Transcript No Slide Title

Protein Structure Prediction
David Wild
Keck Graduate Institute of Applied
Life Sciences
[email protected]
Summary
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Motivation
Secondary Structure Prediction
Tertiary Structure Prediction
Sequence/Structure Approaches
3D profile
Threading
Ab-initio Approaches
Growth of PDB
Functional assignment by homology:
the function-homology gap
yeast data analyzed by GeneQuiz
Russell et al. J. Mol. Biol (1997) 269, 423-439
enterotoxin
homolog: cholera toxin
80% ID 98/103 residues
with rmsd 0.6A
remote homolog: toxic
shock syndrome toxin;
no sequence similarity
but 35/95 residues with
rmsd 2.4A
analog:
tRNA synthetase; no
sequence similarity but
41/103 residues with
rmsd 2.2A
no known functional
similarity
From Hegyi and Gerstein (1999)
• Active site formed by loops between the carboxy end of the
-strands and the amino end of the -helices at one end of the
barrel
From Branden and Tooze (1999)
From Hegyi and Gerstein (1999)
Baker and Sali (2000)
Central Dogma
“The 3D structure of a protein is
determined by its sequence and its
environment without the obligatory
role of extrinsic factors”
• Anfinsen (1973) - renaturation of ribonuclease
• Ignores role of chaperones, disulfide interchange
enzymes etc
Dominant Effects in Protein
Folding
• Net protein stability - diverse chemical properties
of main and side chain atoms give rise to interplay
of non-covalent and entropic effects
• Hydrophobic effect - non-polar core
• Atomic packing - van der Waals interactions favor
close packing
• Conformational entropy - freezing of rotamers
• Electrostatic effects - ion pairs and H-bonds
• Disulfide bridges
Primary
Secondary
Tertiary
Secondary Structure Prediction
• History and Context
• Chou & Fasman
• Lim
• Garnier-Osguthorpe-Robson
• Comparison of Methods
• Newer Approaches
Secondary Structure Prediction by Eye
• Position of insertions and deletions
probable loop
• Conserved Gly/Pro
probable loop
• Short runs of conserved hydrophobics
buried -strand
• i, i+2, i+4 pattern of conserved residues surface -strand
• i, i+3,i+4,i+7 conserved pattern
surface  helix
Helix
Edge strand
Buried strand
From Branden and Tooze (1999)
Single Sequence Methods
Chou & Fasman 1974
• Propensities of formation based upon
frequency of occurrence
• Generate tables for , , turn & random coil
• Strong/weak/indifferent formers & breakers
• Rules for nucleation, propagation &
termination
• 15 protein database - 50% accuracy!
The Lim Method
(1974)
• Theory based on packing of polypeptide chains
e.g.: -helices that make contact with the main
protein body need a hydrophobic side
• Hydrophobic residues must face internally and
pack closely together
• Method defines hydrophobics/hydrophilics and
passageway residues
• Advantage: rules have a clear basis in protein
chemistry theory
• Disadvatange: rules complex & difficult to
understand
-helix
-strand
strong former
glu, ala, leu
met, val, ile
former
his, met, gln, trp, val, phe
cys, tyr, phe, gln, leu, thr, trp
weak former
leu, ile
ala
indifferent former asp, thr, ser, arg, cys
arg, gly, asp
breaker
asn, tyr
lys, ser, his, asn, pro
strong breaker
pro, gly
glu
Single Sequence Methods
Garnier, Osguthorpe, Robson (GOR), 1978
• Window of 17 residues (i-8
i
i+8)
• 4 states - predicted structure is highest value
summed over window
• “Information theoretic” approach
• Single sequence GORI - 55% accuracy
• GORIII - pair information - correlate the type of
residues in a window with the residue to be
predicted
• Sensitive to database size - getting better all the
time
GOR I Example
• For alanine
240 in helix, 150 not in helix, total 390 residues
• For all residues
780 in helix (H), 1050 not in helix (~H), total 1830
P(S=H|A) = 240/390 = 0.615
P(S=~H|A) = 150/390 = 0.385
P(S=H) = 780/1830 = 0.426
P(S=~H) = 1050/1830 = 0.573
I(S=H:~H;A) = ln(0.615/0.385) - ln(0.426/0.573) (log-odds ratio)
= 0.4683 - 0.2964
= 0.7647
Neural networks applied to SS prediction
• Use known structures as target function
• Single sequence methods not that successful, but better
than GOR (Qian & Sejnowski, 1988 ~ 63%)
• Adding information from an alignment substantially
improves accuracy
• Disadvantage: one loses sight of original problem due to
‘black box’ nature of prediction method
• Large number of parameters
Qian and Sejnowski (1988)
13 residue window
Input
-6,-5,-4,-3,-2,-1, X, +1,+2,+3,+4,+5,+6
