Download Sequence-based prediction of protein interaction

BIOINFORMATICS
ORIGINAL PAPER
Vol. 25 no. 5 2009, pages 585–591
doi:10.1093/bioinformatics/btp039
Sequence analysis
Sequence-based prediction of protein interaction sites with an
integrative method
Xue-wen Chen1,2,∗ and Jong Cheol Jeong1
1 Bioinformatics and Computational Life Sciences Laboratory, Information and Telecommunication Technology Center
and 2 Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS 66045, USA
Received on June 2, 2008; revised on January 14, 2009; accepted on January 15, 2009
Advance Access publication January 19, 2009
Associate Editor: Limsoon Wong
ABSTRACT
Motivation: Identification of protein interaction sites has significant
impact on understanding protein function, elucidating signal
transduction networks and drug design studies. With the
exponentially growing protein sequence data, predictive methods
using sequence information only for protein interaction site
prediction have drawn increasing interest. In this article, we propose
a predictive model for identifying protein interaction sites. Without
using any structure data, the proposed method extracts a wide
range of features from protein sequences. A random forest-based
integrative model is developed to effectively utilize these features
and to deal with the imbalanced data classification problem
commonly encountered in binding site predictions.
Results: We evaluate the predictive method using 2829 interface
residues and 24 616 non-interface residues extracted from 99
polypeptide chains in the Protein Data Bank. The experimental
results show that the proposed method performs significantly better
than two other sequence-based predictive methods and can reliably
predict residues involved in protein interaction sites. Furthermore,
we apply the method to predict interaction sites and to construct
three protein complexes: the DnaK molecular chaperone system,
1YUW and 1DKG, which provide new insight into the sequence–
function relationship. We show that the predicted interaction sites
can be valuable as a first approach for guiding experimental methods
investigating protein–protein interactions and localizing the specific
interface residues.
Availability: Datasets and software are available at
http://ittc.ku.edu/~xwchen/bindingsite/prediction.
Contact: [email protected]
Supplementary information: Supplementary data are available at
Bioinformatics online.
1
INTRODUCTION
Protein–protein interaction plays an essential role in nearly all
cell functions, such as promoting chemical reactions and acting as
antibodies. Consequently, identification of protein interaction sites
is critical for understanding protein function and for elucidating
metabolic and signal transduction networks. It could also help in
rational drug design studies (Gallet et al., 2000). A commonly
used technique in identifying protein interaction sites is in silico
∗ To
whom correspondence should be addressed.
methods, which is of great importance in molecular recognition
and are considered as a good starting point to form hypotheses in
searching for potential pharmacological targets for the design of
drugs (Gallet et al., 2000).
Roughly speaking, computational methods can be categorized
into two groups: molecular docking of two proteins with known
structures and the identification of putative interaction sites on an
isolated protein without knowing the structure of its partner or
complex (Gallet et al., 2000). While a number of computational
methods for predicting protein interaction sites have been developed
over the years, most of them require known protein structure
information (Aytuna et al., 2005; Bradford and Westhead, 2005;
Chen and Zhou, 2005; Chung et al., 2006; Fariselli et al., 2002;
Gabb et al., 1997; Helmer-Citterich and Tramontano, 1994; Jiang
and Kim, 1991; Jones and Thornton, 1997a, b; Katchalski-Katzir
et al., 1992; Keskin et al., 2005; Kuntz et al., 1982; Norel et al.,
1995; Palma et al., 2000; Salemme, 1976; Shoichet and Kuntz,
1991; Walls and Sternberg, 1992; Warwicker, 1989; Wodak and
Janin, 1978; Zhou and Shan, 2001). Despite much effort in structural
genomics, the amount of protein structures, determined by timeconsuming and expensive experimental technologies, is significantly
smaller than those of protein sequences produced by large-scale
DNA sequencing methods. For example, by July 29, 2008, there
are 392 667 identified protein sequences in Uniprot/Swissprot
(reviewed, manually annotated) (Uniprot, 2008) and only 47 978
known protein structures in PDB (Berman et al., 2000). Thus, it is
now more important than ever to identify protein interaction sites
from amino acid sequences only, without knowing structural data.
