DEIM Forum 2013 P2-3

Identifying functional site of disordered proteinsChun Fang† Tamotsu Noguchi‡

and Hayato Yamana††School of Fundamental Science and Engineering, Waseda University, 3–4–1 Okubo, Shinjuku-ku, Tokyo, 169–8555, Japan‡Pharmaceutical Education Research Center, Meiji Pharmaceutical University, 2-522-1, Noshio, Kiyose, Tokyo, 204-8588, Japan

E-mail: †{fangchun, yamana}@yama.info.waseda.ac.jp,‡noguchit@my-pharm.ac.jp

Abstract
Molecular recognition features (MoRFs) has been proved to play critical role in molecular-interaction network of the cell,
especially in eukaryotes. They act as molecular switches in the processes of cell signaling and regulation, and have relationshipwith the cause of many diseases. The importance of identifying MoRFs in disordered proteins is becoming increasinglyapparent. However, due to unique structural and particular physiochemical properties of disordered proteins, traditional
methods used for identifying functional sites of ordered proteins are ineffective for predicting MoRFs in disordered proteins. In
this study, we propose a novel method, using a masking encoding scheme which extracts the relatively conservative
information of residues for prediction. We compared our method with 9 other existing methods on a same test-dataset; theexperimental results showed that, our method achieved the best performance, which got an AUC of 0.758. This study
demonstrated that: 1) the flanking regions of MoRFs affected the plasticity of MoRFs; 2) MoRFs were flanked by lessconserved residues; and 3) the masked and filtered PSSM was predictive features for identifying MoRFs．
Keyword MoRFs，disordered proteins，PSSM

1. Introduction

physicochemical preferences were shown in all

With breaking of the traditional concept on protein

MoRFs types compared to the disordered regions instructure and function, the functional importance ofgeneral. Fuxreiter M., et al [3] demonstrated thatdisordered regions has become more and moreboth amino acid composition and charge/hydropathy

apparent. MoRFs are short binding regions located inproperties of MoRFs exhibit a mixture characteristic

intrinsically disordered protein regions. They haveof folded and disordered proteins. AHCHOR [4]noapplied biophysical principles to identify MoRFs.

well-definedstructureinundergoaEvolutionary information has been proved to be adisorder-to-order transition upon binding to partner

predictive feature in identifying protein functional

proteins. The particular dynamic conformation ofsite, because in order to maintain certain function,

MoRFs allows them to interact with multiple targets.

the functional sites of proteins must maintain a high[1]. MoRFs pla y critical role in various cellulardegree of conservation. MoRFs have also been found

functions, such as signaling and regulation; they act

to be more conserved than surrounding residues;ashowever,naturalstate,molecular

three-dimensional
butareswitches

easy

into

molecular-interaction

disordered

proteins

evolverapidly

network of the cell, and assumed to have relationshipcompared to ordered proteins. The standard PSSM

with

Thus,

which incorporates the conservation information ofidentification of MoRFs is a key step to annotate

proteins is ineffective when used directl y. Relative

protein functions and to find applications in drug

local conservation has been proved to be a gooddesign.

feature for motifs discovery in disordered protein

thecausesofmany

diseases.

MoRFs have attracted the interestof many

researchers. Norman E. D [2] analyzed the attributesofMoRFs

andfoundthat,severalstrongregions, and has been used by researches [5-7].
In our previous study [8], we found that there aresome redundant features in standard PSSMs, andfunctional site prediction for ordered proteins. In
order to evaluate whether it is also suitable fordisordered proteins, in this study, we adopt amasking and filtering encoding scheme to develop

percentage composition

ignoring some noise features could improve the20%
15%
10%
5%0%
1the MoRFs predictor. This method could strengthen

3579111315171921232527

29

lengththe high relative local conservative information
while filtering out the low relative local conservative

Figure1.

Length distribution of MoRFs

scores in PSSMs. A traditional PSSM-based method
was also developed for a comparison. Both of themodels used the support vector machines (SVM).

