NAPS: a residue-level nucleic acid-binding prediction server.
ABSTRACT Nucleic acid-binding proteins are involved in a great number of cellular processes. Understanding the mechanisms underlying these proteins first requires the identification of specific residues involved in nucleic acid binding. Prediction of NA-binding residues can provide practical assistance in the functional annotation of NA-binding proteins. Predictions can also be used to expedite mutagenesis experiments, guiding researchers to the correct binding residues in these proteins. Here, we present a method for the identification of amino acid residues involved in DNA- and RNA-binding using sequence-based attributes. The method used in this work combines the C4.5 algorithm with bootstrap aggregation and cost-sensitive learning. Our DNA-binding model achieved 79.1% accuracy, while the RNA-binding model reached an accuracy of 73.2%. The NAPS web server is freely available at http://proteomics.bioengr.uic.edu/NAPS.
- SourceAvailable from: Yuedong Yang[Show abstract] [Hide abstract]
ABSTRACT: As more and more protein sequences are uncovered from increasingly inexpensive sequencing techniques, an urgent task is to find their functions. This work presents a highly reliable computational technique for predicting DNA-binding function at the level of protein-DNA complex structures, rather than low-resolution two-state prediction of DNA-binding as most existing techniques do. The method first predicts protein-DNA complex structure by utilizing the template-based structure prediction technique HHblits, followed by binding affinity prediction based on a knowledge-based energy function (Distance-scaled finite ideal-gas reference state for protein-DNA interactions). A leave-one-out cross validation of the method based on 179 DNA-binding and 3797 non-binding protein domains achieves a Matthews correlation coefficient (MCC) of 0.77 with high precision (94%) and high sensitivity (65%). We further found 51% sensitivity for 82 newly determined structures of DNA-binding proteins and 56% sensitivity for the human proteome. In addition, the method provides a reasonably accurate prediction of DNA-binding residues in proteins based on predicted DNA-binding complex structures. Its application to human proteome leads to more than 300 novel DNA-binding proteins; some of these predicted structures were validated by known structures of homologous proteins in APO forms. The method [SPOT-Seq (DNA)] is available as an on-line server at http://sparks-lab.org.PLoS ONE 01/2014; 9(5):e96694. · 3.73 Impact Factor
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ABSTRACT: Protein-RNA interactions are central to essential cellular processes such as protein synthesis and regulation of gene expression and play roles in human infectious and genetic diseases. Reliable identification of protein-RNA interfaces is critical for understanding the structural bases and functional implications of such interactions and for developing effective approaches to rational drug design. Sequence-based computational methods offer a viable, cost-effective way to identify putative RNA-binding residues in RNA-binding proteins. Here we report two novel approaches: (i) HomPRIP, a sequence homology-based method for predicting RNA-binding sites in proteins; (ii) RNABindRPlus, a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA-binding proteins. Although highly reliable, HomPRIP cannot make predictions for the unaligned parts of query proteins and its coverage is limited by the availability of close sequence homologs of the query protein with experimentally determined RNA-binding sites. RNABindRPlus overcomes these limitations. We compared the performance of HomPRIP and RNABindRPlus with that of several state-of-the-art predictors on two test sets, RB44 and RB111. On a subset of proteins for which homologs with experimentally determined interfaces could be reliably identified, HomPRIP outperformed all other methods achieving an MCC of 0.63 on RB44 and 0.83 on RB111. RNABindRPlus was able to predict RNA-binding residues of all proteins in both test sets, achieving an MCC of 0.55 and 0.37, respectively, and outperforming all other methods, including those that make use of structure-derived features of proteins. More importantly, RNABindRPlus outperforms all other methods for any choice of tradeoff between precision and recall. An important advantage of both HomPRIP and RNABindRPlus is that they rely on readily available sequence and sequence-derived features of RNA-binding proteins. A webserver implementation of both methods is freely available at http://einstein.cs.iastate.edu/RNABindRPlus/.PLoS ONE 01/2014; 9(5):e97725. · 3.73 Impact Factor
