Transcript lecture08_06

Structural Bioinformatics
Proteins
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Specific databases of protein sequences
and structures
 Swissprot
 PIR
 TREMBL (translated from DNA)
 PDB (Three Dimensional Structures)
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Myoglobin – the first high resolution protein structure
Solved in 1958 by Max Perutz John Kendrew of Cambridge University.
Won the 1962 and Nobel Prize in Chemistry.
“ Perhaps the most remarkable features of the molecule are its
complexity and its lack of symmetry. The arrangement seems to
be almost totally lacking in the kind of regularities which one
instinctively anticipates.”
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Why Proteins Structure ?
 Proteins are fundamental components of all living
cells, performing a variety of biological tasks.
 Each protein has a particular 3D structure that
determines its function.
 Protein structure is more conserved than protein
sequence , and more closely related to function.
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There Are Four Levels of Protein Structure
Primary: amino acid linear
sequence.
Tertiary: the 3D shape of the
fully folded polypeptide chain
Secondary: -helices,
β-sheets and loops.
Quaternary: arrangement of
several polypeptide chains.
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Symbols for the 20 amino acids
A ala alanine
C cys cysteine
D asp aspartic acid
E glu glutamic acid
F phe phenylalanine
G gly glycine
H his histidine
I ile isoleucine
K lys lysine
L leu leucine
M met
N asn
P pro
Q gln
R arg
S ser
T thr
V val
W trp
Y tyr
methionine
aspargine
proline
glutamine
arginine
serine
threonine
valine
tryptophane
tyrosine
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Secondary Structure
Secondary structure is usually divided into
three categories:
Alpha helix
Beta strand (sheet)
Anything else –
turn/loop
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Alpha Helix: Pauling (1951)
• A consecutive stretch of 5-40 amino
acids (average 10).
• A right-handed spiral conformation.
• 3.6 amino acids per turn.
3.6
residues
5.6 Å
• Stabilized by H-bonds in the
backbone between C=O of residue n,
and NH of residue n+4.
• Side-chains point out.
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Beta Strand: Pauling and Corey (1951)
•
Different polypeptide chains run alongside each
other and are linked together by hydrogen bonds.
• Each section is called β -strand,
and consists of 5-10 amino acids.
β -strand
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3.47Å
Beta Sheet
4.6Å
(a)Antiparallel
(b)Parallel
3.25Å
The strands become
adjacent to each other,
forming beta-sheet.
4.6Å
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Loops
• Connect the secondary
structure elements.
• Have various length and
shapes.
• Located at the surface of
the folded protein and
therefore may have
important role in
biological recognition
processes.
• Proteins that are
evolutionary related have
the same helices & sheets
but may vary in loop
structures.
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How is the 3D Structure Determined ?
1. Experimental methods (Best approach):
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X-rays crystallography.
• NMR.
• Others.
2. In-silico methods (partial solutions based on similarity):.
• Threading - needs a 3D structure, combinatorial complexity.
• Ab-initio structure prediction - not always successful.
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X-ray crystallography
1. Obtain an ordered protein crystal.
2. Check x-ray diffraction.
The crystal is bombarded
with X-ray beams.
The collision of the beams
with the electrons creates
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a diffraction pattern.
X-ray crystallography
3. Analyze diffraction pattern and produce an
electron density map.
4. Thread the known protein sequence into the
density map.
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X-ray crystallography
• The molecules must be very pure in order to
produce perfect and stable crystals.
• The method is time-consuming and
difficult.
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NMR - Nuclear Magnetic
Resonance (since 1945)
• A sample is immersed in a magnetic field
and bombarded with radio waves.
• The molecule’s nucleus resonate (spin).
This motion is determined and is specific
for each molecule type.
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Principles of NMR
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NMR - Nuclear Magnetic
Resonance
• The NMR technique is very time
consuming and expensive, and the sample
has to be in a concentrated solution, and is
limited to small and soluble molecules.
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PDB: Protein Data Bank
• Holds 3D models of biological macromolecules (protein,
RNA, DNA).
• All data are available to the public.
• Obtained by X-Ray crystallography (84%) or NMR
spectroscopy (16%).
• Submitted by biologists and biochemists from around the
world.
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PDB – Protein Data Bank
http://www.rcsb.org/pdb/
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How Many Structures ?
