proteomics: power of technology, expectations and realitysystems biology is an academic field that...
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Proteomics: Power of Technology,Proteomics: Power of Technology,Expectations and RealityExpectations and Reality
Pawel Ciborowski, Ph.D.Pawel Ciborowski, Ph.D.
Departments of Pharmacology and Experimental NeuroscienceDepartments of Pharmacology and Experimental Neuroscienceand Biochemistry and Molecular Biologyand Biochemistry and Molecular Biology
Center for Neurovirology and Neurodegenerative DisordersCenter for Neurovirology and Neurodegenerative DisordersUniversity of Nebraska Medical CenterUniversity of Nebraska Medical Center
Omaha, NEOmaha, NE
pciborowski@[email protected](402) 559-3733(402) 559-3733
© 2006 University of Nebraska Medical Center
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This presentation was presented at the 1st Biannual IDeAConference held in Washington, D.C. July 20-22, 2006.
The presentation is available on-line based on requests frommany participants of the Proteomics Workshop.
Also, I understand that for those who did not attend theworkshop, some slides may not be fully informative. In the nearfuture, the presentation will be modified to make it more useful.
Please reference this work/presentation when you part or all foreducational or other purposes.
Thank you,
Pawel Ciborowski, Ph.D.
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Table of Contents
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6IPG STRIPSIPG STRIPS
LC-MS/MSLC-MS/MS
mRNA:
SDS-PAGE: SDS-PAGE: Sodium Dodecyl Sulfate-polyacrylamideGel Electrophoresis
MS/MSMS/MS
SELDI: SELDI: Surface-Enhanced Laser Desorption/Ionization
HPLC: HPLC: high-performance liquid chromatography
FPLCFPLC
CE:CE:
MALDI: MALDI: Matrix-Assisted Laser Desorption/Ionization
WCLWCL
DIGEDIGE
IEFIEF
CSF:CSF: cerebrospinal fluidcerebrospinal fluid
HAD: HAD: HIV-1 associated dementiaHIV-1 associated dementia
LCQDecaPlusLCQDecaPlus
BSABSA
IgGIgG, , IgAIgA
TFATFA
TCATCA
IMAC: IMAC: Immobilized Metal AffinityImmobilized Metal Affinity ChromatographyChromatography
HPA: HPA: Human Protein AtlasHuman Protein Atlas
ALQHALQH
AIWQHAIWQHAL- QHAL- QH
AIWQHAIWQHA- LQHA- LQH
BLASTBLAST
MDM: MDM: monocyte derived macrophagesmonocyte derived macrophages
ExPASyExPASy: : Expert Protein Analysis SystemExpert Protein Analysis System
SIB: SIB: Swiss Institute of BioinformaticsSwiss Institute of Bioinformatics
PIR: PIR: Protein Information ResourceProtein Information Resource
UniProtUniProt: : Universal Protein ResourceUniversal Protein Resource
TOF: Time of Flight
AbbreviationsAbbreviations
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Part 1Part 1
History and DefinitionsHistory and Definitions
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19751975 19801980 19901990 20002000
PROTEOMICSPROTEOMICS
CHIP BASEDCHIP BASEDAPPROACHESAPPROACHES
2D SDS-PAGE2D SDS-PAGE
M A S S S P E C T R O M E T R YM A S S S P E C T R O M E T R Y
How Proteomics Was Born?How Proteomics Was Born?
ALGORITHMSALGORITHMS
COMPLEX MIXTURECOMPLEX MIXTUREANALYSIS LC-MS/MSANALYSIS LC-MS/MS
IPG STRIPSIPG STRIPS
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The dream of having genomes completely sequenced is now a realityThe dream of having genomes completely sequenced is now a realityand the complete human sequence is known. However, understandingand the complete human sequence is known. However, understandingprobably half a million human proteins encoded by less than 30,000probably half a million human proteins encoded by less than 30,000genes is still a long way away and the hard work to unravel thegenes is still a long way away and the hard work to unravel thecomplexity of biological systems is yet to come.complexity of biological systems is yet to come.
The discipline of The discipline of proteomicsproteomics has been initiated to complement physical has been initiated to complement physicalgenomic research. Proteomics can be defined as genomic research. Proteomics can be defined as the qualitative andthe qualitative andquantitative comparison of proteomes under different conditions toquantitative comparison of proteomes under different conditions tofurther unravel biological processesfurther unravel biological processes..
Adapted from http://au.Adapted from http://au.expasyexpasy.org.org
A new fundamental concept called proteome (A new fundamental concept called proteome (PROTEPROTEin in complement to acomplement to agengenOMEOME) emerged. This should drastically help to unravel biochemical) emerged. This should drastically help to unravel biochemicaland physiological mechanisms of complex multivariate diseases at theand physiological mechanisms of complex multivariate diseases at thefunctional molecular level.functional molecular level.
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GENOMEGENOME
Isoforms of the same protein and/or their Isoforms of the same protein and/or their subcellular subcellular localization maylocalization mayplay different roles in cell function. Therefore, the discovery of proteinplay different roles in cell function. Therefore, the discovery of proteinexpression or expression or isoforms isoforms by genomic approaches will always be limited.by genomic approaches will always be limited.
All of the geneticAll of the geneticinformationinformation
In biology, the suffix In biology, the suffix -omics-omics generally refers to the study of a completegenerally refers to the study of a completegroup or system of group or system of biomoleculesbiomolecules. Just as genomics is the study of an. Just as genomics is the study of anorganism's genome, proteomics is the study of an organism's entireorganism's genome, proteomics is the study of an organism's entirecomplement of proteins.complement of proteins.
PROTEOMEPROTEOME
All of the proteinAll of the proteinmakeupmakeup
TRANSCRIPTOMETRANSCRIPTOME
mRNAmRNA
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Accelerating discovery of disease-related genes and proteins doesAccelerating discovery of disease-related genes and proteins doesnot eliminate the necessity for old-fashioned bench science !!!not eliminate the necessity for old-fashioned bench science !!!
Our Our expectationsexpectations and realityand reality
We are far from understanding what a protein structure representsWe are far from understanding what a protein structure representsin defining biological function, not to mention physiologicalin defining biological function, not to mention physiologicalfunction. Proteins are at least at a level of complexity above thefunction. Proteins are at least at a level of complexity above thegenome.genome.
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……to identify protein structures, interactions and pathways soto identify protein structures, interactions and pathways sothatthat we will better understand changes underlying pathologicalwe will better understand changes underlying pathologicalprocesses, new disease markers and drug targets can beprocesses, new disease markers and drug targets can beidentified that will help create new products to prevent,identified that will help create new products to prevent,diagnose, and treat disease.diagnose, and treat disease.
