Transcript Ming Li Talk about Bioinformatics

CS882, Fall 2006
Lecture 7. Computing
Protein Structures
• Current attempts:
• Threading: RAPTOR
• Consensus: ACE
• Fragment assembly
Can we compute the protein structures eventually? Your projects.
Homologous proteins have similar
structure and functions
 Being homologous means that they have
evolved from a common ancestral gene.
Hence at least in the past they had the same
structure and function.
 Caution: old genes can be recruited for new
functions. Example: a structural protein in eye
lens is homologous to an ancient glycolytic
enzyme.
 Homology search is done by BLAST, or
PatternHunter for more sensitivity. BLAST will
work with over 30% sequence identity.
Conserving core regions
 Homologous proteins usually have conserved
core regions.
 When we model one protein after a similar
protein with known structure, the main
problem becomes modeling loop regions.
 Modeling loops can also depend on database
to some degree.
 Side chains: on a few side-chain
conformations frequently occur – they are
called rotamers, there is a such a database.
Primary, secondary, and tertiary
 There are many secondary structure
prediction programs. However, without
considering tertiary structure, we will never be
correct solely predicting secondary
structures.
 Most tertiary structure prediction programs
today depend on good secondary predictions.
This is also not good: you cannot get right
tertiary structure with wrong starting
information.
 They must be done together.
There are not too many candidates!
 There are only about 1000 topologically different





domain structures. There is no reason whatsoever
that we cannot compute their structures accurately.
Ab initio method – we have heard about it.
Another promising method is threading (separate
lecture).
After threading, an important step is “refinement”,
perhaps by fragment assembly. This will be a
separate topic (Xin Gao).
Folding membrane proteins is a quite different topic
(Richard Jang).
Now we go to threading.
Protein Threading
 Make a structure prediction through finding an optimal
placement (threading) of a protein sequence onto each known
structure (structural template)
 “placement” quality is measured by some statistics-based
energy function
 best overall “placement” among all templates may give a
structure prediction
target sequence
MTYKLILNGKTKGETTTEAVDAATAEKVFQYANDNGVDGEWTYTE
template library
Threading Example
Introduction to Linear Program





Optimize (Maximize or Minimize) a linear objective function
 e.g.
2x+3y+4z
The variables satisfy some linear constraints. e.g.
1. x+y-z >=1
2. 2x+y+3z=3
integer program (IP) =linear program (LP) + integral variables
LP can be solved within polynomial time --- Interior point method.
Simplex method also runs fast. We used IBM package.
Polynomial time for IP not likely, NP-hard
 IP can be relaxed to LP, solve the non-integral version
 Branch-and-bound or branch-and-cut (may cost exponential
time)
Why Integer Programming?
 Treat pairwise potentials rigorously
 critical for fold-level targets
 Existing Exact algorithms for pairwise
potentials


High memory requirement, or
Expensive computational time
 Exploit correlations between various kinds of
item scores in the energy function
 99% real data generate integral solutions
directly, no branch-and-bound needed.
Different approaches
 Approximation Algorithm
 Interaction-Frozen Algorithm (A. Godzik et al.)
 Monte Carlo Sampling (T. Madej et al.)
 Double dynamic programming (D. Jones et al.)
 Recursive dynamic programming (R. Thiele et al.)
 Exact Algorithm

Branch-and-bound (R.H. Lathrop et al.)


Exploit the relationship among various scoring
parameters, fast self-threading
Divide-and-conquer (Y. Xu et al.)

