proteomics & bioinformatics part ii
DESCRIPTION
Proteomics & Bioinformatics Part II. David Wishart 3-41 Athabasca Hall [email protected]. 3 Kinds of Proteomics*. Structural Proteomics High throughput X-ray Crystallography/Modelling High throughput NMR Spectroscopy/Modelling Expressional or Analytical Proteomics - PowerPoint PPT PresentationTRANSCRIPT
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3 Kinds of Proteomics*
• Structural Proteomics– High throughput X-ray Crystallography/Modelling– High throughput NMR Spectroscopy/Modelling
• Expressional or Analytical Proteomics– Electrophoresis, Protein Chips, DNA Chips, 2D-HPLC– Mass Spectrometry, Microsequencing
• Functional or Interaction Proteomics– HT Functional Assays, Ligand Chips– Yeast 2-hybrid, Deletion Analysis, Motif Analysis
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Historically...
• Most of the past 100 years of biochemistry has focused on the analysis of small molecules (i.e. metabolism and metabolic pathways)
• These studies have revealed much about the processes and pathways for about 400 metabolites which can be summarized with this...
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More Recently...
• Molecular biologists and biochemists have focused on the analysis of larger molecules (proteins and genes) which are much more complex and much more numerous
• These studies have primarily focused on identifying and cataloging these molecules (Human Genome Project)
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Nature’s Parts Warehouse
The protein universe
Living cells
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The Protein Parts List
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However...
• This cataloging (which consumes most of bioinformatics) has been derogatively referred to as “stamp collecting”
• Having a collection of parts and names doesn’t tell you how to put something together or how things connect -- this is biology
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Remember: Proteins Interact*
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Proteins Assemble*
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For the Past 10 Years...
• Scientists have increasingly focused on “signal transduction” and transient protein interactions
• New techniques have been developed which reveal which proteins and which parts of proteins are important for interaction
• The hope is to get something like this..
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Protein Interaction Tools and Techniques -
Experimental Methods
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3D Structure Determination*• X-ray crystallography
– grow crystal– collect diffract. data– calculate e- density– trace chain
• NMR spectroscopy– label protein– collect NMR spectra– assign spectra & NOEs– calculate structure using
distance geom.
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Quaternary Structure
Some interactionsare real
Others are not
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Protein Interaction Domains*
http://pawsonlab.mshri.on.ca/ 82 domains
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Protein Interaction Domains
http://pawsonlab.mshri.on.ca/
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Yeast Two-Hybrid Analysis*
• Yeast two-hybrid experiments yield information on protein protein interactions
• GAL4 Binding Domain• GAL4 Activation Domain• X and Y are two proteins of
interest• If X & Y interact then
reporter gene is expressed
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Invitrogen Yeast 2-Hybrid
LexA
lacZLexA
X
Y
Y
B42
B42
lacZ
lacZLexA
X
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Example of 2-Hybrid Analysis*
• Uetz P. et al., “A Comprehensive Analysis of Protein-Protein Interactions in Saccharomyces cerevisiae” Nature 403:623-627 (2000)
• High Throughput Yeast 2 Hybrid Analysis
• 957 putative interactions
• 1004 of 6000 predicted proteins involved
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Example of 2-Hybrid Analysis
• Rain JC. et al., “The protein-protein interaction map of Helicobacter pylori” Nature 409:211-215 (2001)
• High Throughput Yeast 2 Hybrid Analysis
• 261 H. pylori proteins scanned against genome
• >1200 putative interactions identified
• Connects >45% of the H. pylori proteome
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Another Way?*
• Ho Y, Gruhler A, et al. Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415:180-183 (2002)
• High Throughput Mass Spectral Protein Complex Identification (HMS-PCI)
• 10% of yeast proteins used as “bait”
• 3617 associated proteins identified
• 3 fold higher sensitivity than yeast 2-hybrid
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Affinity Pull-down*
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HMS-PCI*
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Synthetic Genetic Interactions*
• Two mutations are synthetically lethal if cells with either of the single mutations are viable but cells with both mutations are non-viable
• Two types of synthetic lethal genetic interactions (lethal, slow growth)
• Mate two mutants without phenotypes to get a daughter cell with a phenotype
• Genetic interactions provide functional data on protein interactions or redundant genes
• About 23% of known SLs (1295 - YPD+MIPS) are known protein interactions in yeast
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Synthetic Lethality*
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Cell PolarityCell Wall Maintenance Cell StructureMitosisChromosome StructureDNA Synthesis DNA RepairUnknownOthers
Synthetic Genetic Interactions in Yeast
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Protein Chips*
Antibody Array Antigen Array Ligand Array
Detection by: SELDI MS, fluorescence, SPR, electrochemical, radioactivity, microcantelever
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Protein (Antigen) Chips
His6
GST
ORF
Nickel coating
H Zhu, J Klemic, S Chang, P Bertone, A Casamayor, K Klemic, D Smith, M Gerstein, M Reed, & M Snyder (2000).Analysis of yeast protein kinases using protein chips. Nature Genetics 26: 283-289
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Protein (Antigen) Chips
Nickel coating
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Arraying Process
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Probe with anti-GST Mab
Nickel coating
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Anti-GST Probe
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Probe with Cy3-labeled Calmodulin
Nickel coating
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“Functional” Protein Array*
Nickel coating
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Antigen Array (ELISA Chip)*
Mezzasoma et al. Clinical Chem. 48:121 (2002)
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Diagnostic Antigen Array
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Protein Chips
Antibody Array Antigen Array Ligand Array
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Ciphergen “Ligand” Chips*
• Hydrophobic (C8) Arrays
• Hydrophilic (SiO2) Arrays
• Anion exchange Arrays
• Cation exchange Arrays
• Immobilized Metal Affinity (NTA-nitroloacetic acid) Arrays
• Epoxy Surface (amine and thiol binding) Arrays
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Ciphergen (BioRad) ProteinChip*
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Peptide/Protein Profile
E. coli
Salmonella
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Protein Interaction Tools and Techniques -
Computational Methods
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Sequence Searching Against Known Domains*
http://pawsonlab.mshri.on.ca/
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Motif Searching Using Known Motifs
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Text Mining*
• Searching Medline or Pubmed for words or word combinations
• “X binds to Y”; “X interacts with Y”; “X associates with Y” etc. etc.
