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Protein Sequence Motifs
Aalt-Jan van Dijk
Plant Research International, Wageningen UR
Biometris, Wageningen UR
[email protected]
www.bioinformatics.nl
Plant Bioinformatics

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Genomics
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•
•
Next Generation Sequencing
Genome assembly & annotation
(Comparative) genome analysis
SNP analysis, marker development
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Computational infrastructure
Database development
Webbased analysis tools
Software- development
Workflow management systems
machine learning
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Data (pre-)processing pipelining
Alternative splicing
Protein interactions networks
Metabolomics
•
•
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Alternative splicing
EST analysis
Proteomics
•
•
•
Technology
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Integrated analysis of omics datasets
 Transcriptomics
Database- development
Data (pre-)processing pipelining
Metabolite and pathway-identification
Systems biology

network modelling (bottom-up)
• Protein interactions networks
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My research

Protein complex structures
 Protein-protein docking
 Correlated mutations

Interaction site
prediction/analysis
 Protein-protein interactions
 Protein-DNA interactions

Motif search
 Enzyme active sites
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Overview

Protein Motif Searching
Hydrophobicity & Transmembrane Domains
Protein Interactions
Sequence-motifs to predict interaction sites

Secondary Structure Prediction

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Protein Motif Searching
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What is a motif?


A motif is a description of a particular element of
a protein that contains a specific sequence
pattern
Motifs are identified by


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3D structural alignment
Multiple sequence alignment
Pattern searching programs
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What is a motif?


A motif is a description of a particular element of
a protein that contains a specific sequence
pattern
Motifs are identified by



3D structural alignment
Multiple sequence alignment
Pattern searching programs
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Protein Motif Searching

Strict consensus pattern

use only strictly conserved residues
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
C
CxxxxxCxxxPxxxxxC
C
P
C
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Protein Motif Searching

Strict consensus pattern

use only strictly conserved residues
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
C
CxxxxxCxxxPxxxxxC
C
P
C
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Protein Motif Searching

Strict consensus pattern


use only strictly conserved residues
But what about:


variable residues?
gaps?
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
C
CxxxxxCxxxPxxxxxC
C
P
C
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Protein Motif Searching

Strict consensus patterns contain




no alternative residues
no flexible regions
no mismatches
no gaps
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
CxxxxxCxxxPxxxxxC
C
C
P
C
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Protein Motif Searching


Most motifs defined as regular expressions
Motifs can contain


alternative residues
flexible regions
C-x(2,5)-C-x-[GP]-x-P-x(2,5)-C
CXXXCXGXPXXXXXC
|
| | |
|
FGCAKLCAGFPLRRLPCFYG
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The PROSITE Syntax

A-[BC]-X-D(2,5)-{EFG}-H


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
A
B or C
anything
2-5 D’s
not E, F, or G
H
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PROSITE entries

Mandatory motifs characterise a protein (super-)
family
ID SUBTILASE_ASP; PATTERN.
DE Serine proteases, subtilase family, aspartic acid active site.
PA [STAIV]-x-[LIVMF]-[LIVM]-D-[DSTA]-G-[LIVMFC]-x(2,3)-[DNH].
ID SUBTILASE_HIS; PATTERN.
DE Serine proteases, subtilase family, histidine active site.
PA H-G-[STM]-x-[VIC]-[STAGC]-[GS]-x-[LIVMA]-[STAGCLV]-[SAGM].
ID SUBTILASE_SER; PATTERN.
DE Serine proteases, subtilase family, serine active site.
PA G-T-S-x-[SA]-x-P-x(2)-[STAVC]-[AG].
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Exercise


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Find the three subtilase motifs in prosite
(prosite.expasy.org)
Compare the lists of proteins in which the motifs
occur – what does this tell you?
Similarly, compare protein structures in which the
motifs occur
Have a look at the “sequence logo”
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Protein Motif Searching

Some motifs occur frequently in proteins; they
may not actually be present, such as

Post-translational modification sites
ID
DE
PA
ASN_GLYCOSYLATION; PATTERN.
N-glycosylation site.
N-{P}-[ST]-{P}.
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Exercise

Use a glycosylation site predictor such as
http://www.cbs.dtu.dk/services/NetNGlyc/

Input: your favorite set of sequences

Do you observe that some N-{P}-[ST] sites are likely to
be glycosylated and others not?
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Profiles
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Many motifs cannot be easily defined using
simple patterns
Such motifs can be defined using profiles
A profile is constructed from a multiple sequence
alignment. For each position, each amino acid is
given a score depending on how likely it is to
occur
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Calculating a Profile


