DBCP: a web server for disulfide bonding
connectivity pattern prediction without the prior
knowledge of the bonding state of cysteines
Hsuan-Hung Lin1,2
and Lin-Yu Tseng3,4,
*
1
Department of Applied Mathematics, National Chung Hsing University, 2
Department of Management
Information System, Central Taiwan University of Science and Technology, 3
Institute of Networking and
Multimedia and 4
Department of Computer Science and Engineering, National Chung Hsing University, Taichung,
Taiwan, ROC
Received February 4, 2010; Revised May 11, 2010; Accepted May 21, 2010
ABSTRACT
The proper prediction of the location of disulfide
bridges is efficient in helping to solve the protein
folding problem. Most of the previous works on
the prediction of disulfide connectivity pattern use
the prior knowledge of the bonding state of cysteines.
The DBCP web server provides prediction of
disulfide bonding connectivity pattern without the
prior knowledge of the bonding state of cysteines.
The method used in this server improves the
accuracy of disulfide connectivity pattern prediction
(Qp) over the previous studies reported in the
literature. This DBCP server can be accessed
at http://120.107.8.16/dbcp or http://140.120.14.
136/dbcp.
INTRODUCTION
Disulfide bonds play an important structural role in
stabilizing protein conformations. For the protein
folding prediction, a correct prediction of disulfide
bridges can greatly reduce the search space (1,2). The prediction
of disulfide bonding pattern helps, to a certain
degree, predict the 3D structure of a protein and hence
its function because disulfide bonds impose geometrical
constraints on the protein backbones. Some recent
research works had shown the close relation between the
disulfide bonding patterns and the protein structures (3,4).
In the realm of the disulfide bond prediction, four
problems are addressed. The first is the protein chain classification:
to classify if the protein contains disulfide
bridge(s) or not, the second is the residue classification:
to predict the bonding state of cysteines, the third is the
bridge classification and the last is the prediction of the
disulfide bonding pattern. Over the past years, significant
progress has been made on the prediction of the disulfide
bonding states (5–8) and the disulfide bonding pattern
(9–17). For disulfide bonding pattern prediction, with
the exception of the methods proposed by Ferre` and
Clote (11, 12) and Cheng et al. (15), the others assume
that the bonding states are known. The method
proposed by Ferre` and Clote (11,12) and Cheng et al.
(15) can be applied whether the bonding states are
known or not.
In this study, the coordinate (X, Y, Z) of the C of each
amino acid in the protein predicted by MODELLER
(18) is used as the feature. The support vector machine
(SVM) is then trained to compute the connectivity
probabilities of cysteine pairs. The Edmonds–Gabow
maximum weight perfect matching algorithm (19) is
utilized to find the connectivity pattern.
SYSTEM
The flowchart of this server illustrated by an example is
shown in Figure 1.
FEATURE
With the exception of the protein’s secondary structure,
the features used in the previous studies on disulfide
bonding connectivity prediction are protein sequence
features and not related to the protein structure. In this
study, we propose to use the structure-related feature. The
MODELLER (18) is used to predict the coordinate (X, Y,
Z) of the Ca of each amino acid in the protein sequence.
Having the coordinates, we can compute the Euclidean
*To whom correspondence should be addressed. Tel: 886 4 22874020; Fax: 886 4 22853869; Email: lytseng@cs.nchu.edu.tw
The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors.
Published online 8 June 2010 Nucleic Acids Research, 2010, Vol. 38, Web Server issue W503–W507
doi:10.1093/nar/gkq514
ß The Author(s) 2010. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
distance Dij between the amino acid at the i-th position
and the amino acid at the j-th position. We further extend
the definition of Euclidean distance to the pair
distance (PD). Let the positions of cysteine i and
cysteine j be Pi and Pj, respectively. The PD between
cysteine i and cysteine j is defined to be a vector
(DPiÀ[w/2], PjÀ[w/2], . . . , DPiÀ1, PjÀ1, DPi, Pj, DPi+1, Pj+1,. . .,
DPi+[(wÀ1)/2], Pj+[(wÀ1)/2]) that contains w Euclidean
distances, where w is the window size. If we have k cysteines
in the protein, there are as many as Ck
2 ¼ kðk À 1Þ=2
cysteine pairs. Since most cysteine pairs will not constitute
a disulfide bond, by examining DPi, Pj of the
cysteine pairs that constitute a disulfide bond, we set a
threshold of value 15 for DPi, Pj. In other words, if DPi, Pj
is >15, this pair of cysteines will not be considered as a
candidate that may have a disulfide bond. In order to
make the values proper to be input to the SVM, which
is À1 to 1, each component of the vector PD is normalized
by the equation (Dij À 7.5)/7.5. The resultant vector
is called the normalized PD (NPD) and is the input to
the SVM.
