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蛋白质代谢的信息处理进展
Neurosciences Research Building关慎恒
Mass Spectrometry Facility/Department of Pharmaceutical Chemistry,Institute for Neurodegenerative Diseases and Department of Neurology,
University of California, San Francisco
第二届中国计算蛋白质组学研讨会
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Identification (Qualitative)-Peptide/protein IDs
-PTM IDs and site assignment-Interaction partners
Quantification-Expression differences
-PTM occupancies-Interaction strength
Dynamics-Turnover-Transport
-Intrinsic transient behaviors
Biological Insight
More detailed information
HigherThroughput
Isotope labeling isessential
Isotope labeling isnot necessary
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Study Protein Turnover on A Proteomic Scale
Many neurodegenerative diseases are closely related to protein turnover
•Alzheimer's disease: A aggregation/breakdown of tau in brain•Parkinson’s disease: accumulation of alpha-synuclein•CJD: transmission and accumulation of misfolded prion
Amino Acids Food Source
Waste
Proteins
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15NInorganic
salt
Label AlgaeWith 15N
feed miceharvesttissues
over time
extractproteins
digestLCMSMS
data processing
GO inference
FunctionLocalizationProcesses
Dynamic Proteomics by 15N Metabolic Labeling
PNAS2010v107p14508
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Correlations between function and turnover rates
PNAS2010v107p14508
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Protein Turnover in Human Plasma
AnalyticalBiochemistry2012v420p73
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7mcp.M112.021162
Metabolic Labeling Reveals Proteome Dynamics of Mouse Mitochondria
314 and 386 proteins in heart and liver mitochondriaHalf live of heart and liver mitochondria: 17.2 d and 4.26 d
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8JBC2010v285p3341
Kinetics of Methylation on Histones
• Marking methyl groups with isotope labeled methionine• Kinetic modeling of isotope incorporation into methylated Lysines
mono-, di-, and trimethylation rates: progressively smalleractive genes = faster rates; silent genes = slower rates
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MS-based measurement and modeling of histone methylation kinetics (M4K)
• Use SRM to measure labeled co-occupant methylation states• Use labeled arginine to measure protein turnover• Kinetic modeling of co-occupant methylation states
PNAS2012v109p13549
me2me3 rates 100X smaller for H3K27 or H3K36More methyltransferase MMSET, higher rates
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RAW Files
MSMSPeaklists
PeptideID List
MS2Extract
DatabaseSearch
14N SurveyXIC
fitXIC
15N Distributions
CrossExtract
15N SurveyMS Peaklists
NN LeastSquares
PeptideCurves
ProteinCurves
ProteinTurnover
CurveConstruct
Pep2Prot
fitCurve
Data Processing Pipelinefor Mammalian Protein Turnover Studies
MCP2011v10: M110.005785
LTQFTQ ExactiveSensitivity!
LC alignmentSelectivity!
Compartment (Pool) ModelsAccuracy/Biological significance
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10 20 30 40 50 60 70-6
-4
-2
0
2
4
6x 10
8
10 20 30 40 50 60 70-6
-4
-2
0
2
4
6x 10
8
Original Basepeak Chromatogram
After LC alignment
LC Alignment for 15N Isotopomer Extraction
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Protein Turnover - Empirical Modeling
•Mass shift is an independent and fast process
•Incorporation curve may be modeled as a delayed exponential
•The model seems universal applicable (to the whole proteomes)
PNAS2010v107p14508
)1()( )( 0ttketRIA
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0 5 10 15 20 25 30 350
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Incorporation Time (day)
15N
Rel
ativ
e F
ract
ion
phosphatidylethanolamine-binding protein 1, P70296 in brain
Protein incorporation curve is constructed from 13 peptide curves
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14JBiolChem1939v130p703
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15PhysMedBiol1957v2p36
Compartment Modeling of Protein Turnover
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Compartment Modeling and NonCompartmental Analysisin Drug Development
AdvDrugDelivertRew2001v48p249
Pharmacokinetics (PK) studies
Industry Standard Software Package == WinNonLin
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Compartment (Pool) Modeling “分池模型”
)1(
V
R
dt
d ARA[A]T
RA[A]
V
TA
A
][
][
0],[
0,0
0,][
0,0
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tAR
toutput
tAR
tinput
A
A
TA
0),1()(
tett
V
RA
AnalChem2012v84p4014
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Relative Fractional Label Concentration
938 939 940 941 942 943 944 945 946 947 948 949 950m/z
8.528.02
9.01
4.514.01
5.019.52
5.510.49 10.010.990.00 1.90 7.526.01 10.512.90 3.52 11.016.492.52
7.39
ALFQDVQKPSQDEWGK2+
Nlabel = 2
What is the physical or chemical significance of the SILAC ratio?
