PREDICTING PROTEIN
SECONDARY STRUCTURE
USING ARTIFICIAL
NEURAL NETWORKS
Sudhakar Reddy
Patrick Shih
Chrissy Oriol
Lydia Shih
Proteins
And Secondary Structure
Sudhakar Reddy
Project Goals

To predict the secondary structure
of a protein using artificial neural
networks.
STRUCTURES

Primary structure: linear arrangement
of amino acid (a.a) residues that
constitute the polypeptide chain.
SECONDARY STRUCTURE

Localized organization of parts of a polypeptide chain,
through hydrogen bonds between different residues.

Without any stabilizing interactions , a polypeptide assumes
random coil structure.

When stabilizing hydrogen bond forms, the polypeptide
backbone folds periodically in to one of two geometric
arrangements viz.

ALPHA HELIX
BETA SHEET

U-TURNS

ALPHA HELIX

A polypeptide back bone is folded in to spiral that is held
in place by hydrogen bonds between backbone oxygen
atoms and hydrogen atoms.

The carbonyl oxygen of each peptide bond is hydrogen
bonded to the amide hydrogen of the a.a 4 residues toward
the C-terminus

Each alpha helix has 3.6 a.a per turn

From the backbone side chains point outward

Hydrophobic/hydrophilic quality of the helix is determined
entirely by side chains, because polar groups of the
peptide backbone are already involved H-bonding in the
helix and thus are unable to affect its
hydrophobic/hydrophilic.
ALPHA HELIX
THE BETA SHEET

Consists of laterally packed beta strands

Each beta strand is a short (5-8 residues), nearly fully
extended polypeptide chain

Hydrogen bonding between backbone atoms in a adjacent
beta strands, within either the same or different
polypeptide chains forms a beta sheet.

Orientation can be either parallel or anti-parallel. In both
arrangements side chains project from both faces of the
sheet.
THE BETA SHEET
THE BETA SHEET
TURNS
Composed of 3-4 residues , are compact, U-shaped
secondary structures stabilized by H-bonds
between their end residues.
 Located on the surface of the protein, forming a
sharp bend that redirects the polypeptide
backbone back toward the interior.
 Glycine and proline are commonly present.
 Without these turns , a protein would be large,
extended and loosely packed.

TURNS
MOTIFS

MOTIFS: regular combinations of secondary
structure.
–
Coiled coil motif
–
Helix-loop-helix(Ca+)
–
Zinc finger motif.
COILED-COIL MOTIF
HELIX-LOOP-HELIX (CA+)
ZINC-FINGER MOTIF
FUTURE

Protein structure identification is key to understanding
biological function and its role in health and disease

Characterizing a protein structure helpful in the
development of new agents and devices to treat disease

Challenge of unraveling the structure lies in developing
methods for accurately and reliably understanding this
relationship

Most of the current protein structures have been
characterized by NMR and X-Ray diffraction

Revolution in sequencing studies-growing data base-only
3000 known structures
ADVANTAGE

Very few confirmations of protein are
possible and structure and sequence are
directly related to each other, we can
unravel the secondary structure by
developing an efficient algorithm, which
compares new sequences with the ones
available, and use them in health care
industry.
WHY SECONDARY STRUCTURE?

Prediction of secondary structure is an essential
intermediate step on the way to predicting the
full 3-D structure of a protein

If the secondary structure of a protein is known,
it is possible to derive a comparatively small
number of possible tertiary structures using
knowledge about the ways that secondary
structural elements pack
Artificial Neural Network
(ANN)
Peichung Shih
Biological Neural Network
Artificial Neural Network
X1k : Input from X1
X2k : Input from X2
W1k : Weight of X1
W2k : Weight of X2
X0k : Bias term
W0k : Weight of bias
term
S Q : Threshold
1
: Nonlinear
function
qk : Output of node k
-1
Artificial Neural Network - Example
X0 = 1
X 1= 1
X2 =
2
W0 = 2
W1 = 1
+
W2 = 2
+
Q=
6
2
 Xi  Wi  1 2  11  2  2  7
i 0
if (7  Q) output
else exit (0);
 Xi Wi
;
F(x) = ( 1 + e-x )-1
7
-1
-1
F(7 ) 
1
1
1 7
e
Output 1
1
 0.9991  1
Paradigms of ANN - Overview
Topology
Feedback
Learning
Binary Adaptive
Resonance
Theory (ART1)
Unsupervised  Analog Adaptive
Resonance
Theory (ART2)