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ABSTRACT: Protein-RNA complexes play key roles in several cellular processes by the interactions of amino acids with RNA. To understand the recognition mechanism, it is important to identify the specific amino acids involved in RNA binding. Various computational methods have been developed for predicting RNA binding residues from protein sequence. However, their performances mainly depend on the training dataset, feature selection for developing a model and learning capacity of the model. Hence, it is important to reveal the correspondence between the performance of methods and properties of RNA-binding proteins (RBPs). In this work, we have collected all available RNA binding residues prediction methods and revealed their performances on unbiased, stringent and diverse datasets for RBPs with less than 25% sequence identity based on structural class, fold, superfamily, family, protein function, RNA type, RNA strand and RNA conformation. The best methods for each type of RBPs and the type of RBPs, which require further refinement in prediction, have been brought out. We also analyzed the performance of these methods for the disordered regions, structures which are not included in the training dataset and recently solved structures. The reliability of prediction is better than randomly choosing any method or combination of methods. This approach would be a valuable resource for biologists to choose the best method based on the type of RBPs for designing their experiments and the tool is freely accessible online at www.iitm.ac.in/bioinfo/RNA-protein/.PLoS ONE 01/2014; 9(3):e91140. · 3.73 Impact Factor
NAPS: a residue-level nucleic acid-binding
Matthew B. Carson, Robert Langlois and Hui Lu*
Department of Bioengineering/Bioinformatics, University of Illinois at Chicago, Chicago, IL, USA
Received February 25, 2010; Revised April 16, 2010; Accepted April 24, 2010
Nucleic acid-binding proteins are involved in a great
the mechanisms underlying these proteins first
requires the identification of specific residues
involved in nucleic acid binding. Prediction of NA-
binding residues can provide practical assistance in
the functional annotation of NA-binding proteins.
Predictions can also be used to expedite mutagen-
esis experiments, guiding researchers to the correct
binding residues in these proteins. Here, we present
a method for the identification of amino acid
residues involved in DNA- and RNA-binding using
sequence-based attributes. The method used in
this work combines the C4.5 algorithm with boot-
strap aggregation and cost-sensitive learning. Our
while the RNA-binding model reached an accuracy
of 73.2%. The NAPS web server is freely available at
Nucleic acid-binding (NA-binding) proteins are involved
in a great number of cellular processes. Protein–DNA
interactions are an integral part of the gene regulation
process and are also responsible for DNA repair.
Protein–RNA interactions are directly involved with
activities such as protein synthesis, the regulation of
gene expression, and mRNA processing. Both DNA-
and RNA-binding proteins are essential to the replication
of specific types of viruses. Understanding the mechanisms
underlying these proteins first requires the identification of
Prediction of NA-binding residues can provide practical
assistance in the functional annotation of NA-binding
proteins. Predictions can also be used to expedite muta-
genesis experiments, guiding researchers to the correct
binding residues in these proteins. Identifying such
residues is a complex and difficult problem. The charac-
teristic traits of a residue, which enable its binding, are
largely unknown. Whether or not certain characteristics
of its neighbors affect a residue’s binding capability is
also poorly understood, further complicating the issue.
For these reasons machine learning has often been
employed in an attempt to discover precisely which
residues confer binding functionality.
NA-binding proteins almost always contain a smaller
number of binding than non-binding residues. This
means that any residue-level data set calculated from
these proteins will be unbalanced in terms of these two
classes, and thus prediction results will be unbalanced in
terms of sensitivity and specificity due to degeneracy (1),
or the tendency during prediction for a classifier to assign
examples to the most common class. For instance, if
binding residues made up the positive class in a training
set and non-binding residues comprised the negative class,
validation results would reveal much lower sensitivity than
specificity, indicating that less true positive examples had
been found. To get around this problem, many in the past
have removed a number of non-binding residues from the
data set in order to achieve a class balance. However,
balancing a training set would seem to remove important
information about the non-binding class and prevent the
classifier from developing a clear distinction between the
two types of examples, and this has in fact been shown to
result in a less robust model and to cause poor test per-
formance (1,2). Therefore, results using models built with
balanced training sets may not actually provide reliable
results. Ideally, we would like to provide machine
learning classifiers with as much of the data set as
possible in order to increase accuracy, while avoiding the
problem of degeneracy. One way to do this is to force the
developed classifier to apply a weight to the examples in
cost-sensitive learning or costing, has been used in
previous research to improve prediction. Sorzano et al.
