Nucleic Acids Research, 2007, 1–3
doi:10.1093/nar/gkm259
WoLF PSORT: protein localization predictor
Paul Horton1
, Keun-Joon Park1,2
, Takeshi Obayashi3
, Naoya Fujita1,3
,
Hajime Harada1
, C.J. Adams-Collier4
and Kenta Nakai3,
*
1
Computational Biology Research Center, AIST, Tokyo, Japan, 2
Center for Genome Science, National
Institute of Health, Korea Center for Disease Control & Prevention, 5 Nokbeon-Dong, Eunpyung-Gu,
Seoul 122-701 Korea, 3
Human Genome Center, Institute of Medical Science, University of Tokyo, Tokyo, Japan
and 4
Collier Technologies, Everett, WA, USA
Received January 30, 2007; Revised March 26, 2007; Accepted April 8, 2007
ABSTRACT
WoLF PSORT is an extension of the PSORT II
program for protein subcellular location prediction.
WoLF PSORT converts protein amino acid
sequences into numerical localization features;
based on sorting signals, amino acid composition
and functional motifs such as DNA-binding motifs.
After conversion, a simple k-nearest neighbor
classifier is used for prediction. Using html, the
evidence for each prediction is shown in two ways:
(i) a list of proteins of known localization with the
most similar localization features to the query, and
(ii) tables with detailed information about individual
localization features. For convenience, sequence
alignments of the query to similar proteins and
links to UniProt and Gene Ontology are provided.
Taken together, this information allows a user to
understand the evidence (or lack thereof) behind
the predictions made for particular proteins.
WoLF PSORT is available at wolfpsort.org
INTRODUCTION
Bilipid membranes divide eukaryotic cells into various
types of organelles containing characteristic proteins
and performing specialized functions. Thus, subcellular
localization information gives an important clue to a
protein’s function. Although localization signals in
mRNA appear to play some role (1), the main determinant
of a protein’s localization residues in the protein’s
amino acid sequence. (We recommend wikipedia.org/wiki/
Protein_targeting for a brief overview and Alberts et al.
(2) for a textbook description.)
Numerous experiments to determine protein localization
have been performed to date. These can broadly
be classified as: small-scale experiments—the results of
which continue to accumulate in public databases, such
as UniProt (3) and Gene Ontology (4); and large-scale
experiments using epitope (5) or green fluorescent protein
(GFP) (6) tagging, or by separation of organelles by
centrifugation combined with protein identification by
mass spectrometry (7,8).
Although they provide invaluable information, the
coverage of experimental data is only high for model
organisms, particularly yeast. Moreover, the agreement
amongst large-scale experimental data is only 75–80%
(6–9). Thus, computational prediction of localization from
amino acid remains an important topic.
Numerous computational methods are available
[reviewed in (10,11)]. Some (including WoLF PSORT)
have recently been benchmarked by Sprenger et al. (12),
who found the computational methods to be useful for
sites, such as the nucleus, for which many training
examples can be easily obtained from UniProt (which
is the source of most or all of the training data for
most prediction methods—including WoLF PSORT). The
different methods they benchmarked were found to have
different strengths. Here, we describe the public server for
our WoLF PSORT method.
PREDICTION METHOD
WoLF PSORT is an extension of PSORT II (13,14) and
also uses the PSORT (15) localization features for
prediction. In addition, WoLF PSORT uses some features
from iPSORT (16) and amino acid composition. Those
features are used to convert amino acid sequences into
numerical vectors, which are then classified with a
weighted k-nearest neighbor classifier. WoLF PSORT
uses a wrapper method to select and use only the most
relevant features. This reduces the amount of information
which needs to be considered (and displayed) for the user
to interpret individual predictions and may also make
the predictor less prone to over learning. The prediction
method has described in more detail elsewhere (17).
*To whom correspondence should be addressed. Tel: þ 81-3-5449-5131; Fax: þ 81-3-5449-5133; Email: knakai@ims.u-tokyo.ac.jp
ß 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/
by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Nucleic Acids Research Advance Access published May 21, 2007
Dataset
The WoLF PSORT dataset is divided into fungi, plant
and animal containing 2113, 2333 and 12771 proteins,
respectively. The current data was primarily obtained
from UniProt (3) version 45, but subcellular localization
information from Gene Ontology (4) was also used.
Entries with evidence codes {TAS, IDA, IMP} were
included, with manual revisions in a few cases. We intend
to update these datasets regularly in the future.