Brain-State-in-a-Box
(BSB)
 Fuzzy Cognitive Map
(FCM)

Supervised
Feedforward


Fuzzy Associative
Memory (FAM)
Learning Vector
Quantization (LVQ)
Perceptron
Perceptron
Adaline
Adaline&&Madaline
Madaline
Backpropagation
Backpropagation(BP)
(BP)
Paradigms of ANN - Feedforward
Topology
Learning
Unsupervised
Supervised
Feedback
Feedforward
Paradigms of ANN - feedback
Topology
Learning
Unsupervised
Supervised
Feedback
Feedforward
Paradigms of ANN - supervised
Topology
Learning
Unsupervised
Supervised
Feedback
Feedforward
Paradigms of ANN - Unsupervised
Topology
Learning
Unsupervised
Supervised
Feedback
Feedforward
Paradigms of ANN - Overview
Topology
Feedback
Learning
Binary Adaptive
Resonance
Theory (ART1)
Unsupervised  Analog Adaptive
Resonance
Theory (ART2)

Brain-State-in-a-Box
(BSB)
 Fuzzy Cognitive Map
(FCM)

Supervised
Feedforward


Fuzzy Associative
Memory (FAM)
Learning Vector
Quantization (LVQ)
Perceptron
Perceptron
Adaline
Adaline&&Madaline
Madaline
Backpropagation
Backpropagation(BP)
(BP)
Perceptron
 One of the earliest learning networks was proposed by
Rosenblatt in the late 1950's.
RULE:
net = w1I1 + w2I2
if net > Q then output = 1,
otherwise o = 0.
MODEL:
Perceptron Example : AND Operation
y
QQ
W=W
Output
correct?
N
O=1;T0
Initial Network:
QQ1
Q = 1.5
0.5
1
+ 0.5
1
O=0;T1
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND Operation
Input
I1
Input
I2
Target
1
1
1
y
QQ
W=W
Output
correct?
0
N
0.5
O=1;T0
Q = 1.5
0.5 0.5
1
QQ1
+ 0.5 1.5
1
O=0;T1
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND Operation
Input
I1
Input
I2
Target
1
0
0
y
QQ
W=W
Output
correct?
0
N
O=1;T0
Q = 0.5
0.5
1
O=0;T1
QQ1
1.5
0
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND Operation
Input
I1
Input
I2
Target
0
1
0
y
QQ
W=W
Output
correct?
1
N
1.5
O=1;T0
Q = 0.5
0.5
0
QQ1
1.5 0.5
1
O=0;T1
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND Operation
Input
I1
Input
I2
Target
0
0
0
y
QQ
W=W
Output
correct?
0
N
O=1;T0
Q = 1.5
0.5
0
O=0;T1
QQ1
0.5
0
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND
Input
I1
Input
I2
Target
1
1
1
Operation
y
QQ
W=W
Output
correct?
0
N
0.5
O=1;T0
Q = 1.5
1.5
1
0.5
QQ1
0.5 1.5
1
O=0;T1
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND Operation
Input
I1
Input
I2
Target
1
0
0
y
QQ
W=W
Output
correct?
1
N
1.5
O=1;T0
Q = 0.5
0.5
1
1.5
O=0;T1
QQ1
1.5
0
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND Operation
Input
I1
Input
I2
Target
0
1
0
y
QQ
W=W
Output
correct?
0
N
O=1;T0
Q = 1.5
0.5
0
O=0;T1
QQ1
1.5
1
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Perceptron Example : AND Operation
Input
I1
Input
I2
Target
0
1
0
y
QQ
W=W
Output
correct?
0
N
O=1;T0
Q = 1.5
0.5
0
O=0;T1
QQ1
1.5
1
QQ-1
I0
I1
I0
I1
W=W
W=W-1
W=W
W=W+1
Hidden Layer