Our goal isOur goal is……
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Systems-level understanding of biological systems that takes intoSystems-level understanding of biological systems that takes intoaccount complex interactions of gene, protein, and cell elements.account complex interactions of gene, protein, and cell elements.
www.www.genpromaggenpromag..com/Glossary~LETTER~Scom/Glossary~LETTER~S.html.html
Systems biology is an academic field that seeks to integrate high-Systems biology is an academic field that seeks to integrate high-throughput biological studies to understand how biologicalthroughput biological studies to understand how biologicalsystems function. By studying the relationships and interactionssystems function. By studying the relationships and interactionsbetween various parts of a biological system (e.g. metabolicbetween various parts of a biological system (e.g. metabolicpathways, organelles, cells, physiological systems, organisms etc.),pathways, organelles, cells, physiological systems, organisms etc.),it is hoped that eventually a comprehensible model of the wholeit is hoped that eventually a comprehensible model of the wholesystem can be developed.system can be developed.
en.en.wikipediawikipedia..org/wiki/Systems_biologyorg/wiki/Systems_biology
Definitions of Systems BiologyDefinitions of Systems Biologyon the Web:on the Web:
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In the In the bottom-upbottom-up approach, approach,we integrate all knownwe integrate all knowncomponents and interactionscomponents and interactionsto model the system.to model the system.
In the In the top-downtop-down approach approachwe start with the intactwe start with the intactsystem and dissect it intosystem and dissect it intocomponents andcomponents andinteractions.interactions.
Approaches in Systems BiologyApproaches in Systems Biology
Rozek W and Ciborowski P “Proteomics and Genomics inNeuroinflammation,” in Neuroimmune Pharmacology, ed.Ikezu, T. and Gendelman, H.E. Springer, in press 2006
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Approaches in proteomicsApproaches in proteomics
1D or 2D SDS-PAGE separation1D or 2D SDS-PAGE separation
Mass spec analysisMass spec analysis(various instrumentation)(various instrumentation)
In situIn situ in-gel in-gel tryptic tryptic digestdigest
Database searchDatabase search
Protein identificationProtein identification
Bottom-up ApproachBottom-up ApproachTop-down ApproachTop-down Approach
Protein mixtureProtein mixture
Protein ionizationProtein ionization
Fragmentation/DissociationFragmentation/Dissociation
Mass spec analysisMass spec analysisof protein fragmentsof protein fragments
Database search or Database search or de novode novo analysis analysis
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Part 2Part 2
TechnologyTechnology
Which proteomic platformWhich proteomic platformdo I need?do I need?
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••High throughput MS/MS basedHigh throughput MS/MS based
••High throughput MALDI basedHigh throughput MALDI based
••Single cellSingle cell
••Other/CustomOther/Custom
••All of the aboveAll of the above
Different flavors of proteomics Different flavors of proteomics
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Using proteomics in theUsing proteomics in thesystematic analysis of proteinssystematic analysis of proteinsrequires four key technologies:requires four key technologies:
1.1. Methods of identification of differences in protein Methods of identification of differences in proteinexpression.expression.
2. Methods of separating these proteins.2. Methods of separating these proteins.
3.3. High-throughput method of High-throughput method of protein identification.protein identification.
4.4. Bioinformatics Bioinformatics tools for data analysis and storage.tools for data analysis and storage.
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Our approach to proteomics analysisOur approach to proteomics analysis
FingerprintsFingerprints
ProteinProteinidentificationidentification
Data AnalysisData Analysis
FractionationFractionation
2D SDS-PAGE and SELDI-TOF2D SDS-PAGE and SELDI-TOF
HPLC and 1D SDS-PAGEHPLC and 1D SDS-PAGE
LC-MS/MSLC-MS/MS
Statistics and BioinformaticsStatistics and Bioinformatics
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SELDI-TOFSELDI-TOF fast samplefast sample accurate massaccurate mass analyticalanalyticalprocessingprocessing of finalof finalproduct, roboticsproduct, robotics
2D SDS-PAGE2D SDS-PAGE long procedurelong procedure approximatedapproximated analytical andanalytical and mass and pImass and pI preparativepreparative
Protein ArraysProtein Arrays availability and qualityavailability and quality identificationidentification analyticalanalyticalof antibodiesof antibodies
HPLC/FPLCHPLC/FPLC fractionationfractionation biochemicalbiochemical preparativepreparative propertiesproperties (analytical)(analytical)
IEFIEF fractionationfractionation isoelectric pointisoelectric point preparativepreparative analyticalanalytical
1D SDS-PAGE1D SDS-PAGE fractionationfractionation approximate size (approximate size (DaDa)) preparativepreparative analyticalanalytical
CapillaryCapillary fractionationfractionation biochemicalbiochemical analyticalanalyticalElectrophoresis (CE)Electrophoresis (CE) propertiesproperties preparativepreparative
LC-MS/MSLC-MS/MS moderate throughputmoderate throughput identificationidentification analytical analytical
MALDI-TOFMALDI-TOF high-throughputhigh-throughput identificationidentification analyticalanalytical
Utility Utility
Met
ho
d
Met
ho
d
Review of methods used in proteomicsReview of methods used in proteomics
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From: B. From: B. DomonDomon, and R. , and R. AebersoldAebersold. Mass Spectrometry and Protein Analysis. Science 2006, 312: 212-217.. Mass Spectrometry and Protein Analysis. Science 2006, 312: 212-217.
Which mass spectrometer do I need?Which mass spectrometer do I need?
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SELDI-TOFSELDI-TOF
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What SELDI-TOF technology platform offers?What SELDI-TOF technology platform offers?
1. The 1. The CiphergenCiphergen’’s s ProteinChip®ProteinChip® Biomarker System has potentially broadBiomarker System has potentially broadapplications such as: protein biomarker screening, characterization, and protein-applications such as: protein biomarker screening, characterization, and protein-based assay applications.based assay applications.
2.2. One step pre-purificationOne step pre-purification of crude biological samples using of crude biological samples using CiphergenCiphergen’’ssProteinChip® Biomarker System.ProteinChip® Biomarker System.
Advantage of SELDI-TOFAdvantage of SELDI-TOF
- Fairly easy processing and operation- Fairly easy processing and operation- Robotics available- Robotics available- Less complex ms spectrum- Less complex ms spectrum- F-moles of proteins required- F-moles of proteins required
Disadvantage of SELDI-TOFDisadvantage of SELDI-TOF
- Some protein(s) of interest may not bind effectively to the surface.- Some protein(s) of interest may not bind effectively to the surface.- Two proteins of interest have opposite characteristic - Two proteins of interest have opposite characteristic –– more than one chip required. more than one chip required.- Highly abundant proteins (e.g. serum albumin), decrease binding (detection) of - Highly abundant proteins (e.g. serum albumin), decrease binding (detection) of important proteins of low abundance.important proteins of low abundance.
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Protein Profiling:Protein Profiling:Multidimensional SeparationMultidimensional Separation
Selectivity
Selectivity
Selectivity
Selectivity
Stringency
Surfa
ceIn
tera
ctio
n
Pote
ntia
l 1 2 3 4 6 7 85
IMAC
1 2 3 4 6 7 85(+)1 2 3 4 6 7 85
(-)
1 2 3 4
1 2 3 4 6 7 85RP
Source: Source: CiphergenCiphergen, Inc., Inc.