Input Layer
13 groups, each of 21 units
(20 residues plus space)
Hidden layer
Output Layer
3 groups (H, E, C)
Prediction of center
residue X
• Binary coding of amino acid residues
– 20 residues require 5 bits
– for instance
ala = 00001
cys = 00010
asp = 00011
…
trp = 10100
• Could alternatively encode 5
properties, e.g.: hydrophobicity, side
chain size etc...
PHD Neural Network
Rost & Sander, 1993
• Uses multiple independent neural networks as
prediction engine
• Balanced training - present network with one structural
class at a time
• Addition of evolutionary information improves
prediction quality
How…
1. Sequence to structure - input coded as a profile,
trained against known structure
2. Structure to structure - predicted SS trained against
known structure
3. Jury decision - numerical average over number of
different level 2 networks
Profile/PSSM
• Position Specific Scoring Matrix, or
weight matrix, is calculated based on
observed frequencies in a column
GCGGTGATAATGGTTGCATG
TTGGGTATATTTGACTATGG
ATGCATACACTATAGGTGTG
TGCAGTAAGATACAAATGGC
ATGGTTATAGTATGCCCATG
Acknowledgement: Mike Gribskov
Weight Matrix Methods
• Position specific scoring matrix (PSSM)
• Feature is represented as a matrix with a score for every
possible character
• A simple weight matrix for the bacterial promoter -10
region, values here are simply % frequencies
A
C
G
T
2
9
10
79
T
95
2
1
3
A
26
14
16
44
T
59
13
15
13
A
51
20
13
17
A
Acknowledgement: Mike Gribskov
1
3
0
96
T
From Baldi and Brunak (2001)
Nearest Neighbor Methods
Salamov & Solovyev, NSSP 1995
• Use database of proteins of known structure
• Match each segment of query sequence
against all sequences in database
• Choose secondary structure state of the
majority of its neighbors as the prediction
• Neighbors are decided upon by using amino
acid substitution tables and scoring tables
Indentifying factors that affect secondary structure
King & Sternberg, DSC 1996
• Relative aa position in chain
• Treatment of insertions/deletions
• Hydrophobic moment
• %aa content
• not a ‘black box’ technique
CASP2 - Blind Prediction of Protein Secondary Structure
Server Predictions M=Multiple S=Single
PHD-M
SSP-M
Method
NNSSP-M
NNPRED-S
SSPRED-M
GOR-S
DSC-M
0
10
20
30
40
50
Accuracy
Zemla et al. Proteins (1997) Suppl. 1, 140-150
60
70
80
Issues
• Definition of secondary structure from 3D coordinates is not exact
• Different algorithms to define secondary structure
DSSP, STRIDE, DEFINE, Author, P-Curve
give different definitions:
DSSP/Stride
Stride/Define
DSSP/Define
95%
74%
73%
• Definition itself is open to interpretation - there are more than 3
states defined:
H, E, G, I, T, C, B, S
H, E, C
-sheets are formed by long
range interactions
Generative probabilistic models
(Schmidler et al. (2000); Chu et al. (2004))
Tertiary Structure Prediction
• Comparative modeling
–Homology modeling
• Fragment-based
–COMPOSER
–SWISS-MODEL
• 3D distance constraints
–MODELER
• Fold Recognition/Threading/Inverse Folding
• Proteins may have undetectable sequence similarity but
striking structural similarity.
• Glimmers in the twilight zone (Doolittle, 1987)
Sequence Alignment Accuracy:
%correctly aligned residues vs. %sequence identity
120
%Res-Res (in reliable regions)
100
80
60
40
20
0
0
20
40
60
80
%ID
Saqi et al. Prot. Eng (1998)
100
120
Russell et al. J. Mol. Biol (1997) 269, 423-439
Fold Recognition Methods
• Sequence profile
– PSI-BLAST
– HMM
– Environmental PSSM
• Structural profile
– 3D-1D profile
• Threading
– Pair potential based fold recognition
Ab initio/De Novo Folding
 Combinatorial approaches
• Secondary structure prediction + Docking
Energy minimization
Monte Carlo simulation
• Fragments of highly resolved protein structures are joined
together and the feasibility of the fold is evaluated with a
potential function.
Lattice simulations
– Still mainly developer based usage.
From Higgins and Taylor (2000)
Bowie, Luthy and Eisenberg (1991)
Threader Jones et al 1999
• Structural role of residue described in terms of
interactions
• ‘Network’ of pairwise interatomic energy terms
(potentials) from a statistical analysis of proteins of known
structure and inverse Boltzman equation (Sippl 1990) used
as sequence-structure compatibility function
For specified atoms in a pair of residues {a,b},
with a sequence separation of k and distance
interval s, the potential is given by
f kab ( s )
 E ab