1
(0, 1)
(1, 1)
1
0
(0, 0)
(1, 0)
0
XOR
OR
AND
Hidden Layer
Input
I1
Input
I2
Target
1
1
0
1
0
1
0
1
1
0
0
0
0
??
Hidden Layer
Input
I1
Input
I2
Target
1
1
0
1
0
1
0
1
1
0
0
0
0.5
1
-2
1
1
1
1
1.5
1
1
1
1
1
How Many Hidden Nodes?
We have indicated the number of layers
needed. However, no indication is provided as
to the optimal number of nodes per layer.
There is no formal method to determine this
optimal number; typically, one uses trial and
error.
Hidden Units
Q3(%)
0
62.50
5
61.60
10
61.50
15
62.60
20
62.30
30
62.50
40
62.70
60
61.40
JNET AND JPRED
CHRISSY ORIOL
JNET
•Multiple Alignement
•Neural Network
•Consensus of methods
TRAINING AND TESTS
• 480 proteins train (1996 PDB)
• 406 proteins test (2000 PDB)
 Blind test
 7-fold cross validation test
MULTIPLE ALIGNMENTS
ALIGNMENTS
• Multiple sequence alignment constructed
• Generation of profiles
 Frequency counts of each residue / total residue in the column
(expressed as percentage)
 Each residue scored by its value from BLOSUM62 and the scores
were averaged based on the number of sequence in that column
Profile HMM generated by HMMER2
 PSI-BLAST (Position Specific Iterative Basic Local Alignment
Search Tool)
o Frequency of residue
o PSSM (Position Specific Scoring Matrix)
• Uses:
HMM PROFILE
 Statistical descriptions of a sequence family's
consensus
 Position-specific scores for residues, insertions and
deletions
• Profiles:
 Captures important information about the degree of
conservation at different positions
 Varying degree to which gaps and insertions and
deletions are permitted
PSI-BLAST PROFILE
Full length seq. from the initial PSIBlast search,
extracted from the database, and ordered by pvalue
Align [a] and [b]
Remove gaps in [a] and
the column below the
gaps to form a
restrained profile
which better
represents sequence
[a]
Align [c] to profile
of [a] and [b]
Iterate addition
of each sequence
from PSIBlast
search until all
are aligned
Alignment profile based on the
query sequence to be predicted
PSI-BLAST PROFILE
• Iterative
 Low complexity sequences polluted searching
profile
• Filtered database to “mask” out:
 Low complexity sequences (SEG)
 Coiled-coil regions (HELIXFILT)
 Transmembrane helices (HELIXFILT)
NUERAL NETWORK
NUERAL NETWORK
•
Two Nueral Network Used
 1st
o Sliding window of 17 residues
o 9 hidden nodes
o 3 outputs
 2nd
o Sliding window of 19 residue
o 9 hidden nodes
o 3 outputs
CONSENSUS COMBINATION
OF PREDICTION METHODS
CONSENSUS COMBINATION
OF PREDICTION METHODS
“Jury Agreement” (Identical
predictions by all methods Q3 = 82%)
•
• “No Jury” (Q3 = 76.4%)
 Trained another neural network
Q
3
predicted 100
 

observed
(i H ,E ,C )
ASSESMENT OF ACCURACY
Segment Overlap:
min ov (sobs ; spred )  d
Sov 
len(s1 )

