Significantly improved prediction of subcellular localization by integrating text and protein sequence data

Abstract

Computational prediction of protein subcellular localization is a challenging problem. Several approaches have been presented during the past few years; some attempt to cover a wide variety of localizations, while others focus on a small number of localizations and on specific organisms. We present a comprehensive system, integrating protein sequence-derived data and text-based information. Itis tested on three large data sets, previously used by leading prediction methods. The results demonstrate that our system performs significantly better than previously reported results, for a wide range of eukaryotic subcellular localizations.

Full text

Significantly Improved Prediction of Subcellular Localization by Integrating Text and
Protein Sequence Data
Annette Hoglund, Torsten Blum, Scott Brady, Pierre Donnes, John San Miguel, Matthew
Rocheford, Oliver Kohlbacher, and Hagit Shatkay
Pacific Symposium on Biocomputing 11:16-27(2006)

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SIGNIFICANTLY IMPROVED PREDICTION OF SUBCELLULAR
LOCALIZATION BY INTEGRATING TEXT AND PROTEIN SEQUENCE DATA
ANNETTE H ¨
OGLUND , TORSTEN BLUM , SCOTT BRADY ,
PIERRE D ¨
ONNES , JOHN SAN MIGUEL , MATTHEW ROCHEFORD ,
OLIVER KOHLBACHER , HAGIT SHATKAY
Div. for Simulation of Biological Systems, ZBIT/WSI,
University of T¨ubingen, Sand 14, D-72076 T¨ubingen, Germany
School of Computing, Queen’s University,
Kingston, Ontario, Canada K7L 3N6
Computational prediction of protein subcellular localization is a challenging problem. Several
approaches have been presented during the past few years; some attempt to cover a wide va-
riety of localizations, while others focus on a small number of localizations and on specific
organisms. We present a comprehensive system, integrating protein sequence-derived data and
text-based information. It is tested on three large data sets, previously used by leading prediction
methods. The results demonstrate that our system performs significantly better than previously
reported results, for a wide range of eukaryotic subcellular localizations.
1. Introduction
In this paper we introduce a new system for computationally assigning proteins
to their subcellular localization. By integrating several types of sequence-derived
features and text-based information, the achieved performance is the best reported
so far, in terms of sensitivity, specificity, and overall accuracy. Unlike several
recent systems which focus on a few subcellular localizations or on a specific
organism , our system is applicable to – and retains its good performance
across – a wide variety of organisms and subcellular localizations. Moreover, we
show that the integrated system, which combines sequence and text, performs sig-
nificantly better than its individual components, based on each data source alone.
The task of protein subcellular localization prediction is important and well-
studied . Knowing a protein’s localization helps elucidate its function, its
role in both healthy processes and in the onset of disease, and its potential
use as a drug target. Experimental methods for protein localization range from
immunolocalization to tagging of proteins using green fluorescent protein (G FP)
To whom correspondence should be addressed: shatkay@cs.queensu.ca. H S is supported by NS ERC
Discovery grant 298292-04.

