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Plant Physiol, January 2003, Vol. 131, pp. 16-26
ARAMEMNON, a Novel Database for Arabidopsis Integral Membrane
Proteins1
Rainer
Schwacke,*
Anja
Schneider,
Eric
van
der Graaff,
Karsten
Fischer,
Elisabetta
Catoni,
Marcelo
Desimone,
Wolf B.
Frommer,
Ulf-Ingo
Flügge, and
Reinhard
Kunze
Universität zu Köln, Botanisches Institut,
Gyrhofstrasse 15, 50931 Köln, Germany (R.S., A.S., E.v.d.G.,
K.F., U.-I.F., R.K.); and Universität Tübingen, Zentrum
für Molekularbiologie der Pflanzen, Auf der Morgenstelle 1, 72076 Tübingen, Germany (E.C., M.D., W.B.F.)
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ABSTRACT |
A specialized database (DB) for Arabidopsis membrane
proteins, ARAMEMNON, was designed that facilitates the interpretation of gene and protein sequence data by integrating features that are
presently only available from individual sources. Using several publicly available prediction programs, putative integral membrane proteins were identified among the approximately 25,500 proteins in the
Arabidopsis genome DBs. By averaging the predictions from seven
programs, approximately 6,500 proteins were classified as transmembrane
(TM) candidate proteins. Some 1,800 of these contain at least four TM
spans and are possibly linked to transport functions. The ARAMEMNON DB
enables direct comparison of the predictions of seven different TM span
computation programs and the predictions of subcellular localization by
eight signal peptide recognition programs. A special function displays
the proteins related to the query and dynamically generates a protein
family structure. As a first set of proteins from other organisms, all
of the approximately 700 putative membrane proteins were extracted from
the genome of the cyanobacterium Synechocystis sp. and
incorporated in the ARAMEMNON DB. The ARAMEMNON DB is accessible at the
URL http://aramemnon.botanik.uni-koeln.de.
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INTRODUCTION |
Biological membranes constitute a
chemical barrier to the environment and are thus the prerequisite for
the establishment and maintenance of a controlled intracellular milieu,
the cytoplasm. In eukaryotes, membranes are also responsible for the
formation of chemically distinct intracellular compartments. The lipid
bilayer membranes contain a great diversity of proteins that fulfill
different functions and serve as an interface to the environment and
between different compartments. Among these membrane proteins are
receptors involved in signaling cascades and pathogen defense
reactions, enzymes such as the apparatus for cell wall biosynthesis,
and transporters responsible for the import and export of solutes and
ions and the establishment of electrochemical gradients across membranes, thereby connecting the different metabolic pathways of the
cellular compartments and organelles.
Many plant transport proteins were identified by complementation of
yeast mutants that were deficient in certain transport or metabolic
functions (Frommer and Ninnemann, 1995 ). Membrane proteins have a modular structure, consisting of hydrophobic domains and hydrophilic loops or termini that extend into the cytoplasm, the
organelle, or point to the extracellular space. The hydrophobic transmembrane (TM) domains consist of amphipathic  -helices or  -barrels that pass across or dip into the hydrophobic membrane lipid
bilayer. During recent years, the three-dimensional structures of more
than 160 TM proteins or domains were determined at varying resolution,
and it appears that modularity is a general feature of polytopic
membrane proteins (http://www.rcsb.org/pdb/;
http://www.ncbi.nlm.nih.gov:80/Structure/; Berman et al.,
2002; Wang et al., 2002).
Arabidopsis is the first plant for which the genome has been deciphered
completely (Arabidopsis Genome Initiative, 2000). Automatic gene predictions and annotations have been performed for the
full genome and are continuously being improved at The Institute for
Genomic Research (TIGR;
http://www.tigr.org/tdb/e2k1/ath1/ath1.shtml), The
Arabidopsis Information Resource
(http://www.Arabidopsis.org/aboutarabidopsis.html), and the
Munich Information Center for Protein Sequences
(http://mips.gsf.de/proj/thal/db/about/about_frame.html). Gene
predictions are sustained by expressed sequence tag analyses, full-length mRNA sequencing and individual research projects. Accordingly, the Arabidopsis genome offers the possibility to perform
bioinformatic analyses and data mining that are not yet possible with
other plant species. In the future, these analyses will also become
possible for the recently completed rice (Oryza sativa)
genome (Goff et al., 2002; Yu et al.,
2002).