There are several studies attempted to address the sequence-based
interaction site prediction problem. Kini and Evans (1996) observed
that proline is the most common residue in a large number of
protein interaction sites. Pazos et al. (1997) used multiple sequence
alignment to detect correlated changes to a group of interacting
protein domains for predicting contacting pairs of residues. Gallet
et al. (2000) analyzed hydrophobicity distribution and amino acid
frequencies in known interaction sites for identifying linear stretches
of sequences. Most recently, more complicated machine learning
methods are applied to predict interaction sites. Yan et al. (2003)
applied support vector machines (SVMs) to predict interface sites
with features extracted from sequence neighbors for each target
residue. Wang et al. (2006) also employed SVMs as classifiers
with features extracted from spatial sequence and evolutionary
conservation scores based on a phylogenetic tree.
© The Author 2009. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected]
585
X.-w.Chen and J.C.Jeong
Sequence-based method, increasingly important in protein
interaction site prediction, is still in its infancy. Several issues
exist that make the prediction from sequences a very difficult
task. The two main problems are: (i) the biological properties
that are responsible for protein–protein interactions are not fully
understood, which leads to the difficulty of extracting informative
features common to all the binding sites; and (ii) the number
of interacting sites of a protein is much smaller than that of
non-interacting sites, which leads to a very challenging problem,
the so-called imbalanced data classification problem. Our article
addresses these problems by extracting a wide variety of features
from amino acid sequences and by developing a random forestbased method to effectively integrate these features and at the same
time deal with imbalanced-data problems. The extracted features
can be grouped into three categories: physicochemical properties
and evolutionary conservation score, residue-based distance matrix
and sequence profile. While each group of features may represent
common characteristics for a certain number of interaction sites,
none of the features is the dominant factor that is capable of
describing the common effect among all the interface residues. For
example, hydrophobicities may be useful for predicting interaction
sites in homodimers, they are, however, of moderate power to
predict interaction sites in other type of complexes (Jones and
Thornton, 1996; Lo Conte et al., 1999). Thus, effectively integrating
a large number of features is critical for a reliably predictive model.
To utilize all the extracted features, an integrative random forest
framework is developed. Our results evaluated on 99 polypeptide
chains show that the proposed method outperforms two other
sequence-based methods. Furthermore, we apply this method to
identify potential interaction sites for three protein complexes: the
DnaK molecular chaperone system, 1YUW and 1DKG, which may
provide new insight into the sequence–function relationship.
2
2.1
METHODS
Extracting a large number of features
To build a predictor that can distinguish interface residues from noninterface sites, we extract features based on physicochemical property,
evolutionary conservation score, amino acid distances, and position-specific
score matrix (PSSM). Instead of using one long feature vector, we divide
the features into three groups based on their sources, as features extracted
from different sources may have different distribution (e.g. hydrophobicity
in the physicochemical feature set versus the shortest distance of amino acid
residues to a target residue in distance features) (see Supplementary Material
for detailed discussions).
Group 1—physicochemical features and evolutionary conservation score:
the first two features are hydrophobicity and hydrophobic moments, which
were initially used to distinguish membrane α-helix proteins from soluble
proteins (Eisenberg et al., 1982, 1984) and later to predict protein binding
sites in the apolipoprotein E sequence (De Loof et al., 1986; Gallet et al.,
2000). For each amino acid, a sliding window centered at this amino acid is
moved along the protein sequence and the mean hydrophobicity and mean
hydrophobic moment are calculated as follows and assigned to the center
amino acid.
N
1
hn(i )
(1)
< Hi >=
2N +1
n=−N
< µHi >=
586
(i)
(2N +1) is the size of the sliding window centered around amino acid i, hn
is the hydrophobicity of the amino acid (AA) that is nAA’s away from the
AA i, and δn is the gyration angle between two consecutive residues in the
sequence. Gallet et al. (2000) found the method to be most successful when
they used N = 5 and δn = 100˚. The hydrophobicities of each amino acid are
taken from the scale developed by Eisenberg et al. (1984).
We also extract seven other physicochemical properties including
hydrophilicity, hydrophilic moment, propensity, propensity moment,
isoelectric point, isoelectric moment and mass (Jones and Thornton, 1997a;
Voet and Voet, 2004). The residue interface propensities quantify whether
an amino acid is possibly exposed to solvent or buried in an interface (Jones
and Thornton, 1997b). Furthermore, evolutionary conservation score for each
residue is also calculated by HSSP (Schneider and Sander, 1996) and used
as a feature. Thus, for each residue, we can extract nine physicochemical
features and one evolutionary conservation score.