For each alignment position: take the
(weighted) average of the appropriate rows
from the scoring matrix
An (extremely
simple) example:
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seq_01
seq_02
seq_03
seq_04
seq_05
seq_06
seq_07
seq_08
seq_09
seq_10
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
A
W
A
A
A
A
A
A
A
A
W
W
A
A
A
A
A
A
A
W
W
W
A
A
A
A
A
A
W
W
W
W
A
A
A
A
A
W
W
W
W
W
A
A
A
A
W
W
W
W
W
W
A
A
A
W
W
W
W
W
W
W
A
A
W
W
W
W
W
W
W
W
A
W
W
W
W
W
W
W
W
W
W
W
W
W
W
W
W
W
W
W
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Excerpt from the EBLOSUM62 matrix:
A R N D C Q E G H I L K M F P S T W Y V
A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0
W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3
A
4.0
N
-2.0
C
0.0
P
-1.0
D
-2.0
Q
-1.0
E
-1.0
R
-1.0
F
-2.0
S
1.0
G
0.0
T
0.0
H
-2.0
V
0.0
I
-1.0
W
-3.0
K
-1.0
Y
-2.0
L
-1.0
M
-1.0
A
5A+5W: 1.0
N
-6.0
C
-2.0
P
-5.0
D
-6.0
Q
-3.0
E
-4.0
R
-4.0
F
-1.0
S
-2.0
G
-2.0
T
-2.0
H
-4.0
V
-3.0
I
-4.0
W
8.0
K
-4.0
Y
0.0
L
-3.0
M
-2.0
A
-3.0
N
-4.0
C
-2.0
P
-4.0
D
-4.0
Q
-2.0
E
-3.0
R
-3.0
F
1.0
S
-3.0
G
-2.0
T
-2.0
H
-2.0
V
-3.0
I
-3.0
W
11.0
K
-3.0
Y
2.0
L
-2.0
M
-1.0
10A:
10W:
prophecy (EMBOSS), using Henikoff profile type, and BLOSUM62
matrix;
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Pattern Searching

Short linear motifs: e.g.
http://dilimot.russelllab.org/
Profiles: meme
http://meme.sdsc.edu/meme/cgi-bin/meme.cgi

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Exercise
Use a number of sequences wich contain the
prosite subtilase motif and find motifs in those
sequences with MEME
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Hydropathy Plot
Prediction hydrophobic and hydrophilic regions in a
protein
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Partition Coefficients
Hydrophilic
Hydrophobic
Oil
Water
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Hydrophobicity/Hydrophilicity Values
hydrophilic
hydrophobic
R
K
D
Q
N
E
H
S
T
P
Y
C
G
A
M
W
L
V
F
I
Fauchere & Pliska
-1.37
-1.35
-1.05
-0.78
-0.85
-0.87
-0.40
-0.18
-0.05
0.12
0.26
0.29
0.48
0.62
0.64
0.81
1.06
1.08
1.19
1.38
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Kyte & Doolittle
-4.50
-3.90
-3.50
-3.50
-3.50
-3.50
-3.20
-0.80
-0.70
-1.60
-1.30
2.50
-0.40
1.80
1.90
-0.90
3.80
4.20
2.80
4.50
Hopp & Woods
3.00
3.00
3.00
0.20
0.20
3.00
-0.50
0.30
-0.40
0.00
-2.30
-1.00
0.00
-0.50
-1.30
-3.40
-1.80
-1.50
-2.50
-1.80
Eisenberg
-2.53
-1.50
-0.90
-0.85
-0.78
-0.74
-0.40
-0.18
-0.05
0.12
0.26
0.29
0.48
0.62
0.64
0.81
1.06
1.08
1.19
1.38
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Hydrophobicity Plot



Sum amino acid hydrophobicity values in a given
window
Plot the value in the middle of the window
Shift the window one position
ik
1
Hi 
Hn

2k  1 n i  k
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Sliding Window Approach

Calculate property for first sub-sequence

Use the result (plot/print/store)

