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Napovedovanje imunskega odziva
iz peptidnih mikromrež
Mitja Luštrek1 (2),
Peter Lorenz2, Felix Steinbeck2, Georg Füllen2, Hans-Jürgen Thiesen2
1
Odsek za inteligentne sisteme, Institut Jožef Stefan
2 Univerza v Rostocku
1. Introduction
2. Immune response prediction
3. Interpretation
1. Introduction
2. Immune response prediction
3. Interpretation
Peptide
= part of protein = short sequence of amino acids
Image taken from
EMBL website
Peptide
= part of protein = short sequence of amino acids
SNDIVLT
Image taken from
EMBL website
= string of letters from 20-letter alphabet
(1 letter = 1 amino acid, 20 standard amino acids)
Epitope
Antigen
protein
Antibody
binding
Antibody
Epitope
Epitope
Antigen
protein
Antibody
binding
Antibody
Epitope
Epitope
Antigen
protein
Peptide
Epitope
Epitope
Antigen
protein
Epitope
Epitope
Antigen
protein
Antibody
binding
Antibody
Epitope
Epitope
Antigen
protein
Antibody
binding
Antibody
Epitope
Epitope
Antigen
protein
Antibody
binding
Antibody
Epitope
Epitope
Antigen
protein
Epitope
Epitope
Antigen
protein
Peptide arrays
Peptide
array
Peptides
(15 amino acids)
Glass slide
Peptide arrays
IVIg antibody
mixture
Peptide
array
Peptides
(15 amino acids)
Glass slide
Peptide arrays
IVIg antibody
mixture
Red = epitopes (bind antibodies)
Black = non-epitopes
Peptide
array
Peptides
(15 amino acids)
Glass slide
Peptide arrays
Red = epitopes (bind antibodies)
Black = non-epitopes
Antibody
against
antibody
+ dye
Antibody
Peptide
Glass slide
Peptide arrays
Peptide
Class
PGIGFPGPPGPKGDQ non-ep.
Red = epitopes (bind antibodies)
Black = non-epitopes
PNMVFIGGINCANGK non-ep.
DGIGGAMHKAMLMAQ non-ep.
REDNLTLDISKLKEQ non-ep.
TPLAGRGLAERASQQ non-ep.
DQVHPVDPYDLPPAG non-ep.
...
RRMISRMPIFYLMSG epitope
LPPGFKRFTCLSIPR epitope
EFSQMESYPEDYFPI epitope
...
1. Introduction
2. Immune response prediction
3. Interpretation
Our task
Peptide
RRKGGLEEPQPPAEQ
SEDLENALKAVINDK
EDHVKLVNEVTEFAK
GEKIIQEFLSKVKQM
ILVSRSLKMRGQAFV
YTCQCRAGYQSTLTR
...
Our task
Peptide
Peptide
Class
RRKGGLEEPQPPAEQ
RRKGGLEEPQPPAEQ non-ep.
SEDLENALKAVINDK
SEDLENALKAVINDK non-ep.
EDHVKLVNEVTEFAK
EDHVKLVNEVTEFAK non-ep.
GEKIIQEFLSKVKQM
Machine
learning
GEKIIQEFLSKVKQM non-ep.
ILVSRSLKMRGQAFV
ILVSRSLKMRGQAFV epitope
YTCQCRAGYQSTLTR
YTCQCRAGYQSTLTR epitope
...
...
Our task
Peptide
Peptide
Class
RRKGGLEEPQPPAEQ
RRKGGLEEPQPPAEQ non-ep.
SEDLENALKAVINDK
SEDLENALKAVINDK non-ep.
EDHVKLVNEVTEFAK
EDHVKLVNEVTEFAK non-ep.
GEKIIQEFLSKVKQM
Machine
learning
GEKIIQEFLSKVKQM non-ep.
ILVSRSLKMRGQAFV
ILVSRSLKMRGQAFV epitope
YTCQCRAGYQSTLTR
YTCQCRAGYQSTLTR epitope
...
...
Training set: 13,638 peptides (3,420 epitopes)
Test set: 13,640 peptides (3,421 epitopes)
Balanced until
the final testing
Machine learning
Peptide
Class
PGIGFPGPPGPKGDQ non-ep. / epitope
Machine learning
Class
Peptide
PGIGFPGPPGPKGDQ non-ep. / epitope
Attribute 1
Attribute 2
value 1
value 2
...
Class
non-ep. / epitope
Attribute
representation
Machine learning
Class
Peptide
PGIGFPGPPGPKGDQ non-ep. / epitope
Attribute 1
Attribute 2
value 1
value 2
...