N 
max ov (sobs ; spred )
s
1
Confidence = 10 C (outmax - outnext)
RIBONUCLEASE A
KEY
“H” – helix
“E” – strand
“B” - buried
residue
“-” exposed
residue
“*” – no jury
JNET OUTPUT
YourSeq
YA60_PYRHO
TF19_HUMAN
Q9VUZ8
YRGK_CAEEL
Y691_METJA
YK68_ARCFU
YF69_SCHPO
YMW4_YEAST
:
:
:
:
:
:
:
:
:
MRQQLEMQKKQIMMQILTPEARSRLANLRLTRPDFVEQIELQLIQLAQMGRVRSKITDEQLKELLKRVAGKKREIKISRK
ERALIEAQIQAILRKILTPEARERLARVKLVRPELARQVELILVQLYQAGQITERIDDAKLKRILAQIEAKRREFRIKW.
..KHREAEMRSILAQVLDQSARARLSNLALVKPEKTKAVENYLIQMARYGQLSEKVSEQGLIEILKKVSQQEKTTTVKFN
..MRAQEEMKSILSQVLDQQARARLNTLKVSKPEKAQMFENMVIRMAQMGQVRGKLDDAQFVSILESVNAQQSKSSVKYD
ARAENQETAKGMISQILDQAAMQRLSNLAVAKPEKAQMVEAALINMARRGQLSGKMTDDGLKALMERVSAQQKATSVKFD
..ALLEAEMQALLRKILTPEARERLERIRLARPEFAEAVEVQLIQLAQLGRLPIPLSDEDFKALLERISALKRKREIKIV
MRRQVEAQKKAILRAILEPEAKERLSRLKLAHPEIAEAVENQLIYLAQAGRIQSKITDKMLVEILKRVQPKKRETRIIRK
..QEVQDEMRNLLSQILEHPARDRLRRIALVRKDRAEAVEELLLRMAKTGQISHKISEPELIELLEKISGEKRNETKIVI
.AGGGENSAPAAIANFLEPQALERLSRVALVRRDRAQAVETYLKKLIATNNVTHKITEAEIVSILNGIAKQQNNSKIIFE
OrigSeq
: 1---------11--------21--------31--------41--------51--------61--------71-------- :
: MRQQLEMQKKQIMMQILTPEARSRLANLRLTRPDFVEQIELQLIQLAQMGRVRSKITDEQLKELLKRVAGKKREIKISRK : OrigSeq
jalign
jfreq
jhmm
jnet
jpssm
:
:
:
:
:
Jpred
: -HHHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHH-----EEEE-- : Jpred
MCoil
MCoilDI
MCoilTRI
Lupas 21
Lupas 14
Lupas 28
:
:
:
:
:
:
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
:
:
:
:
:
:
MCoil
MCoilDI
MCoilTRI
Lupas 21
Lupas 14
Lupas 28
Jnet_25
Jnet_5
Jnet_0
Jnet Rel
:
:
:
:
---BB---B--BBB-BB---B--BB--B-BB---BB-BBB-BBB-BB-BB-B---B----BB-BB--B--------B-------------BB--B----B---B--B----------B---B--B--------------B--BB----------------------------------------------------B---B--B--------------B------------------79889998888998643697888849188454657899999999988626987657778999999986007883747728
:
:
:
:
Jnet_25
Jnet_5
Jnet_0
Jnet Rel
--HHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHHHH----EE---HHHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHH----EEEEE--HHHHHHHHHHHHHHH---HHHHHHHHHHH----HHHHHHHHHHHHHHH--------HHHHHHHHHHHHHH---EEEEE-HHHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHH-----EEEEE--HHHHHHHHHHHHHH--HHHHHHH-HEEEE---HHHHHHHHHHHHHHH--------HHHHHHHHHHHH-----EEE---
:
:
:
:
:
:
:
:
:
:
:
:
:
:
YourSeq
YA60_PYRHO
TF19_HUMAN
Q9VUZ8
YRGK_CAEEL
Y691_METJA
YK68_ARCFU
YF69_SCHPO
YMW4_YEAST
jalign
jfreq
jhmm
jnet
jpssm
JPRED SERVER
Consensus web server
•JNET – default method
•PREDATOR
• Neural network focused on predicting hydrogen bonds
•PHD - PredictProtein
• Neural network focused on predicting hydrogen bonds
JPRED SERVER cont.
•NNSSP – Nearest-neighbor SS prediction
•DSC – Discrimination of protein Secondary
structure Class
• Based on dividing secondary structure prediction into the basic
concepts for prediction and then use simple and linear statistical
methods to combine the concepts for prediction
•ZPRED
• physiochemical information
•MULPRED
•Single sequence method combination
YourSeq
OrigSeq
: MRQQLEMQKKQIMMQILTPEARSRLANLRLTRPDFVEQIELQLIQLAQMGRVRSKITDEQLKELLKRVAGKKREIKISRK : YourSeq