(3) used a Naı¨ve Bayes algorithm to improve recognition
of particles in images from cryo-electron microscopy ex-
periments. Fan et al. (4) used their own version of costing
*To whom correspondence should be addressed. Tel: +1 312 413 2021; Fax: +1 312 413 2018; Email: firstname.lastname@example.org
Published online 16 May 2010Nucleic Acids Research, 2010, Vol. 38, Web Server issueW431–W435
? 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.
to improve accuracy for intrusion detection systems.
Stolfo et al. (5) employed a cost-based method to
improve fraud detection in financial transactions.
The NAPS web server uses ensemble classifiers based on
C4.5 (6), bootstrap aggregation (7) and a cost-sensitive
learning algorithm (8) to predict binding residues within
DNA- and RNA-binding proteins. Given a protein
sequence, the server returns a list of binding and
non-binding residues within the protein along with a
score, which measures the confidence in the prediction.
In comparison with five other commonly used algorithms
for NA-binding residue prediction, we find that our
ensemble method performs better for sequence-based pre-
diction of both DNA and RNA binding. Additionally, we
built our classifiers over several DNA- and RNA-binding
protein data sets from previous works and found that we
are able to improve on the previously published results.
The NAPS server prediction models were created using the
Malibu Machine Learning Workbench (http://proteomics
in-house. Malibu features a collection of algorithms pri-
marily focused on supervised learning problems and is
comprised of both third-party and native implementa-
tions. Our models were constructed using a modified
version of the C4.5 decision tree algorithm. One of the
biggest advantages of decision trees is the fact that,
unlike some other classification algorithms such as
support vector machines (SVM) and neural networks,
the decision tree represents rules that can be understood
such that some knowledge may be gleaned from the
output. Other advantages of this algorithm include its
ability to handle large data sets without requiring data
preprocessing, to handle missing values, and to avoid
overfitting. Additionally, if the attributes used in classifi-
cation are discrete values, this method is very computa-
tionally efficient. One of the disadvantages of the
decision tree algorithm is that small changes to the
data set may result in a different feature being chosen
at a certain node, which will affect the structure of the
tree in subsequent nodes. By modifying the algorithm as
described below, we aim to address this disadvantage.
C4.5 with bagging, or bootstrap aggregating (7),
attempts to offset the instability of the C4.5 decision tree
by building many different trees from the training data set
using random sampling with replacement through a
uniform probability distribution. The resulting classifier
then uses majority voting to decide to which class the
example belongs. When used in combination with an
unstable classifier such as a decision tree, bagging can
help to improve accuracy by reducing variance. For our
models, out-of-bag partitioning was used for parameter
selection and the accuracy metric was used to choose the
best model. We ran 500 bagging iterations and used
10-fold, stratified cross-validation to test our models.
Our final models combined the bagging method
Cost-sensitive learning is useful for problems in which,
as is the case with nucleic acid-binding residue prediction,
one class is more highly represented than the other. The
costing algorithm determines the importance of an
example by considering its class label and performs an
importance-weighted classification by applying weights
to each class based on class distribution (8). The final clas-
sifier is created from an average of multiple rounds of
rejection sampling. For each round, we constructed a
model by first selecting residues in the data set. We
investigated two types of residue selection, one in which
a random sampling was taken from all proteins in the data
set and the other in which the samples came only from
within individual proteins. We found that both methods
created very similar results. In the final analysis, we used
We used 10-fold, stratified cross-validation over the
training set for one run during both the selection and
the validation cycles. We applied a weight, which was
equal to the class distribution, to the positive class of
the data set. Two hundred costing iterations were per-
formed. Net accuracy was used to select the best param-
eters for the model. See Supplementary Data for details on
the data sets used to build our models.