Group 2—amino acid distance: the frequency of occurrence of proline
residues was initially used for analyzing interaction sites by Kini and Evans
(1996). They examined 1600 protein–protein interaction sequences and
found proline residues on at least one side of 88.2% of the binding sites.
The proline residues generally occurred within four residues of the binding
site and often within two residues. Inspired by this idea, we examine the
shortest distance from the current residue to 20 amino acid residues. This
will create a vector with a size of 20 for each residue.
Group 3—PSSM: PSSM is calculated by HSSP using multiple sequence
alignment. The likelihood of 20 amino acid substitutions at a given alignment
position is used as our PSSM features.
For each target residue we are considering, its features are extracted by
using a sliding window with a size of 21 centered on this target residue,
i.e. the feature vector for the central residue consists of features extracted
from itself and 10 amino acids on each side of this residue. Consequently,
the total numbers of features for each target residue are 210, 420 and 420
for groups 1, 2 and 3, respectively. While each group of features may not be
the dominant factor to describe the common effect among all the interface
residues, collectively, they are capable of effectively characterizing protein
interface sites. Since the total number of features is 1050, a carefully designed
classifier is needed to effectively utilize the large size of features. Next, we
describe the random forest method.
1
2N +1

2 N
2  12
N
(i )
(i )

hn sin δn
+
hn cos (δn )  (2)
n=−N
n=−N
2.2
Constructing an integrative random forest model
To effectively utilize the large number of extracted features and to deal
with the imbalanced data classification problems, we herein describe an
integrative random forest method for predicting interaction sites. Random
forest tree has been applied to protein–protein interaction prediction in
our recent work (Chen and Liu, 2005), but not to binding site problems.
When the input space is extraordinarily large as in our application, random
subspace feature selection introduced by Ho (1998) can improve classifier
diversity. A random forest consists of an ensemble of decision trees from
randomly sampled subspaces of the input features, and final classification is
obtained by combining results from the trees via voting (Breiman, 2001). It
is crucial to produce a large number of sufficiently different trees when using
the combined power of multiple trees for increase in accuracy. The use of
randomization in feature selection is a way to explore various possibilities
of subspaces. While most classification methods suffer from the curse of
dimensionality, the random subspace feature selection method can take
advantage of the high dimensionality. In contrast to the Occam’s Razor,
the method improves accuracy as it grows in complexity (Ho, 1998).
The random forest can also deal with imbalanced data problems. It
constructs many decision trees and each is grown from a different subset
of training data. To construct individual decision tree, training samples are
randomly selected with replacement from the original training dataset. In
our application, to build each tree, we randomly select the same number
of samples for each class, which converts the imbalanced data problem to
multiple balanced data classification problems. If the number of positive
samples (minority class) in the original training set is N, then N samples are
Sequence-based prediction of protein interaction sites
randomly drawn with replacement for each class. At each splitting or decision
node, the best splitting feature is chosen from a randomly selected subspace
of m features where m is much smaller than M total number of features. Each
tree in the forest is grown to the largest extent possible without pruning. To
classify a new object, each tree in the forest gives a classification which is
interpreted as the tree ‘voting’ for that class. The final classification of the
object is determined by majority votes among the classes decided by the
forest of trees.
Furthermore, for each group of features, we generate a random forest
classifier. This is because features from different groups have different
distribution. Building a forest classifier for each group of feature can
effectively integrate all the features for better performance. For each feature
group, we generate 100 trees: each tree is built using 100 randomly selected
features from each feature group and the same number of positives and
negatives. The final decision is made by majority vote.
3
EXPERIMENTAL RESULTS
3.1
Data sources
The proteins used in this article were extracted from a set of 70
protein–protein heterocomplexes used in the studies of Chakrabarti
and Janin (2002) and Yan et al. (2004). Redundant proteins and
molecules with fewer than 10 residues and proteins with sequence
identity ≥30% were removed. Some proteins which are not available
in HSSP and DSSP programs (Kabsch and Sander, 1983) were
also omitted. Finally, we end up with 54 heterocomplexes for our
studies. Table 1 lists the 99 polypeptide chains extracted from the
54 heterocomplexes downloaded in PDB, which can be grouped
into six categories: antibody–antigen, protease–inhibitor, enzyme
complexes, large protease complexes, G-proteins and miscellaneous.