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and isotopes . Such methods are accurate but, even at their best, are slow and
labor-intensive compared with large-scale computational methods. Computational
tools for predicting localization are useful for a large-scale initial “triage”, espe-
cially for proteins whose amino acid sequence may be determined from the ge-
nomic sequence, but are hard to produce, isolate, or locate experimentally.
The past decade, and most notably the last five years, has seen much progress
in computational prediction of protein localization from sequence data. Nakai and
Kanehisa introduced PSort, a rule-based expert system, which was later im-
proved upon by a probabilistic and by a K-nearest neighbor classifier. Another
pair of prominent systems, TargetP and ChloroP , based on artificial neural net-
works, demonstrated a significantly higher accuracy when applied to a limited set
of subcellular localizations in plant and animal cells. Other recent systems use a
variety of machine learning techniques. Most of them focus on a few subcellular
localizations and improve upon – or just meet – the state of the art on those .
Several recent publications have examined the possibility of using text to sup-
port subcellular localization. Specifically, Stapley et al. represented yeast pro-
teins as vectors of weighted terms from all the PubMed articles mentioning their
respective genes. They then trained a support vector machine (SVM) on protein-
text-vectors, to distinguish among subcellular localizations. The performance was
favorable when compared to a classifier trained on amino acid composition alone,
but it was not compared against any state-of-the-art localization system, and the
reported results do not suggest an improvement over earlier systems. Moreover,
while their text-based classifier performed better than an amino acid composition
classifier, combining the two forms of data did not significantly improve perfor-
mance with respect to the text-based classifier alone.
Nair and Rost used the text taken from Swiss-Prot annotations of proteins to
represent these proteins, and trained a subcellular classifier using this representa-
tion. They concentrate on a few subcellular localizations, and report results that
are compatible – but do not improve upon – the state of the art at that time. Their
work was elaborated upon by Eskin and Agichtein , who added subsequences
from the protein’s amino acid sequence as part of the terms considered in the text
representation. The system was not tested against existing systems or data sets,
and the reported results do not indicate improvement over previous systems.
The best performing comprehensivesystems reportedso far, which were tested
on a large set of proteins, are PLOC and, more recently, MultiLoc . While they
report the best accuracy until now, on a broad range of organisms and localiza-
tions, there is still room for improvement.
The work reported here, similarly to that reported by Nair and Rost , uses
Swiss-Prot as a text source. Unlike them though, we use the PubMed abstracts

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referenced by Swiss-Prot, rather than the annotation text placed by Swiss-Prot cu-
rators. Furthermore, unlike Stapley et al. who use all abstracts that contain the
gene name for the protein, we use only abstracts that are referenced by Swiss-Prot,
and moreover, rather than use all the terms in them with a standard (TF*IDF )
weighting, as done by Stapley et al., we select terms based on a distinguishing
criterion described in Section 2, and apply a probability-based weighting scheme.
We train an SVM as a text-based classifier, and combine it with a sequence-based
classifier, to produce a comprehensive subcellular categorizer. Our integrated sys-
tem is tested on a number of publicly available, extensive, homology-reduced,data
sets which were used for evaluating earlier systems (TargetP, PLOC, and Multi-
Loc). For each system, we first conduct a comparison using the same data and
the same subcellular localizations as reported in the paper published about that
system. We then conduct a test using all the proteins in Swiss-Prot for which a
subcellular annotation is assigned, among the 11 localizations: chloroplast, cy-
toplasm, endoplasmic reticulum, extracellular space, Golgi apparatus, lysosome,
mitochondria, nucleus, peroxisome, plasma membrane, and vacuole. On each of
the data sets our system performs better than the state-of-the-art systems in terms
of overall prediction accuracy, and other standard measures.
The next section outlines the methods used, while in Section 3 we demonstrate
the performance of our system. Section 4 concludes and outlines future work.
2. Methods
Our system combines five separate classifiers, four sequence-based and one text-
based. Their output is integrated through a sixth classifier to produce an improved
prediction of protein subcellular localization. The sequence-based classifiers have
been successfully used before by the MultiLoc system and are briefly described
below. Section 2.2 then presents the novel text-based method, while Section 2.3
explains how all these classifiers are combined to form an integrated prediction
system. Four of the five classifiers are based on support vector machines (SVMs),
using the LI BSV M implementation . The latter supports soft, probabilistic cate-
gorization for -class tasks , assigning to each classified item an -dimensional
vector denoting its probability to belong to each of the classes. Radial Basis
Function kernels were used throughout this study. Further details are given below.
2.1. Sequence-based methods
Each of the sequence-based classifiers utilizes a different approach to derive bio-
logically informative features that can be used to predict localization, and classi-
fies the input protein sequence to its respective localization using these features.
An acronym for Term Frequency, Times Inverse Document Frequency.

