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
Biological
Insight
More detailed
information
Dynamics
-Turnover
-Transport
-Intrinsic transient behaviors
Isotope labeling is
essential
Quantification
-Expression differences
-PTM occupancies
-Interaction strength
Identification (Qualitative)
Higher
Throughput
-Peptide/protein IDs
-PTM IDs and site assignment
-Interaction partners
Isotope labeling is
not necessary
2
Study Protein Turnover on A Proteomic Scale
Food Source
Proteins
Amino Acids
Waste
Many neurodegenerative diseases are closely related to protein turnover
•Alzheimer's disease: Ab aggregation/breakdown of tau in brain
•Parkinson’s disease: accumulation of alpha-synuclein
•CJD: transmission and accumulation of misfolded prion
3
Dynamic Proteomics by 15N Metabolic Labeling
15N
Inorganic
salt
Label Algae
With 15N
feed mice
harvest
tissues
over time
extract
proteins
data processing
GO
inference
PNAS2010v107p14508
digest
LCMSMS
Function
Localization
Processes
4
Correlations between function and turnover rates
PNAS2010v107p14508
5
Protein Turnover in Human Plasma
AnalyticalBiochemistry2012v420p73
6
Metabolic Labeling Reveals Proteome Dynamics of Mouse Mitochondria
314 and 386 proteins in heart and liver mitochondria
Half live of heart and liver mitochondria: 17.2 d and 4.26 d
mcp.M112.021162
7
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 smaller
active genes = faster rates; silent genes = slower rates
JBC2010v285p3341
8
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
me2me3 rates 100X smaller for H3K27 or H3K36
More methyltransferase MMSET, higher rates
9
Data Processing Pipeline
for Mammalian Protein Turnover Studies
LTQFTQ Exactive
Sensitivity!
Compartment (Pool) Models
Accuracy/Biological significance
fitXIC
LC alignment
Selectivity!
RAW Files
Protein
Turnover
14N Survey
XIC
Cross
Extract
MS2
Extract
MSMS
Peaklists
15N Survey
MS Peaklists
Database
Search
fitCurve
Protein
Curves
NN Least
Squares
Peptide
ID List
15N
Distributions
Pep2Prot
Peptide
Curves
Curve
Construct
MCP2011v10: M110.005785
10
LC Alignment for 15N Isotopomer Extraction
8
6
x 10
4
2
0
-2
-4
Original Basepeak Chromatogram
-6
10
20
30
40
50
60
70
60
70
8
6
x 10
4
2
0
-2
-4
-6
10
After LC alignment
20
30
40
50
11
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)
RIA(t )  (1  e  k (t t0 ) )
PNAS2010v107p14508
12
phosphatidylethanolamine-binding protein 1, P70296
in brain
15N Relative Fraction
Protein incorporation curve is constructed from 13 peptide curves
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
5
10
15
20
25
30
35
Incorporation Time (day)
13
JBiolChem1939v130p703
14
Compartment Modeling of Protein Turnover
PhysMedBiol1957v2p36
15
Compartment Modeling and NonCompartmental Analysis
in Drug Development
Pharmacokinetics (PK) studies
Industry Standard Software Package == WinNonLin
AdvDrugDelivertRew2001v48p249
16
Compartment (Pool) Modeling
“分池模型”
d R A

(1   )
dt
V
RA[A]T
[ A]

[ A]T
[ A]0  0
V
 0, t  0
input  
 R A [ A]T , t  0
 0, t  0
output  
 R A [ A], t  0
RA[A]
 (t )  (1  e
AnalChem2012v84p4014