: ERALIEAQIQAILRKILTPEARERLARVKLVRPELARQVELILVQLYQAGQITERIDDAKLKRILAQIEAKRREFRIKW. : YA60_PYRHO
: ..KHREAEMRSILAQVLDQSARARLSNLALVKPEKTKAVENYLIQMARYGQLSEKVSEQGLIEILKKVSQQEKTTTVKFN : TF19_HUMAN
: ..MRAQEEMKSILSQVLDQQARARLNTLKVSKPEKAQMFENMVIRMAQMGQVRGKLDDAQFVSILESVNAQQSKSSVKYD : Q9VUZ8
: ARAENQETAKGMISQILDQAAMQRLSNLAVAKPEKAQMVEAALINMARRGQLSGKMTDDGLKALMERVSAQQKATSVKFD : YRGK_CAEEL
: ..ALLEAEMQALLRKILTPEARERLERIRLARPEFAEAVEVQLIQLAQLGRLPIPLSDEDFKALLERISALKRKREIKIV : Y691_METJA
: MRRQVEAQKKAILRAILEPEAKERLSRLKLAHPEIAEAVENQLIYLAQAGRIQSKITDKMLVEILKRVQPKKRETRIIRK : YK68_ARCFU
: ..QEVQDEMRNLLSQILEHPARDRLRRIALVRKDRAEAVEELLLRMAKTGQISHKISEPELIELLEKISGEKRNETKIVI : YF69_SCHPO
: .AGGGENSAPAAIANFLEPQALERLSRVALVRRDRAQAVETYLKKLIATNNVTHKITEAEIVSILNGIAKQQNNSKIIFE : YMW4_YEAST
: --3-273433568336-522-43--25838573836556-2384484316682-37581274298238323542-3422- : consv
: 1---------11--------21--------31--------41--------51--------61--------71-------- :
: MRQQLEMQKKQIMMQILTPEARSRLANLRLTRPDFVEQIELQLIQLAQMGRVRSKITDEQLKELLKRVAGKKREIKISRK : OrigSeq
jalign
jfreq
jhmm
jnet
jpssm
mul
nnssp
phd
pred
zpred
:
:
:
:
:
:
:
:
:
:
Jpred
: -HHHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHHH----EEEE-- : Jpred
PHDHtm
MCoil
MCoilDI
MCoilTRI
Lupas 21
Lupas 14
Lupas 28
:
:
:
:
:
:
:
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
:
:
:
:
:
:
:
PHDHtm
MCoil
MCoilDI
MCoilTRI
Lupas 21
Lupas 14
Lupas 28
PHDacc
Jnet_25
Jnet_5
Jnet_0
:
:
:
:
----B---B-BBBBBBB---B---BB-B-BB----B-BB-BBBB-BB-BB-B---B----B--BB--B------B-B-U---BB---B--BBB-BB---B--BB--B-BB---BB-BBB-BBB-BB-BB-B---B----BB-BB--B--------B-------------BB--B----B---B--B----------B---B--B--------------B--BB----------------------------------------------------B---B--B--------------B-------------------
:
:
:
:
PHDacc
Jnet_25
Jnet_5
Jnet_0
PHD Rel
Pred Rel
Jnet Rel
: 97527999999999999899999999986315269999999999999964332235649999999999962356225319 : PHD Rel
: 00777700999990990609990999886606668099999999009677787757768989909999957077777000 : Predator Rel
: 79889998888998643697888849188454657899999999988626987657778999999986007883747728 : Jnet Rel
YA60_PYRHO
TF19_HUMAN
Q9VUZ8
YRGK_CAEEL
Y691_METJA
YK68_ARCFU
YF69_SCHPO
YMW4_YEAST
consv
--HHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHHHH----EE---HHHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHH----EEEEE--HHHHHHHHHHHHHHH---HHHHHHHHHHH----HHHHHHHHHHHHHHH--------HHHHHHHHHHHHHH---EEEEE-HHHHHHHHHHHHHHHHHHHHHHHHHHHHH---HHHHHHHHHHHHHHHH--------HHHHHHHHHHHH-----EEEEE--HHHHHHHHHHHHHH--HHHHHHH-HEEEE---HHHHHHHHHHHHHHH--------HHHHHHHHHHHH-----EEE----HHHHHHHHHHHHHHHHH--HHHHHHHH-H--HHHHHHHHHHHHHH----------HHHHHHHHHHHHHHH--H-EEEHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH--HHHHHHHHHHHHHHHHH--------HHHHHHHHHHHHH-----EEEEE
---HHHHHHHHHHHHHHHHHHHHHHHHHHH--HHHHHHHHHHHHHHHHH--------HHHHHHHHHHHHHH----EEE----HHHHHHHHHHHHHHHHHHHHHHHHHHHHH-HHHHHHHHHHHHHHH-------HHHHHHHHHHHHHHHHHHHHH-----HHHHHHHHHHHHHEHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHHH-EE----HHHHHHHHHHHHHHHHH---EE--
:
:
:
:
:
:
:
:
:
:
jalign
jfreq
jhmm
jnet
jpssm
mul
nnssp
phd
pred
zpred
Accuracy Evaluation
By Liang-Yu Shih
Methods