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One-step SELDI-TOF protein profiling ofOne-step SELDI-TOF protein profiling ofWCL of HIV-1-infected (ADA strain) andWCL of HIV-1-infected (ADA strain) and
control human macrophagescontrol human macrophages
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Canonical analysis of SELDI-TOF profilingCanonical analysis of SELDI-TOF profiling
+ bone marrow brain spleen
30 SELDI-TOF spectra per group, total 180 spectra, WCX2 chip30 SELDI-TOF spectra per group, total 180 spectra, WCX2 chip
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Minutes0 10 20 30 40 50 60 70
mVolts
0
1000
2000SPD-10Avp Ch1-220nm
(kDa)17
14
6
BM SPL MG
BM
SPL
MG
m/z
Inte
nsit
y
From SELDI-TOF to protein identificationFrom SELDI-TOF to protein identification
14kDa14kDa
SODSOD
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2D SDS-PAGE2D SDS-PAGE
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From GenotypeFrom Genotypeto Protein Isoformsto Protein Isoforms
to Phenotypeto Phenotype
Multiple Multiple isoforms isoforms of proteins resulting from post-of proteins resulting from post-translational modifications, gene splicing, or genotypictranslational modifications, gene splicing, or genotypicvariants are most commonly identified throughvariants are most commonly identified throughdifferences in isoelectric point and molecular weight thatdifferences in isoelectric point and molecular weight thatalter their mobility in the 2-D gel.alter their mobility in the 2-D gel.
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INFECTEDINFECTED
CONTROLCONTROL
2D SDS-PAGE using DIGE technology2D SDS-PAGE using DIGE technology
Only controlOnly control
Only infectedOnly infected
BothBoth
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50 50 ugug--
25 25 ugugHADHAD
--50 50 ugug25 25 ugugNDND
Cy 5Cy 5
(red)(red)Cy 3Cy 3
(green)(green)Cy 2Cy 2
(yellow)(yellow)
1. All samples were mixed and loaded on one 24 cm gel strip (1. All samples were mixed and loaded on one 24 cm gel strip (Immobiline DryStrip Immobiline DryStrip pH 3-10)pH 3-10)
2. IEF 35,000 2. IEF 35,000 Vhrs Vhrs (>8hrs) (>8hrs) –– first dimension first dimension
3. Equilibration (reduction and 3. Equilibration (reduction and alkylationalkylation))
4. SDS PAGE in 12% gel - second dimension4. SDS PAGE in 12% gel - second dimension
5. Imaging (Typhoon Scanner)5. Imaging (Typhoon Scanner)
6. Data Analysis 6. Data Analysis –– DeCyder DeCyder 2D 6.52D 6.5
Fluorescent labelingFluorescent labeling((CyDyeCyDye DIGE Fluor Minimal Labeling Kit, GE Healthcare) DIGE Fluor Minimal Labeling Kit, GE Healthcare)
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2 D DIGE of CSF samples from HIV-1 infected individuals with or without cognitive impairment (HAD).2 D DIGE of CSF samples from HIV-1 infected individuals with or without cognitive impairment (HAD).Green spots (Cy3) are from HAD negative, while red spots (Cy5) are from HAD patientsGreen spots (Cy3) are from HAD negative, while red spots (Cy5) are from HAD patients
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Prostaglandin D2 synthase 21 Prostaglandin D2 synthase 21 kDa kDa (brain)(brain)Acc. # 54898708 Mw 21,028 pI 7.66Acc. # 54898708 Mw 21,028 pI 7.66
ApolipoproteinApolipoprotein A-1 precursor A-1 precursorAcc. # 55637005 Mw 30,777 pI 5.58Acc. # 55637005 Mw 30,777 pI 5.58
1) 1) TransthyretinTransthyretinAcc. # 37483 Mw 15,887 pI 5.52Acc. # 37483 Mw 15,887 pI 5.52
2) 2) ClusterinClusterin isoformisoform 1 1Acc. # 42718297 Mw 52,484 pI 5.88Acc. # 42718297 Mw 52,484 pI 5.88
Human Human TransthyretinTransthyretin with bond chloride with bond chlorideAcc. # 73535808 Mw 13,863 pI 5.53Acc. # 73535808 Mw 13,863 pI 5.53
R-State HumanR-State HumanCarbonmonoxyhemoglobinCarbonmonoxyhemoglobin Alpha-A53s Alpha-A53sAcc. # 3212437 Mw 15,142 pI 8.73Acc. # 3212437 Mw 15,142 pI 8.73
Cy3 (ND) Cy5 (HAD)Cy3 (ND) Cy5 (HAD)
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Spot matching across all gels, resulting in a reference library consisting of 2115 spots. Library spotsSpot matching across all gels, resulting in a reference library consisting of 2115 spots. Library spotsnumber derived from control (grey bars) and asthma (black bars) are shown. A. number derived from control (grey bars) and asthma (black bars) are shown. A. ““Spot must be present in allSpot must be present in allgelsgels”” used as selection criterion. B. used as selection criterion. B. ““Spot must be present at least 6 timesSpot must be present at least 6 times”” used as selection used as selection criteioncriteion..
From: R. From: R. Houtman Houtman et al. Proteomics, 2003, 3:2008-2018.et al. Proteomics, 2003, 3:2008-2018.
Reproducibility of 2D SDS-PAGEReproducibility of 2D SDS-PAGE
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What 2D SDS-PAGE technology platform offers?What 2D SDS-PAGE technology platform offers?
1. The 2D SDS-PAGE has broad applications in simultaneous studies of thousands1. The 2D SDS-PAGE has broad applications in simultaneous studies of thousandsof proteins.of proteins.
2.2. Two steps of protein fractionation: based on isoelectric point (pI) and molecularTwo steps of protein fractionation: based on isoelectric point (pI) and molecularweight (m.w.).weight (m.w.).
Advantage of 2D SDS-PAGEAdvantage of 2D SDS-PAGE
- Analytical and preparative tool- Analytical and preparative tool- Standardized IPG strips of various ranges for first dimension (pI)- Standardized IPG strips of various ranges for first dimension (pI)- With fluorescent labeling of proteins it is possible to perform quantitative analysis.- With fluorescent labeling of proteins it is possible to perform quantitative analysis.- Separates 2000 to 3000 proteins in one gel- Separates 2000 to 3000 proteins in one gel
Disadvantage of 2D SDS-PAGEDisadvantage of 2D SDS-PAGE
- Proteins with high molecular weight, hydrophobic and very basic are not well- Proteins with high molecular weight, hydrophobic and very basic are not well separated. separated.- Two or more proteins can co-migrate.- Two or more proteins can co-migrate.- Highly abundant proteins, e.g. serum albumin, may limit detection of important- Highly abundant proteins, e.g. serum albumin, may limit detection of important proteins of low abundance.proteins of low abundance.
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Mass SpectrometryMass Spectrometry
LC-MS/MSLC-MS/MSMALDI-TOFMALDI-TOF
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Sample extractionSample extractionand and trypsinizationtrypsinizationor in-gel digestionor in-gel digestion
1. Sample preparation1. Sample preparation 2. Sample peptides analysis2. Sample peptides analysis
3. Selected peptide3. Selected peptidegated into MSgated into MScollision chambercollision chamberfor controlledfor controlledfragmentationfragmentation
5. Obtained fragments are searched5. Obtained fragments are searchedagainst database to determine aminoagainst database to determine aminoacid sequence.acid sequence.
