1. Protein Sequence Motifs
Protein Analysis
Protein Sequence Motifs
Aalt-Jan van Dijk
Plant Research International, Wageningen UR
Biometris, Wageningen UR
© 2008, Jack A.M. Leunissen

2. Plant Bioinformatics Integrated analysis of omics datasets Genomics
27/01/2018
Genomics
Next Generation Sequencing
Genome assembly & annotation
(Comparative) genome analysis
SNP analysis, marker development
Technology
Computational infrastructure
Database development
Webbased analysis tools
Software- development
Workflow management systems
machine learning
Integrated analysis of omics datasets
Transcriptomics
Alternative splicing
EST analysis
Proteomics
Data (pre-)processing pipelining
Protein interactions networks
Metabolomics
Database- development
Metabolite and pathway-identification
Systems biology
network modelling (bottom-up)

3. My research Protein complex structures Protein-protein docking
27/01/2018
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

4. Overview Protein Motif Searching
Protein Analysis
Overview
Protein Motif Searching
Hydrophobicity & Transmembrane Domains
Protein Interactions
Sequence-motifs to predict interaction sites
Secondary Structure Prediction
© 2008, Jack A.M. Leunissen

5. Protein Motif Searching
Protein Analysis
Protein Motif Searching
© 2008, Jack A.M. Leunissen

6. Protein Analysis
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
© 2008, Jack A.M. Leunissen

7. Protein Analysis
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
© 2008, Jack A.M. Leunissen

8. Protein Motif Searching
Protein Analysis
Protein Motif Searching
Strict consensus pattern
use only strictly conserved residues
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
CxxxxxCxxxPxxxxxC
C C P C
© 2008, Jack A.M. Leunissen

9. Protein Motif Searching
Protein Analysis
Protein Motif Searching
Strict consensus pattern
use only strictly conserved residues
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
CxxxxxCxxxPxxxxxC
C C P C
© 2008, Jack A.M. Leunissen

10. Protein Motif Searching
Protein Analysis
Protein Motif Searching
Strict consensus pattern
use only strictly conserved residues
But what about:
variable residues?
gaps?
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
CxxxxxCxxxPxxxxxC
C C P C
© 2008, Jack A.M. Leunissen

11. Protein Motif Searching
Protein Analysis
Protein Motif Searching
Strict consensus patterns contain
no alternative residues
no flexible regions
no mismatches
no gaps
C--QASCDGIPLKMNDC
C---VTCEGLPMRMDQC
CERTLGCQPMPVH---C
C C P C
CxxxxxCxxxPxxxxxC
© 2008, Jack A.M. Leunissen

12. Protein Motif Searching
Protein Analysis
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
© 2008, Jack A.M. Leunissen

13. The PROSITE Syntax A-[BC]-X-D(2,5)-{EFG}-H A B or C anything 2-5 D’s
Protein Analysis
The PROSITE Syntax
A-[BC]-X-D(2,5)-{EFG}-H A
B or C
anything
2-5 D’s
not E, F, or G H
© 2008, Jack A.M. Leunissen

14. Protein Analysis
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].
© 2008, Jack A.M. Leunissen

15. Protein Analysis
Exercise
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”
© 2008, Jack A.M. Leunissen

16. Protein Motif Searching
Protein Analysis
Protein Motif Searching
Some motifs occur frequently in proteins; they may not actually be present, such as
Post-translational modification sites
ID ASN_GLYCOSYLATION; PATTERN.
DE N-glycosylation site.
PA N-{P}-[ST]-{P}.
© 2008, Jack A.M. Leunissen

17. Protein Analysis
Exercise
Use a glycosylation site predictor such as
Input: your favorite set of sequences
Do you observe that some N-{P}-[ST] sites are likely to be glycosylated and others not?
© 2008, Jack A.M. Leunissen

18. Profiles Many motifs cannot be easily defined using simple patterns
Protein Analysis
Profiles
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
© 2008, Jack A.M. Leunissen

19. Protein Analysis
Calculating a Profile
For each alignment position: take the (weighted) average of the appropriate rows from the scoring matrix
An (extremely simple) example:
seq_01 A A A A A A A A A A W
seq_02 A A A A A A A A A W W
seq_03 A A A A A A A A W W W
seq_04 A A A A A A A W W W W
seq_05 A A A A A A W W W W W
seq_06 A A A A A W W W W W W
seq_07 A A A A W W W W W W W
seq_08 A A A W W W W W W W W
seq_09 A A W W W W W W W W W
seq_10 A W W W W W W W W W W
© 2008, Jack A.M. Leunissen

