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Chapter 8 Proteomics

Chapter 8 Proteomics. 暨南大學資訊工程學系 黃光璿 2004/06/07. proteome the sum total of an organism’s proteins genome the sum total of an organism’s genetic material. 8.1 From Genomes to Proteomes. We want to know what proteins are present in cells; what those proteins do and how they function.

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Chapter 8 Proteomics

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  1. Chapter 8 Proteomics 暨南大學資訊工程學系 黃光璿 2004/06/07

  2. proteome • the sum total of an organism’s proteins • genome • the sum total of an organism’s genetic material

  3. 8.1 From Genomes to Proteomes We want to know • what proteins are present in cells; • what those proteins do and how they function. However, it’s not easy.

  4. Why? • The longevity (壽命) of an mRNA and the protein it codes for are very different. • Many proteins are extensively modified after translation. • Many proteins are not functionally relevant until they are assembled into larger complexes or delivered to an appropriate location.

  5. Proteins require more careful handling than DNA. • Function may change. • Protein identification requires • mass spectrometric analysis • specific antibodies. • Obtaining large numbers of protein molecules requires chemical isolation for living cells.

  6. 8.2 Protein Classification Based on • protein function • six categories • evolutionary history & structural similarity • 1000 homologous families

  7. 8.2.1 Enzyme Nomenclature • Started at 1950s International Union of Biochemistry and Molecular Biology

  8. 8.2.2 Family and Superfamily • Modern-day proteins may be derived from ~ 1000 original proteins. • folds  superfamilies  families • databases • SCOP, CATH, DALI

  9. fold • the same major secondary structure & topological connections • superfamily • probable evolutionary relationships • family • clear evolutionary relationships

  10. 8.3 Experimental Techniques • 2D Electrophoresis • Mass Spectrometry

  11. 2D Electrophoresis liver kidney http://tw.expasy.org/cgi-bin/map1

  12. Problems • tens of thousand v.s. thousands • under presentation of membrane-bound proteins • difficult to determine exactly which protein is represented

  13. 8.3.2 Mass Spectrometry 2D  mass spectrometry, for identification

  14. 8.3.3 Protein Microarrays • Use antibodies as probes. Problems • Single proteins will interact with multiple probes. • The binding kinetics of each probe are different. • Proteins are sensitive to their environment.

  15. 8.4 Inhibitors and Drug Design • development & testing of a new drug • ~ 15 years, US$ 700 million • discovery • target identification • lead discovery & optimization • toxicology (毒理學) • pharmacokinetics • testing

  16. HIV protease • has an active site; • cuts a single, large polypeptide chain into many proteins.

  17. 8.5 Ligand Screening

  18. 8.5.1 Ligand Docking Determine how two molecules of known structure will interact. Three issues: • Identify the energy of a particular molecular conformations. • Search for the conformation that minimizes the free energy.

  19. How to deal with flexibility in both the protein and the putative ligand. • Lock and key approaches • rigid protein structure, flexible ligand structure • induced fit docking • flexible in both protein and ligand

  20. Softwares • AutoDock • FTDock • DOCK • Hammerhead • Gold • FlexX

  21. 8.5.2 Database Screening • Primary consideration • complete and accurate search • with a reasonable computational complexity • SLIDE • Fig. 8.4

  22. 8.6 X-Ray Crystal Structures • W. C. Roentgen (1895) discovered X-rays. • M. von Laue (1912) discovered crystals diffract X-rays. • D. Hodgkin, etc. (1950s), crystallized complex organic molecules and determined their structures.

  23. grow a crystal of the protein

  24. File formats • PDB formatted text • mmCIF (MacroMolecular Crystallographic Information File)

  25. databases & resources • PDB • PIR • ExPASy

  26. Visualizing Tools • Fig. 8.8 • RasMol • Swiss PDB viewer • VMD (Visual Molecular Dynamics) • Spock • Protein explorer • DINO

  27. 8.7 NMR Structures • ~ 200 amino acids • the structures determined are not unique

  28. 8.8 Empirical Methods and Prediction Techniques • Example: • Fig. 8.9 • extracting features • learning, training • testing

  29. 8.9 Post-Translational Modification Prediction • Remove segments of a protein. • Covalently attach sugars, phosphates, or sulfate groups into surface residues. • Cross-link residues within a protein (disulfide bond).

  30. 8.9.1 Protein Sorting

  31. associated with membranes • not associated with membranes Table 8.3 (Case 2)

  32. PSORT: nearest neighbor classifier • Prediction of protein subcellular localization • SignalP: artificial neural networks • Prediction of signal peptide cleavage sites

  33. 8.9.2 Proteolytic Cleavage • chymotrypsin • cleaves polypeptides on the C-terminal side of bulky and aromatic residues • trypsin • cleaves on the carboxyl side • elastase • cleaves on the C-terminal side of small residues

  34. Prediction • proteasomes, > 98%, by neural network

  35. 8.9.3 Glycosylation • The process of covalently linking an oligosaccharide to the side chain of a protein surface residue (科學人) • N-linked, 75% • O-linked, 85% by neural network

  36. 8.9.4 Phosphorylation • kinases : add • phosphatases : remove • signal • NetPhos, > 70%, neural network

  37. 參考資料及圖片出處 • Fundamental Concepts of BioinformaticsDan E. Krane and Michael L. Raymer, Benjamin/Cummings, 2003. • Merrian-Webster Dictionary

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