PDB Content Growth
http://www.rcsb.org/pdb/holdings.html
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Structure Prediction: Motivation
• Hundreds of thousands of gene sequences translated to
proteins (genbanbk, SW, PIR)
• Only about 28000 solved structures (PDB)
Experimental methods are time consuming and not
always posible
• Goal: Predict protein structure based
on sequence information
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Structure Prediction: Motivation
• Understand protein function
– Locate binding sites
• Broaden homology
– Detect similar function where sequence differs
• Explain disease
– See effect of amino acid changes
– Design suitable compensatory drugs
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Prediction Approaches
• Primary (sequence) to secondary structure
– Sequence characteristics
• Secondary to tertiary structure
– Fold recognition
– Threading against known structures
• Primary to tertiary structure
– Ab initio modelling
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Can we predict the secondary structure from sequence ?
-helix
b-sheet
nonpolar
polar
polar
polar
Non-polar
Secondary structures have an amphiphilic nature :
one face polar and the other non polar
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Secondary Structure Prediction
Methods
• Chou-Fasman / GOR Method
– Based on amino acid frequencies
• Artificial Neural Network (ANN) methods
– PHDsec and PSIpred
• HMM (Hidden Markov Model)
• Best accuracy now ~80%
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Chou and Fasman (1974)
The propensity of an amino
acid to be part of a certain
secondary structure (e.g. –
Proline has a low
propensity of being in an
alpha helix or beta sheet 
breaker)
Name
Alanine
Arginine
Aspartic Acid
Asparagine
Cysteine
Glutamic Acid
Glutamine
Glycine
Histidine
Isoleucine
Leucine
Lysine
Methionine
Phenylalanine
Proline
Serine
Threonine
Tryptophan
Tyrosine
Valine
P(a)
142
98
101
67
70
151
111
57
100
108
121
114
145
113
57
77
83
108
69
106
P(b)
83
93
54
89
119
037
110
75
87
160
130
74
105
138
55
75
119
137
147
170
P(turn)
66
95
146
156
119
74
98
156
95
47
59
101
60
60
152
143
96
96
114
50
Success rate of 50%
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Secondary Structure Method
Improvements
‘Sliding window’ approach
• Most alpha helices are ~12 residues long
Most beta strands are ~6 residues long
 Look at all windows of size 6/12
 Calculate a score for each window. If >threshold
 predict this is an alpha helix/beta sheet
TGTAGPOLKCHIQWMLPLKK
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Improvements in the 1980’s
• Adding information from conservation in
MSA
• Smarter algorithms (e.g. HMM, neural
networks).
Success -> ~80%
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PHDsec and PSIpred
• PHDsec
– Rost & Sander, 1993
– Based on sequence family alignments
• PSIpred
– Jones, 1999
– Based on Position Specific Scoring Matrix
Generated by PSI-BLAST
• Both consider long-range interactions
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HMM
• HMM enables us to calculate the
probability of assigning a sequence of
hidden states to the observation
TGTAGPOLKCHIQWML
HHHHHHHLLLLBBBBB
observation
p=?
Hidden state
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Beginning
with an αhelix
α-helix
followed by
α-helix
The probability
of observing
Alanine as part
of a β-sheet
The probability of observing a residue which belongs to an αhelix followed by a residue belonging to a turn = 0.15
Table built according to large database of known secondary
structures
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HMM
• The above table enables us to calculate the
probability of assigning secondary structure
to a protein
• Example
TGQ
HHH
p = 0.45 x 0.041 x 0.8 x 0.028 x 0.8 x 0.0635 =
0.0020995
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SS prediction using ANN
A
C
D
E
F
G
H
I
K
L
M
N
P
Q
R
S
T
V
W
Y
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Amino
acid at
position
Inputs for one
position
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PHDsec Neural Net
A
C
D
E
F
G
H
I
K
L
M
N
P
Q
R
S
T
V
W
Y
.
Amino
acid at
position
Inputs for one
position
Outputs
H= helix
E= strand
C= Coil
Confidence
0=low,9=high
Hidden
layer
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Secondary structure prediction
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AGADIR - An algorithm to predict the helical content of peptides
APSSP - Advanced Protein Secondary Structure Prediction Server
GOR - Garnier et al, 1996
HNN - Hierarchical Neural Network method (Guermeur, 1997)
Jpred - A consensus method for protein secondary structure prediction at University
of Dundee
JUFO - Protein secondary structure prediction from sequence (neural network)
nnPredict - University of California at San Francisco (UCSF)
PredictProtein - PHDsec, PHDacc, PHDhtm, PHDtopology, PHDthreader, MaxHom,
EvalSec from Columbia University
Prof - Cascaded Multiple Classifiers for Secondary Structure Prediction
PSA - BioMolecular Engineering Research Center (BMERC) / Boston
PSIpred - Various protein structure prediction methods at Brunel University
SOPMA - Geourjon and Del‫י‬age, 1995
SSpro - Secondary structure prediction using bidirectional recurrent neural networks
at University of California
DLP - Domain linker prediction at RIKEN
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