• Requires a list of known gene names or protein names for a given organism (a protein/gene thesaurus)
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iHOP (Information hyperlinked over proteins)
http://www.ihop-net.org/UniPub/iHOP/
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PolySearch*
http://wishart.biology.ualberta..ca/polysearch
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Rosetta Stone Method
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Interologs, Homologs, Paralogs*...
• Homolog– Common Ancestors– Common 3D Structure– Common Active Sites
• Ortholog– Derived from Speciation
• Paralog– Derived from Duplication
• Interolog– Protein-Protein Interaction
YM2
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Finding Interologs*
• If A and B interact in organism X, then if organism Y has a homolog of A (A’) and a homolog of B (B’) then A’ and B’ should interact too!
• Makes use of BLAST searches against entire proteome of well-studied organisms (yeast, E. coli)
• Requires list of known interacting partners
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A Flood of Data
• High throughput techniques are leading to more and more data on protein interactions
• This is where bioinformatics can play a key role
• Some suggest that this is the “future” for bioinformatics
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Interaction Databases
• DIP– http://dip.doe-mbi.ucla.edu/dip/
Main.cgi
• MINT– http://mint.bio.uniroma2.it/mint/
• String– http://string.embl.de/
• IntAct– http://www.ebi.ac.uk/intact/
main.xhtml
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DIP Database of Interacting Proteins
http://dip.doe-mbi.ucla.edu/dip/Main.cgi
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DIP Query Page
CGPC
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DIP Results Page
click
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DIP Results Page
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MINT Molecular Interaction Database
http://mint.bio.uniroma2.it/mint/
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MINT Resultsclick
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IntAct*
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IntAct
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KEGG Kyoto Encyclopedia of Genes and Genomes*
http://www.genome.ad.jp/kegg/kegg2.html
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KEGG
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KEGG
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TRANSPATH
http://www.gene-regulation.com/pub/databases.html
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BIOCARTA*
• www.biocarta.com
• Go to “Pathways”
• Web interactive links to many signalling pathways and other eukaryotic protein-protein interactions
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Visualizing Interactions
DIP
MINT
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Visualizing Interactions*
Cytoscape (www.cytoscape.org) Osprey http://biodata.mshri.on.ca/osprey/servlet/Index
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Pathway Visualization with BioCarta*
http://www.biocarta.com/genes/allpathways.asp
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Pathway Database Comparison*KEGG BioCyc GenMAPP Reactome BioCarta TransPATH
Organisms 181 (varied)
E.Coli, human (20
others)
Human, mouse, rat, fly, yeast
Human, rat, mouse,
chicken, fugu, zebrafish
Human, mouse
Human, mouse
Pathway types
Metabolic, genetic,
signaling,
complexes
Metabolic, complexes
Metabolic, signaling,
complexes
Metabolic, signaling,
complexes
Metabolic, signaling,
complexes
Signaling, genetic
Tools/ viewing
linked to from many
Pathway Tools
GenMAPP PathView applets
none Pathway Builder
Images Static box flow
diagrams
Detailed flow
diagrams
Static box flow
diagrams
“starry sky” “Graphics rich” cell diagrams
Graphics rich cell
diagrams
Download Formats
KGML
XML
SBML
BioPax
SBML
MAPP format
SBML
MySQL
Just images
Propietary XML files
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Other Databases
http://www.imb-jena.de/jcb/ppi/jcb_ppi_databases.html
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Functional Proteomics
• Mixture of experimental and computational techniques
• Trying to reach a point where functions and interactions can be predicted and modelled
• The future of proteomics (and bioinformatics)
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Final Exam• Short answer to long answer format• Bring calculators• Typically one question from each of the
lectures in the last ½ of the course• Some questions/answers will involve recall• Most questions require analysis or some
thinking or explaining• Dec. 13, 9:00 am - 2 hours not 3 hours• This room, M-229
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Typical Questions
• What is the correlation between protein expression and transcript expression? Provide three reasons to explain the difference
• Describe the algorithm or diagram a flow chart for XXXXX
• Explain the differences and similarities between functional proteomics and structural proteomics
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Typical Questions• Here is some YYYY data from some XXXX
experiment – interpret it and explain what it means
• Explain the difference between the XXX algorithm and the YYY algorithm. Give some examples or provide an illustration
• Here are two small molecules, calculate their difference distance matrix, show calculations. What is the difference between the two?
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Typical Questions• Define normalization. Provide 3
examples. Show equations or algorithms• What are the three different kinds of
proteomics, compare and contrast• Show the equations and explain the
algorithm you would use to rotate, expand and translate this small molecule