2o structure, TM regions, and solvent accessibility
Chapter 29, Du and Bourne “Structural Bioinformatics”
Topic 13
The Truth (Information) is Out (In) There
The Truth (Information) is Out (In) There
But we’re still having a tough time finding it.
Protein Secondary Structure Prediction
Given a protein sequence (primary structure), predict its secondary structures
GHWIATRGQLIREAYEDYRHFSSECPFIP
E: -strand
H: -helix
C: coil
CEEEEECCCEEEEECCCHHHHHHCCCCCC
H: ( H: - helix, G: 310 helix, I: -helix )
E: (E: -strand, B: bridge)
C: (T: -turn, S: bend, C: coil)
Assumption: short stretches of residues have propensity to adopt certain
conformation ⇒ conformation of the central residue in a sequence fragment
depends only on flanking residues (sliding window)
Why secondary structure prediction?
-- Because we can (kind of).
-- Because it could be a first step towards prediction of protein tertiary
structure.
“Have solution, need problem.” Nearly every imaginable algorithm has been
applied to secondary structure prediction.
Secondary Structure Prediction Methods
1. First generation: Single amino acid propensities
Chou-Fasman method (1974), GOR I-IV
~56-60% accuracy
2. Second generation: Segments of 3-51 adjacent residues
NNSSP, SSPAL
~65% accuracy
3. Neural network
PHD, Psi-Pred, J-Pred
4. Support vector machine (SVM)
5. Hidden Markov Models (HMM)
Third generation methods
using evolutionary information
~76% accuracy
Secondary Structure Prediction Accuracy
1. three-state per-residue prediction accuracy
3
Q3  100
M
i 1
N obs
ii
Mii, number of residues observed in state i and predicted in state i
Nobs, the total number of residues observed in 3 states
2. per-segment prediction accuracy (SOV, Segment of OVerlap)
Per-stage segment overlap:
S1: observed SS segment
S2: predicted SS segment
Single Residue Propensity Methods
Calculate the propensity for a given amino acid to adopt a certain ss-type
P( | aai )
p( , aai )
P 

p( )
p( ) p(aai )
i

i, amino acid
, secondary structure state
Example: from a data set with 30 proteins
#Ala=2,000, #residues=20,000, #helix=4,000, #Ala in helix=580
p(,aa) = 580/20,000, p() = 4,000/20,000, p(aa) = 2,000/20,000
P = 580 / (4,000/10) = 1.45
Amino Acid Propensities to Secondary Structures
P(H)
P(H)
T
S
P
T
A
E
L
M
R
S
T
G
69
77
57
69
142
151
121
145
98
77
69
57
T
S
P
T
A
E
L
M
R
S
T
G
69
77
57
69
142
151
121
145
98
77
69
57
P(H)
T
S
P
T
A
E
L
M
R
S
T
G
69
77
57
69
142
151
121
145
98
77
69
57
Chou-Fasman method
Nearest Neighbor Methods
* The idea is simple: predict SS of the central residue of a given
segment from homologous segments (neighbors).
For example, from database, find some number of the closest sequences
to a subsequence defined by a window around the central residue, then
use max (N, N, Nc) to assign the SS.
E
Homologous
C
sequences
C
RSTEVRASRQLAKEKVN
H
H
Window size
C
C
Key parameters:
1. How to define similarity?
2. What size window of sequence should be examined?
3. How many close sequences should be selected?
C
The Devil is in the details…
Psi-Pred Method





D. Jones, J. Mol. Boil. 292, 195 (1999).
Method : Neural network
Input data : PSSM generated by PSI-BLAST
Bigger and better sequence database
 Combining several database and data filtering
Training and test sets preparation
 Ss prediction only makes sense for proteins with no homologous
structure.
 No sequence & structural homologues between training and test sets
by CATH and PSI-BLAST (mimicking realistic situation).
Psi-Pred Method--Neural Network



Window size = 15
Two networks
First network (sequence-to-structure):






Second network (structure-to-structure):





