1. Combining Predictors for Short and Long Protein Disorder
Zoran Obradovic, Slobodan Vucetic and Kang Peng
Information Science and Technology Center, Temple University, PA 19122
A. Keith Dunker and Predrag Radivojac
Center for Computational Biology and Bioinformatics, Indiana University, IN 46202
NIH grant R01 LM A1 to A.K. Dunker and Z. Obradovic is gratefully acknowledged

2. 4 levels of protein structure
Introduction
Protein Structure - under physiological condition, the amino acid sequence of a protein folds spontaneously into specific (native) three dimensional (3-D) structure or conformation
hydrogen bond
-strand
4 levels of protein structure
hydrogen bond

3. Importance of Protein Structure
The “central dogma” – amino acid sequence determine protein structure, and protein structure determine its biological function
> 1NLG:_ NADP-LINKED GLYCERALDEHYDE-3-PHOSPHATE EKKIRVAINGFGRIGRNFLRCWHGRQNTLLDVVAINDSGGVKQASHLLKYDSTLGTFAAD VKIVDDSHISVDGKQIKIVSSRDPLQLPWKEMNIDLVIEGTGVFIDKVGAGKHIQAGASK VLITAPAKDKDIPTFVVGVNEGDYKHEYPIISNASCTTNCLAPFVKVLEQKFGIVKGTMT TTHSYTGDQRLLDASHRDLRRARAAALNIVPTTTGAAKAVSLVLPSLKGKLNGIALRVPT PTVSVVDLVVQVEKKTFAEEVNAAFREAANGPMKGVLHVEDAPLVSIDFKCTDQSTSIDA SLTMVMGDDMVKVVAWYDNEWGYSQRVVDLAEVTAKKWVA
Function: Gene Transfer
Amino Acid Sequence
3-D Structure
Biological Function
Thus, it is important to know a protein’s structure to understand its function and other biological properties

4. Protein Structure Prediction
The sequence-structure gap
Current experimental structure determination techniques, e.g. X-ray diffraction and NMR spectroscopy, are still slow, expensive and have their limitations
As a result, there are less than 30,000 experimental protein structures, compared to more than 1.6 million known protein sequences
Protein structure prediction – predicting protein structures from amino acid sequences using computational methods
Aspects of protein structure prediction
1D – secondary structures, solvent accessibility, transmembrane helices, signal peptides/cleavage sites, coiled coils, disordered regions
2D – inter-residue contacts, inter-strand contacts
3D – individual atom coordinates in the tertiary structure (the ultimate goal)

5. # of participating groups
The CASP Experiments
Critical Assessment of Techniques for Protein Structure Prediction
The primary goal
To obtain an in-depth and objective assessment of current methods for predicting protein structure from amino acid sequence
The procedure
Proteins with “soon to be solved” structures are selected as prediction targets, and their amino acid sequences are made available
Prediction teams submit their prediction models before the experimental structures are released
Prediction models are compared to experimental structures for detailed evaluation by independent assessors
# of targets
# of participating groups
# of submitted models
CASP6 (2004) 76
208
41283
CASP5 (2002) 67
215
28728
CASP4 (2000) 43
163
11136
CASP3 (1998) 98
3807
CASP2 (1996) 42 72
947
CASP1 (1994) 33 35
135
CASP Website:

6. Prediction Categories in CASP6
Tertiary structure (3-D coordinates for individual atoms) prediction
Comparative/Homology modeling
Fold recognition
New fold modeling
Disordered region prediction (since CASP5)
Domain boundary prediction (new)
Residue-residue contact prediction (new)
Secondary structure prediction was excluded in CASP6
In CASP6 there were 20 groups participated in Disordered Region prediction, while only 6 groups in CASP5

7. Disordered Region (DR)
Part of a protein or a whole protein that does NOT have stable 3D structure in its native state
Perform important biological functions
Have distinct sequence properties
Evolve faster than ordered regions
Common in nature
Kissinger et al, 1995
Other definitions of disordered region
Missing coordinates (used by CASP)
High B-factors
Random coils
NOn-Regular Secondary Structure (NORS)