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Three of these classifiers are SVM-based. The fourth scans the protein sequences
for short sequence motifs indicative of structure and function. The four classifiers
are briefly described below (see the MultiLoc paper for further details).
SVMTarget – This classifier uses the N-terminal targeting peptide (TP) to pre-
dict a few subcellular categories. It distinguishes among four plant (chloroplast
(ch), mitochondria (mi), secretory pathway (SP), and other (OT)) and three non-
plant (mi, SP, OT) localizations. The targeting peptides are represented by their
partial amino acid composition, motivated by the observation that TPs for specific
localizations have a similar amino acid composition while their actual sequence
may differ. Given an input protein, the classifier outputs a three-dimensional vec-
tor (four-dimensional for plant) of class probabilities. SVMTarget alone demon-
strated a slightly better performance than TargetP in a comparative study .
SVMSA – Some proteins of the secretory pathway carry a signal anchor (SA)
that, unlike the targeting peptide, is usually located further away from the N-
terminus and contains a longer hydrophobic component. SVMSA can predict
secretory pathway (SP) proteins that are hard to detect using SVMTarget. It is
a binary classifier, trained to distinguish proteins carrying SA from those that do
not. It outputs, given an input sequence, its probability to contain a signal anchor.
SVMaac – This method uses the whole protein amino acid composition (aac),
and categorizes proteins into any of the possible localizations. It combines a col-
lection of binary classifiers, each trained to distinguish one class from all others,
although one classifier in the collection was especially trained to distinguish cy-
tosolic (cy) from nuclear (nu) proteins, as these are hard to separate using the
one-against-all approach. Given an input protein, , with possible localizations,
the classifier outputs an -dimensional probability vector containing ’s probabil-
ity to belong to each localization.
MotifSearch – Proteins from several subcellular localizations can be charac-
terized by a few types of short sequence motifs, such as Nuclear Localization Sig-
nal and DNA-binding domains. The motifs were obtained from the PROSITE
and from the NLSdb databases. This classifier outputs a discrete, binary vec-
tor, representing the presence (1) or the absence (0) of each type of motif in the
query protein sequence.
2.2. Text-based method
The idea underlying the text-based classifier is the representation of each protein
as a vector of weighted text features. While text-based localization has been pre-
sented before , the key differences between the current work and previous ones
is in the text source used, the feature selection, and the term weighting scheme.
First, for each protein the text comes from the abstracts curated for the protein

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in its Swiss-Prot entry. We used a script that scanned each protein in Swiss-Prot
for all the PubMed identifiers occurring in its Swiss-Prot entry, and obtained the
respective title and abstract from PubMed. Each protein is thus assigned a set
of PubMed abstracts, based on Swiss-Prot. This choice of abstracts is different
from that of Stapley et al. who used all the PubMed abstracts mentioning the
gene’s name, and from that of Nair and Rost – who use Swiss-Prot annotation
text rather than PubMed abstracts. The assigned abstracts are then tokenized into
a set of terms, consisting of singleton and pairs of consecutive words, with a list of
standard stop words excluded from consideration. The results reported here also
include the application of Porter stemming to all the words in the terms.
Second, from all the extracted terms, we select a subset of distinguishing
terms. This is done by scoring each term with respect to each subcellular lo-
calization, where the score reflects the probability of the term to occur in abstracts
that are associated with proteins of this certain localization. Intuitively, a term
is distinguishing for a localization , if it is much more likely to occur in ab-
stracts associated with localization than with abstracts associated with all other
localizations. We formalize this idea in the following paragraphs.
Let be a term, a localization, and a protein. If protein is known to be
localized in , we denote this . We also define the following sets:
The set of all PubMed abstracts associated with protein according to Swiss-
Prot, denoted ;
The set of all proteins known to be localized at , denoted ;
The set of abstracts that are associated with a localization , denoted , is
defined as: . It is the set of all the abstracts associ-
ated with the proteins that are in localization . The number of documents in
this set is denoted .
The probability of a term to be associated with a localization , denoted , is
defined as the conditional probability of the term to appear in a document, given
that the document is associated with the localization: .
A maximum likelihood estimate for this probability is simply the proportion of
documents containing among all those associated with the localization:
of documents . For each term and each localization ,
the estimate for the probability is calculated.
Based on this probability, a term is called distinguishing for localization , if
and only if its probability to occur in localization , , is significantly different
from its probability to occur in any other localization , . The statistical test
applied, uses the Z-score , which evaluates the difference between two binomial
Without using any of the MeSH terms.