LOCALIZATION SITES AND PREDICTION
ACCURACY
WoLF PSORT classifies proteins into more than 10 localization
sites, including dual localization such as proteins
which shuttle between the cytosol and nucleus. Based on
our cross-validation studies (17), we estimate sensitivity
and specificity of around 70% for: nucleus, mitochondria,
cytosol, plasma membrane, extracellular and (in plants)
chloroplast. For other sites, such as peroxisome, Golgi, etc.
the sensitivity is very low, but useful predictions are still
made in some cases. For example, the Arabidopsis seed
protein 12S1_ARATH is reasonably predicted to localize
to the vacuole even though only one of its neighbors
(see below) shares significant sequence similarity.
An independent test (12) on mouse proteins gave
a significantly lower estimate of WoLF PSORT’s prediction
accuracy (around 50%). This discrepancy may be
explained by the over-representation of well-studied
proteins in the WoLF PSORT training data and perhaps
also by the size of their test data (in particular, their
‘LOC2145’ test set contained only 87 cytosolic proteins)
or differences in site definition.
PREDICTION RESULTS DISPLAY
The k-nearest neighbors classifier allows for an intuitive
display of the prediction results which is exactly analogous
to sequence similarity search. Using multifasta format,
multiple sequences can be given in a query. The first page
returned from the server gives a one line summary of the
result for each query sequence. For example the prediction
summary line for the TCOF_HUMAN protein is:
TCOF_HUMAN details nucl: 27.5, cyto_nucl: 17, cyto:
3.5, extr: 1
The localization sites are abbreviated to four letter codes
(documented on the server) with dual localization denoted
by joining the four letter codes with an underscore
character. The numbers roughly indicate the number of
nearest neighbors to the query which localize to each
site—but are adjusted to account for the possibility of
dual localization (17).
Neighbor list
Details about the queries neighbor list and localization
signals can be obtained by following the ‘details’ link.
The first part of the display page is a neighbor list table
such as the one shown in Figure 1. This list gives
information regarding the query’s neighbors (proteins in
the WoLF PSORT training data that have the most
similar localization features). For user convenience, the
percent identity and a link to the alignment of each
neighbor to the query is given. Sequence similarity is
not used for prediction but can provide additional
corroborating evidence in many cases. Links to the
relevant entries in UniProt, Gene Ontology and TAIR
(www.arabidopsis.org) for many Arabidopsis entries are
also provided.
Localization feature table
By scrolling down on the detailed results pages, one can
find a feature table giving the values of each localization
feature for the query and its neighbors. In some cases,
the individual values can help support (or question)
the predicted site. For example in the case of
TCOF_HUMAN (Figure 2), the 99 percentile value of
the PSORT localization feature ‘nuc’ (which is based on
nuclear localization signals and DNA-binding site motifs),
is consistent with the nuclear prediction. Below the
normalized table, a similar table with the raw feature
values is displayed.
IMPLEMENTATION
The server is implemented with Mason (www.
masonhq.com), which allows convenient embedding of
logic and computed results into html via the Perl
programming language. Multiple requests are handled
with the simple strategy of returning the results in a
URI containing an MD5 hash of the query contents.
Figure 1. Part of the list of proteins similar to the query protein, an isoform of TCOF_HUMAN, is shown. For each neighbor the following
is shown: UniProt ID, localization site, the distance in localization features from the query, the percent identity to the query, a link to its UniProt
entry, the subcellular localization line from UniProt and other available localization information.
2 Nucleic Acids Research, 2007
Upon sending a query a wait page is shown, followed by
an automatic redirect to the results page upon task
completion (usually requiring around 40 s). Task scheduling
is delegated to Apache and the Linux operating
system. Multiple sequences are allowed in one query, but
we currently limit the query size to 64 KB. For large-scale
use, such as whole genome annotation, we encourage users
to download the stand-alone package (available on the
server) and run WoLF PSORT locally.
SUMMARY
WoLF PSORT not only provides subcellular localization
prediction with competitive accuracy, but also provides
detailed information relevant to protein localization to
help users to form their own hypotheses.
ACKNOWLEDGEMENTS
KN was partly supported by a grant from the National
Project on Protein Structural and Functional Analyses
by the Ministry of Education, Culture, Sports, Science
and Technology in Japan. The annual budget of the
Human Genome Center was used for the publication of
this paper.
Conflict of interest statement. None declared.
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Figure 2. The localization features for the query and its neighbors are shown. The values are normalized to percentiles relative to the WoLF PSORT
training data. Neighbor values shown in blue are within 10% points to the query value, while those shown in red are 20 or more percentile point
different from the query.
Nucleic Acids Research, 2007 3