RT
ln(
1


)

RT
[
1


]
m
m
ab
ab
k
f k (s)
mab is the number of pairs ab observed at sequence
separation k
 is the weight of each observation
fabk(s) is the equivalent frequency of occurrence of
residue pair ab.
fk(s) is the frequency of occurrence of all residue
pairs at sequence separation k and separation
distance s
Potentials corresponding to short (sequence
separation , k < 11), medium (11  k  22), and
long (k > 30) range interactions, have been
Ab-initio Approaches
LINUS (Srinivasan & Rose, 1995)
Folding by “Hierarchical Condensation”
Cstart
N

6
( ({
j
cycle
1
N-1
Step
2
}))
Generate Trial conformation C*
1. Randomly choose backbone conformation
2. Bump check C*
3. Calculate energy of C*, U(C*)
4. If U(C*)<U(C) or x < e- E ,
where is x is random and 0<x<1 then C = C*
Fragments of 50 residues, interaction interval 6<<48
Simple potential:
• Contact energy
• H-bonding
• Main chain ‘torsional potential’ ( > 0 except for glycine)
ROSETTA
Simons et al, 1997
• Metropolis Monte Carlo simulated annealing procedure
• 3 and 9 residue fragments of known structures
with local sequences similar to the target sequence
•Potential function - sequence dependent terms
hydrophobic burial
electrostatics and disulfide bonding,
• sequence independent terms
hard sphere packing,
 alpha-helix and beta-strand packing
collection of beta-strands in beta-sheets
FRAGFOLD Jones (1997, 2001)
Library of super-secondary structures fragments
-hairpin
 motif
From Branden and Tooze (1999)
Folding Proteins
with Boltzmann Learning Rule
• NOT traditional ab initio folding
• Learn the potentials that maximize the probability of
known native folds
• Then, use learned potential for future folding
(Ole Winter & Anders Krogh 2003)
Boltzmann Learning Rule
The probability of nativei fold given sequencei and the
model parameters :
Pnative i sequence i , 
The updating of parameters with the rate .
 new   old    ln Pnative i sequence i , 
i
Potentials
• Lennard-Jones between atoms X and Y

   XY
ELJ    XY 5
r

  XY
12

  XY
  6

 rXY



6





• Hydrogen bonds
12
10

  HO  
  HO 







Ehb    HOu angles 
 2

 
r
r

 HO  
• Others
 HO 

Total of more than 1000 model parameters to learn
Assessment
• LiveBench
• CAFASP3 Servers
– Evaluation Results
• CASP5
– Evaluation Results