A couple of databases (DBs) specialized for membrane proteins are
accessible on the internet. For Brewer's yeast (Saccharomyces cerevisiae) a transport protein DB has been established
(http://alize.ulb.ac.be/YTPdb/; Andre, 1995;
Van Belle and Andre, 2001). A comprehensive
classification of transport systems and transport protein families from
20 bacterial, archaeal, and eukaryotic genomes has been
established and is accessible at
http://www-biology.ucsd.edu/~msaier/transport/(Saier,
1999). Tables that summarize genomic comparisons of
membrane transport systems are also published on the Web pages of
the Paulsen laboratory (http://www.biology.ucsd.edu/~ipaulsen/transport/). An
Arabidopsis library of all TM candidate proteins containing more than
one TM span has been published and was used to identify novel membrane protein families not known from other organisms
(http://www.biosci.cbs.umn.edu/Arabidopsis; Ward, 2001). PlantsT is an
Arabidopsis and yeast transporter DB with a focus on metal ion
transporters from Arabidopsis (http://plantst.sdsc.edu; Mäser et al., 2001).
All of these membrane protein DBs use one or two algorithms for TM
prediction. For a given protein, these particular method(s) used may
generate an accurate prediction. However, in many cases, predictions by
different programs vary with respect to the number of TM domains and
their relative location in the polypeptide sequence, and it is not
possible to know which prediction program will generate the most
accurate prediction for a particular protein (Möller et
al., 2001; Ikeda et al., 2002).
Here, we present a novel membrane protein DB, ARAMEMNON, which
integrates features that are presently only available from separate
sources, and thus should facilitate the interpretation of gene/protein
sequence data. The major objectives of the ARAMEMNON DB are to provide
(a) the possibility to directly compare the predictions of (currently)
seven different TM span computation programs and (b) the predictions of
subcellular localization by eight signaling peptide recognition
programs, and (c) to identify protein families ("clusters") that
center around a user-selected protein. The ARAMEMNON DB is accessible
on the Web at the URL http://aramemnon.botanik.uni-koeln.de.
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RESULTS |
Arabidopsis Membrane Protein Predictions and Comparative Graphical
Representation of TM Spans
The complete set of 25,492 predicted Arabidopsis protein sequences
(January 2002) was screened for putative membrane proteins containing
one or more TM domains. In the current version of the DB, the
prediction of TM domains is based on seven different programs (Table
I), and future versions will include
additional predictions (see "Discussion"). Because of different
approaches used, i.e. methods based on hidden Markov models, on the
calculation of hydrophobicities, or on a DB of TM proteins, some
programs recognize (or overlook) membrane spans that are predicted by
others. For example, HmmTop 2.0, TmPred, TMap, and TopPred 2.0 classify
more proteins as TM proteins compared with TmHMM 2.0, SosuiG 1.1, or
Eiconda 0.9 (Table II). It should be
noted that the overall number of predicted TM proteins differs
significantly between the individual programs, and therefore the use of
a single prediction algorithm does not allow high confidence level
conclusions with respect to number and location of predicted TM
spans.
The combination of the overlapping sets of TM proteins predicted by any
one of the seven programs would result in the highly unlikely number of
approximately 18,600 putative Arabidopsis membrane proteins. To select
putative reading frames with a high probability to contain TM spans,
the statistical median of all predicted TM regions was calculated for
each protein. Only such reading frames were classified as membrane
proteins by ARAMEMNON, for which at least three TM spans had been
predicted by five or more of the seven programs, or one or two TM spans
by at least six programs, resulting in an overall number of 6,047 proteins. In a subsequent step, among the disqualified proteins, those
were selected and entered into the DB that share 30% or more sequence
similarity with a member of the 6,047 preselected proteins and that are
predicted by four programs to contain TM spans. This reiterative
procedure increased the total number of putative membrane
genes/proteins to 7,314.
Endoplasmatic reticulum targeting sequences of soluble proteins are
often characterized by a hydrophobic core that may be recognized by
prediction programs as a TM span (Nielsen et al., 1997a). To exclude such proteins from classification as
integral membrane proteins, the 839 reading frames containing a single TM domain at the N terminus within a cleavable signal peptide, which is
consistently predicted by TargetP, SignalP-HMM, SignalP-NN, and iPSORT
(see below) were removed from the DB. In accordance, proteins
supposedly having a non-cleavable signal sequence and thus remaining
anchored to the membrane will be retained in the ARAMEMNON DB. However,
as the recognition of (non-)cleavable signal sequences by the different
programs is not always reliable, some actual membrane anchored proteins
may have been excluded from the fraction of one-TM proteins, whereas
some soluble proteins may have persisted. The excluded proteins are
listed in the ARAMEMNON DB.