Among 27 445 residues in the 99 polypeptide chains, we extract
13 774 surface residues based on their relative solvent accessible
surface areas (RASA) calculated by the DSSP program: a residue is
considered as a surface residue if its RASA is >25%. Furthermore,
a surface residue is defined as an interface residue if the difference
of accessible surface areas (ASA) between its unbound molecule
and bounded complex is >1 Å2 . The definitions for surface residues
and interface residues are commonly used in other literatures (Gong
et al., 2005; Jones and Thornton, 1996; Jones and Thornton, 1997a;
Nguyen et al., 2006; Rost and Sander, 1994; Wang et al., 2006; Yan
et al., 2003). Among the 13 774 surface residues, 2829 residues are
defined as interface residues (positive class). Thus, the number of
non-interface residues including both non-binding surface residues
and non-surface residues (negative class) is 24 616. Apparently, this
is an imbalance data classification problem where the ratio of
negative to positive samples is about 9:1.
3.2
Evaluation criteria
To measure the performance of each predictor, we use leave-oneout cross-validation (LOOCV) and the following criterion functions,
where true positive (TP) is the number of true interface residues that
are predicted correctly; true negative (TN) is the number of true noninterface residues that are predicted correctly; false positive (FP) is
the number of true non-interface residues that are predicted to be
interface residues; and false negative (FN) is the number of true
interface residues that are predicted to be non-interface residues.
TN+TP
Overall accuracy: TN+FP+FN+TP
TP
Sensitivity positive accuracy : FN+TP
TN
Specificity negative accuracy : FP+TN
Balanced accuracy: Positive Accuracy×Negative Accuracy
TP×TN−FP×FN
TP+FN TP+FP TN+FP TN+FN
Correlation coefficient (CC): The overall accuracy is the ratio of the number of correctly
predicted residues (both positive and negative) to the total number
of residues. It measures the overall performance of a classifier.
In our application, since the number of positive samples is much
smaller than that of negative samples, the overall accuracy may not
be a good measure for evaluating the performance of a predictor.
For imbalanced data classification, balanced accuracy and receiver
operating characteristic (ROC) curves are typically used, where
balanced accuracy is related to the product of both positive accuracy
and negative accuracy and ROC curves are generated in terms of
sensitivity and specificity. Additionally, CC, ranging from −1 to +1,
is also a good measure. Its value is -1 for a worst possible predictor,
+1 for a best possible predictor and 0 for a random predictor.
3.3
Leave-one-out test results
To evaluate the performance, we compare the proposed method
to two sequence-based methods. The first method, introduced by
Yan et al. (2003) uses PSSM with 11 neighbor residues. The
second method, proposed by Wang et al. (2006) uses PSSM
and evolutionary conservation score with 11 neighbor residues.
Both methods use support vector machines (SVMs) for prediction.
We implement the same methods and procedures as described in
Table 1. Protein categories and polypeptide chains with PDB ID
Antibody-antigen
Protease-inhibitor
Enzyme
Large-protease
G-proteins
Miscellaneous
1AO7_A, 1AO7_B, 1AO7_D, 1AO7_E, 1DVF_AB, 1DVF_CD, 1IAI_LH, 1IAI_MI, 1JH1_A, 1KB5_AB
1KB5_LH, 1NCA_LH, 1NCA_N, 1NFD_ABCD, 1NFD_EFGH, 1NMB_LH, 1NMB_N, 1NSN_LH, 1NSN_S
1OSP_LH, 1OSP_O, 1QFU_A , 1QFU_B, 1QFU_H, 1QFU_L, 1YQV_LH, 2JEL_LH, 2JEL_P, 3HFM_LH
1ACB_E, 1ACB_I, 1AVW_A, 1AVW_B, 1CHO_I, 1FLE_E, 1FLE_I, 1HIA_ABXY, 1HIA_IJ
1MCT_A, 1STF_E, 1STF_I, 1TGS_I, 1TGS_Z, 2SIC_I, 2SNI_E, 2SNI_I, 3SGB_E, 4CPA_I
1BRS_ABC, 1BRS_DEF, 1DFJ_E, 1DFJ_I, 1DHK_A, 1DHK_B, 1FSS_A
1FSS_B, 1GLA_F, 1GLA_G, 1UDI_E, 1UDI_I, 1YDR_E, 1YDR_I
1BTH_PQ, 1DAN_LH, 1DAN_TU, 1TBQ_LHJK, 1TBQ_RS, 1TOC_ABCDEFGH, 1TOC_RSTU, 4HTC_I
1AGR_AD, 1AGR_EH, 1GG2_A, 1GG2_B, 1GG2_G, 1GOT_A, 1GOT_B
1GOT_G, 1GUA_A, 1GUA_B, 1TX4_A, 1TX4_B, 2TRC_P
1AK4_AB, 1ATN_A, 1ATN_D, 1DKG_AB, 1EFN_AC, 1FC2_C, 1FC2_D, 1HWG_A
1HWG_BC, 1IGC_A, 1IGC_LH, 1SEB_ABEF, 1YCS_A, 1YCS_B, 2BTF_A, 2BTF_P
587
X.-w.Chen and J.C.Jeong
1
True positive rate (sensitivity)
0.9
0.8
0.7
0.6
0.5
0.4
0.3
Our-All
Yan-All
Wang-All
0.2
0.1
0
0
0.1
0.2
0.3
0.4 0.5 0.6
1-specificity
0.7
0.8
0.9
1
Fig. 2. Comparison of prediction performance in terms of the best balanced
accuracy for three sequence-based methods.