RA
t
V
), t  0
17
Relative Fractional Label Concentration
SILAC labels: Lys 6, Lys 8, Arg 6, or Arg8
Stable element labels: 15N, 2H, 13C, etc
8.52
8.02
ALFQDVQKPSQDEWGK2+
Nlabel = 2
60%
9.01
4.51
4.01
32%
5.01
8%
0.49 0.99
0.00
1.90
2.52 2.90 3.52
938
939
940
941
942
9.52
5.51
7.52
6.01
6.49 7.39
943
944
945
946
m/z
10.01
10.51 11.01
947
948
949
950
What is the physical or chemical significance of the SILAC ratio?
0X8%+1X32%+2X60%
Relative Fractional Label Concentration (RF) = -------------------------------------- = 0.76
100% X Nlabel
total moles percent enrichment (MPE) AnalBiochem2011v412p47
18
Two-compartment/two rate constant model
- Brain Proteins
kb’
k0’
VAA
14NAA
15NAA
(t)
Ra*H(t)
Free amino acid
pool (compartment)
k s’
14NP
VP
15NP
b(t)
Protein (of interest)
pool (compartment)
Two-compartment/two rate constant model
V AA
d [14 NAA]
 (k s ' k 0 a ' )[14 NAA]
dt
d [14 NP]
VP
 k s '[14 NAA]  kb '[14 NP]
dt
V AA
d [15 NAA]
 (k s ' k 0 a ' )[15 NAA]  Ra[ AA]
dt
d [15 NP]
VP
 k s '[15 NAA]  k b '[15 NP]
dt
[14 NAA]  [15 NAA]  [ AA]
[14 NP]  [15 NP]  [ P]
2
Solution of two-compartment/two-rate constant model
[15 NAA]
 (t ) 
 1  e  k 0t
[ AA]
kb e  k 0t k 0 e  kb t
[15 NP]
b (t ) 
 1

[ P]
k 0  kb k 0  kb
k s ' k0 ' k0 '
k0 

VAA
VAA
1
0.9
0.8
kb '
kb 
VP
0.7
0.6
b
0.5
0.4
k s ' [ P]

kb ' [ AA]
0.3
0.2
0.1
0
b (t )  1  e
 kbt
, k0  kb
0
5
10
15
20
t
25
30
35
1
Phosphatidyl0.9
ethanolamine-binding 0.8
0.7
protein 1, P70296
RIA 0.6
in brain
0.5
0.4
0.3
0.2
0.1
00
15N Relative Fraction
0.7
b (t )  1 
0.6
Empirical delayed exponential model
y (t )  1  e  k (t t0 )
k  0.0563 day -1
t0  1.23 day -1
R 2  0.988
5
10
15
Incorporation Time (day)
20
25
30
35
kb
k0
e  k 0t 
e  kb t
k 0  kb
k 0  kb
0.5
kb  0.0373 day -1
0.4
k0  0.1587 day -1
0.3
R 2  0.9989
Two-compartment/two rate constant model
0.2
0.1
0
0
5
10
15
20
25
30
Incorporation Time (day)
35
22
Three-compartment/five rate constant model
for Liver Proteins
k0t
kbi
k0a
14NPt
15NPt
VPt
kbt
kst
14NAA
14NPi
ksi
15NAA
15NPi
VAA
VPi
g(t)
Ra*H(t)
23
transitional endoplasmic reticulum ATPase (Q01853), liver
Protein incorporation curve is constructed from 37 of a total of 45 peptide curves
15N Relative Fraction
Two-compartment/two rate constant model
(a)
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
Three-compartment/four rate constant model
(b)
kb =0.137day-1
k0 =1.62X1010day-1
R2 =0.981
5
10
15
20
25
Incorporation Time (day)
30
35
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
kst =0.713day-1
k0 =2.002day-1
kbt =0.026day-1
kbi =0.317day-1
R2 =0.9995
0
5
10
15
20
25
Incorporation Time (day)
30
35
24
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
25
Studies
Cellular models – on going
Aging models – on going
Disease models: Prion infected - planned
Technical improvement
High sensitivity Instrument - installed
LC alignment – implemented
Processing speed and QC – on going
26
神经退化性疾病研究所
John C. Price
Shigenari Hayashi
Alma L. Burlingame
Sina Ghaemmaghami
Stanley B. Prusiner
药化系质谱中心
27