Exploit the topological structure of template
contact graphs
Formulating Protein Threading by LP
•
Protein Threading Needs:
1.
2.
3.
4.
Construction of Template Library
Design of Energy Function
Sequence-Structure Alignment
Template Selection and Model Construction
Threading Energy Function
how preferable to
put two particular
residues nearby: Ep
how well a residue
fits a structural
environment: Es
(Pairwise potential)
(Fitness score)
sequence similarity
between query and
template proteins: Em
alignment gap
penalty: Eg
(gap score)
(Mutation score)
Consistency with the secondary structures: Ess
E= Ep + Es + Em + Eg + Ess
Minimize E to find a sequence-structure alignment
Contact Graph
template
1.
2.
3.
Each residue as a vertex
One edge between two
residues if their spatial
distance is within a given
cutoff.
Cores are the most
conserved segments in the
template: alpha-helix, betasheet
Simplified Contact Graph
Contact Graph and Alignment
Diagram
Contact Graph and Alignment
Diagram
Variables
 x(i,l) denotes core i is aligned to sequence position l
 y(i,l,j,k) denotes that core i is aligned to position l and core j is
aligned to position k at the same time.
Formulation 1
Minim ize
E   ai ,l xi ,l   b(i ,l )( j ,k ) y(i ,l )( j ,k )
s.t.
xi ,l  xi 1,k  1
y(i ,l )( j ,k )  xi ,l x j ,k
Encodes
scoring system
x
lD[ i ]
i ,l
Eg , Ep
Es , Ess , Em
1
xi ,l , y(i ,l )( j ,k )  {0,1}
Encodes interaction structures:
the first makes sure no crosses;
the second is quadratic, but can
be converted to linear: a=bc is
eqivalent to: a≤b, a≤c, a≥b+c-1
Formulation used in RAPTOR
Minim ize
E   ai ,l xi ,l   b(i ,l )( j ,k ) y(i ,l )( j ,k )
Eg, Ep
s.t.
xi ,l 
Encodes
scoring system
, l  D[i]
y
, k  D[ j ]
( i ,l )( j , k )
kR[ i , j ,l ]
x j ,k 
x
lD[ i ]
y
( i ,l )( j , k )
lR[ j , k ,i ]
i ,l
1
xi ,l , y(i ,l )( j ,k ) {0,1}
Es, Ess, En
Encodes interaction
structures
Solving the Problem Practically
1. More than 99% threading instances can be
solved directly by linear programming, the
rest can be solved by branch-and-bound
with only several branch nodes
2. Less memory consumption
3. Less computational time
4. Easy to extend to incorporate other
constraints
CPU Time for CAFASP3 targets
Fold Recognition
 Support Vector Machines (SVM) Approach



Features are extracted from the alignments
A threading pair is treated as a positive pattern
only if they are in at least fold-level similarity
60,000 threading pairs are employed to train
SVM model.
 5% more targets are recognized by SVM
approach than the traditional z-Score
Part II. Experiments
Test
Lindhal et al.
benchmark
Evaluator
us
Data Set
large
Blindness
no
public
no
LiveBench
third-party
small
no
yes
CASP/CAFA
SP
third-party
small
yes
yes
Target Category
CASP5
CM
CM/FR
FR(H)
FR(A)
CAFASP
3
HM easy
(family level)
HM hard
(superfamily
level)
FR (fold level)
# targets
20
12
30
NF/FR
Hard
Easy
Prediction Difficulty
CM: Comparative Modelling, HM: Homology Modelling
FR: Fold Recogniton, NF: New Fold
NF
Lindahl Benchmark Test
RAPTOR
FUGUE
PSI-BLAST
HMMER-PSIBLAST
SAMT98-PSIBLAST
BLASTLINK
SSEARCH
THREADER
family
Top1
Top5
84.8
87.1
82.2
85.8
71.2
72.3
67.7
73.5
70.1
75.4
74.6
78.9
68.6
75.7
49.2
58.9
superfamily
Top1
Top5
47.0
60.0
41.9
53.2
27.4
27.9
20.7
31.3
28.3
38.9
29.3
40.6
20.7
32.5
10.8
24.7
fold
Top1
31.3
12.5
4.0
4.4
3.4
6.9
5.6
14.6
Top5
54.2
26.8
4.7
14.6
18.7
16.5
15.6
37.7
976*975 threading pairs are tested, the results of other servers are taken from
Shi et al.’s paper.
LiveBench Test
LiveBench 6
Month
Rank
August
3
September
4
October
7
LiveBench 7
Month
Rank
Feb
10
March
1
April
3
May
2
June
6
November
14
December
9
Total
6
Total
4
Easy
6
Easy
7
Hard
5
Hard
3
(http://bioinfo.pl/LiveBench)
CASP5/CAFASP3
 62 targets
 Time allowed for each target:
 Individual Servers: 48 hours
 Meta Servers: 48 hours
 Predictors: computer program, no manual
intervention (CAFASP3)
 Evaluated by computer program
 RAPTOR was voted by CASP5 attendees as the
most novel approach, at http://forcasp.org
CAFASP3: The Third Critical Assessment of Fully Automated Structure Prediction
CAFASP3 Evaluation Criteria
 Model


Only the first submission considered for each target,
each server can submit 10 models for each target,
 MaxSub (evaluation program)



Superimpose the predicted structure with the
experimental structure
Calculate the length of maximum superimposable
subsegment within 5Å RMSD
one prediction is regarded as correct only if the length
is above a given value.
CAFASP3 Evaluation Criteria
 Sensitivity (N-1 Rule)

