Combining Predictors for Short

and Long Protein DisorderZoran 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 LM007688-01A1 to A.K. Dunker and Z. Obradovic is gratefully acknowledged

IntroductionProtein Structure - under physiological condition, the amino acid sequence of a protein folds spontaneously into specific (native) three dimensional (3-D) structure or conformation

4 levels of protein structure

-strand

hydrogen bond

hydrogen bond

Importance of Protein Structure

Amino Acid Sequence

3-D Structure

Biological Function

> 1NLG:_ NADP-LINKED GLYCERALDEHYDE-3-PHOSPHATE EKKIRVAINGFGRIGRNFLRCWHGRQNTLLDVVAINDSGGVKQASHLLKYDSTLGTFAAD VKIVDDSHISVDGKQIKIVSSRDPLQLPWKEMNIDLVIEGTGVFIDKVGAGKHIQAGASK VLITAPAKDKDIPTFVVGVNEGDYKHEYPIISNASCTTNCLAPFVKVLEQKFGIVKGTMT TTHSYTGDQRLLDASHRDLRRARAAALNIVPTTTGAAKAVSLVLPSLKGKLNGIALRVPT PTVSVVDLVVQVEKKTFAEEVNAAFREAANGPMKGVLHVEDAPLVSIDFKCTDQSTSIDA SLTMVMGDDMVKVVAWYDNEWGYSQRVVDLAEVTAKKWVA

Function: Gene Transfer

The “central dogma” – amino acid sequence determine protein structure, and protein structure determine its biological function

Thus, it is important to know a protein’s structure to understand its function and other biological properties

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)

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 41283CASP5 (2002) 67 215 28728CASP4 (2000) 43 163 11136CASP3 (1998) 43 98 3807CASP2 (1996) 42 72 947CASP1 (1994) 33 35 135

CASP Website: http://predictioncenter.llnl.gov/

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

Disordered Region (DR)

Perform important biological functions Have distinct sequence properties Evolve faster than ordered regions Common in nature

Part of a protein or a whole protein that does NOT have stable 3D structure in its native state

Kissinger et al, 1995

Other definitions of disordered region Missing coordinates (used by CASP) High B-factors Random coils NOn-Regular Secondary Structure (NORS)

Prediction of Disordered Regions

One example for each sequence position (residue)

Class label 0/1:disordered / ordered

Input Windowof 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 …

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

Amino Acid Compositions of Short DRs

Consequence – a predictor specialized for short disordered regions is necessary

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W C F I Y V L H M A T R G Q S N P D E KResidues

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Rigid OrderFlexible OrderShort DisorderLong Disorder

Radivojac et al., Protein Science, 2004

Amin

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Our Approach in CASP6Idea – 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

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 0 0

XRAYd 381 24 329

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): 566-572, 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):1060-1072, 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 2004e) TOTAL - the merged sequences are non-redundant with less than 50% identity

Specialized Disorder Predictors

Optimized for long and short disordered regions, respectively

Predictor AttributesWindow Length Accuracyc (%)

Wina Wout

b short DR long DR orderLong Disorder

(>30aa)• Amino acid frequencies• K2-Entropy• Flexibility index• Hydropathy/net charge ratio

41 31 50.13.6 76.54.2 85.10.9

Short Disorder (30aa)

(In addition to the attributes above)

• PSI-BLAST profile• Secondary structure prediction (PSIPred)

• An indicator of terminal regions

15 5 81.52.1 66.73.5 82.40.5

a) Length of input window for attribute constructionb) Length of output window for post-filteringc) 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

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

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

uuuuuuuuuuuuuu00000000000000000000uuuuuuuuuuuuuuuu

A Short Disordered Region (8aa)

Ordered Region

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:

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 90 12,520

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, 52:573-584, 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): 566-572, 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)

Length range

1-3 4-15 16-30 31-100 >100 order

Acc

urac

y (%

)0

20

40

60

80

100VL2VL3ENEWNEW/shortNEW/long

Length range

1-3 4-15 16-30 31-100 >100 order

Acc

urac

y (%

)

0

20

40

60

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100VL2VL3ENEWNEW/shortNEW/long

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

1 20 40 60 80 100 120 140 160 180 200 2200

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NEWVL3EVL2

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Prediction on Short Disordered Regions

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

Notes: (1) red segments indicate disordered regions (of missing coordinates), (2) The threshold for predicting disorder is 0.5

Correlation with High B-factor Regions

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T0203 (1-4, 105-111, 377-382)

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T0233 (1-13, 81-92, 106-108, 137-138)

50 100 150 200 250 300 350

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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

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: http://predictioncenter.llnl.gov/casp6/meeting/presentations/DR_assessment_RD.pdf

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

The End

Thank You!!