8. Prediction of Disordered Regions
One example for each sequence position (residue)
Class label 0/1:
disordered / ordered
Input Window
of length Win
Amino Acid Sequence
K Q L L W C Y L A A M A H Q F G A G K L K C T S A T T W Q G
Attributes derived from the local window
20 AA frequencies
K2-entropy (sequence complexity)
Flexibility
Hydropathy
more …

9. Long DR Predictors on Short DR
Disordered regions can be divided into 2 groups according to their lengths
short DRs – 30 consecutive residues or shorter
long DRs – longer than 30 consecutive residues
Our previous disorder predictors were specific to long DRs
Predictors – VL-XT, VL2, VL3, VL3H, VL3P, VL3B
Accuracies – 70% (VL-XT) ~ 85% (VL3P)
They were less successful on short DRs, as shown in CASP5
25~66% per-residue accuracy on short DRs
75~95% per-residue accuracy on long DRs
Possible reasons
The window lengths for attribute construction and post-filtering were optimized for long DRs
Training data did NOT include any short DRs
Short DRs are different from long DRs in terms of amino acid compositions, flexibility index, hydropathy and net charge

10. Amino Acid Compositions of Short DRs
Radivojac et al., Protein Science, 2004
Amino acid frequency difference from Globular-3D
Consequence – a predictor specialized for short disordered regions is necessary

11. Our Approach in CASP6
Idea – two specialized predictors for long and short disordered regions, and a meta predictor to estimate which specialized predictor is more suitable for current input
Long Disorder Predictor (>30aa)
Short Disorder Predictor (30aa) 
Meta Predictor OL OS wL wS
Final
Prediction
Input
In CASP5, we used only Long Disorder Predictor component

12. The Training Dataset Dataset Number of Chains Number of long DRs
Number of short DRs
LONGa
153
163 24
SHORTc
511 43
630
ORDERa,b
290
XRAYd
381
329
TOTALe
1335
230
983
a) LONG and ORDER – training data for VL3 predictors (Z. Obradovic, K. Peng, S. Vucetic, P. Radivojac, C. J. Brown, A. K. Dunker, Proteins, 53 (S6): , 2003; K. Peng, S. Vucetic, P. Radivojac, C. J. Brown, A. K. Dunker, Z. Obradovic, Journal of Bioinformatics and Computational Biology, in press)
b) ORDER – training data for a B-factor predictor and used in a study of flexibility index (P. Radivojac, Z. Obradovic, D. K. Smith, G. Zhu, S. Vucetic, C. J. Brown, J. D. Lawson, A. K. Dunker, Protein Science, 13 (1):71-80, 2004; D. K. Smith, P. Radivojac, Z. Obradovic, A. K. Dunker, G. Zhu, Protein Science, 12 (5): , 2003)
c) SHORT – training data for a short disorder predictor (Radivojac et al., Protein Science, 13 (1):71-80, 2004)
d) XRAY – a non-redundant set of PDB chains released between June 2003 and May 2004
e) TOTAL - the merged sequences are non-redundant with less than 50% identity

13. Specialized Disorder Predictors
Optimized for long and short disordered regions, respectively
Predictor
Attributes
Window Length
Accuracyc (%)
Wina
Woutb
short DR
long DR
order
Long Disorder
(>30aa)
Amino acid frequencies
K2-Entropy
Flexibility index
Hydropathy/net charge ratio 41 31
50.13.6
76.54.2
85.10.9
Short Disorder
(30aa)
(In addition to the attributes above)
PSI-BLAST profile
Secondary structure prediction (PSIPred)
An indicator of terminal regions 15 5
81.52.1
66.73.5
82.40.5
a) Length of input window for attribute construction
b) Length of output window for post-filtering
c) Out-of-sample per-chain accuracies were estimated by 1) randomly split the 1335 sequences into 75%:25%, 2) the first part for training and the second for testing, 3) repeat steps 1 and 2 for 30 times and average the accuracies

14. The Prediction Process
For each sequence position (residue)
The three predictors construct attributes and output OL, OS and OG
The final output is calculated as O = OL * OG + OS * (1 – OG)
If O > 0.5, predict disorder
Otherwise, predict order
Long Disorder Predictor (>30aa)
Short Disorder Predictor (30aa) 
Meta Predictor OL OS OG
1-OG
The final output
O = OL* OG + OS * (1 - OG)
Input