Move to next residue position, and repeat
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Hydrophobicity Plot
MEZCALTASTESVERYNICE
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Hydrophobicity Plot
1.5
1
0.5
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
-0.5
-1
-1.5
-2
MEZCALTASTESVERYNICE
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Hydrophobicity Plot
1.5
1
0.5
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
-0.5
-1
-1.5
-2
MEZCALTASTESVERYNICE
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Hydrophobicity Plot
1.5
1
0.5
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
-0.5
-1
-1.5
-2
MEZCALTASTESVERYNICE
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Hydrophobicity Plot
1.5
1
0.5
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
-0.5
-1
-1.5
-2
MEZCALTASTESVERYNICE
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Hydrophobicity Plot
1.5
1
0.5
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
-0.5
-1
-1.5
-2
MEZCALTASTESVERYNICE
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Hydrophobicity Plot
1.5
1
0.5
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
-0.5
-1
-1.5
-2
MEZCALTASTESVERYNICE
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Transmembrane Regions
Rotation is 100 degrees per amino acid
Climb is 1.5 Angstrom
per amino acid residue
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Transmembrane Regions
30 angstrom
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So we need approx.
30 / 1.5 = 20 amino
acids to span the
membrane
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Adapting the window size to
the size of the membrane
spanning segment makes the
picture easier to interpret
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window = 1
window = 9
window = 19
window = 121
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Protein Interactions
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Protein Interactions
hemoglobin
Obligatory
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Protein Interactions
hemoglobin
Obligatory
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Mitochondrial Cu transporters
Transient
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Experimental approaches (1)
Yeast two-hybrid (Y2H)
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Experimental approaches (2)
Affinity Purification + mass spectrometry (AP-MS)
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Interaction Databases

STRING http://string.embl.de/
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Interaction Databases
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Interaction Databases
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
STRING http://string.embl.de/
HPRD http://www.hprd.org/
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Interaction Databases
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Interaction Databases



STRING http://string.embl.de/
HPRD http://www.hprd.org/
InteroPorc http://biodev.extra.cea.fr/interoporc/Default.aspx
Many others….
E.g. see

http://nar.oxfordjournals.org./content/39/suppl_1.toc
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Yeast protein interaction network
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Sequence-based Protein Binding
Site Prediction
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Binding site
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Binding site
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Predefined motifs
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Predefined motifs
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Predefined motifs
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Predefined motifs
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Motif search in groups of proteins
• Group proteins which have same interaction partner
• Use motif search, e.g. find PWMs
Neduva Plos Biol 2005
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Motif search in groups of proteins
• Group proteins which have same interaction partner
• Use motif search
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Correlated Motif Search
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Correlated Motif Search
Interactors
AARLL PLTEQ
MARLT DLTEP
VVRLM MMTER
Non-interactors
AARLL MARLT
VVRLM MARLT
PLTEQ DLTEP
Correlated Motif Pair: (RL,TE)
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Experimental validation
Van Dijk et al, Plos Comp Biol 2010
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New approach: slider
•
•
Faster approach  genome wide searching for interaction motifs
Improve mining algorithm with a priori biological knowledge
(conservation score, surface accessibility)
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Boyen et al, IEEE/ACM Trans Comput Biol Bioinform. 2011


THE END…..
Questions?
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Secondary Structure Prediction
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Secondary Structure Prediction

Traditional methods (statistical and/or rule-based)