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Table 1. Examples of distinguishing stemmed terms for several localizations
Localization Example Terms
Nucleus bind, control, dna, histon, nuclear, promot, transcript
Mitochondria coa (CoA), complex, cytochrom, dehydrogenas, mitochondri, oxidas, respiratori
Golgi Apparatus acceptor, catalyt domain, fucosyltransferas, galactos, glycosyltransferas, golgi, transferas
Endoplasmic Reticulum calcium, chaperon, disulfid isomeras, endoplasm, lumen, microsom, transmembran
probabilities, , and , as follows:
where
When , the hypothesis that the two probabilities , are
different is accepted with a confidence level greater than . Therefore, if the
term has a localization such that for any other localization ,
is considered distinguishing for localization , and is included in the set of
distinguishing terms. In our representation of proteins as term vectors, we use
only distinguishing terms. In the experiments described in Section 3, using several
different proteins sets, the average number of PubMed abstracts is on the order
of , while that of distinguishing terms is about . Some examples of
distinguishing terms for several localizations are shown in Table 1.
Finally, once the collection of distinguishing terms, denoted as , was
established, each protein is represented as an N-dimensional vector, where the
weight at position , (where ), is the conditional probability
of the term to appear in the abstracts associated with the protein , given all
the PubMed abstracts related to the protein, (the set ) This probability is es-
timated as the ratio between the total number of times the term occurs in the
abstracts associated with the protein and the total number of all the occurrences
of distinguishing terms in these same abstracts. Formally it is calculated as:
of times occurs in
of times occurs in
where the sums are taken over all the abstracts in the set of abstracts associated
with the protein , .
The representation of proteins as weighted term vectors, is then partitioned
into training and test sets for each subcellular localization, and as before, an SVM
is trained to classify these protein vectors into their respective localization. This
classifier, like SVMacc described above, produces an -dimensional probability
vector denoting the probability of the protein to be in each of the localizations.

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2.3. Integrated method
The output from the five classifiers above, is a set of four probability vectors and
one binary-valued vector (resulting from MotifSearch). These are all concatenated
to form one integrated feature vector for each protein. Again, an SVM classifier
is trained on these feature vectors to produce a prediction. This classifier consists
of a set of one-against-one classifiers (each of which distinguishes between a pair
of localizations) and its output, yet again, is a probabilistic vector, holding for
each localization the probability of the protein to belong to it. Based on this
final classification step, a protein is assigned to the localization with the highest
probability value in the last output vector. The training and evaluation procedure
uses strict five-fold cross-validation, where no test protein was used to train any
of the classifiers comprising the system.
3. Experiments and Results
To train and to evaluate our integrated system, we used three different data sets,
namely those used for training and testing TargetP, MutliLoc, and PLOC. These
sets provide the basis for an extensive and sound comparison. The data sets, the
evaluation procedure, and the results are described throughout this section.
3.1. Experimental setting
The data sets used in our experiments are the following:
TargetP – This data set contains a total of 3,415 distinct proteins representing
four plant (ch,mi,SP, and OT) and three non-plant (mi,SP, and OT) localizations.
Homologs were removed from it by the TargetP authors. The SP category includes
proteins from several localizations in the secretory pathway: endoplasmic retic-
ulum (er), extracellular space (ex), Golgi apparatus (go), lysosome (ly), plasma
membrane (pm), and vacuole (va). The OT category includes cy and nu proteins.
MultiLoc – The MultiLoc data set contains a total of 5,959 protein se-
quences, which were extracted from the Swiss-Prot database release 42.0 . An-
imal, fungal, and plant proteins with an annotated subcellular localization were
grouped into eleven eukaryotic localizations: cy,ch,er,ex,go,ly,mi,nu, per-
oxisome (pe), pm,va. In the experiments reported here homologous proteins
with identity higher than , (the same threshold used by PLOC ), were ex-
cluded from the set, to avoid the occurrence of highly similar sequences in both
the training and the test sets . Further details about the data set extraction and the
implications of homology reduction are available in the MultiLoc publication .
Excluding proteins whose annotation was commented by similarity or potential.
We also conducted experiments with a more lenient and more stringent homology constraints, of
and identity, respectively (data not shown).