Class
non-ep. / epitope
ML
Classifier
Proability for epitope p
Attribute
representation
Machine learning
Class
Peptide
PGIGFPGPPGPKGDQ non-ep. / epitope
Attribute 1
Attribute 2
value 1
value 2
...
Class
non-ep. / epitope
ML
Classifier
Proability for epitope p
Attribute
representation
Machine learning
Class
Peptide
PGIGFPGPPGPKGDQ non-ep. / epitope
Attribute
representation 1
...
ML
Classifier 1
Attribute
representation 8
ML
...
Classifier 8
Machine learning
Class
Peptide
PGIGFPGPPGPKGDQ non-ep. / epitope
Attribute
representation 1
Attribute
representation 8
...
ML
ML
Classifier 1
...
Classifier 8
Probabilities for epitope
p1
p2
p3
p4
p5
Class
p6
p7
p8
non-ep. / epitope
Final proability
for epitope p
Meta
classifier
Machine learning
Class
Peptide
PGIGFPGPPGPKGDQ non-ep. / epitope
Attribute
representation 1
SVM (SMO),
Logistic
regression
Attribute
representation 8
...
ML
ML
Classifier 1
...
Final proability
for epitope p
Classifier 8
Linear
regression
Probabilities for epitope
p1
p2
p3
p4
p5
Class
p6
p7
p8
non-ep. / epitope
Meta
classifier
Attribute representation 1
Amino-acid counts
RRMISRMPIFYLMSG
Count of A C D E
F
G H
I
1
1
2
K
L M N P Q R
S
1
2
3
1
3
T
V W Y
1
Attribute representation 2
Amino-acid count differences
RRMISRMPIFYLMSG
Difference in counts of F–G F–I
0
–1
F–L F–M F–P F–R F–S F–Y G–F G–I
0
–2
0
–2
–1
0
0
–1
...
Attribute representation 3
Subsequence counts
RRMISRMPIFYLMSG
Count of
RR RM MI
1
2
1
... RRM RMI
1
1
MIS
1
... ACDE ... ACDEF ...
0
0
Attribute representation 4
Amino-acid class counts
l l l l t l l s l l l l l t t
RRMISRMPIFYLMSG
bbnnnbnnnnnnnnn
Count of
tiny
small
large
basic
acidic
neutral
3
1
11
3
0
12
...
Attribute representation 5
Amino-acid class subsequence counts
l l l l t l l s l l l l l t t
RRMISRMPIFYLMSG
bbnnnbnnnnnnnnn
Count of
ll
lt
tl
ls
sl
tt
8
2
1
1
1
1
...
bb
bn
nb
nn
1
2
1
10
...
Attribute representation 6
Amino-acid pair counts
Rationale: antibodies may bind in two places due to their twochain structure.
Antibody
Peptide
Attribute representation 6
Amino-acid pair counts
Rationale: antibodies may bind in two places due to their twochain structure.
RRMISRMPIFYLMSG
123
Antibody
3
Peptide
Count of pairs at distance
(R,R) at 1 (R,M) at 2 (R,I) at 3
1
1
2
...
(A,C) at 1 (A,C) at 2
0
0
...
Attribute representation 7
Amino-acids at distances from first + first amino acid
Rationale: antibodies may bind in two places, first amino acid
most accesible on the peptide array.
Antibody
Peptide
Attribute representation 7
Amino-acids at distances from first + first amino acid
Rationale: antibodies may bind in two places, first amino acid
most accesible on the peptide array.
Antibody
R RMISRMPIFYLMSG
Peptide
Count of at distance ...
R at 1
1
...
M at 2
1
...
A at 3
C at 3
0
0
...
First
R
Attribute representation 8
Average amino-acid properties
RRMISRMPIFYLMSG
Hydrophobicity
Size
Polarity
Flexibility
Accesibility
0.448
0.596
0.306
0.231
0.376
...
Attribute representation 9 (not used)
Amino-acid counts with a difference
RRMISRMPIFYLMSG
RRMISRMPIWYLMSG
Equivalent for epitope prediction?
Attribute representation 9 (not used)
Amino-acid counts with a difference
RRMISRMPIFYLMSG
RRMISRMPIWYLMSG
Equivalent for epitope prediction?
Count F as:
• 1F
• 0.8 W
• 0.4 Y
• ...
Count W as:
• 1W
• 0.7 F
• 0.3 Y
• ...
Attribute representation 9 (not used)
Amino-acid substitution matrix
A
C
D
...
F
W
Y
A
1
C
D
...
F
W
Y
1
1
1 0.8 0.4
0.7 1 0.3
1
Attribute representation 9 (not used)
Amino-acid substitution matrix
A
C
D
...