2. Research method
2.1 Data Sets

We prepared two dataset s, training-dataset andtest-dataset, both of them were extracted from thedatasets used in the research [1].
Training-datasets:
The research [1] used 421 MoRFs-contained chainsfor training and 419 MoRFs-contained chains for test.
Since we found that some sequences among the 840
chains are sharing a similarity higher than 40%, weused CD-HIT [9] to cluster them, and the chains withsimilarity >40% were discarded. After removed, 447
chains that contain 5,601positive samples (MoRFs)
and 262,732 negative samples (non-MoRFs) wereremained. All the positive samples, and the sameamount of negative samples selected randomly fromthe 262,732 non-Morphs were used to construct thetraining dataset for our experiment.
Test-datasets:
We used the same test dataset with the research ofMoRFPred [1], named as TEST2012, which includes
45 MoRFs-contained chains deposited in PDB fromJanuary 1 to March 11, 2012, and also include thoseof UniProtKB released from February 22, 2012.
2.2 Length distribution of MoRFs

The length distributions of MoRFs in the 447
proteins were analyzed. As shown in Figure 1, mostof them had length between 5 to25 residues.

22.3 Composition analysis

We calculated the composition of the 447 proteins.
The sequences are divided into 3 regions: the MoRFs,
the flanking regions of MoRFs within 5 residues, andthe non-MoRFs in the general disordered region. The
composition percentage of the 20 amino acid residues
is shown in Figure 2 (a). Figures 2 (a) demonstrates
that Ile, Leu, Phe, Tyr, Lys, Arg residues areoverrepresented in MoRFs, most of them arehydrophobic amino acids. While amino acids Ala,Gly, Lys, Ser, Pro are overrepresented inflanking-MoRFs regions, most of them are small andtiny amino acids.
12%
10%
8%
MoRF

6%

non_MoRF
flank

4%
2%0%
I L V C A G M F Y W H K R E Q D N S T P

Figure 2(a). Composition distribution of the 20
amino acids for the 3 kinds of regions
2.4 Physicochemical properties preference

We also calculated the physicochemical properties
propensit y for each kind of regions. 10
discriminative physicochemical features of residue
were considered in our study. They were hydrophobic,polar, small, proline, tiny, aliphatic, aromatic,
positive, negative, and charged. The distribution of
10 physicochemical properties is shown in Figure 2(b). Figure 2 (b) demonstrates that physicochemical
characteristics propensity of flanking regions seems
to vary widely compared to the flanking regions andgeneral disordered regions. Properties such as polar,small, tiny and charged are over-represented inflanking regions.
25%
20%
15%
MoRF
10%
non_MoRF
5%

flank

0%

Figure 2(b). 10 physicochemical characteristics
propensit y for the 3 regions

2.5 Prediction model

Based on the above analysis, we considered to use amasked and filtered PSSM, which incorporates theinformation of amino acid position, relative
evolutionary information, and dependency onneighboring residues, to design our prediction model.
The prediction model is shown in Figure 3.

size. N is the length of a protein.
2.7 Masking and filtering the PSSM

The masked PSSM is used to describe the relative
evolutionary information of each residue in a protein;
it was converted from a standard PSSM according tothe formula (1).
Firstl y, a masking sliding window with appropriate
size was used to calculate the mean conservation
score for each residue in a standard PSSM, and thenthe relative conservation scores were calculated.
After that, in order to strengthen the highconservative scores while filtering out the low
conservative scores, the positions below a meanconservation score were sited to 0 according to theformula (2).
1 j i n(1)
Masking _ Ci  Ci Cj2n  1 j i n
Masking _ Ci , ( Masking _ Ci  0)
Filtering _ Ci  
0, ( Masking _ Ci  0)

(2)

Masking_C i is the mean conservation score of residue
i, C i is the standard conservation score in PSSM,
2*n+1 is the masking window size.
2.8 SVM

Figure 3. Prediction model. The length of sliding
window is represented by 2h+1; n is the length ofsequence.
2.6 Evolutionary information (PSSM)