LC-MS
LC-MS/MS
Identification of Proteins by Tandem MS (MS/MS)Identification of Proteins by Tandem MS (MS/MS)
4. Mass/charge analysis of4. Mass/charge analysis offragments.fragments.
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LC-MS/MS (LC-MS/MS (LCQDecaPlusLCQDecaPlus) analysis of BSA ) analysis of BSA tryptic tryptic digestdigest
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How much and how good is the informationHow much and how good is the informationwe get from we get from microsequencingmicrosequencing??
Automated analysis Automated analysis –– infusion of RP-HPLC separated sample infusion of RP-HPLC separated sample
MS/MS conditions are setMS/MS conditions are setLess sample required, one time Less sample required, one time ““shotshot””
Manual analysis Manual analysis –– direct infusion, usually without separation direct infusion, usually without separation
MS/MS conditions changed at real timeMS/MS conditions changed at real timeMore sample required, focused analysisMore sample required, focused analysis
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Protein Fingerprinting MALDI-TOFProtein Fingerprinting MALDI-TOF
Proteins are digested with trypsinProteins are digested with trypsin(cleaves at R-X and K-X except when X(cleaves at R-X and K-X except when Xis Pro.is Pro.
A peptide mass fingerprint producedA peptide mass fingerprint producedby analyzing the digested proteinby analyzing the digested proteinwith a MALDI-TOF MS.with a MALDI-TOF MS.
MS
Denature
Proteins are Proteins are denaturated denaturated (e.g. urea, heat)(e.g. urea, heat)
Digest
MITGIQITMITGIQITKKAANDLLNDSFAANDLLNDSFRRLLDSLLDSKKGEACVAAGYAEVVSGEACVAAGYAEVVSRREYPQLTIVSGQQEYPQLTIVSGQQRRFNSLTPSLFNSLTPSL
MITGIQITMITGIQITKK AANDLLNDSF AANDLLNDSFRR LLDS LLDSKK GEACVAAGYAEVVS GEACVAAGYAEVVSRR EYPQLTIVSGQQ EYPQLTIVSGQQRR FNSLTPSL FNSLTPSL
m/zm/z
Ab
un
dan
ceA
bu
nd
ance
100%100%
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Part 3Part 3
Sample preparationSample preparation
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Effective method of sample preparation will provide:Effective method of sample preparation will provide:
•• Reproducible Reproducible solubilization solubilization of all proteins and/or proteins of interest.of all proteins and/or proteins of interest.
•• Prevent protein aggregation during analysis (IE, SELDI-TOF). Prevent protein aggregation during analysis (IE, SELDI-TOF).
•• Prevent chemical modification of the proteins. Prevent chemical modification of the proteins.
•• Remove interfering molecules. Remove interfering molecules.
•• Yield proteins of interest at detectable levels, which may involve the Yield proteins of interest at detectable levels, which may involve the removal of abundant proteins or non-relevant classes. removal of abundant proteins or non-relevant classes.
Sample preparationSample preparation
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Sample preparation cont.Sample preparation cont.
2.2. Amount of sample available and required sensitivity will determine types ofAmount of sample available and required sensitivity will determine types ofmass spectrometry and associated chromatographic instrumentationmass spectrometry and associated chromatographic instrumentationwhich will be employed.which will be employed.
1.1. Choice of analytical system (mass spectrometers) will depend onChoice of analytical system (mass spectrometers) will depend onproperties of the properties of the analyte analyte (proteins, peptides, etc.) including its molecular(proteins, peptides, etc.) including its molecularweight (useable weight (useable m/z m/z range), polarity and volatility, and state (solid, liquid orrange), polarity and volatility, and state (solid, liquid orvapor).vapor).
3.3. Amount of sample required for successful analysis ranges from lowAmount of sample required for successful analysis ranges from lowfemtogramsfemtograms to several milligrams depending on the complexity of the to several milligrams depending on the complexity of thesample and the type of analysis used.sample and the type of analysis used.
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101066 cells (macrophage) usually yields 40 cells (macrophage) usually yields 40 µµg of proteing of protein
~ 3000 proteins are found in whole cell lysate~ 3000 proteins are found in whole cell lysate
100 100 µµgg/3000 = ~ /3000 = ~ 33 33 ngng of one protein on averageof one protein on average
How much of a protein?How much of a protein?(an example)(an example)
33 ng/100,000 33 ng/100,000 Da Da = = 330 330 fmolesfmoles 33 ng/10,000 33 ng/10,000 Da Da = = 3.3 3.3 pmolespmoles
Trypsin in-gel digest - 30% recoveryTrypsin in-gel digest - 30% recovery 100 100 fmolesfmoles toto 1 1 pmolepmole
Post gel manipulation Post gel manipulation –– 90% recovery 90% recovery 90 90 fmolesfmoles toto 900 900 fmolesfmoles
MS loading (MS loading (autosamplerautosampler) ) –– lost of 10 to 20% ~ lost of 10 to 20% ~ 75 75 fmolesfmoles toto 750 750 fmolesfmoles
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Summary of protein yield afterSummary of protein yield afterdepletion of 6 most abundant proteinsdepletion of 6 most abundant proteins
(albumin, (albumin, IgGIgG, , IgAIgA, , transferrintransferrin, , haptoglobinhaptoglobin, , antitrypsinantitrypsin) and 2D) and 2DClean-Up from 6 CSF samplesClean-Up from 6 CSF samples
8.5 - 11.7 %8.5 - 11.7 %60 - 120 60 - 120 ugug100 100 ululCSFsCSFs after afterdepletion anddepletion and2 D Clean-Up2 D Clean-Up
N/AN/A700 - 1020 700 - 1020 ugug1000 1000 ululOriginal CSFOriginal CSFSamplesSamples
Recovery afterRecovery afterdepletiondepletion
(%)(%)
Total amount ofTotal amount ofprotein (range)protein (range)VolumeVolumeSampleSample
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Sample preparation for MALDI-TOFSample preparation for MALDI-TOF
1.1. For MALDI-TOF analysis usually a 1 For MALDI-TOF analysis usually a 1 picomole picomole per per microliter microliter ororgreater concentration is required.greater concentration is required.
2.2. Samples should be mixed and suspended in solventsSamples should be mixed and suspended in solventspreventing formation of any precipitates, then allowed to airpreventing formation of any precipitates, then allowed to airevaporate on the target. Nonvolatile solvents should be avoided.evaporate on the target. Nonvolatile solvents should be avoided.
3.3. Matrix solution should be maintained at a pH below 4.0 toMatrix solution should be maintained at a pH below 4.0 toprevent ionization of the matrix. If a higher pH occurs, theprevent ionization of the matrix. If a higher pH occurs, theaddition of 0.1% TFA may improve results.addition of 0.1% TFA may improve results.
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Protein FingerprintingProtein FingerprintingMALDI-TOF MALDI-TOF
The The monoisotopic monoisotopic masses of the peptides seen in the MALDI-TOF mass spectrummasses of the peptides seen in the MALDI-TOF mass spectrumare software selected and used to search a protein. The database has beenare software selected and used to search a protein. The database has beentheoretically digested with trypsin and the experimentally generated mass list istheoretically digested with trypsin and the experimentally generated mass list iscompared to the theoretically digested database. The match is scored on number ofcompared to the theoretically digested database. The match is scored on number offactors, depending on the search program utilized.factors, depending on the search program utilized.