Eventually, 6,475 (about 25%) of the nuclear encoded Arabidopsis
proteins are classified as membrane proteins and listed in the
ARAMEMNON DB (Table II). In addition, 78 membrane proteins of the
organellar genomes were added. At present, approximately 40% of all
Arabidopsis TM proteins are annotated in the TIGR DB as "unknown
protein," "hypothetical protein," or "putative protein."
The ARAMEMNON DB was primarily developed for the analysis of
Arabidopsis membrane proteins. However, to augment comparative analyses
of similar proteins, supplementary data sets of orthologs from other
organisms will also be incorporated. As a first step, all 706 membrane
proteins were extracted from the cyanobacterium Synechocystis sp. genome (3,165 genes; Kaneko et al.,
1996) by applying the same procedure as with Arabidopsis
protein sequences and included in the ARAMEMNON DB. For 500 Arabidopsis
membrane proteins, ARAMEMNON finds 123 related Synechocystis
sp. membrane proteins. ChloroP or PCLR remarkably predict only
approximately 130 of these Arabidopsis proteins to be localized in the chloroplasts.
Table II shows the numbers and frequencies of Arabidopsis proteins with
less than 17 TM spans in the ARAMEMNON DB in comparison with the
numbers predicted by the individual programs. Major differences between
the individual programs are obviously attributable to recognition of
proteins containing one to three TM spans. Programs that identify more
proteins containing one to three TM spans, i.e. HmmTop 2.0, TmPred,
TMap, and TopPred 2.0, also classify the highest overall number of
Arabidopsis proteins as TM proteins. TmPred, HmmTop, TopPred, and TMap
also predict the highest fraction of "false positives" (proteins
predicted only by this but no other program), whereas, except for TMap,
the programs predicting a lower overall number of TM proteins tend to
generate more "false negatives" (proteins not predicted by this
program but by all other programs).
Because the TM predictions by individual algorithms sometimes deviate
dramatically, the reliability of TM topology predictions can be
significantly improved by combining the results from several prediction
methods according to a "majority-vote" principle (Nilsson et
al., 2000). Also for most Arabidopsis proteins, the programs generate deviating predictions. For each class of proteins with a
median TM span number between one and 14, the proportion of proteins
was determined; for that, the same number of TM domains is predicted by
all seven programs used, or by six, five, four, or less than four
programs, respectively (Fig. 1). For at
least 40% of all proteins with a median of three or more TM spans,
maximally three programs predict the same number of TM spans. Except
for the 4-TM proteins, five or more programs predict the same TM span number only for less than one-third of the proteins. Moreover, it has
to be pointed out that despite predicting the same number of TM spans,
different programs frequently recognize putative TM domains in
different locations (for example, see Fig.
2B).
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Figure 1.
Uniformity of TM span predictions. For each class
of proteins with a median TM span number between one and 14, the
proportion of proteins indicated for that same number of TM domains is
predicted by all seven programs used or by six, five, four, or less
than four programs, respectively.
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Figure 2.
Graphical representation of TM span predictions.
ARAMEMNON displays plots of the TM predictions by seven programs. A, TM
span predictions for an aquaporin MIP-like protein (At3g54820). All
seven programs uniformly predict six TM spans at almost the same
positions, and TmHMM 2.0, HmmTop 2.0, TmPred and TMap predict the same
orientation within the membrane. The shading intensity of the membrane
span candidate segments indicates the mean hydrophobicity range
according to a normalized hydrophobicity scale (Eisenberg et
al., 1984): white, 0 to 0.24; light gray, 0.25 to 0.49; dark
gray, 0.50 to 0.74; and black, 0.75 to 0.99. B, TM span predictions for
the PPT2 protein (At3g01550). The predictions differ in the number of
TM spans, location of TM spans, and orientation of the protein within
the membrane. C, For each prediction, the details are shown. #,
Predicted TM spans starting from the N terminus; Pos, location of the
TM span in the protein; HyPhob, mean hydrophobicity within the membrane
span candidate segment (Eisenberg et al., 1984). The
shading intensity correlates to that in A; AmPhil, relative maximal
amphiphilicity within the membrane candidate segment.