E.g. Garnier, Osguthorpe & Robson
• Statistical method

Accuracy ~ 60%
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GOR Helix Parameters
i-8
Gly -5
ala 5
val 0
leu 0
ile 5
ser 0
thr 0
asp 0
glu 0
asn 0
gln 0
lys 20
his 10
arg 0
phe 0
tyr -5
trp -10
cys 0
met 10
pro -10
-10
10
0
5
10
-5
0
-5
0
0
0
40
20
0
0
-10
-20
0
20
-20
i-6
-15
15
0
10
15
-10
0
-10
0
0
0
50
30
0
0
-15
-40
0
25
-40
-20
20
0
15
20
-15
-5
-15
0
0
0
55
40
0
0
-20
-50
0
30
-60
i-4
i-2
-30 -40 -50 -60
30 40 50 60
0
0
5 10
20 25 28 30
25 20 15 10
-20 -25 -30 -35
-10 -15 -20 -25
-20 -15 -10
0
10 20 60 70
-10 -20 -30 -40
5 10 20 20
60 60 50 30
50 50 50 30
0
0
0
0
0
5 10 15
-25 -30 -35 -40
-50 -10
0 10
0
0 -5 -10
35 40 45 50
-80-100-120-140
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i
-86
65
14
32
6
-39
-26
5
78
-51
10
23
12
-9
16
-45
12
-13
53
-77
-60
60
10
30
0
-35
-25
10
78
-40
-10
10
-20
-15
15
-40
10
-10
50
-60
i+2
-50
50
5
28
-10
-30
-20
15
78
-30
-20
5
-10
-20
10
-35
0
-5
45
-30
-40
40
0
25
-15
-25
-15
20
78
-20
-20
0
0
-30
5
-30
-10
0
40
-20
i+4
-30
30
0
20
-20
-20
-10
20
78
-10
-10
0
0
-40
0
-25
-50
0
35
-10
-20
20
0
15
-25
-15
-5
20
70
0
-5
0
0
-50
0
-20
-50
0
30
0
i+6
-15
15
0
10
-20
-10
0
15
60
0
0
0
0
-50
0
-15
-40
0
25
0
-10
10
0
5
-10
-5
0
10
40
0
0
0
0
-30
0
-10
-20
0
20
0
i+8
-5
5
0
0
-5
0
0
5
20
0
0
0
0
-10
0
-5
-10
0
10
0
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I S G A R N I E R H E L I X P R E D I C T
i-8
Gly -5
ala 5
val 0
leu 0
ile 5
ser 0
thr 0
asp 0
glu 0
asn 0
gln 0
lys 20
his 10
arg 0
phe 0
tyr -5
trp -10
cys 0
met 10
pro -10
-10
10
0
5
10
-5
0
-5
0
0
0
40
20
0
0
-10
-20
0
20
-20
i-6
-15
15
0
10
15
-10
0
-10
0
0
0
50
30
0
0
-15
-40
0
25
-40
-20
20
0
15
20
-15
-5
-15
0
0
0
55
40
0
0
-20
-50
0
30
-60
i-4
i-2
-30 -40 -50 -60
30 40 50 60
0
0
5 10
20 25 28 30
25 20 15 10
-20 -25 -30 -35
-10 -15 -20 -25
-20 -15 -10
0
10 20 60 70
-10 -20 -30 -40
5 10 20 20
60 60 50 30
50 50 50 30
0
0
0
0
0
5 10 15
-25 -30 -35 -40
-50 -10
0 10
0
0 -5 -10
35 40 45 50
-80-100-120-140
www.bioinformatics.nl
i
-86
65
14
32
6
-39
-26
5
78
-51
10
23
12
-9
16
-45
12
-13
53
-77
-60
60
10
30
0
-35
-25
10
78
-40
-10
10
-20
-15
15
-40
10
-10
50
-60
i+2
-50
50
5
28
-10
-30
-20
15
78
-30
-20
5
-10
-20
10
-35
0
-5
45
-30
-40
40
0
25
-15
-25
-15
20
78
-20
-20
0
0
-30
5
-30
-10
0
40
-20
i+4
-30
30
0
20
-20
-20
-10
20
78
-10
-10
0
0
-40
0
-25
-50
0
35
-10
-20
20
0
15
-25
-15
-5
20
70
0
-5
0
0
-50
0
-20
-50
0
30
0
i+6
-15
15
0
10
-20
-10
0
15
60
0
0
0
0
-50
0
-15
-40
0
25
0
-10
10
0
5
-10
-5
0
10
40
0
0
0
0
-30
0
-10
-20
0
20
0
i+8
-5
5
0
0
-5
0
0
5
20
0
0
0
0
-10
0
-5
-10
0
10
0
www.bioinformatics.nl
GOR Prediction
beta sheet
helix
www.bioinformatics.nl
www.bioinformatics.nl
Secondary Structure Prediction

Recent methods

Neural networks
Multiple alignments
Heuristics

Or a combination of the above



= flexible statistics
= variability
= common sense
Accuracy ~ 70%
www.bioinformatics.nl
www.bioinformatics.nl
Heuristics


Conserved parts are structurally and/or
functionally important
Segments with many gaps must be in loop
regions
www.bioinformatics.nl
www.bioinformatics.nl
Secondary Structure Prediction

Strategy

Use as many methods as possible

Use homologous sequences

Combine predictions into consensus prediction
www.bioinformatics.nl
www.bioinformatics.nl
Why can’t it be 100% correct?

All current 2D prediction schemes are based
upon observation of occurrence of 2D elements in
3D structures

Deduction of 2D elements from structures is
ambiguous!

DSSP, Stride, and the PDB (human) annotation do not
always agree upon the assigned elements
www.bioinformatics.nl
www.bioinformatics.nl
Do these residues still belong to the helix?
www.bioinformatics.nl
www.bioinformatics.nl