Advantages:Advantages:
Rapid analysis and turn around timeRapid analysis and turn around timeHigh sensitivityHigh sensitivityInexpensiveInexpensiveSuitable for large numbers of samplesSuitable for large numbers of samples
Disadvantages:Disadvantages:
Protein must be in the databaseProtein must be in the databaseGenerally not suitable for proteins <15kDa in sizeGenerally not suitable for proteins <15kDa in sizeMatch based on peptide masses, not sequence informationMatch based on peptide masses, not sequence informationGenerally only able to suggest post-translational modificationsGenerally only able to suggest post-translational modifications
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Tissues or cultured cells will liberate active or activate endogenousTissues or cultured cells will liberate active or activate endogenousproteases.proteases.
Because Because proteolytic proteolytic degradation of proteins greatly complicates all anddegradation of proteins greatly complicates all andany proteomic analysis, one should apply measures to address thisany proteomic analysis, one should apply measures to address thisproblem.problem.
Addition of a cocktail of protease inhibitors is an effective method.Addition of a cocktail of protease inhibitors is an effective method.
Proteases can also be inactivated by other methods such as immediateProteases can also be inactivated by other methods such as immediatesnap-freezing or denaturing samples with e.g. 10% TCA, 8M urea, or 2%snap-freezing or denaturing samples with e.g. 10% TCA, 8M urea, or 2%SDS.SDS.
Protecting samples against proteolysisProtecting samples against proteolysis
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Keratins: is this a real problem?Keratins: is this a real problem?
http://www.thermo.com/eThermo/CMA/PDFs/Articles/articlesFile_21631.pdfhttp://www.thermo.com/eThermo/CMA/PDFs/Articles/articlesFile_21631.pdf
If keratins are present in concentrations exceeding that of analyzedIf keratins are present in concentrations exceeding that of analyzedprotein(s), their abundance can obscure the peptides of interest. Veryprotein(s), their abundance can obscure the peptides of interest. Veryimportant in data dependent type of LC-MS/MS analysis. At lowimportant in data dependent type of LC-MS/MS analysis. At lowconcentration keratins do not pose a problem.concentration keratins do not pose a problem.
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Part 4Part 4
Sample enrichmentSample enrichment
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1.1. Proteomics has generated a large number of experimentsProteomics has generated a large number of experimentsand vast amounts of data.and vast amounts of data.
2.2. Proteomics experiments can be very expensive, but withoutProteomics experiments can be very expensive, but withoutcareful experimental design and analysis they can have verycareful experimental design and analysis they can have verylittle value.little value.
3.3. However, with an appropriate experimental design,However, with an appropriate experimental design,proteomics experiments can be a central tool for discoveringproteomics experiments can be a central tool for discoveringimportant information relating protein expression to disease.important information relating protein expression to disease.
Points for considerationPoints for consideration
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Complexity
Reproducibility
Why fractionate, why enrich?Why fractionate, why enrich?•• Proteomic samples can vary greatly in complexity Proteomic samples can vary greatly in complexity depending on sample source and preparation. depending on sample source and preparation.
•• Each biological sample has different characteristics !!! Each biological sample has different characteristics !!!
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Sample enrichmentSample enrichment
Sample preparation is often a key to successful proteomic analysis.Sample preparation is often a key to successful proteomic analysis.Methods can range from simple Methods can range from simple solubilization solubilization to complex extractionsto complex extractionsusing a wide range of agents.using a wide range of agents.
Sample preparation is very much an art requiring much experimentationSample preparation is very much an art requiring much experimentationbefore the correct conditions for a particular sample are found. Forbefore the correct conditions for a particular sample are found. Forexample, slight variations in concentrations of the example, slight variations in concentrations of the solubilizing solubilizing agentsagentscan have a dramatic effect on the final outcome.can have a dramatic effect on the final outcome.
Sample fractionation is an integrated part of the workflow for solvingSample fractionation is an integrated part of the workflow for solvingbiological questions using mass spectrometry.biological questions using mass spectrometry.
54
Fractionation of proteins allow to enhance the resolution of proteins (alsoFractionation of proteins allow to enhance the resolution of proteins (alsothose in question), and increases ability to detect low abundant proteinsthose in question), and increases ability to detect low abundant proteinsthrough the removal of potentially co-migrating proteins.through the removal of potentially co-migrating proteins.
Fractionation and enrichment can be accomplished via multiple ways e.g.Fractionation and enrichment can be accomplished via multiple ways e.g.based on protein modification.based on protein modification.
Depletion techniques are widely used to remove heavily abundant proteinsDepletion techniques are widely used to remove heavily abundant proteinsfrom serum samples, but can be applied also to other studies.from serum samples, but can be applied also to other studies.
Sample fractionationSample fractionation
55
1. SAMPLE COLLECTION AND EXTRACTION1. SAMPLE COLLECTION AND EXTRACTION
2. SAMPLE STORAGE AND STABILITY 2. SAMPLE STORAGE AND STABILITY
3. SAMPLE PREPARATION FOR GENOMIC VS. PROTEOMIC APPLICATIONS 3. SAMPLE PREPARATION FOR GENOMIC VS. PROTEOMIC APPLICATIONS
Points for considerationPoints for consideration
4. REFERENCE SAMPLE4. REFERENCE SAMPLE
56
Most abundant proteins areMost abundant proteins are……
4.5%Other
1.3%α1-Acid Glycoprotein
2.0%IgM
3.0%Haptoglobin
3.3%Transferrin
3.5%IgA
3.6%α2-Macroglobulin
3.8%α1-Antitrypsin
4.0%Apolipoproteins
17.0%IgG
54.0%Albumin
57From: NL. Anderson, and NG. Anderson. The Human Plasma Proteome. Molecular & Cellular Proteomics. 2002, 1: 845-867.From: NL. Anderson, and NG. Anderson. The Human Plasma Proteome. Molecular & Cellular Proteomics. 2002, 1: 845-867.
……and dynamic rangeand dynamic range
58
1 1 –– CSF before HPLC CSF before HPLC
2 2 –– CSF after HPLC CSF after HPLC
M M –– Molecular Weight Markers Molecular Weight Markers
MM 2211
118 _118 _
62 _ 62 _
98 _ 98 _
49 _ 49 _
28 _ 28 _
38 _ 38 _
17 _ 17 _
14 _ 14 _
6 _ 6 _
kDakDa
Human Serum AlbuminHuman Serum Albumin
IgIg Heavy Chain Heavy Chain
IgIg Light Chain Light Chain
SDS PAGE analysis of CSF proteins after SDS PAGE analysis of CSF proteins after immunodepletionimmunodepletion
59
Protein concentration in 40 CSF samplesProtein concentration in 40 CSF samples
0.00
0.50
1.00
1.50
2.00
0.5 1.5
Pro
tein
co
nce
ntr
atio
n (
mg
/ml)
Low 0.16Low 0.16
High 1.48High 1.48
Ave. 0.54Ave. 0.54
60
Although 2-D SDS-PAGE is the most highly resolvingAlthough 2-D SDS-PAGE is the most highly resolvingmethod available for the parallel separation (purification)method available for the parallel separation (purification)from mixtures of thousands of proteins and remains thefrom mixtures of thousands of proteins and remains themethod of choice for studying changes in complexmethod of choice for studying changes in complexmixtures of proteins, other methods and experimentalmixtures of proteins, other methods and experimentalapproaches are also available and should be considered.approaches are also available and should be considered.