Per-residue accuracy
 Q3 measurement: traditional way
 Mathew’s correlation coefficient:

Per-segment accuracy
 SOV measurement: CASP2

Subcategorizing the incorrect
prediction
 Over: predict alpha/beta when it is coil
 Under: predict coil when it is alpha/beta
 Wrong: predict alpha when it is beta or
vice versa
How to measure Q3
Qindex:
 Qhelix, Qstrand and Qcoil: for a single conformational state:
Qi = [(number of residues correctly predicted
in state i)/(number of residues observed in state i)] x
100
 Q3: for all three states
Q3 = [(number of residues correctly
predicted)/(number of all residues)] x 100
How to measure Matthew
coefficients
Problems in per-residue accuracy
1.
It does not reflect 3D structure.

2.
Example: assigning the entire myoblobin
chain as a single helix gives a Q3 score of
80.
Conformational variation observed
at secondary structure segment
ends.

Example: low Q3 value but can predict
folding well.
Q: What is a good measure?
A: A structurally oriented measure

A structurally oriented measure consider the
following………..
1.
Type and position of secondary structure
segments rather than a per-residue
assignment of conformational state.
Natural variation of segment boundaries
among families of homologous proteins.
2.
How to measure SOV
SOV Example
Observed (S1):
CCEEECCCCCCEEEEEECCC
Predicted (S2):
CCCCCCCEEEEECCCEECCC
Minov
#
##
Maxov
SOV Example Cont.
Sov(E) =
1
11 2  2
100 *
*(

) * 6  34.6
663
10
6
EEECCCCCCEEEEEE
S(E’) S(E’) S(E)
S(E)
[minov(s1, s2) + delta(s1,s2)] / maxov(s1, s2)
Delta(s1,s2)=min[(10-1);(1);(15/2);(10/2)]
Delta(s1,s2)=min[(6-2);(2);(15/2);(10/2)]
Evaluation-Step 1
(query sequence)
Hypothetical Protein :
MRQQLEMQKKQIMMQILTPEARSRLANLRLTRPDFVEQI
QLIQLAQMGRVRSKITDEQLKELLKRVAGKKREIKISRK
80 residues
 Methanothermobacter thermautotrophicus
 Structures solved by NMR
 Christendat,D., et al. Nat. Struct. Biol. 7 (10),
903-909 (2000)