INPUT
The inputs to the DBCP include three parts:
(1) Query name is an optional name for the user to
distinguish his or her queries. If a user sends more
than one query and selects sending the results by
the e-mail, the order in which he or she receives the
results may not match the order in which he or she
sends the queries.
(2) The sequence of a protein (plain sequence without
the header, the maximum size is 9000 residues) may
be input in the input box. If one presses the [Sample
Data] button, the sample input sequence will be
displayed on the protein sequence input box.
Figure 1. The flowchart of the DBCP illustrated by an example.
W504 Nucleic Acids Research, 2010, Vol. 38, Web Server issue
(3) The email address (If ‘send the result by Email’ is
checked, otherwise, the results will be displayed on
the web page).
METHODOLOGY
(1) Run BLAST to get the template sequence of the
input sequence. The parameters of BLAST are set
as follows: the Expectation value (E) threshold for
saving hits is set to a very large value 10 000 and the
database is set to pdb that contains sequences
derived from the 3D structure records from the
Protein Data Bank. If the E-value of the template
sequence is >10 or the template sequence shares
identity <25% to the input sequence, instead of
going to Step 2, we use the method previously developed
by us (20) to predict the disulfide bonding
pattern.
(2) Align the input sequence and the template sequence.
(3) Feed the alignment file into MODELLER and run
the procedure to evaluate the model of the input
sequence using the template sequence.
(4) Get the coordinate (X, Y, Z) of the Ca (a Carbon)
of each residue.
(5) Coding each cysteine pair as the NPD, this will be
the input to the SVM.
(6) Feed the coding file into the SVM to predict the
bonding probability of each cysteine pair with the
trained model. The multiple trajectory search (21) is
tightly integrated with the SVM training. For more
details, please refer to the Supplementary Data on
the DBCP web server.
(7) Coding the input file with the probabilities from the
SVM output and using the modified weighted
perfect matching algorithm to get the first level disulfide
bonding connectivity.
(8) Justify the first level disulfide bonding connectivity
with the thresholds to get the final result.
(9) Display the result on the web page or send the
result to the user.
In Step 1, if the E-value of the template sequence is >10
or the template sequence shares identity <25% to the
input sequence, a previously proposed method (20)
is used for prediction. In this method, the positionspecific
scoring matrix, the normalized bond lengths, the
predicted secondary structure of protein and the
physicochemical properties index of the amino acid were
used as features. The multiple trajectory search and the
SVM training were tightly integrated to train the predictor.
For more details, please refer to (20).
ENVIRONMENT
The DBCP web server is free and open to all users and
there is no login requirement. This prediction software
was implemented using C language and the server-side
scripting language PHP, and it employed the web page
on the Apache web server.
OUTPUT
In this subsection, we introduce the results of the DBCP,
as listed below:
(1) Job ID: the id assigned by the server for this query
job.
(2) Query name: the same as that was input by the
user.
(3) SEQUENCE: the input protein sequence.
(4) Positions of the cysteines.
(5) E-value: the E-value of the template sequence found
by the BLAST. If the E-value is >10, the sequence
identity and the E-value will be marked in red to
indicate a warning.
(6) Identity: the identity between the template sequence
and the input sequence. This value is provided by
the MODELLER. If the template sequence shares
<25% identity with the input sequence, the sequence
identity and the E-value will be marked in red to
indicate a warning.
(7) Probability: the prediction probability of each cysteine
pair.
(8) Metal binding site score: a rough estimation that
the cysteine pair may be involved in the metal
binding site.
(9) Positions of oxidized cysteines.
(10) Predicted disulfide bonding connectivity pattern.
(11) Predicted positions of cysteines in metal binding
site: this is only a rough prediction and users
should consult other methods or web services for
more accurate prediction of cysteines involved in
metal binding sites (22).
EVALUATION OF WEB SERVER
We found four web sites that provided the prediction of
the disulfide bonding connectivity pattern without prior
knowledge of bonding state of cysteines (12,14–16).
Cheng et al. (15) tested their prediction method by a
10-fold cross validation on the data set SPX (15). As a
comparison, we also tested our method by a 10-fold cross
validation on the same data set, and the results were
shown in the Supplementary Data. The method
proposed by Song et al. (16) can process only protein sequences
that have less than 12 cysteines. Therefore, we
conducted a test to compare our method only with the
other three methods. We took 56 protein sequences from
the SWISS-PROT database release no. 56.3 that are
neither in the SWISS-PROT release no. 39 nor in the
data set SPX, this set of sequences is denoted as
‘SP56NS’. The prediction accuracies of our method and
the other three methods on this data set are shown in
Table 1.
Since the present version of the web server was trained
by using the data set SPX, we also took 50 sequences
from the SWISS-PROT database release no. 56.3 that
are neither in the SWISS-PROT release no. 39 nor in
the data set SPX. Furthermore, the pairwise sequence
identity of these 50 sequences and the sequences in SPX
is <25%. This set of sequences is denoted as ‘SP56NS_25’.