FractionationFractionation…… and Profiling and Profiling
61
Based on solubilityBased on solubilityhydrophobicityhydrophobicity
Based on cellular localizationBased on cellular localizationnuclearnuclearmitochondriamitochondriaplasma membraneplasma membrane
Based on chemical make upBased on chemical make upphosphorylationphosphorylationglycosylationglycosylationcharge (ion exchange)charge (ion exchange)isoelectric point (isoelectric point (pIpI))
Based on Based on electrophoretic electrophoretic mobilitymobility
Fractionation and Profiling (cont.)Fractionation and Profiling (cont.)
62
Fractionation and ProfilingFractionation and Profiling
Source: Source: ProteomeLab ProteomeLab (PF2D) from Beckman-Coulter, Inc.(PF2D) from Beckman-Coulter, Inc.
63
•• Protein Protein glycosylation glycosylation is one of the major post-translational eventsis one of the major post-translational events
•• It has significant effects on protein properties and functions It has significant effects on protein properties and functions
• Glycosylation Glycosylation makes protein multidimensional through additionmakes protein multidimensional through addition of functions such as quality control, stability, destination, interactions of functions such as quality control, stability, destination, interactions with with lectinslectins, etc. , etc.
•• Complexity of Complexity of glycans glycans is attributed to diversity of linkage isomersis attributed to diversity of linkage isomers
•• Glycans Glycans are not direct products of genesare not direct products of genes
GlycomicsGlycomics
64
Strategies in Strategies in glycomicsglycomics
1. Extraction of 1. Extraction of glycoproteinsglycoproteins, purification of target , purification of target glycoproteinsglycoproteins
2. Group purification of 2. Group purification of glycoproteinsglycoproteins on on lectinlectin columns columns
3. 3. GlycoGlyco-catch procedure (recapture)-catch procedure (recapture)
4. Assignment of glycoprotein genes and 4. Assignment of glycoprotein genes and glycosylationglycosylation sites sites
1. Purification of target 1. Purification of target glycoproteinsglycoproteins
2. Structural analysis2. Structural analysis
3. Gene identification3. Gene identification
4. Liberation of 4. Liberation of glycanglycan chains, chains, glycoformglycoform analysis analysis
65
•• Phosphorylation acts like a switch, turning the function of a protein Phosphorylation acts like a switch, turning the function of a protein on or off. on or off.
•• There are two major groups of enzymes regulating phosphorylation: There are two major groups of enzymes regulating phosphorylation: kinases and kinases and phosphatasesphosphatases..
•• Primarily tyrosine, serine and Primarily tyrosine, serine and threonine threonine residues are phosphorylated.residues are phosphorylated.
PhosphoproteomicsPhosphoproteomics
66
•• Proteins separated by 2D PAGE are blotted onto membranes and Proteins separated by 2D PAGE are blotted onto membranes and phosphoproteins phosphoproteins are detected using are detected using phosphospecific phosphospecific antibodies followedantibodies followed by mass spectrometry identification. by mass spectrometry identification.
•• Phosphopeptides Phosphopeptides are enriched using Immobilized Metal Affinityare enriched using Immobilized Metal Affinity Chromatography (IMAC) chromatography followed by mass spectrometry. Chromatography (IMAC) chromatography followed by mass spectrometry.
•• Chemical Chemical derivitization derivitization steps are used to modify the site(s) ofsteps are used to modify the site(s) of phosphorylation ( phosphorylation (3232P, P, ββ-elimination, etc.). -elimination, etc.).
Strategies in Strategies in phosphoproteomicsphosphoproteomics
67
Part 5Part 5
New and Emerging TechnologiesNew and Emerging Technologies
68
New and Emerging TechnologiesNew and Emerging Technologies
1.1. Tissue MALDI-TOFTissue MALDI-TOF2.2. Protein arraysProtein arrays
69
Tissue imagingTissue imaging is performed on a small number of samples where is performed on a small number of samples wherethe goal is to obtain an image with relatively high resolutionthe goal is to obtain an image with relatively high resolutionshowing the distribution of various proteins in the tissue section.showing the distribution of various proteins in the tissue section.
MALDI matrix is uniformly deposited over the entire tissue sectionMALDI matrix is uniformly deposited over the entire tissue sectionand mass spectra are acquired. The intensity of any signal in theand mass spectra are acquired. The intensity of any signal in themass spectra is plotted as a function of position on the tissuemass spectra is plotted as a function of position on the tissuesurface. Resulting two-dimensional density plots give visualsurface. Resulting two-dimensional density plots give visualrepresentations of protein distributions.representations of protein distributions.
Tissue imagingTissue imaging
Source: http://www.mc.Source: http://www.mc.vanderbiltvanderbilt..edu/msrc/tissue/tissue_profiling_and_imagingedu/msrc/tissue/tissue_profiling_and_imaging..phpphp
70Source: http://www.mc.Source: http://www.mc.vanderbiltvanderbilt..edu/msrc/tissue/tissue_profilingedu/msrc/tissue/tissue_profiling..phpphp
Tissue imagingTissue imaging
71
Tissue profilingTissue profiling usually involves multiple samples of several types usually involves multiple samples of several typesof tissues. The goal is to discover patterns in the protein profilesof tissues. The goal is to discover patterns in the protein profilesof the samples that can classify the samples based on biologicalof the samples that can classify the samples based on biologicalstate (e.g., tumor vs. normal) and that can predict biologicalstate (e.g., tumor vs. normal) and that can predict biologicaloutcomes (e.g., the prognosis of a patient).outcomes (e.g., the prognosis of a patient).
MALDI matrix is deposited on the tissue sections in discreetMALDI matrix is deposited on the tissue sections in discreetdroplets, and each droplet is analyzed. The spectra from eachdroplets, and each droplet is analyzed. The spectra from eachspot on each section are then subjected to extensive bio -spot on each section are then subjected to extensive bio -statistical analyses.statistical analyses.
Tissue profilingTissue profiling
Source: http://www.mc.Source: http://www.mc.vanderbiltvanderbilt..edu/msrc/tissue/tissue_profiling_and_imagingedu/msrc/tissue/tissue_profiling_and_imaging..phpphp
72Source: http://www.mc.Source: http://www.mc.vanderbiltvanderbilt..edu/msrc/tissue/tissue_profilingedu/msrc/tissue/tissue_profiling..phpphp
Tissue profilingTissue profiling
73
Protein ArraysProtein Arrays
http://catalog.invitrogen.com/productimages/3400/3347.jpg
74
The Swedish Human Protein Atlas (HPA) program has been designed for theThe Swedish Human Protein Atlas (HPA) program has been designed for thesystematic exploration of the human proteome using Affinity (Antibody)systematic exploration of the human proteome using Affinity (Antibody)Proteomics by combining high-throughput generation of affinity-purifiedProteomics by combining high-throughput generation of affinity-purified(mono-specific) antibodies with protein profiling using tissue arrays.(mono-specific) antibodies with protein profiling using tissue arrays.