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PLOC – The PLOC data set was used by Park and Kanehisa and consists of
proteins extracted from Swiss-Prot release 39.0, covering 12 localizations. In con-
trast to MultiLoc, (aside for the older Swiss-Prot version), this data set introduces
an additional category within the cy proteins, namely, the cytoskeleton (cs). There
are 41 cs proteins, compared to 1,245 cy proteins. The total number of sequences
is 7,579 (max. sequence identity 80%). This set is larger than the MultiLoc data
set due to a less restrictive data extraction, assigning proteins to localization even
when the localization annotation includes the words “potential” or “by similarity”.
Using these three data sets, the performance of our integrated system is com-
pared to that of TargetP, PLOC, and MultiLoc . In addition, we also compare
the performance of the integrated system to that of an SVM classifier applied to
the text data alone. Following previous evaluations , we consistently employ
five-fold cross-validation. For comparison against the PLOC data set we use the
same split as the one used by Park and Kanehisa . For the TargetP data, as the
split used by Emanuelsson et al. was not provided, we ran the five-fold cross-
validation procedure five times, each using a different randomized five-way split,
to ensure robustness. The reported results are averaged over all the 5 folds, and
over the 5 randomized splits when those are used.
Since the performance of previous systems was evaluated using several
different metrics, for a fair comparison we calculated these same performance
measures. Thus, for each system and data set the performance is measured,
for each localization, in terms of the sensitivity (Sens), specificity (Spec), and
Matthews correlation coefficient (MCC). These are defined as:
and
where denote the number of true positives, true negatives,
false positives, and false negatives, respectively, with respect to a given localiza-
tion. Like Park and Kanehisa we also measure the overall accuracy, namely,
, where is the number of correctly classified proteins over all the
localizations, and is the total number of classified proteins. They also measured
the average sensitivity, over all the localizations, a metric they call local accuracy,
which we calculate as well. This last measure, which we denote as Avg, gives an
equal weight to the categorization performance on each localization, regardless of
the number of proteins known to be associated with it.
Comparison to PSort is not included here, since MultiLoc has already demonstrated a higher pre-
diction accuracy compared to this method .

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3.2. Results
We present the results of running the sequence-based system, MultiLoc, the text-
based classifier alone (denoted Text), and the integrated system (denoted Multi-
LocText), on all the three data sets. For completeness, we also present the results
reported by the authors of PLOC and of TargetP on the respective data sets.
These numbers were directly taken from the respective publications.
Table 2 summarizes the results, showing the overall accuracy (Acc) and the
average local accuracy (Avg) for both the TargetP and the PLOC data sets. For
TargetP the results are shown for plant and non-plant proteins, while for PLOC
results are shown for plant, animal, and fungal proteins. Table 3 compares the
performance of TargetP and PLOC with our integrated system, with respect to the
individual subcellular localizations.
Table 2. An overview of the prediction results using the TargetP and PLOC data sets. Both the total (Acc)
and the average (Avg) prediction accuracies are shown for all the methods. The highest values appear in bold.
Standard deviations, (denoted ) are provided where available.
Data set Method Acc [%] ( Standard Deviation) / Avg [%] ( Standard Deviation)
TargetP Plant Non-Plant
TargetP 85.3 ( 3.5) / 85.6 (n/a) 90.0 ( 0.7) / 90.7 (n/a)
MultiLoc 89.7 ( 1.6) / 90.2 ( 2.0) 92.5 ( 1.2)/ 92.8 ( 1.1)
Text 81.2 ( 2.6) / 78.1 ( 3.2) 88.7 ( 1.1)/ 89.8 ( 1.6)
MultiLocText 94.7 ( 1.5) / 94.4 ( 1.6) 96.2 ( 0.8) / 96.7 ( 0.9)
PLOC Plant Animal Fungal
PLOC 78.2 ( 0.9)/ 57.9 ( 2.1) 79.6 ( 0.9)/ 59.9 ( 3.3) 79.5 ( 0.9)/ 56.8 ( 1.9)
MultiLoc 73.6 ( 0.7) / 71.3 ( 2.8) 76.0 ( 0.7) / 73.6 ( 3.9) 75.8 ( 0.8) / 72.5 ( 2.5)
Text 68.7 ( 0.7) / 73.5 ( 1.8) 70.2 ( 0.7) / 75.5 ( 2.7) 67.8 ( 0.5) / 72.4 ( 2.6)
MultiLocText 85.3 ( 1.2) / 84.2 ( 2.4) 86.4 ( 0.8) / 84.5 ( 3.6) 85.4 ( 0.8) / 83.8 ( 2.8)
Table 3. Localization specific results using the TargetP (left), and the PLOC (right) data sets. For
both sets, the results reported in the respective papers are compared to results of our integrated
system (MultiLocText). As PLOC localization-specific results are averaged over all three organ-
isms, we show such averaged results for our system as well. Specificity and MCC values were not
available for PLOC, hence only its Sensitivity is listed and compared with our sensitivity values.
The highest compared values for each data set are shown in bold.
TargetP Data Set PLOC Data Set
Loc TargetP MultiLocText Loc PLOC MultiLocText
Plant (Sens Spec MCC) Avg. Sens Avg. (Sens Spec MCC)
ch ch
mi mi
OT cs
SP cy
Non-Plant (Sens Spec MCC)er
mi ex
OT go
SP nu
pe
pm
va
ly
A comparison of the performance of our three systems (MultiLoc alone, Text
alone, and the integrated MultiLocText) using five-fold cross-validation over the