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The ARAMEMNON DB provides a function for comparative graphical
representation of TM spans. It displays plots with TM spans predicted
by the seven different prediction programs, that allow the immediate
evaluation of the predictions. Figure 2A shows an example of a
aquaporin-like MIP protein that is consistently predicted by all seven
programs. In contrast, the overall numbers and locations of TM spans
predicted by the alternative algorithms for the
phosphate/phosphoenolpyruvate translocator PPT2 deviate
considerably between each other (Fig. 2B). For each prediction, the TM
details "location," "mean hydrophobicity," and "relative
maximal amphiphilicity" can be displayed (Fig. 2C).
Prediction of Subcellular Localization
Subcellular localization predictions were performed by eight
programs (Table III). However, only
TargetP and iPSORT predict targeting to chloroplasts, to mitochondria,
or to the secretory pathway. The other programs offer predictions for
one or two specific compartments only. Overall, at least four
predictions are available for each subcellular target (Table III).
ARAMEMNON enables queries for proteins predicted to be located in
plastids or mitochondria or to be secreted, if a absolute majority of
programs predict one target compartment.
The direct comparison of subcellular localization predictions
implemented in the ARAMEMNON DB exemplifies that the reliability of the
individual predictions is rather ambiguous (see also Emanuelsson and von Heijne, 2001). This is illustrated by proteins for
which clear experimental data exist. For example, the plastidic
localization of the xylulose-5-phosphate/phosphate translocator
(At5g17630; M. Eicks, Universität zu Köln, personal
communication) is correctly predicted by TargetP, ChloroP, Predotar,
iPSORT, and PCLR, whereas the chloroplast inner envelope-localized
triose phosphate translocator AtTPT (At5g46110; P. Niewiadomski,
Universität zu Köln, personal communication) is predicted
to be targeted to mitochondria (Table IV). The only hint toward a plastidic
localization is the relatively high score generated by the PCLR
program. Therefore, if experimental data for subcellular localization
of a given protein were available (presently only few), this has been
indicated in the membrane topology view and a reference to respective
publication is provided.
Family Structure Analysis of TM Proteins
All proteins in the ARAMEMNON DB were subjected to pairwise local
alignments. All pairs with a minimal similarity of 28% excluding gaps
and a minimal Smith-Waterman score of 310 were registered. The
ARAMEMNON DB provides this rather static view of membrane proteins
related to a user-selected query sequence as a list ordered by the
degree of similarity with a lower cut-off level of 28% (Fig.
3A).
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Figure 3.
TPT-related PTs identified by ARAMEMNON DB.
A, List-form display of TPT-related proteins. The columns show (from
left to right): amino acid similarity excluding gaps and Smith-Waterman
score; organism (At, Arabidopsis) and chromosome number; gene
annotation (with links to relevant publications); gene name (with links
to TIGR and Munich Information Center for Protein Sequences DBs);
protein accession number in GenBank (with link to National Center for
Biotechnology Information [NCBI]); button to call protein, cDNA,
genomic DNA, 5'- and 3'-untranslated region sequence display; button to
call the TM and signal sequence prediction display; button to call the
family structure (cluster) display, as shown in B. B, Columns from left
to right: average subcluster amino acid similarity levels; members of
the subclusters; chromosomal location of genes. C, NJ tree based on a
multiple alignment of PT sequences performed by ClustalX. The numbers
beside branches indicate the frequency (%) with which the branch was
found in 1,000 bootstrap replicas. The shaded branches correspond to
the subclusters generated by ARAMEMNON DB.
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To create a more dynamic representation of a protein family, especially
with respect to subfamilies, a "cluster" generation function was
implemented in the ARAMEMNON DB. For each protein with similarity to
the query protein, all other related proteins are retrieved and the
pairwise similarities between all sequences are determined. According
to these similarities, the retrieved sequences are merged into a
"cluster" that contains "subclusters" with similarity threshold
levels of 28%, 40%, 50%, and an upper resolution limit of 70% (see
"Materials and Methods" and Figs. 3-5). The
proteins selected by this means have at least 28% sequence similarity
to at least one other member of the cluster, but not necessarily to the
original query sequence. Proteins of the cluster with less than 28%
similarity to the query protein are marked in Figures 4 and 5 by
"~~."