Evolutionary information were obtained from PSSMs,
which were generated by PSI-BLAST [10], searching
against NCBI non-redundant (nr) database [11] by
three times iteration with an e-value of 0.001.
Evolutionary information for each amino acid wasencapsulated in a vector of 20 dimensions; the size ofPSSM of a protein with N residues is 20 × N. 20
dimensions were considered as a standard amino acid

3Prediction of MoRFs can be addressed as a
two-classification problem; determining whether agiven residue belong to MoRFs or not. Our prediction
model was trained by the LIBSVM software package
which was written by in Chih-Jen [12-13]. The
Radial Basis Function (RBF kernel) was adopted toconstruct the SVM classifiers. In order to obtain theoptimal sliding window size, the standard PSSM
based model was analyzed as an example. Results ofthe success rate according to different slidingwindow sizes were listed in Figure 4. The
performance tends to be stable from the window sizes
21. Thus, we chose the relatively best size 27 as thesliding window size for our models.

Figure 4. Success rate of the standard PSSM based model
at different sliding window sizes

3. Evaluation criteria

The area under the corresponding receiver operating
characteristic (ROC) curve (AUC) was adopted toevaluate the performance of the classifiers. the ROC
plots with the AUC values were created by using theR statistical package [14]. The accuracy, truepositive rate (TPR), false negative rate (FPR) aredefined as follows:
TPTP  FN(3)

FPTN  FP(4)

TPR FPR Accuracy TP  TN
TP  TN  FP  FNFigure 5. ROC Plots of the MFPSSMPred at different(5)masking window size

Where TP, TN, FP and FN represents true positive,true negative, false positive and false negative
respectively.

4. Results and discussion
4.1 Performance comparison with SVM-PSSM

We used the test dataset TEST2012 to evaluate theperformance of our developed models. The standard
PSSM based model (SVM-PSSM) was developed fora comparison. Both the MFPSSMPred model and theSVM-PSSM model were used a same training set,with a same sliding window size of 27. Since
MFPSSMPred method requires a particular masking
window size, we compared the ROCs calculated withdifferent masking window size, as shown in Figure 5.
Among them, the mode with a masking size of 15 got
the best ROC (yellow ROC). Thus, 15 was chosen as
the masking window size for the MFPSSMPred. Thedetail ROC plots of the MFPSSMPred andSVM-PSSM are shown in Figure 6 and 7 respectively.
Figure 6 demonstrates that the AUC of MF PSSMPredis 0.7578 which is higher than the AUC ofSVM-PSSM (0.749) showed in Figure 7.

Figure 6. ROC Plot of the MFPSSMPred model at masking
window size 15.

Figure 7. ROC Plot of the SVM-PSSM model
For facilitating comparison, the two ROC plots arealso integrated into Figure 8. From Figure 8, we cansee that, the ROC shape of the MFPSSMPred is more
perfect besides its bigger area under the ROC curve4compared to the ROC curve of SVM-PSSM.

4.3 Performance on unbalanced training samples

Figure 8. Integrated ROC Plots of the SVM-PSSM
model and MFPSSMPred model

Details of the accuracy, TPR, FPR of the FPSSMPredaccording to different evaluation thresholds wereshown in table 1.
Table 1. Accuracy, TPR and FPR of the MFPSSMPred atdifferent threshold

There are 5,601positive samples and 262,732
negative samples in our dataset; the ratio between
them was 1:46.9, in order to analyze whether thisimbalance bias the prediction method, we also
developed the training model with a 2:1 ratio
between the non-MoRF and Morph residues, that is,5,601 MoRFs with 112,02 non-Morphs residues.
Results tested on the TEST2012 are shown in Figure
9. Figure 9 demonstrates that, the difference between
the results trained on 1:1 ratio and 2:1 ratio is notvery obvious.

