1 Chapter 8Proteomics 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? 1. The longevity ( 壽命 ) of an mRNA and the protein it codes for are very different. 2. Many proteins are extensively modified after translation. 3. Many proteins are not functionally relevant until they are assembled into larger complexes or delivered to an appropriate location.

5 4. 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

Enzyme Nomenclature Started at 1950s International Union of Biochemistry and Molecular Biology

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

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

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Experimental Techniques 2D Electrophoresis Mass Spectrometry

13 2D Electrophoresis liverkidney

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

Mass Spectrometry 2D  mass spectrometry, for identification

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.

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

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

Ligand Screening

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.

23 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

24 Softwares  AutoDock AutoDock  FTDock FTDock  DOCK DOCK  Hammerhead  Gold Gold  FlexX FlexX

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

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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.

28 grow a crystal of the protein

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31 File formats  PDB formatted text PDB formatted text  mmCIF (MacroMolecular Crystallographic Information File) mmCIF

32 databases & resources  PDB PDB  PIR PIR  ExPASy ExPASy

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

NMR Structures ~ 200 amino acids the structures determined are not unique

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

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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).

Protein Sorting

39 associated with membranes not associated with membranes Table 8.3 (Case 2)

40 PSORT: nearest neighbor classifier PSORT  Prediction of protein subcellular localization SignalP: artificial neural networks SignalP  Prediction of signal peptide cleavage sites

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

42 Prediction  proteasomes, > 98%, by neural network

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

Phosphorylation kinases : add phosphatases : remove signal NetPhos, > 70%, neural network NetPhos

45 參考資料及圖片出處 1. Fundamental Concepts of Bioinformatics Dan E. Krane and Michael L. Raymer, Benjamin/Cummings, Fundamental Concepts of Bioinformatics 2. Merrian-Webster Dictionary Merrian-Webster Dictionary