Evaluation-Step 2 (programs)
Explicit rules
First Generation
(information is
from a single
residue, of a
single sequence)
NearestNeighbors
Neural-Networks
based prediction
Levin et al 1986
Nishikawa and
Ooi 1986
Holley and Karplus
1989
Qian and Sejnowski
1988
PSIProfile
HMM
Lim 1974
Second
Generation
(Local
interactions)
PREDATOR 1996
APSSP1995
Third
Generation
(Information is
from
homologous
sequences)
SAM-T99sec
PHD 1993
Jpred 1999
PROFsec2000
SSPRO2
Severs
1.
2.
3.
4.
5.
6.
APSSPhttp://imtech.ernet.in/raghava/aps
sp/
JPred http://jura.ebi.ac.uk:8888/
PHDhttp://cubic.bioc.columbia.edu/predic
tprotein
PROFsechttp://cubic.bioc.columbia.edu/pr
edictprotein
PSIpredhttp://insulin.brunel.ac.uk/psifor
m.html
SAM-T99sec
http://www.cse.ucsc.edu/research/compbi
o/HMM-apps/T99-query.html
Evaluation-Step 3
Conversion of DSSP secondary
structure from 8 states to 3
states:
DSSP
USED
H
H
G
H
H: alpha helix
E: beta strand
L: coil (others)
I
H
E
E
B
E
T
L
S
L
''
L
Evaluation-Step 4
•First column: protein sequence (AA) in one-letter code
•Second column: observed (OSEC) secondary structure
•Third column: predicted (PSEC) secondary structure
http://predictioncenter.llnl.gov/local/sov/sov.html
Evaluation-Result
Method
Measurement
Jpred
Q3
Apssp
Sam-T99
PHD
Predator
SSRPO
ALL
HELIX
STRAND
COIL
73.8
100.0
100.0
47.5
SOV
62.2
80.5
100.0
48.1
Q3
72.5
97.5
100.0
47.5
SOV
67.3
93.8
100.0
46.9
Q3
72.5
100.0
100.0
45.0
SOV
65.8
93.8
100.0
44.2
Q3
67.5
97.5
100.0
37.5
SOV
56.5
80.0
100.0
38.5
Q3
70.0
95.5
100.0
45.0
SOV
66.4
89.4
100.0
48.0
Q3
77.5
100.0
100.0
55.0
SOV
69.1
94.0
100.0
50.0
EVA: Evaluation of Automatic
protein structure prediction
http://cubic.bioc.columbia.edu/eva/sec/graph/common3.jpg
Conclusion
 Jpred
is the pioneer of methods
which give high Q3 and SOV scores.
 The 2ndary structure prediction using
a jury of neural networks is one of
the best methods.
REFERENCES
1.
Cuff JA, Clamp ME, Siddiqui AS, Finlay M, Barton GJ. “Jpred: A consensus secondary
structure prediction server,” Bioinformatics, 1998;14:892-893.
2.
Cuff,J.A. and Barton, G.J. “Evaluation and improvement of multiple sequence methods for
protein secondary structure prediction.” Proteins: Structure, Functions, and Genetics,
1999;34:508-519.
3.
Cuff,J.A. and Barton, G.J. “Application of multiple sequence alignment profiles to improve
protein secondary structure prediction.” Proteins: Structure, Functions, and Genetics,
2000;40:502-511.
4.
Zemla et al. A modified definition of Sov, a Segment-Based Measure for Protein Secondary
Structure Prediction Assessment. Protein; 1999:34:220-223
5. Defay T, Cohen F. Evaluation of current techniques for ab initio protein structure prediction.
Proteins 1995; 23:431-445.
6. Barton GJ. Protein secondary structure prediction. Curr Opin Struct Biol 1995; 5:372-376
7. Schulz GE. A critical evaluation of methods for prediction of secondary structures. Ann Rev
Biophys Chem 1988; 17:1-21
8. Zhu Z-Y. A new approach to the evaluation of protein secondary structure predictions at the level
of the elements of secondary strucuter. Protein Eng 1995; 8:103-108