20. prophecy (EMBOSS), using Henikoff profile type, and BLOSUM62 matrix;
Protein Analysis
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 W
A C D E F G H I K L M
10A:
N P Q R S T V W Y
5A+5W:
10W:
prophecy (EMBOSS), using Henikoff profile type, and BLOSUM62 matrix;
© 2008, Jack A.M. Leunissen

21. Protein Analysis
Pattern Searching
Short linear motifs: e.g.
Profiles: meme
© 2008, Jack A.M. Leunissen

22. Protein Analysis
Exercise
Use a number of sequences wich contain the prosite subtilase motif and find motifs in those sequences with MEME
© 2008, Jack A.M. Leunissen

23. Protein Analysis
Hydropathy Plot
Prediction hydrophobic and hydrophilic regions in a protein
© 2008, Jack A.M. Leunissen

24. Partition Coefficients
Protein Analysis
Partition Coefficients
Hydrophilic
Hydrophobic
Oil
Water
© 2008, Jack A.M. Leunissen

25. Hydrophobicity/Hydrophilicity Values
Protein Analysis
Hydrophobicity/Hydrophilicity Values
Fauchere & Pliska Kyte & Doolittle Hopp & Woods Eisenberg R K D Q N E H S T P Y C G A M W L V F I
hydrophilic hydrophobic
© 2008, Jack A.M. Leunissen

26. Protein Analysis
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
© 2008, Jack A.M. Leunissen

27. Sliding Window Approach
Protein Analysis
Sliding Window Approach
Calculate property for first sub-sequence
Use the result (plot/print/store)
Move to next residue position, and repeat
© 2008, Jack A.M. Leunissen

28. MEZCALTASTESVERYNICE
Protein Analysis
Hydrophobicity Plot
MEZCALTASTESVERYNICE
© 2008, Jack A.M. Leunissen

29. MEZCALTASTESVERYNICE
Protein Analysis
Hydrophobicity Plot
MEZCALTASTESVERYNICE
© 2008, Jack A.M. Leunissen

30. MEZCALTASTESVERYNICE
Protein Analysis
Hydrophobicity Plot
MEZCALTASTESVERYNICE
© 2008, Jack A.M. Leunissen

31. MEZCALTASTESVERYNICE
Protein Analysis
Hydrophobicity Plot
MEZCALTASTESVERYNICE
© 2008, Jack A.M. Leunissen

32. MEZCALTASTESVERYNICE
Protein Analysis
Hydrophobicity Plot
MEZCALTASTESVERYNICE
© 2008, Jack A.M. Leunissen

33. MEZCALTASTESVERYNICE
Protein Analysis
Hydrophobicity Plot
MEZCALTASTESVERYNICE
© 2008, Jack A.M. Leunissen

34. MEZCALTASTESVERYNICE
Protein Analysis
Hydrophobicity Plot
MEZCALTASTESVERYNICE
© 2008, Jack A.M. Leunissen

35. Transmembrane Regions
Protein Analysis
Transmembrane Regions
Rotation is 100 degrees per amino acid
Climb is 1.5 Angstrom
per amino acid residue
© 2008, Jack A.M. Leunissen

36. Transmembrane Regions
Protein Analysis
Transmembrane Regions
So we need approx. 30 / 1.5 = 20 amino acids to span the membrane
30 angstrom
© 2008, Jack A.M. Leunissen

37. Protein Analysis
© 2008, Jack A.M. Leunissen

38. Protein Analysis
Adapting the window size to the size of the membrane spanning segment makes the picture easier to interpret
© 2008, Jack A.M. Leunissen

39. window = 1 window = 9 window = 19 window = 121 Protein Analysis
© 2008, Jack A.M. Leunissen

40. Protein Analysis
Protein Interactions
© 2008, Jack A.M. Leunissen

41. Protein Interactions hemoglobin Obligatory
Explain homodimer, heterodimer
Obligatory

42. Protein Interactions hemoglobin Mitochondrial Cu transporters
Obligatory
Transient

43. Experimental approaches (1)
Yeast two-hybrid (Y2H)
Old / small-scale: co-immunoprecipitation
Disadvantages: problem with homodimers, problem with large complexes