15. Training the Meta Predictor
The meta predictor was then trained as a 2-class classifier (short disorder vs. long disorder)
Constructing labeled dataset for training of meta predictor
Used same attributes as for the short disorder predictor
Residues from long DRs and their flanking regions were labeled as class 1
Residues from short DRs (3aa) and their flanking regions were labeled as class 0
The remaining residues were discarded (u)
Disorder labels:
Class labels:
GKKGAVAEDGDELRTEPEAKKSKTAAKKNDKEAAGEGPALYEDPPDHKTS
ooooooooooooooooooooDDDDDDDDoooooooooooooooooooooo
uuuuuuuuuuuuuu uuuuuuuuuuuuuuuu
A Short Disordered Region (8aa)
Ordered Region
Sequence:
Current Residue
Input Window
(Length Win)
The input window (of length Win =61) centered at current residue must overlap with more than half of a disordered region
Example:

16. CASP6 Targets
63 targets with 3-D coordinates information available, with 90 disordered regions and 90 ordered regions
Length range
Number of regions
Number of residues
Disordered regions
1-3 35 58
4-15 41
304
16-30 9
201
31-100 4
266
>100 1
102
Total 90
931
Ordered regions
12,520

17. Prediction Accuracy (a) per-region accuracy (b) per-residue accuracy
VL2 (CASP6 model-3) – a previously developed long disorder predictor (S. Vucetic, C.J. Brown, A.K. Dunker and Z. Obradovic, Proteins: Structure, Function and Genetics, 52: , 2003)
VL3E(CASP6 model-2) – a previously developed long disorder predictor (Z. Obradovic, K. Peng, S. Vucetic, P. Radivojac, C. J. Brown, A. K. Dunker, Proteins, 53 (S6): , 2003; K. Peng, S. Vucetic, P. Radivojac, C. J. Brown, A. K. Dunker, Z. Obradovic, Journal of Bioinformatics and Computational Biology, in press )
NEW (CASP6 model-1) – the combined predictor
NEW/short – the specialized predictor for short disordered regions (30aa)
NEW/long – the specialized predictor for long disordered regions (>30aa)

18. Prediction on Long Disordered Regions
(a) Prediction by component predictors
(b) Comparison to previous predictors
Notes: (1) red segments indicate disordered regions (of missing coordinates), (2) The threshold for predicting disorder is 0.5

19. Prediction on Short Disordered Regions
Notes: (1) red segments indicate disordered regions (of missing coordinates), (2) The threshold for predicting disorder is 0.5
In both targets, all short DRs were identified, but with considerable amount of false positives. More detailed analysis shows that the new predictor tend to over-predict at N- and C- termini

20. Correlation with High B-factor Regions
Notes: (1) red segments indicate disordered regions (of missing coordinates), (2) The threshold for predicting disorder is 0.5, (3) no B-factor data for disordered regions

21. Conclusion by CASP6 Assessor
“Group 193 is best on all measures, on both no-density segments and B-factors, and is significantly better than next 3 groups, 096, 003, 347 on no-density segments, who are about the same as each other. Groups 3, 347, and 472 are good at B-factors”
Group IDs:
193 ISTZORAN (Zoran Obradovic, Temple University)
096 CaspIta (Tosatto et al., Univ. of Padova)
003 Jones UCL (David Jones, University College London)
347 DRIP PRED (server from Bob MacCallum, Stockholm)
472 Softberry (good at B-factor correlation)
Assessor’s report is available at CASP6 website:

22. Future Directions
The length threshold 30 for dividing DRs into long and short is artificial and may not be the best choice
A better method for partitioning the DRs into more homogenous length groups (maybe more than 2)
The new predictor produced considerable amount of false positives, especially at the N- and C- terminals.
Build predictors specific to terminal and internal regions, and combine them (a similar approach to VL-XT)
The dataset contains noises, i.e. mislabeling, since not all missing coordinate regions may not necessarily be due to disorder

23. The End
Thank You!!