315 = (20 + 1)  15 inputs
extra unit to indicate where the windows spans either N or C terminus
Data are scaled to [0-1] range by using 1/[1+exp(-x)]
75 hidden units
3 outputs (H, E, L)
Structural correlation between adjacent sequences
60 = (3 + 1)  15 inputs
60 hidden units
3 outputs
Accuracy ~76%
Sample Psi-Pred Output
Conf: Confidence (0=low, 9=high) ---very important!!!!
Pred: Predicted secondary structure (H=helix, E=strand, C=coil)
AA: Target sequence # PSIPRED HFORMAT (PSIPRED V2.3 by David Jones)
Conf: 966899999997542002357777557999999716898188034435788873356776
Pred: CCHHHHHHHHHHHHHHHCCCCCCCHHHHHHHHHHHCCCCCEEECCCCEEEEEEECCCCCC
AA:
MMWEQFKKEKLRGYLEAKNQRKVDFDIVELLDLINSFDDFVTLSSCSGRIAVVDLEKPGD
10
20
30
40
50
60
Conf: 777179998337888888988751235636899718261220179868899999998557
Pred: CCCCEEEEEECCCCCHHHHHHHHHCCCCCEEEEECCCEEEEECCCHHHHHHHHHHHHHCC
AA:
KASSLFLGKWHEGVEVSEVAEAALRSRKVAWLIQYPPIIHVACRNIGAAKLLMNAANTAG
70
80
90
100
110
120
Conf: 200242314703799714651435541487355188999999999999999889999999
Pred: CCCCCCEECCCEEEEEECCCEEEEEECCCCCEEECHHHHHHHHHHHHHHHHHHHHHHHHH
AA:
FRRSGVISLSNYVVEIASLERIELPVAEKGLMLVDDAYLSYVVRWANEKLLKGKEKLGRL
130
140
150
160
170
180
***Compare the prediction for residues 9 and 17***
Sample Psi-Pred Output-II
Again, voting rules methods tend to be best
ATKAVCVLKGDGPVQGTIHFEAKGDTVVVTGSITGLTEGDHGFHVHQFGDNTQGCTSAGP
CCCCCCCCCCCCCCCCEEHCCHHECEEEEEEEEEEEECCCCCCCCCCCCCCCCCCCCCCC
CCHEEEEECCCCCCCCEEEHHHCCCEEEEEEEEECECCCCCCEEEECCCCCCCCCCCCCC
CCCEEEEEECCCCCEEEEEEEECCCEEEEEEEEEEEECCCCCEEEEECCCCCCCCCCCCC
CCCEEEEECCCCCCCEEEEEECCCCEEEEEEEEECCCCCCCCEEEEEECCCCCCCCCCCC
HHHCEEEECCCCCCCEEEEEECCCCEEEEEECEEEEEECCCCEEEEECCCCCCEEECCCC
CCCCEEEECCCCCCCCCEEECCCCCCEEEEECEEECCCCCCCEEEECCCCCCCCEEECCC
CCCCEEEEECCCCCCCCCEEECCCCCEEEECCCCCCCCCCCEEEEEEEECCCCCCCCCCC
CCCCEEEECCCCCCCCEEEEECCCCEEEEEEEEEEECCCCCCEEEEECCCCCCCCCCCCC
---EEEEE------EEEEEEEEE--EEEEEEEEE-----EEEEEEEE-------------
2SOD
BPS
D_R
DSC
GGR
GOR
H_K
K_S
JOI
2SOD
HFNPLSKKHGGPKDEERHVGDLGNVTADKNGVAIVDIVDPLISLSGEYSIIGRTMVVHEK
CCCCCCCCCCCCCCCCCCCCCCECCCCCCHEECCCCCCCCCECCEECEEEEEEEEEEECC
CCCCCCCCCCCCCCCHHCECCCCCECCCCCCEEEEEEECCEEEECCCEEEEEEEEEEECC
CCCCCCCCCCCCCCEEEEECCCCCCCCCCCCEEEEEECCCCCCCCCCEEEEEEEEEEECC
CCCCCCCCCCCCCCCCEEECCCCCCCCCCCCCEEEEECCCCCCCCCCEEEECEEEEEECC
CCCCCCCCCCCCCCHHEEECCCCCCCCCCCCEEEEEEECCEEECCCCEEEEEEEEEECCC
CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCEECCCCCCCCCCCCCCHHHHHHEECCC
CCCCCCCCCCCCCCCCEEECCCCCCCCCCCCCEEEEEEEEEEEEECCCEEECCEEEEEEE
CCCCCCCCCCCCCCCCEEECCCCCCCCCCCCEEEEEECCCCECCCCCEEEEEEEEEEECC
--------------------EEEEEE------EEEEEEE--------------EEEEE--
2SOD
BPS
D_R
DSC
GGR
GOR
H_K
K_S
JOI
2SOD
Prediction Accuracy (EVA)
25
P SIP RED
SSp ro
P ROF
P HDps i
JP red 2
P HD
Percentage of all 150 proteins
20
15
10
5
0
30
40
50
60
70
80
90
1 00
P ercen tag e co rrectl y pred i cted resi d ues per p rot ei n
EVA: Automatic evaluation of prediction servers
How Far Can We Go?
 Currently ~76%
 Proteins with more than 100 homologues 80%
 Assignment is ambiguous (5-15%). Recall DSSP vs STRIDE.
-- non-unique protein structures (dynamic), H-bond cutoff, etc.
 Different secondary structures between homologues (~12%).
 Non-locality. Secondary structure is influenced by long-range interactions.
-- Some segments can have multiple structure types (chameleon
sequences).
Solvent accessibility
 Conceptually similar problem to SS prediction: Buried vs. Exposed.
 Weighted Ensemble Solvent Accessibility predictor:
http://pipe.scs.fsu.edu/wesa.html
E
E
E
E
B
B
B
B
B
B
E
E
Why bother?
 To provide structural context for putative mutations that one wants to
characterize biochemically or biophysically.
Transmembrane Segment Prediction
 Again, conceptually similar problem to SS prediction: TM vs. Not.