Function6 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. CS790 – BioinformaticsProtein Structure and Function7 Practical Assignment #1  Get entry 2APR from the PDB. This is an Aspartic Protease structure.  Download Rasmol or Raswin and load 2APR.  Render the molecule as sticks with CPK coloring and print the image.  Render the molecule as either a ribbons or cartoon image, showing secondary structure.  Rotate the molecule to show at least one beta sheet and one alpha helix. Print this image and turn it in as well.

8. CS790 – BioinformaticsProtein Structure and Function8 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)

9. CS790 – BioinformaticsProtein Structure and Function9 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  Anfinsen’s experiments Denatured ribonuclease Spontaneously refolded into enzymatically active form  Verified for numerous proteins

10. CS790 – BioinformaticsProtein Structure and Function10 Folding intermediates  Levinthal’s paradox – Consider a 100 residue protein. If each residue can take only 3 positions, there are 3 100 = 5  10 47 possible conformations. If it takes 10 -13 s to convert from 1 structure to another, exhaustive search would take 1.6  10 27 years!  Folding must proceed by progressive stabilization of intermediates Molten globules – most secondary structure formed, but much less compact than “native” conformation.

11. CS790 – BioinformaticsProtein Structure and Function11 Ideas on protein folding  It is believed that hydrophobic collapse is a key driving force for protein folding Hydrophobic core!  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

12. CS790 – BioinformaticsProtein Structure and Function12 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

13. CS790 – BioinformaticsProtein Structure and Function13 Sickle Cell Anemia Sequestering hydrophobic residues in the protein core protects proteins from hydrophobic agglutination.

14. CS790 – BioinformaticsProtein Structure and Function14 Computational Protein Folding  Two key questions: Evaluation – how can we tell a correctly-folded protein from an incorrectly folded protein?  H-bonds  Electrostatics  Hydrophobic exposure  Etc. Optimization – once we get an evaluation function, can we optimize it?  Simulated annealing  EC  Etc.

15. CS790 – BioinformaticsProtein Structure and Function15 Evaluation of Protein Folds  Empirical potential functions Residue-based: spatial relationships among residues Stereochemistry-based: molecular interactions (covalent, electrostatic, etc.) with coefficients  Ab-initio potential functions  Procheck, etc.  Full molecular dynamics Very computationally expensive

16. CS790 – BioinformaticsProtein Structure and Function16 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

17. CS790 – BioinformaticsProtein Structure and Function17 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

18. CS790 – BioinformaticsProtein Structure and Function18  The “hydrophobic zipper” effect: Learning from Lattice Models Ken Dill ~ 1997

19. CS790 – BioinformaticsProtein Structure and Function19 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

20. CS790 – BioinformaticsProtein Structure and Function20 Secondary Structure Prediction AGVGTVPMTAYGNDIQYYGQVT… A-VGIVPM-AYGQDIQY-GQVT… AG-GIIP--AYGNELQ--GQVT… AGVCTVPMTA---ELQYYG--T… AGVGTVPMTAYGNDIQYYGQVT… ----hhhHHHHHHhhh--eeEE…

21. CS790 – BioinformaticsProtein Structure and Function21 A Peek at Protein Function  Serine proteases – cleave other proteins Catalytic Triad: ASP, HIS, SER

22. CS790 – BioinformaticsProtein Structure and Function22 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

23. CS790 – BioinformaticsProtein Structure and Function23 Specificity Binding Pocket

24. CS790 – BioinformaticsProtein Structure and Function24 onward  Apo-proteins and prosthetic groups  Lab techniques for proteins Gels Xtal Digests  Some computational areas of interest Folding Docking, screening