4.2 Performance comparison with 9 existing methods

There exist some tools for MoRFs predication. Here
we also list them out for a comparison. The results ofother classifiers are quoted from research [1]. All themethods were tested on the TEST2012 dataset.
Results are shown in Table 2. Table 2 demonstrates
that our MFPSSMPred predictor achieves the bestAUC.
Table 2. Performance of MFPSSMPred and 9 otherpredictors on dataset TEST2012

5Figure 9. ROC Plots of MFPSSMPred based on unbalanced
training samples

5. Conclusions
In this paper, we proposed a Masked and filtered
PSSM based method, which used the masking andfiltering skills to extract the relative highevolutionary information, while filtering out the lowconservative information of residues for MoRFs
prediction. The traditional PSSM based method and 9other existing methods were analyzed forcomparison s. We tested all the methods on the samedataset TEST2012. Experimental results showed that,when comparing with the traditional PSSM based
method, the MFPSSMPred method performed better
than the SVM-PSSM method, which not only
obtained a bigger AUC but also got a more perfect
ROC curved shape. When comparing with the 9 otherexisting methods, our method demon strated the best
AUC of 0.758, which was 0.109~0.246 higher thanothers.
In summary, this study demonstrated that themasked and filtered PSSM, which incorporated
relative evolutionary information and filtered thenoise features of residues, was predict ive foridentifying MoRFs. It also revealed some hallmarksof MoRFs: the flanking regions of MoRFs affected
the plasticity of MoRFs; MoRFs were flanked by lessconserved residues. Though the performance of our
method is still not satisfactory due to the complexityof disorder proteins, we shall try our best to further
improve the performance of our model.

References[1] Fatemeh M.D Wei-Lun H, Marcin J.M, ChristopherJ.O, Bin X, A. Keith D, Vladimir N.U, Lukasz K.
MoRFpred, a computational tool for sequence -based
prediction
andcharacterizationofshortdisorder-to-order transitioning binding regions inproteins, Bioinformatics (2012) 28 (12): i75 -i83.
[2] Norman E. D, Kim V. R, Robert J. W. Attributes ofshort linear motifs. Molecular BioS ystems. 2012, 8,268-281.
[3] Monika F, Peter T and Istva´ n S. Local structural
disorder imparts plasticity on linear motifs .
Bioinformatics (2007) 23 (8): 950-956.
[4] Dosztan yi Z, Mészáros, Simon I. ANCHOR: webserver for predicting protein binding regions indisordered proteins. Bioinformatics (2009) 25 (20) :2745-2746.
[5] Norman E. D,
Denis C. S andRichard J. E.
Masking residues using context-specific evolutionar yconservation significantly improves short linear
motif discovery. Bioinformatics (2009) 25 (4) :443-450.
[6] Norman E. Davey1,Joanne L. C,
Denis C. S,
Toby J. G.
Mark J. C. andRichard J. E.
SLiMPrints:
conservation -based
discoveryoffunctional motif fingerprints in intrinsicall ydisordered protein regions. Nucleic Acids Research,2012, 1–14
[7] Niall J Haslam and Denis C Shields . Profile-based
short linear protein motif discovery, BMC

6

Bioinformatics 2012, 13:104[8] Chun F, Tamotsu N, and Hayato Y: Using
physicochemicalfeatures
tocondense
theposition-specific scoring matrix for flavin adenine
dinucleotide-binding predi ction. BioDataMing, inassessing.
[9] Weizhong Li, Adam G. Cd-hit: a fast program forclustering and comparing large sets of protein or
nucleotide sequences. Bioinformatics (2006) 22 (13) :1658-1659.
[10] Stephen F. A, Thomas L. M, Alejandro A. S, Jinghui Z,Zheng Z. Gapped BLAST and PSI-BLAST: a newgeneration of protein database search programs .
Nucleic Acids Research. 1997, 25 (17): 3389-3402.
[11] NR [ftp://ftp.ncbi.nih.gov/blast/db/fasta/nr.gz ].
[12] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: alibrary for support vector machines. ACM
Transactions on Intelligent Systems and Technology.
2011, 2:27:1-27:27[13] A librar y for support vector machines :[http://www.csie.ntu.edu.tw/~cjlin/libsvm/ ][14] http://www.r-project.org/