New Initiatives New Initiatives –– Human Protein Atlas Human Protein Atlas
http://www.hpr.se/object.php
75
From data to informationFrom data to informationto knowledgeto knowledge
Part 6Part 6BioinformaticsBioinformatics
76
We are able to generate a great amount of We are able to generate a great amount of datadata which whichneed to be transformed into need to be transformed into informationinformation. This step. This steprequires data cleansing and organization.requires data cleansing and organization.
Acquired information needs to be analyzed andAcquired information needs to be analyzed andsubsequently used to build subsequently used to build knowledgeknowledge. This difficult task. This difficult taskrequires interpretation of what the data/information meansrequires interpretation of what the data/information meansand how this can be used to address questions ofand how this can be used to address questions ofbiological importance.biological importance.
Where is the problem?Where is the problem?
77
""Biomedical InformaticsBiomedical Informatics is an emerging discipline that has is an emerging discipline that hasbeen defined as the study, invention, and implementationbeen defined as the study, invention, and implementationof structures and algorithms to improve communication,of structures and algorithms to improve communication,understanding and management of medical information."understanding and management of medical information."
“…“…medical informatics is more concerned with structures andmedical informatics is more concerned with structures andalgorithms for the manipulation of medical data, rather than withalgorithms for the manipulation of medical data, rather than withthe data itself. This suggests that one difference betweenthe data itself. This suggests that one difference betweenbioinformaticsbioinformatics and and medical informaticsmedical informatics as disciplines lies with as disciplines lies withtheir approaches to the data; there are their approaches to the data; there are bioinformaticistsbioinformaticistsinterested in the theory behind the manipulation of that data interested in the theory behind the manipulation of that data andandthere are bioinformatics scientists concerned with the data itselfthere are bioinformatics scientists concerned with the data itselfand its biological implications.and its biological implications.””
Source: http://www.Source: http://www.colorbasepaircolorbasepair..com/what_is_bioinformaticscom/what_is_bioinformatics.html.html
Bioinformatics or Biomedical Informatics?Bioinformatics or Biomedical Informatics?
78
Sensitivity of bioinformatics similarity search algorithms centersSensitivity of bioinformatics similarity search algorithms centersaround two areas:around two areas:
1. How well can the method detect biologically meaningful1. How well can the method detect biologically meaningfulrelationships between two related sequences in the presence ofrelationships between two related sequences in the presence ofmutations and sequencing errors?mutations and sequencing errors?
2. How does the heuristic nature of the algorithm affect the probability2. How does the heuristic nature of the algorithm affect the probabilitythat a matching sequence will not be detected? At the user'sthat a matching sequence will not be detected? At the user'sdiscretion, the speed of most similarity search programs can bediscretion, the speed of most similarity search programs can besacrificed in exchange for greater sensitivity - with an emphasis onsacrificed in exchange for greater sensitivity - with an emphasis ondetecting lower scoring matches.detecting lower scoring matches.
SensitivitySensitivity
It is in userIt is in user’’s discretion how to filter (accept or reject) data.s discretion how to filter (accept or reject) data.
79
From Data to InformationFrom Data to Information
80
The aim of a sequence alignment is to match "the most similar elements" of two sequences. ThisThe aim of a sequence alignment is to match "the most similar elements" of two sequences. Thissimilarity must be evaluated somehow. For example, letsimilarity must be evaluated somehow. For example, let’’s consider the following sequence ands consider the following sequence andtwo alignments:two alignments:
If we think of the letters as amino acid residues rather than elements of strings, alignment (a) is theIf we think of the letters as amino acid residues rather than elements of strings, alignment (a) is thebetter one, because better one, because isoleucine isoleucine (I) and (I) and leucine leucine (L) have similar side chains, while (L) have similar side chains, while tryptophan tryptophan (W) has a(W) has avery different structure. This is a very different structure. This is a physico-chemical physico-chemical measure.measure.
(a)(a)
AIWQHAIWQHAL- QHAL- QH
(b)(b)
AIWQHAIWQHA- LQHA- LQH
Sequence AlignmentSequence Alignment
These sequences seem quite similar: both contain one gap (These sequences seem quite similar: both contain one gap (indelindel) and one substitution, just at) and one substitution, just atdifferent positions.different positions.
On the other hand, it is much more likely that a mutation changed I into L and that W was lost, as inOn the other hand, it is much more likely that a mutation changed I into L and that W was lost, as in(a), rather than W changed into L and I was lost. We would expect that a change from I to L would not(a), rather than W changed into L and I was lost. We would expect that a change from I to L would notaffect the function as much as a mutation from W to L, but this is another issue.affect the function as much as a mutation from W to L, but this is another issue.
ALQHALQH
81
ExampleExample
Identification of one peptide derived from gp120 envelope glycoproteinIdentification of one peptide derived from gp120 envelope glycoproteinfound in culture supernatant of MDM infected with HIV-1found in culture supernatant of MDM infected with HIV-1ADAADA..
Number of hits using BLAST search = 500, all gp120 from HIV-1Number of hits using BLAST search = 500, all gp120 from HIV-1
HIV-1HIV-1ADAADA
82
From Information to KnowledgeFrom Information to Knowledge
83
Data MiningData Mining
•• Peptide and protein identifications are made based onPeptide and protein identifications are made based onthreshold(s) which can be arbitrarily set.threshold(s) which can be arbitrarily set.
•• Manual validation: how useful is this Manual validation: how useful is this ““goldgoldstandardstandard””??
•• Statistical models are necessary for accurateStatistical models are necessary for accuratedescription of false positive and false negative results.description of false positive and false negative results.
•• Clear rules and standards should be applied.Clear rules and standards should be applied.
84
Data ValidationData Validation
Extensive manual validation of data is impossible !!!Extensive manual validation of data is impossible !!!
85
The The ExPASy ExPASy Molecular Biology ServerMolecular Biology ServerExExpertpert PProteinrotein AAnalysisnalysis SySystemstem
August 1, 1993 the August 1, 1993 the ExPASyExPASy molecular biology server, release 0, beta version was installed. molecular biology server, release 0, beta version was installed.
http://http://us.expasy.orgus.expasy.org//
The Swiss Institute of Bioinformatics (SIB) has three missions: research & development, education,The Swiss Institute of Bioinformatics (SIB) has three missions: research & development, education,and service. and service. It undertakes specific research and development of activities related to theIt undertakes specific research and development of activities related to thedatabases and software developed within the Institute.databases and software developed within the Institute.