Fig. 1. The ROC curves for three sequence-based predictors.
their papers. All the methods are trained and tested on the same
datasets. To evaluate the performance, a LOOCV is used: each time,
one of the 99 polypeptide chairs (including all the interface and noninterface residues in this polypeptide chain) is used as test data and
the remaining 98 chains are used as training data; this process is
repeated 99 times and the final results are averaged over the test
results.
Figure 1 shows the ROC curves for three sequence-based
predictors. An ROC curve is a plot of the sensitivity versus
(1 − specificity) for a binary classifier as its decision boundary is
moved. Sensitivity measures the capability of predicting positive
samples (interface residues) correctly and specificity determines
if any non-interface residues are incorrectly predicted as interface
residues. The ROC curve of our method is constructed by changing
the threshold we place in the majority vote of decision trees.
Typically, majority votes win where the threshold is zero. A threshold
at five implies that at least five more votes of binding sites than
these of non-binding sites are necessary to classify a residue as
binding site. Otherwise, the residue is predicted as non-binding
site. Therefore, with different thresholds, our model will produce
different values of specificity and sensitivity. The ROC curve for
Yan’s method is constructed by varying the threshold (bias) for
a SVM decision boundary. Wang’s method consists of five SVM
models. Thus, the ROC curve for Wang’s method is constructed by
varying both the decision boundary of each SVMs with the same
bias and the threshold for the majority vote of SVMs. The proposed
method significantly outperforms Yan’s and Wang’s methods in
terms of ROC curves: for example, with a specificity rate of 70%,
the sensitivities of Yan’s, Wang’s and our methods are 30%, 39% and
73%, respectively. Another function we use is CC, which measures
how predicted results correlate with actual data. The CC values range
from negative one (worst possible prediction) to positive one (perfect
prediction). For a random predictor, the CC value is zero. With a
specificity rate of 70%, the CC values are 0.00, 0.06 and 0.28 for
Yan’s, Wang’s and our methods, respectively. With a sensitivity rate
of 70%, the CC values are 0.02, 0.05 and 0.28 for Yan’s, Wang’s and
our methods, respectively. Thus, the proposed method outperforms
other two methods and is significantly better than random guessing.
We also compare the best results on balanced accuracies (the square
root of product of positive accuracy and negative accuracy), which
are commonly used for imbalanced data classification problems. Our
method improves 23% and 17% in balanced accuracy compared with
588
(a)
(b)
Fig. 3. Predicted results of chains 1IAI_LH and 1IAI_MI in 1IAI using (a)
our method, and (b) Wang’s method.
Yan’s and Wang’s methods, respectively (Fig. 2). The results clearly
demonstrate that the proposed method is capable of predicting
protein interaction sites with significantly better performance than
these previous sequence-based methods.
We further examine the predicted results using Jmol (Jmol) and
VMD software (Humphrey et al., 1996). For the results presented
in Figures 3 and 4, the model is trained using training data and
decisions are made without changing the threshold (e.g. in our
method, simple majority vote is used). In Figures 3 and 4, each
sphere represents an atom. Green sphere denotes true positives
(true interface residues that are correctly predicted), blue sphere
represents false negatives (interface residues that are predicted as
non-interface residues) and red sphere indicates false positives (noninterface residues that are predicted as interface residues). Figure 3
shows the predicted interaction sites for four chains, L, H, M and I
in idiotype-anti-idiotype Fab complex (Ban et al., 1994), using our
method (Fig. 3a) and Wang’s method (Fig. 3b) obtained by leaving
out each chain and training the predictive models on the remaining
chains. Result from Yan’s method is similar to Figure 3b. Figure 4
shows the predicted interaction sites for two chains of 2CIO. Note
that the structure of 2CIO was not available when we originally
trained the model. Thus, it was not included in the group of 99 chains.