F
W
Y
A
1
C
D
...
F
W
Y
1
Optimize
with a genetic algorithm
to maximize
classification accuracy
1
1 0.8 0.4
0.7 1 0.3
1
Results – training set
Attribute representation
Amino-acid counts
Amino-acid count differences
Subsequence counts
Amino-acid class counts
Amino-acid class subsequence counts
Amino-acid pair counts
Amino acids at distances from the first
Average amino-acid properties
AUC
Accuracy
0.870
80.7 %
0.868
80.3 %
0.867
80.5 %
0.873
81.2 %
0.866
80.5 %
0.865
80.6 %
0.873
81.2 %
0.863
80.3 %
Results – training set
Attribute representation
Amino-acid counts
Amino-acid count differences
Subsequence counts
Amino-acid class counts
Amino-acid class subsequence counts
Amino-acid pair counts
Amino acids at distances from the first
Average amino-acid properties
Combined
AUC
Accuracy
0.870
80.7 %
0.868
80.3 %
0.867
80.5 %
0.873
81.2 %
0.866
80.5 %
0.865
80.6 %
0.873
81.2 %
0.863
80.3 %
0.881
83.3 %
Results – test set
Attribute representation / dataset
Best single / training set
Combined / training set
Combined / test set
AUC
Accuracy
0.873
81.2 %
0.881
83.3 %
0.883
83.7 %
Results – test set
Attribute representation / dataset
Best single / training set (balanced)
Combined / training set (balanced)
Combined / test set (balanced)
Combined / test set (original)
AUC
Accuracy
0.873
81.2 %
0.881
83.3 %
0.883
83.7 %
0.884
85.9 %
Epitope : non-epitope = 1 : 1
Epitope : non-epitope = 1 : 3
Results – test set
Attribute representation / dataset
Best single / training set (balanced)
Combined / training set (balanced)
Combined / test set (balanced)
Combined / test set (original)
EL-Manzalawy / test set (balanced)
EL-Manzalawy / test set (original)
State of the art:
SVM + string kernel
(EL-Manzalawy et al., 2008)
Trained and tested on our data.
AUC
Accuracy
0.873
81.2 %
0.881
83.3 %
0.883
83.7 %
0.884
85.9 %
0.868
82.0 %
0.874
83.9 %
Results – test set
Our results
Balanced: 0.883 / 83.7 %
Original: 0.884 / 85.9 %
EL-Manzalawy
Balanced: 0.868 / 82.0 %
Original: 0.874 / 83.9 %
1. Introduction
2. Immune response prediction
3. Interpretation
Rules
Interpretable classifier:
• Interpretable attributes
(frequencies, properties of amino acids)
• RIPPER (JRip) to induce rules
Rules
Interpretable classifier:
• Interpretable attributes
(frequencies, properties of amino acids)
• RIPPER (JRip) to induce rules
Property
Aromaticity
Low/high
High
Applies to peptides
53.8 %
If a peptide has a high aromaticity, it binds antibodies.
This applies to 53.8 % of peptides that bind antibodies.
(Aromaticity is the percentage of aromatic amino acids in the
peptide.)
Rules
Property
Aromaticity
Polarity
Frequency of tyrosine
Hydrophobicity
Frequency of arginine
Summary factor 2
Acidity
Preference for -sheets
Summary factor 5
Low/high
High
Low
High
Low
High
High
Low
Low
High
Applies to peptides
53.8 %
27.7 %
26.2 %
22.5 %
19.7 %
16.7 %
11.4 %
4.3 %
3.0 %
Epitope propensity
Frequency in peptides with epitopes,
divided by
frequency in peptides without epitopes
Epitope propensity
Aromatic
Epitope propensity
Non-polar
Epitope propensity
Tyrosine
(Un)classifiable peptides
Simplified classifier:
• Interpretable attributes
(frequencies, properties of amino acids)
• Logistic regression to train the classifier
Peptides
All
AUC
Accuracy
0.860
83.0 %
(Un)classifiable peptides
Simplified classifier:
• Interpretable attributes
(frequencies, properties of amino acids)
• Logistic regression to train the classifier
Peptides
All
Classifiable
Unclassifiable
Classified correctly
AUC
Accuracy
0.860
83.0 %
Classified incorrectly
(Un)classifiable peptides
Simplified classifier:
• Interpretable attributes
(frequencies, properties of amino acids)
• Logistic regression to train the classifier
Peptides
All
Classifiable
Unclassifiable
AUC
Accuracy
0.860
83.0 %
0.999
98.8 %
0.956
91.5 %
Expected
Strange?