THE NAPS WEB SERVER
The NAPS web server (http://proteomics.bioengr.uic
.edu/NAPS) takes a DNA- or RNA-binding protein
sequence as input and returns a list of residues, the pre-
dicted class (binding or non-binding), and a score
indicating the classifier’s confidence in the decision
(Figure 1). The model classifier assigns a confidence
score between 0 and 1 for each residue in the test
protein. This score reflects the level of certainty in the
assigned class with 0.5 as the threshold. Residues with a
confidence score between 0 and 0.5 are classified as
non-binding residues; those with a score between 0.5
and 1 are classified as binding residues (Figure 2). A
table of calculated statistics, including the total number
of residues binned by confidence score, the number of
binding and non-binding residues in the protein, the per-
centage of each class, and the mean confidence value, is
also returned. The server calculates a total of 301
sequence-based attributes for each residue in the test
protein. We consider a ‘sequence-based attribute’ to be
any residue feature that can be calculated without the
use of a crystal structure (i.e. only protein sequence).
The descriptors are described in more detail below.
Residue ID. A 20D feature vector representing the 20
common amino acids is used to identify each residue,
where a single non-zero entry indicates the current residue.
Residue charge. Since DNA molecules are negatively
charged, positively charged, basic amino acid residues
can play an important role in NA binding. Accordingly,
we include a charge attribute for each residue. Arginine
and lysine residues are assigned a charge of+1, histidines
+0.5, and all others 0.
W432Nucleic Acids Research, 2010,Vol.38, Web Server issue
consider the level of evolutionary conservation of each
position-specific scoring matrix (PSSM) for each residue
in the test protein. Along with the NCBI-NR90 database
(10), which contains <90% sequence identity between any
two proteins, PSI-BLAST (11) is used to create a matrix
representing the distribution of all 20 amino acids at each
position in the protein sequence. A 7-residue sliding
window, which represents the distribution of amino acid
residues at the positions occupied by three sequence neigh-
bors on either side of the central residue, is subsequently
created. This results in a 140-element feature vector for
each residue. A similar 7-residue window is created using
non-position-specific evolutionary conservation informa-
tion for the sequence neighborhood of each residue, re-
sulting in another 140-element feature vector.
ofevolutionaryconservation. In orderto
in order tocapture
EVALUATION OF NAPS MODELS
Comparisons with other algorithms
We evaluated the performance of the NAPS models
againstfive other classification
Alternating Decision Tree, WillowBoost, C4.5 with
Adaptive Boosting, and C4.5 with bootstrap aggregation).
We built two models for each using sequence-based
features: one for DNA-binding proteins and one for
RNA-binding proteins. Supplementary Table 1 describes
the results for this comparison and shows the performance
of each algorithm in terms of accuracy, sensitivity, speci-
ficity, precision, Matthews correlation coefficient (MCC),
and the area under the Receiver Operating Characteristic
curve (AUC). The AUC provides a measure of a model’s
ability to separate positive and negative examples and is
generated from a plot of the true positive rate versus the
false positive rate for each example in the data set (see
Supplementary Figures 1 and 2). A perfect model would
have an AUC of 1, while a random model would have an
AUC of 0.5.
Evaluation using previous data sets
In order to demonstrate the stability of our classifiers, we
built models using previously compiled data sets for both
Supplementary Table 2 shows the comparisons between
the original classifier and ours using two previously
compiled DNA-binding protein data sets and one
RNA-binding protein data set used in seven publications
(10,13–18). The classifiers were created using 10-fold
cross-validation for both selection and validation. For
the costing algorithm, the weight assigned to each class
was equal to the class distribution and 200 costing iter-
ations were run. Net accuracy was used to find the best
model. The prediction metrics from previous works shown
are either those reported as the best results from the pub-
lications, or if the author’s intended best result is unclear,
the results with the best accuracy or MCC.