We use 2CIO as a third, independent data for validating the three
models. We also observe that our method identifies significantly
more interaction residues for some complexes than the other two
methods (more results can be found in the Supplementary Material:
Figs S3 and S4).
In conclusion, we showed that the proposed method outperformed
two other sequence-based methods. To understand whether the
improvement is due to the choice of the predictive model or the use
of new features, we conducted tests using our random forest tree with
the same PSSM features as those used in Wang’s method. The areas
under ROC curve (AUC) for random forest and Wang’s classifier
Sequence-based prediction of protein interaction sites
(a)
Fig. 5. DnaK molecular chaperone system: (a) DnaJ (PDB ID 1XBL), (b)
the structure of DnaK C-terminal and (c) the structure of DnaK N-terminal.
Orange structure denotes ATPase domain.
(b)
(c)
Fig. 4. Predicted results of two chains in 2CIO using (a) our method, (b)
Wang’s method and (c) Yan’s method.
together with PSSM features are 0.75 and 0.58, respectively. The
AUC of our method is 0.80. Thus, both the classifier and the
new features contribute to the improvement: the AUCs obtained
from random forest tree method with PSSM features only and
with all the new features increase 0.17 and 0.22, respectively,
compared with Wang’s method. As expected, random forest method
is capable of dealing with imbalanced data classification problems.
We also used the student’s t-test to rank the features: among the
top 50 features, the best four are PSSM features, the remaining
features are physicochemical properties and the distance profiles
(Supplementary Table S4).
3.4
Fig. 6. Predicted results of 1YUW using our method. Black color denotes
C-terminal (amino acid 395–554), blue color denotes N-terminal (amino
acid 1–383) of DnaK, and atoms in predicted binding sites shown as purple
spheres.
Blind test
To show the applicability of the proposed method, three blind tests
are conducted. Without knowing the true binding sites, the blind tests
evaluate the capability of predicting interface residues with peptide
complexes. To construct 3D structures, we used the VMD software
(Humphrey et al., 1996). Figures 5–7 show the results, where again,
each sphere represents an atom and purple spheres denote these
atoms in the predicted potential interface residues, and the orange
spheres in Figure 7 denote the atoms also shown in Figure 5.
First, we test three structural components of the DnaK (eukaryotic
Hsp70) molecular chaperone system, which is used in another
study (Fariselli et al., 2002). A chaperone system aids protein
folding/unfolding. The first two components are two DnaK domains:
a C-terminal domain (1DKX, PDB ID) and a N-terminal domain
(1DKG, PDB ID), which are binding and releasing together. The
third component is DnaJ (1XBL, PDB ID). The Hsp70 DnaK
proteins are aided by the so-called J-domain cochaperones (Hsp40
proteins in eukaryotes, and DnaJ in prokaryotes), which dramatically
increase the ATP activity of the Hsp70s. ATP hydrolysis causes
locking in of substrates into the substrate-binding cavity of Hsp70
and cochaperone DnaJ modulates these ATP hydrolysis and substrate
binding and is associated with conformational changes in DnaK
(Gassler et al., 1998; Greene et al., 1998; Suh et al., 1998).
DnaJ structures (PDB ID 1XBL) consist of four α-helices and a
loop region containing HPD motif (tripeptide of histidine, proline,
and aspartic acid residues) between two α-helices (Fig. 5a). This
HPD motif is highly conserved and presented in almost all known J
domains and this is critical to stimulate Hsp70 ATPase activity and
mutations on the conserved tripeptide HPD of the J-domain abolish
the ability of proteins to function with Hsp70 proteins; therefore, the
HPD tripeptide could mediate specific interactions between Hsp40
and Hsp70 proteins (Fariselli et al., 2002; Gassler et al., 1998;
Greene et al., 1998; Hennessy et al., 2000; Suh et al., 1998). Our
method predicted two amino acid residues, 30-MET and 36-ARG
which are near the HPD motif (33-HIS, 34-PRO, and 35-ASP). This
is similar to the results of Greene et al. (1998) that the potential
binding sites are residues between 1 and 35 and binding sites are
589
X.-w.Chen and J.C.Jeong
reported binding regions II, III, IV, V and VI. We also predicted
some binding sites in the upper right corner on chain B in GrpE, as
a result of the fact that GrpE exists as a dimmer.