One miss allowed for each server, i.e., the first
models of N-1 out of N targets ranked
 Specificity



Rank the first models of all targets according
to their zScores
S(M): # Correct before the first M false
positives
Average of S(1),S(2),…,S(5)
Specificity Example
Predicted
Model
zScore
Correct ?
(by MaxSub)
T1
9.1
Yes
T2
8.4
Yes
T3
7.8
No
T4
7.6
Yes
T5
7.5
No
T6
7.4
Yes
…
…
…
T30
…
…
S(1)=2
S(2)=3
First false positive
Second false positive
Sensitivity on FR targets (1)
30 FR
targets
54 servers
Servers
Sum MaxSub Score
# correct
3ds5 robetta
5.17-5.25
15-17
pmod 3ds3 pmode3
4.21-4.36
13-14
RAPTOR
3.98
13
shgu
3.93
13
3dsn orfeus
3.64-3.90
12-13
pcons3
3.75
12
fugu3 orf_c
3.38-3.67
11-12
…
…
…
pdbblast
0.00
0
…
…
…
blast
0.00
0
(http://ww.cs.bgu.ac.il/~dfischer/CAFASP3, released on Dec., 2002.)
Sensitivity on FR targets (2)
CM/FR
FR(H)
FR(A)
NF/FR
NF
# Correct
6
4
2
1
0
# Targets
7
7
6
5
5
1. RAPTOR is weak at recognizing FR(A) targets (need improvement )
2. RAPTOR cannot deal with NF targets at all (normal)
Sensitivity on Hard HM targets
Ran
k
Servers
Score
#
Correct
1
3ds5
5.13
12
2
3ds3 shgu
4.93-5.02
12
4
pmod pmod3
4.60-4.68
12
6
orfeus orfb 3dpsm raptor
fugu3 pco3 robetta
4.33-4.43
12
8
samt02
4.18
12
…
…
…
…
11
pdbblast
4.28
12
…
…
…
…
blast
0.32
2
Specificity of Servers
Rank
Servers
Specificity
1
3ds5
24.8
2
pmodel 3dsn 3ds3
pmodel3
22.0-22.6
6
pcons3 shgu
21.4-21.6
8
inbgu fugu3
19.0-19.8
10
ffas03 orfeus fugsa
18.2-18.4
13
raptor 3dpsm orf_c
17.4-17.8
…
…
…
pdbblast
13.0
blast
4.0
Out of 33 Targets
CAFASP3 Example
 Target ID: T0136_1
 Target Size:144
 Superimposable size
within 5Å: 118
 RMSD:1.9Å
Red: Experimental Structure
Blue/green: RAPTOR model
CASP6, T0199-2, ACE buffalo rank: 9th
From RAPTOR rank 1 model. TM=0.4183 MaxSub=0.2857. Good
parts: 116-134, 286-332
Left: predicted structure. Right: experimental structure
CASP6, T0203 ACE buffalo rank: 1st
From RAPTOR 2nd model. TM=0.6041, MaxSub=0.3485. Good parts:
19-57, 89-94, 139-178, 224-239, 312-372
RAPTOR first
Model ranks
5th
Predicted
Experimental
CASP6, T0262-2, ACE buffalo rank: 4th
From Fugue3 6th model. TM=0.4306, MaxSub=0.3459.
Good parts: 162-203
Fugue’s
top
model
ranks
low
Predicted
Experimental
CASP6, T0242, NF, ACE buffalo rank: 1
From RAPTOR rank 5 model.
TM score=0.2784, MaxSub score=0.1645
However,
RAPTOR top
model
ranks 44th !
Trivial error?
Predicted
Experimental
CASP6, T0238, NF ACE buffalo rank 1st
From RAPTOR 8th model TM=0.2748, MaxSub=0.1633
Good part: 188-237. High TM score, low MaxSub
Raptor
top
model
ranks 4th
Predicted
Experimental
About RAPTOR
 Jinbo Xu’s Ph.D. thesis work.
 The RAPTOR system has benefited
significantly from PROSPECT (Ying Xu, Dong
Xu, et al).
 Currently distributed by BSI.
 References: J. Xu, M. Li, D. Kim, Y. Xu, Journal of
Bioinformatics and Computational Biology, 1:1(2003), 95-118.
J. Xu, M. Li, PROTEINS: Structure, Function, and Genetics,
CASP5 special issue.