Overall we found that, based on the metrics reported in
these previous publications, we were able to improve on
those results over each of three previously compiled data
sets. First, we built our own classifier on the PDNA-62
data set, which was originally compiled by Selvaraj et al.
(2) and used for binding residue prediction in three sub-
sequent publications (10,14,17). Our model (C.45 with
bagging and costing) achieved ?78% accuracy, ?80%
sensitivity, ?77% specificity, ?86% AUC, and an MCC
of 0.57, which is an improvement of+0.12 in the MCC for
Figure 1. An example of the NAPS output.
Figure 2. Illustration of the NAPS confidence score.
Nucleic AcidsResearch, 2010, Vol.38, WebServer issueW433
the best previous result (14). The second data set we tested
was compiled and used by Ofran et al. (15) and consisted
of 274 proteins. Our classifier reached ?86% accuracy,
?85% sensitivity, ?88% specificity, ?93% AUC, and
an MCC of 0.725. The only directly comparable metric
reported in this previous work is accuracy. While our
accuracy is slightly lower than that reported by Ofran
et al., we believe that our model actually offers a more
reliable result. In their work, Ofran et al. used sequence
to derive evolutionary profiles, sequence neighborhood,
and predicted structural features. Their SVM classifier
gave its best performance at 89% accuracy. However,
coverage’ (sensitivity) were imbalanced. For example, at
a sensitivity rate of ?80% (the number of true positive
examples correctly classified), the precision rate is quite
low (?55%), which indicates that the classifier has low
confidence that the predicted positive examples are
actually positive. Finally, we tested 109 RNA-binding
protein chains originally collected by Terribilini et al.
(16) and used in three works (13,16,18). Our model
achieved ?76% accuracy, ?75% sensitivity, ?77% speci-
ficity, ?83% AUC, and an MCC of 0.523 over this set,
which is an improvement of +0.07 in the MCC over the
best result (18).
The sequence-based feature sets used in the previous
publications varied between works, as did the type of clas-
sifier used for prediction and the type of validation per-
formed. While comparisons of this type are not ideal, they
do demonstrate that, toward the goal of distinguishing
binding from non-binding residues, each of the classifiers
we have built using C4.5 with bagging and costing
provides consistent results in terms of overall accuracy
when trained over various data sets, thus increasing our
confidence in this ensemble method.
DISCUSSION AND FUTURE DIRECTIONS
Here we have presented a web utility for predicting
binding residues in DNA- and RNA-binding proteins.
Our method is built on C4.5 (6), bootstrap aggregation
(7), and cost-sensitive learning (8) and is capable of
producing balanced sensitivity, specificity, and precision
with high accuracy when working with unbalanced data
sets. We have compared these results with those from five
commonly used algorithms and found that our classifiers
achieve superior results. We also tested our method using
previously published data sets and were able to show that
our models outperform the previous results.
It is possible that a DNA-binding protein may contain
residues which are predicted to be RNA-binding and vice
versa. This can occur when the attributes calculated for
the test protein residues during the prediction have very
similar values to residues in both the DNA- and
RNA-binding training sets. One issue that complicates
NA-binding protein and residue prediction is the fact
that certain proteins are known to bind both DNA and
RNA molecules (19). The authors suggest paying close
attention to the confidence values calculated during
prediction as these will guide the user toward the most
likely binding residues.
We have recently developed a sequence-based method
for predicting DNA-binding proteins (20). This algorithm,
along with another for RNA-binding protein prediction,
will be added to the NAPS server. This prediction service
will let the user first identify whether a sequence of interest
belongs to a DNA- or RNA-binding protein before pre-
dicting binding residues within the protein. This feature
will be useful in cases where the function of a test protein
is unknown. Other future plans include adding the ability
to handle structural features and the option to upload a
PDB file for the calculation of these features (21,22). In
addition, we plan to add knowledge-mining capability to
our web server, which will allow users to analyze each rule
for every residue within the model built by the classifier.
This feature is intended to help to determine which attri-
butes play an important role in identifying binding
Supplementary Data are available at NAR Online.
Funding for open access charge: University of Illinois at
Conflict of interest statement. None declared.
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