4
(a)
(b)
Fig. 7. Predicted results of three chains in 1DKG using our method. (a)
DnaK N-terminal chain D of 1DKG, orange spheres are these atoms in these
predicted interface residues shown in Figure 5c, and (b) GrpE, chain A and
B of 1DKG. All spheres show our predicted atoms: purple spheres denote
the atoms in the predicted interface residues and orange spheres denote the
atoms also shown in Figure 5.
concentrated on the outer surface of helix II which is a right-side
α-helix where our prediction, 30-MET, is located; therefore, our
predictions on DnaJ are reasonable. In addition 71-HIS and 74-PHE
are also shown as conserved residues based on the consensus of
amino acid position (Hennessy et al., 2000).
For Hsp70 DnaK-Nterminal (PDB ID 1DKG) in Figure 5c, our
prediction detected three residues (13-ASN, 116-SER, 174-ALA) on
ATPase domain (orange color in Fig. 5c). Other researches (Davis
et al., 1999; Gassler et al., 1998) showed that most of the mutants,
which affect interaction with C-terminal domain, are located in the
bottom of ATPase domain. Our predictions are spatially very close
to those mutants observed in Davis and Gassler’s studies.
For Hsp70 DnaK-Cterminal (PDB ID 1DKX) in Figure 5b, our
method predicted 6-THR, 405-GLY, 447-ARG and 483-ILE as
interface residues. Mutants observed by Davis and Montgomery
are located in the loops on sandwich sub-domain which are close
to peptide-binding site; therefore, our predictions are in agreement
with their experimental results (Davis et al., 1999; Montgomery
et al., 1999). We believe that the predicted binding will provide new
insights into the interaction.
Figure 6 shows the direct binding between DnaK C-terminal
and N-terminal on the protein 1YUW in Bus Taurus (Jiang et al.,
2005). Notice that 1YUW is different from 1DKX in Escherichia
coli (Zhu et al., 1996) (also shown in Fig. 5) in terms of both Cterminal sequences and their binding sites. Our results show that
most predicted interface residues are condensed in alpha helix of
C-terminal, which are in agreement with these in Jiang et al. (2005).
We also observe some predicted binding sites in N-terminal, which
are not in the binding regions between C-terminal and N-terminal.
Some of these may be the interaction sites between N-terminal and
other chains (e.g. GrpE).
Finally, we examined another DnaK effector GrpE by using
1DKG (Harrison et al., 1997) co-crystallized with 1DKG N-terminal
and GrpE. GrpE is a nucleotide-exchange factor that binds substoichiometrically to the ATPase unit which is DnaK N-terminal.
Figure 7a shows the predicted binding sites in DnaK N-terminal,
which are very close to the reported binding residues in the regions
III, V and VI (Harrison et al., 1997). In Figure 7b, the predicted
interaction residues on chain A of GrpE are also located in the
590
CONCLUSIONS
As genome-sequencing projects provide biologists with ready access
to the rapidly increasing pool of protein sequences, there is a
growing demand for developing advanced computational methods
for predicting potential protein binding sites by using sequence
information only. In this article, we demonstrate a predictive
system that can reliably identify protein interface sites for protein
complexes. The proposed predictive system is based on the analysis
of protein sequence information, without knowing protein structures.
A wide variety of physicochemical properties and sequence profiling
properties are effectively integrated using a random forest tree
framework.
The predicted interaction sites can be valuable as a first approach
for guiding experimental methods investigating protein–protein
interactions and localizing the specific interface residues. We
illustrate the usefulness of the proposed method for predicting
putative binding sites for the DnaK molecular chaperone system,
1YUW and 1DKG. In our future work, we will evaluate the relative
importance of the features, which should help to understand the
underlining binding process.
ACKNOWLEDGEMENTS
We wish to thank Ozlen Keskin for helping us in modeling 3D
protein structures with VMD software. We also thank the reviewers
for their valuable suggestions.
Funding: National Science Foundation award (IIS-0644366).
Conflicts of Interest: none declared.
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