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Localization prediction of
transmembrane proteins
Stefan Maetschke, Mikael Bodén and Marcus Gallagher
The University of Queensland
Protein classes
Protein
Membrane
Soluble
Integral
Peripheral
Anchored
Transmembrane
-barrel
-helical
Multi-spanning
Maetschke et al, The University of Queensland
Single-spanning
2
Transmembrane protein types
Type-I
signal
peptide
Type-II
C
N
Type-IV
(multi-spanning)
Type-III
N
C
C
N
Cytosol (inside)
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Eukaryotic cell
Peroxisome
Nucleus
Mitochondrion
RNA
Ribosome
Endoplasmic Reticulum
ERGIC
Golgi Complex
Lysosome
Endosome
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Secretory and endocytic pathway
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Problem and hypothesis
• Sorting signals for transmembrane proteins serve multiple
purposes (targeting, retention, retrieval, avoidance) and
are largely unknown (the problem is challenging/multifaceted)
• Current localization prediction of eukaryotic
transmembrane proteins is poor (models based on soluble
proteins are ill-suited) (previous work is
inadequate/incomplete)
• Localization prediction for transmembrane proteins is
virtually unexplored (paucity/variance of data) (it is an open
problem)
• Explicit modelling of protein topology should enhance
localization prediction accuracy
(parameter tuning receives explicit guidance to biologically
sensible solutions) (the way to do it!)
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Hidden Markov model

State sequence:

Inital state probabilities:
s1 s1 s1 s2 s2 s2 s2 s2 s2 s3
    i    q1  Si 

b1
A  aij  P(qt  S j | qt 1  Si )
A

Observation probabilities:
B   bi (k )   P(ot  Vk | qt  Si )
a33
a23
S2
1
2
A
R
V
1
2
V
20
Maetschke et al, The University of Queensland
S3
b3
b2
...
R

a12
S1
State transition probabilities:
  
a22
a11
A
R
1
2
...
Observation sequence:
...

V
20
20
7
2-order Hidden Markov model

Observation sequence:

State sequence:

Inital state probabilities:
s1 s1 s1 s2 s2 s2 s2 s2 s2 s3
    i    q1  Si 
S2
a33
a23
S3
b3
State transition probabilities:
b1
A  aij  P(qt  S j | qt 1  Si )
AA
1
AA
1
AA
1
AR
2
AR
2
AR
2
Observation probabilities:
AN
3
AN
3
AN
3
B   bi (k )   P(ot  Vk | qt  Si )
AD
4
AD
4
AD
4

VV
400
Maetschke et al, The University of Queensland
VV
...
  
b2
...

a12
S1
...

a22
a11
400
VV
400
8
3-order Hidden Markov model
Observation sequence:

State sequence:

Inital state probabilities:
s1 s1 s1 s2 s2 s2 s2 s2 s2 s3
    i    q1  Si 

a12
S1
State transition probabilities:
  
a22
a11

A  aij  P(qt  S j | qt 1  Si )
b1
AAA
1
2
Observation probabilities:
AAN
B   bi (k )   P(ot  Vk | qt  Si )
AAD
a23
3
4
AAC
5
AAQ
1
AAR
VVV
8000
Maetschke et al, The University of Queensland
AAA
1
AAR
2
AAN
2
AAN
3
AAD
3
AAD
4
AAC
4
AAC
5
AAQ
5
AAQ
6
...
...
6
AAA
S3
b3
b2
AAR

S2
a33
6
...

VVV
VVV
8000
8000
9
Signal peptide
N-terminal
region
hydrophobic core
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cleavage
region
mature
protein
10
Transmembrane domain
icap
TMD
ocap
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Protein topology model
SP
N-term
outside ocap
TMD
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icap
inside C-term
12
Localization model (5 x topology models)
Peroxisome
Nucleus
Mitochondrion
ERGIC
Endoplasmic Reticulum
Lysosome
Golgi Complex
Endosome
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LOCATE dataset
Subset LOCATE database
873
Plasma Membrane
261
Endoplasmic Reticulum
141
Golgi Complex
45
Lysosome
31
Endosome
FANTOM3, Mouse proteome
 Filter for transmembrane proteins
 No multi-targeted proteins
 Redundancy reduced (<25%)
 TMDs and SPs are labeled (predicted)
 High quality localization annotation

1351
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Prediction performance
Prediction Performance (MCC)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
LOCATE dataset
 Mean correlation coefficient
 10 fold, 10 times
 Five locations (ER, PM, GO, EN, LY)
 SVM: linear kernel
 1-, 2- and 3-order HMMs

SVM-1
SVM-2
HMM-1
HMM-2
Confusion Matrix HMM-2
HMM-3
=> Di-peptide composition superior to
single amino acid composition
=> Topological model superior to
non-topological model
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Predictor comparison
Prediction accuracy in %
75
80
Test set
(20 PM, 20 ER, 20 Golgi)
 HMM: only three classes but
test set  train set
 Other predictors: more classes but
test set  train set

70
60
48
50
40
33
30
20
18
→ difficult to compare!
10
0
CELLO
WolfPSort
PAnalyst
CELLO 2.5:
WolfPSort:
ProteomeAnalyst 2.5:
HMM-2:
HMM-2
http://cello.life.nctu.edu.tw/
http://wolfpsort.seq.cbrc.jp/
http://www.cs.ualberta.ca/~bioinfo/PA/Sub/
http://pprowler.itee.uq.edu.au/TMPHMMLoc
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Conclusion
• Novel predictor for subcellular localization of transmembrane
proteins along the secretory pathway:
http://pprowler.itee.uq.edu.au/TMPHMMLoc
• Protein model has less states than topology predictors (TMHMM,
HMMTOP, etc) but is of second order
• Localization model is trained and tested using LOCATE, a recent,
high-quality localization dataset
• Overall better performance than current localization predictors
(transmembrane proteins, eukaryotic, secretory pathway)
– Di-peptide composition superior to single amino acid composition
– "Topological" model superior to "non-topological" baseline model
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