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5,959 proteins of the MultiLoc data set, is presented in Table 4. The sensitivity
(Sens), specificity (Spec), and Matthews MCC values for the plant and animal
versions are listed. (Similar results were obtained for the fungal version, and are
not shown here due to space limitation).
The results in Tables 2, 3, and 4 clearly show that the combined classifier,
which integrates text and sequence data, outperforms earlier prediction methods.
It also outperforms its own text-based (Text) and sequence-based (MultiLoc) com-
ponents, if taken separately. A significance test was performed to evaluate the
differences between the values obtained from MultiLocText and those obtained
from each of MultiLoc and Text alone, (Table 4). The improved performance
values of MultiLocText are highly statistically significant ( ), for almost
all the subcellular localizations. The only exceptions are the Golgi ( , animal
and plant), where there is no significant difference in sensitivity with respect to
text-alone, as well as the peroxisome predictions ( , animal and plant), where
MultiLocText does not outperform the text-alone system.
4. Discussion and Conclusion
The methods, experiments, and results presented here clearly demonstrate a sig-
nificant improvement in the prediction of protein subcellular localization through
the integration of sequence- and text-based methods. Table 4 shows that the two
Table 4. Prediction performance of MultiLoc, Text, and MultiLocText on
the MultiLoc data set. Both localization-specific values (sens, spec, M CC)
and overall results (Acc and Avg) are shown. Highest values appear in bold.
Loc MultiLoc Text MultiLocText
Plant (Sens Spec MCC)
ch
cy
er
ex
go
mi
nu
pe
pm
va
Acc [%] 74.6 73.1
Avg [%] 75.2 76.0
Animal (Sens Spec MCC)
cy
er
ex
go
ly
mi
nu
pe
pm
Acc [%]
Avg [%]

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types of methods distinctly complement each other. MultiLoc, which is based on
sequence data, typically performs well predicting protein localizations that are di-
rected by N-terminal signals such as the mitochondria and the chloroplast. The use
of text information complements and significantly boosts its performance for lo-
calizations whose sequence-based signal is not as overt, including the peroxisome
and localizations related to the secretory pathway such as the Golgi apparatus and
the endoplasmic reticulum.
In this work we have demonstrated, using five-fold cross-validation, that our
system can reproduce, with unprecedented sensitivity and specificity, localiza-
tions of proteins which were already annotated in Swiss-Prot. A natural next step
is to apply the method to yet un-localized proteins. We are developing the means
to predict subcellular localization of proteins for which PubMed reference exist
in Swiss-Prot but no localization assigned, as well as for those with no curated
PubMed reference. Our current use of “raw text” from PubMed abstracts (in con-
trast, for instance, to the use of Swiss-Prot annotation text as was done before ),
is expected to make our approach amenable to such extensions. We are also in-
vestigating methods for the localization of proteins with no PubMed references,
through the use of alternative data sources.
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