0X8%+1X32%+2X60%Relative Fractional Label Concentration (RF) = -------------------------------------- = 0.76
100% X Nlabel
60%
32%
8%
SILAC labels: Lys 6, Lys 8, Arg 6, or Arg8Stable element labels: 15N, 2H, 13C, etc
total moles percent enrichment (MPE) AnalBiochem2011v412p47
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14NAA
15NAA
14NP
15NP
ks’
Ra*H(t) (t)
k0’kb’
(t)
VAA VP
Two-compartment/two rate constant model- Brain Proteins
Free amino acid pool (compartment)
Protein (of interest)pool (compartment)
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Two-compartment/two rate constant model
]14)[''(]14[
0 NAAkkdt
NAAdV asAA
]14[']14[']14[
NPkNAAkdt
NPdV bsP
][]15)[''(]15[
0 AARaNAAkkdt
NAAdV asAA
]15[']15[']15[
NPkNAAkdt
NPdV bsP
][]15[]14[ AANAANAA
][]15[]14[ PNPNP
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Solution of two-compartment/two-rate constant model
tkeAA
NAAt 01
][
]15[)(
b
tk
b
tkb
kk
ek
kk
ek
P
NPt
b
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AAAA
s
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k
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kkk
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P
bb V
kk
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][
][
'
'
AA
P
k
k
b
s
0 5 10 15 20 25 30 350
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
t
btk kket b
0,1)(
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Empirical delayed exponential model
Incorporation Time (day)
988.02
day 23.1
day 0.0563
1)(
1-0
1-
)0(
R
t
k
ety ttk
0 5 10 15 20 25 30 3500.10.20.30.40.50.60.70.80.9
1
RIA
Incorporation Time (day)
15N
Rel
ativ
e F
ract
ion
0 5 10 15 20 25 30 350
0.1
0.2
0.3
0.4
0.5
0.6
0.7
9989.02
day 0.1587
day 0.0373
1)(
1-0
1-
0
0
0
0
R
k
k
ekk
ke
kk
kt
b
tk
b
tk
b
b b
Two-compartment/two rate constant model
Phosphatidyl-ethanolamine-binding
protein 1, P70296 in brain
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14NAA
15NAA
14NPi
15NPi
ksi
k0a
Ra*H(t)
(t)
14NPt
15NPt
kbi
VPt VAA VPi
k0t
kst
kbt
Three-compartment/five rate constant modelfor Liver Proteins
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0 5 10 15 20 25 30 350
0.10.20.30.40.50.60.70.80.91
0 5 10 15 20 25 30 350
0.10.20.30.40.50.60.70.80.91
(a) (b)
15N
Rel
ativ
e F
ract
ion
Incorporation Time (day) Incorporation Time (day)
kst =0.713day-1
k0 =2.002day-1
kbt =0.026day-1
kbi =0.317day-1
R2 =0.9995
kb =0.137day-1
k0 =1.62X1010day-1
R2 =0.981
Two-compartment/two rate constant model Three-compartment/four rate constant model
transitional endoplasmic reticulum ATPase (Q01853), liver
Protein incorporation curve is constructed from 37 of a total of 45 peptide curves
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Compartment Modeling
1. Fit better to experimental data with a minimal
number of parameters
2. Fitting parameters have biological significance
3. Individual rate constants are determined
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Studies
Cellular models – on goingAging models – on going Disease models: Prion infected - planned
Technical improvement
High sensitivity Instrument - installedLC alignment – implementedProcessing speed and QC – on going
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John C. Price Sina Ghaemmaghami
Stanley B. Prusiner
Shigenari Hayashi
Alma L. Burlingame
神经退化性疾病研究所
药化系质谱中心