44. Experimental approaches (2)
Affinity Purification + mass spectrometry (AP-MS)
Also indirect interactions

45. Interaction Databases
STRING

46. Interaction Databases
PPI + genetic / function association

47. Interaction Databases
STRING
HPRD

48. Interaction Databases
Only human and only small-scale based

49. Interaction Databases
STRING
HPRD
InteroPorc
Many others….
E.g. see

50. Yeast protein interaction network

51. Sequence-based Protein Binding Site Prediction
Protein Analysis
Sequence-based Protein Binding Site Prediction
© 2008, Jack A.M. Leunissen

52. Binding site

53. Protein Analysis
Binding site
© 2008, Jack A.M. Leunissen

54. Predefined motifs

55. Predefined motifs

56. Predefined motifs

57. Predefined motifs

58. Motif search in groups of proteins
Group proteins which have same interaction partner
Use motif search, e.g. find PWMs
Neduva Plos Biol 2005

59. Motif search in groups of proteins
Group proteins which have same interaction partner
Use motif search

60. Correlated Motif Search

61. Correlated Motif Search
Interactors Non-interactors
AARLL PLTEQ AARLL MARLT
MARLT DLTEP VVRLM MARLT
VVRLM MMTER PLTEQ DLTEP
Correlated Motif Pair: (RL,TE)

62. Experimental validation
Van Dijk et al, Plos Comp Biol 2010

63. New approach: slider
Faster approach  genome wide searching for interaction motifs
Improve mining algorithm with a priori biological knowledge (conservation score, surface accessibility)
Boyen et al, IEEE/ACM Trans Comput Biol Bioinform. 2011

64. Protein Analysis
THE END…..
Questions?
© 2008, Jack A.M. Leunissen

65. Protein Analysis
© 2008, Jack A.M. Leunissen

66. Secondary Structure Prediction
Protein Analysis
Secondary Structure Prediction
© 2008, Jack A.M. Leunissen

67. Secondary Structure Prediction
Protein Analysis
Secondary Structure Prediction
Traditional methods (statistical and/or rule-based)
E.g. Garnier, Osguthorpe & Robson
Statistical method
Accuracy ~ 60%
© 2008, Jack A.M. Leunissen

68. GOR Helix Parameters i-8 i-6 i-4 i-2 i i+2 i+4 i+6 i+8
Protein Analysis
GOR Helix Parameters
i-8 i-6 i-4 i-2 i i+2 i+4 i+6 i+8
Gly
ala
val
leu
ile
ser
thr
asp
glu
asn
gln
lys
his
arg
phe
tyr
trp
cys
met
pro
© 2008, Jack A.M. Leunissen

69. I S G A R N I E R H E L I X P R E D I C T
Protein Analysis
I S G A R N I E R H E L I X P R E D I C T
i-8 i-6 i-4 i-2 i i+2 i+4 i+6 i+8
Gly
ala
val
leu
ile
ser
thr
asp
glu
asn
gln
lys
his
arg
phe
tyr
trp
cys
met
pro
© 2008, Jack A.M. Leunissen

70. GOR Prediction beta sheet helix Protein Analysis
© 2008, Jack A.M. Leunissen

71. Secondary Structure Prediction
Protein Analysis
Secondary Structure Prediction
Recent methods
Neural networks = flexible statistics
Multiple alignments = variability
Heuristics = common sense
Or a combination of the above
Accuracy ~ 70%
© 2008, Jack A.M. Leunissen

72. Protein Analysis
Heuristics
Conserved parts are structurally and/or functionally important
Segments with many gaps must be in loop regions
© 2008, Jack A.M. Leunissen

73. Secondary Structure Prediction
Protein Analysis
Secondary Structure Prediction
Strategy
Use as many methods as possible
Use homologous sequences
Combine predictions into consensus prediction
© 2008, Jack A.M. Leunissen

74. Why can’t it be 100% correct?
Protein Analysis
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
© 2008, Jack A.M. Leunissen

75. Do these residues still belong to the helix?
Protein Analysis
Do these residues still belong to the helix?
© 2008, Jack A.M. Leunissen