Nucleic Acids Research, 2010, Vol. 38, Web Server issue W505
The prediction accuracies of our method and the other
three methods on this data set are shown in Table 2.
For checking the prediction accuracy when the input
sequence has low identity to the overall set of PDB
proteins, we took 32 sequences from the SWISS-PROT
database release no. 56.3, where either the sequence
shares identity <25% to the template sequence found by
the BLAST or the E-value of the template sequence is
>10. This set of sequences is denoted as ‘CHK25’. The
prediction accuracy of DBCP for this data set is shown in
Table 3.
LIMITATIONS
(1) The prediction accuracy may degenerate if the
template sequence found by the BLAST has an
E-value >10 or the identity of the template sequence
and the input sequence is <25%.
(2) The web server is designed aiming to predict the disulfide
bonding connectivity pattern of a sequence
that does not have cysteines involved in the
metal binding sites. The prediction accuracy will
degenerate if the input sequence contains cysteines
involved in the metal binding sites. The metal
binding site score is provided only to indicate
possible metal binding sites. If this score is >0.5,
users are strongly suggested using other methods
or web services to more accurately predict the cysteines
that are involved in the metal binding sites.
The Metal Detector server (http://metaldetector.dsi
.unifi.it/) is one of such web services.
CONCLUSION
A web-based application system called the DBCP is
provided for the prediction of the disulfide bonding connectivity
pattern without the prior knowledge of the
bonding state of cysteines. In previous research works,
without the prior knowledge of the bonding state of cysteines,
to the best of our knowledge, the best accuracy
of disulfide connectivity pattern prediction (Qp) and that
of disulfide bridge prediction (Qc) are 51% and 52%, respectively,
on the data set SPX with 10-fold cross validation.
The method used in this server improved the
prediction accuracies on the same test data set SPX to
84.4% (Qp) and 94.6% (Qc) with 10-fold cross validation.
The comparison of the prediction accuracy of the DBCP
with that of three other state-of-the-arts web services on
the data sets SP56NS and SP56NS_25 also reveals that the
DBCP outperforms the other three methods.
If the template sequence found by the BLAST has an
E-value >10 or the identity of the template sequence and
the input sequence is <25%, another method previously
proposed by us is used for prediction. In this case, the
prediction accuracy may slightly degenerate. Since the
DBCP is designed aiming to predict the disulfide
bonding connectivity pattern of a sequence that does not
have cysteines involved in the metal binding sites, for
protein sequences that contain cysteines involved in the
metal binding sites, other methods that can predict both
the disulfide bonds and the metal binding sites will be
more suitable for prediction. The high metal binding site
score (e.g. >0.5) indicates that there may be cysteines
involved in the metal binding sites. In this case, users are
strongly suggested using other methods in addition to the
DBCP and conclude the prediction result based on the
results of all methods.
SUPPLEMENTARY DATA
Supplementary Data are available at NAR Online.
ACKNOWLEDGEMENTS
The authors like to thank the anonymous reviewers for
pointing out the problems of proteins lacking homology
and sequences containing cysteines involved in metal
binding sites. The comments of all anonymous reviewers
have improved the quality of the paper.
Table 1. Comparison of the prediction accuracies on the data set
SP56NS
Number
of bonds
Number
of sequences
DBCP
(%)
Dipro
(15) (%)
DiANNA
(12) (%)
DISULFIND
(14) (%)
2 10 50 10 10 0
3 10 80 40 10 30
4 10 70 20 0 0
5 10 60 10 0 0
6–9 16 50 0 0 n.a
All 56 60.7 14.3 3.6 5.4
Table 2. Comparison of the prediction accuracies on the data set
SP56NS_25
Number
of bonds
Number
of sequences
DBCP
(%)
Dipro
(15) (%)
DiANNA
(12) (%)
DISULFIND
(14) (%)
2 10 60 20 20 0
3 10 50 30 0 30
4 10 50 30 0 10
5 10 50 0 0 0
6–14 10 30 0 0 n.a
All 50 48 16 4 8
Table 3. The prediction accuracy on the data set CHK25
Number of
bonds
Number of
sequences
Qp (%) Qc (%)
2 15 53.3 76.7
3 11 54.5 69.7
4 3 33.3 33.3
6–9 3 0 43.5
All 32 46.9 61.2
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FUNDING
National Science Council of ROC (contract number
NSC98-2221-E005-049-MY3, partial); Ministry of
Education, Taiwan, ROC under ATU plan (partial);
Central Taiwan University of Science and Technology
(grant CTU99-P-33). Funding for open access charge:
National Chung Hsing University.
Conflict of interest statement. None declared.
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