(Un)classifiable – rules
Attribute
Aromaticity
Polarity
Frequency of arginine
Frequency of tyrosine
Summary factor 5
Antigenicity
Hydrophobicity
Frequency of histidine
Frequency of cysteine
Preference for reverse turns
Occurrence in turns
Frequency of alanine
Classifiable
L/h
Applies
High
74.3 %
Low
58.7 %
High
31.5 %
High
20.7 %
High
15.1 %
High
7.3 %
Low
4.7 %
Low
3.9 %
Unclassifiable
L/h
Applies
Low
53.3 %
High
27.5 %
Low
34.0 %
Low
16.9 %
Low
15.2 %
Low
8.7 %
High
6.5 %
Low
High
Low
High
10.4 %
10.4 %
10.4 %
8.7 %
(Un)classifiable – rules
Attribute
Aromaticity
Polarity
Frequency of arginine
Frequency of tyrosine
Summary factor 5
Antigenicity
Hydrophobicity
Frequency of histidine
Frequency of cysteine
Preference for reverse turns
Occurrence in turns
Frequency of alanine
Classifiable
L/h
Applies
All: 53.8 % 74.3 %
High
LowAll: 27.7 % 58.7 %
High
31.5 %
High
20.7 %
High
15.1 %
High
7.3 %
Low
4.7 %
Low
3.9 %
Unclassifiable
L/h
Applies
Low
53.3 %
High
27.5 %
Low
34.0 %
Low
16.9 %
Low
15.2 %
Low
8.7 %
High
6.5 %
Low
High
Low
High
10.4 %
10.4 %
10.4 %
8.7 %
(Un)classifiable – epitope propensity
(Un)classifiable peptides
Simplified classifier:
• Interpretable attributes
(frequencies, properties of amino acids)
• Logistic regression to train the classifier
Peptides
All
Classifiable
Unclassifiable
AUC
Accuracy
0.860
83.0 %
0.999
98.8 %
0.956
91.5 %
Strange? Not really!
Inevitable or does it mean something?
2nd degree (un)classifiable peptides
• Unclassifiable peptides only
• Simplified classifier
Peptides
All unclassifiable
AUC
Accuracy
0.956
91.5 %
2nd degree (un)classifiable peptides
• Unclassifiable peptides only
• Simplified classifier
Peptides
AUC
Accuracy
Classified correctly
All unclassifiable
0.956
91.5 %
Classifiable unclassifiable
Classified incorrectly
Unclassifiable unclassifiable
2nd degree (un)classifiable peptides
• Unclassifiable peptides only
• Simplified classifier
Peptides
All unclassifiable
Classifiable unclassifiable
Unclassifiable unclassifiable
AUC
Accuracy
0.956
91.5 %
0.992
97.8 %
0.683
65.0 %
2nd degree (un)classifiable peptides
Peptides
All unclassifiable
Classifiable unclassifiable
Unclassifiable unclassifiable
AUC
Accuracy
0.956
91.5 %
0.992
97.8 %
0.683
65.0 %
(Un)classifiable peptides
Peptides
All
Classifiable
Unclassifiable
AUC
Accuracy
0.860
83.0 %
0.999
98.8 %
0.956
91.5 %
Not inevitable!
Inevitable or does it mean something?
2nd degree (un)cl. – epitope propensity
Conclusions
• Epitopes have common characteristics
Conclusions
• Epitopes have common characteristics
– Epitopes are parts of antigens that bind antibodies
Our peptides mostly did not
come from known antigens
Probably partly general and
partly antibody-specific binding
Conclusions
• Epitopes have common characteristics
– Epitopes are parts of antigens that bind antibodies
Our peptides mostly did not
come from known antigens
Probably partly general and
partly antibody-specific binding
• Epitope characteristics are not unexpected
Conclusions
• Epitopes have common characteristics
– Epitopes are parts of antigens that bind antibodies
Our peptides mostly did not
come from known antigens
Probably partly general and
partly antibody-specific binding
• Epitope characteristics are not unexpected
• Two groups of epitopes:
– around 80 % “typical” (classifiable)
– around 20 % “atypical” (unclassifiable)
Conclusions
• Epitopes have common characteristics
– Epitopes are parts of antigens that bind antibodies
Our peptides mostly did not
come from known antigens
Probably partly general and
partly antibody-specific binding
• Epitope characteristics are not unexpected
• Two groups of epitopes:
– around 80 % “typical” (classifiable)
– around 20 % “atypical” (unclassifiable)
Mostly generalpurpose antibodies?
Mostly antigenspecific antibodies?