It maintains databases of international standing (Swiss-Prot, It maintains databases of international standing (Swiss-Prot, PrositeProsite, EPD, Swiss-2Dpage, Human, EPD, Swiss-2Dpage, HumanChromosome 21, Chromosome 21, TrESTTrEST, , TrGenTrGen, AGBD, Hits, Swiss Model Repository, , AGBD, Hits, Swiss Model Repository, GermOnlineGermOnline). It supplies and). It supplies anddevelops services for the biomedical research community worldwide by way of software anddevelops services for the biomedical research community worldwide by way of software andservices that can be accessed from the SIB web servers (services that can be accessed from the SIB web servers (ExPASyExPASy, Melanie, T-COFFEE, PFTOOLS,, Melanie, T-COFFEE, PFTOOLS,ESTScanESTScan, , DotletDotlet, , SEViewSEView, , Snp_detectSnp_detect, , MmsearchMmsearch, Swiss-Model, , Swiss-Model, DeepView/Swiss-PdbViewerDeepView/Swiss-PdbViewer,,MIMAS).MIMAS).
86
Protein Information ResourceProtein Information ResourcePIRPIR
http://pir.georgetown.edu/pirwww/index.shtml
PIR was established in 1984 by the National Biomedical Research FoundationPIR was established in 1984 by the National Biomedical Research Foundation(NBRF) as a resource to assist researchers in the identification and(NBRF) as a resource to assist researchers in the identification andinterpretation of protein sequence information.interpretation of protein sequence information.
87
Universal Protein ResourceUniversal Protein ResourceUniProtUniProt
http://www.pir.uniprot.org/index.shtml
UniProt UniProt (Universal Protein Resource) is the world's most comprehensive(Universal Protein Resource) is the world's most comprehensivecatalogue of information on proteins. It is a central repository of proteincatalogue of information on proteins. It is a central repository of proteinsequence and function created by joining the information contained insequence and function created by joining the information contained inUniProtKB/Swiss-ProtUniProtKB/Swiss-Prot, , UniProtKB/TrEMBLUniProtKB/TrEMBL, and PIR., and PIR.
88
Next Step for ProteomicsNext Step for Proteomics
89
Proteomics
Fractionation Fingerprint Identification Data analysisSamplepreparation
?????? ?? ?? ?? ?????? ???? ?? ??
Sample loss
Reproducibility
Detection level Analytical and Preparative
Method
90
•• Commitment to one technology can be fatal in a rapidCommitment to one technology can be fatal in a rapiddeveloping field.developing field.
•• Computational and information technology (bioinformatics) isComputational and information technology (bioinformatics) iscritical and at the same time limitation for proteomicscritical and at the same time limitation for proteomics
•• New strategies of mass-spectrometric analysis of intactNew strategies of mass-spectrometric analysis of intactproteins, protein complexes, and workflows are emerging.proteins, protein complexes, and workflows are emerging.
•• Bottlenecks in proteomic analyses/experiments remain andBottlenecks in proteomic analyses/experiments remain andneed closer attention.need closer attention.
SummarySummary
91
Summary (cont.)Summary (cont.)
5.5. Mining of collective data and collective mining of theMining of collective data and collective mining of the data will increase efficiency of transforming information data will increase efficiency of transforming information to knowledge. to knowledge.
6. Proteomics is an effort of all people working in this field.6. Proteomics is an effort of all people working in this field.
7.7. Very little proteomic data is publicly accessibleVery little proteomic data is publicly accessiblebecause publications show conclusions but not data.because publications show conclusions but not data.
8.8. Develop and support infrastructure for data sharing andDevelop and support infrastructure for data sharing and mining. mining.
9. Make data access condition for publication.9. Make data access condition for publication.
92
AcknowledgmentsAcknowledgmentsIDeA IDeA Conference OrganizersConference Organizers
Dr. Charles Wood, DirectorDr. Charles Wood, DirectorNebraska Center for VirologyNebraska Center for Virology
University of Nebraska, LincolnUniversity of Nebraska, Lincoln
Dr. Howard Gendelman, Chair and DirectorDr. Howard Gendelman, Chair and DirectorUniversity of Nebraska Medical CenterUniversity of Nebraska Medical Center
Department of Pharmacology and Experimental NeuroscienceDepartment of Pharmacology and Experimental NeuroscienceCenter for Neurovirology and Neurodegenerative DisordersCenter for Neurovirology and Neurodegenerative Disorders
Dr. Dr. Wojtek RozekWojtek Rozek Dr. Mary Dr. Mary Ricardo-DukelowRicardo-DukelowMs. Irena Ms. Irena KadiuKadiu Ms. Melissa Ms. Melissa FladsethFladsethMs. Kristy BernhardtMs. Kristy Bernhardt Dr. Yoshimi EnoseDr. Yoshimi EnoseDr. Eric AndersonDr. Eric Anderson Ms. Sondra HollowayMs. Sondra HollowayMs. Annie MackMs. Annie Mack MrMr. Mark Solomon. Mark Solomon
National NeuroAIDS Tissue ConsortiumNational NeuroAIDS Tissue Consortium
NIH COBRE P20 RR15635 (CW), P01 NS43985, NS034239 (HEG),NIH COBRE P20 RR15635 (CW), P01 NS43985, NS034239 (HEG),and R21 and R21 MH075662 (PC) (PC)
Graphic Support: Robin Taylor, UNMC
93
B. B. DomonDomon, and R. , and R. AebersoldAebersold. Mass Spectrometry and Protein Analysis. Science 2006, 312: 212-217.. Mass Spectrometry and Protein Analysis. Science 2006, 312: 212-217.
CiphergenCiphergen, Inc., Inc.
R. R. Houtman Houtman et al. Proteomics, 2003, 3:2008-2018.et al. Proteomics, 2003, 3:2008-2018.
ProteomeLabProteomeLab (PF2D) from Beckman-Coulter, Inc.(PF2D) from Beckman-Coulter, Inc.
http://www.mc.http://www.mc.vanderbiltvanderbilt..edu/msrc/tissue/tissue_profiling_and_imagingedu/msrc/tissue/tissue_profiling_and_imaging..phpphp
http://www.mc.http://www.mc.vanderbiltvanderbilt..edu/msrc/tissue/tissue_profilingedu/msrc/tissue/tissue_profiling..phpphp
http://catalog.invitrogen.com/productimages/3400/3347.jpg
http://www.hpr.se/object.php
http://www.http://www.colorbasepaircolorbasepair..com/what_is_bioinformaticscom/what_is_bioinformatics.html.html
http://www.http://www.genpromaggenpromag..com/Glossary~LETTER~Scom/Glossary~LETTER~S.html.html
http://en.http://en.wikipediawikipedia..org/wiki/Systems_biologyorg/wiki/Systems_biology
NL. Anderson, and NG. Anderson. The Human Plasma Proteome. Molecular & Cellular Proteomics. 2002, 1: 845-867.NL. Anderson, and NG. Anderson. The Human Plasma Proteome. Molecular & Cellular Proteomics. 2002, 1: 845-867.
http://us.expasy.org
http://www.pir.uniprot.org/index.shtml
http://www.pir.georgetown.edu/pirwww/index.shtmlh
SourcesSources
Rozek W and Ciborowski P “Proteomics and Genomics in Neuroinflammation,” in Neuroimmune Pharmacology, ed.Ikezu, T. and Gendelman, H.E. Springer, in press 2006