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Figure 4.
Amino acid permease (AAP) protein family
structure generated by ARAMEMNON DB in comparison to the NJ tree. A,
ARAMEMNON family structure (cluster) display of the AAP proteins. B,
Schematic graphic to illustrate the relationships between the AAP
protein subclusters listed in A. C, NJ tree of the AAP family, based on
a multiple alignment of amino acid permease sequences performed by
ClustalX.
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Figure 5.
Family structure and TM domain topology of the
putative K/VAG transporters. A, Graphical illustration the of
At1g21870-related membrane protein family structure generated by the
ARAMEMNON DB. B, NJ tree of the At1g21870-related proteins. C, The
membrane topologies of the K/VAG transporters were predicted by using
the TM protein prediction program Eiconda 0.9. Shading of the boxes
that symbolize the TM spans is as in Figure 2. The graphical outputs
are drawn to scale and were manually aligned. The length of the
proteins in amino acids is indicated to the right.
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To test the quality of the clustering function, results obtained for
three protein families were compared with family analysis performed by
neighbor-joining (NJ) tree building based on ClustalX alignments. The
chosen families were the plastidic phosphate translocators (PTs), the
amino acid permeases of the ATF1 superfamily (Wipf et al.,
2002), and a magnesium transporter family (Li et al., 2001).
The PT family consists of four different groups, (a) the triose
phosphate/phosphate translocator (TPT), (b) the
phosphoenolpyruvate/phosphate translocators (PPT), (c) the
Glc 6-phosphate/phosphate translocators (GPT), and (d) the
xylulose-5-phosphate/phosphate translocator (Eicks et al.,
2002). Members of one group share 35% to 50% sequence identity with members of the other groups. The search in the ARAMEMNON DB for proteins similar to the TPT yields six hits (Fig. 3A). The
cluster view initiated from the TPT suggests four similarity groups for
these proteins, including an additional sequence in one group, a GPT
pseudogene (Fig. 3B). Figure 3C shows a tree of the different
Arabidopsis PTs that was calculated using ClustalX 1.8 (Thompson
et al., 1997) for multiple alignment of the protein sequences
(with gaps excluded) and the NJ method for tree building. The groups
generated by the ARAMEMNON cluster representation closely resemble the
tree calculated by commonly used approaches.
The second family analyzed was the larger family of Arabidopsis ATF1
amino acid permeases (Wipf et al., 2002). Again, the subfamilies found by the ARAMEMNON DB after clustering the amino acid
transporters with similarity to the AAP1 amino acid permease (Fig. 4, A
and B) closely resemble the branches of the NJ tree (Fig. 4C).
The third protein family analyzed were the AtMGT magnesium transporters
(Li et al., 2001). Also in this case, the clusters generated by ARAMEMNON resemble the conventionally constructed NJ tree
(data not shown). However, in addition to the published family members
MGT1 to MGT10, ARAMEMNON detects another putative protein, At5g09710,
with a high degree of similarity to MGT8.
The ARAMEMNON DB was used subsequently to analyze a recently
described family of unknown proteins sharing weak homology with the
phosphate transporters of the TPT group and that have been named K/VAG
transporters (Knappe et al., 2003). By initializing a search
with the putative At1g21870 protein, a distant relative to TPT,
ARAMEMNON identified 11 putative membrane proteins in the Arabidopsis
genome (Fig. 5A). The NJ tree confirmed these subgroups (Fig. 5B). The
TM predictions for the K/VAG proteins consistently suggest a similar
distribution of TM spans. Figure 5C shows the manually aligned
predictions by the Eiconda 0.9 program. In contrast, the subcellular
targeting predictions of the different programs were ambiguous for all
K/VAG proteins (data not shown). Interestingly, the sequence alignment
of the K/VAG transporters revealed that two members of the family,
3g10290 and 1g12500, may contain N-terminal hydrophilic extensions
possibly representing transit peptides directing these proteins to
plastids or/and to mitochondria. In 3g10290 and 1g12500, the most
N-terminal TM span, located behind the putative signal peptide,
aligns well with the TM spans at the N termini of eight other paralogs
(Fig. 5C). Mitochondrial and plastidic signal sequences usually are
hydrophilic and lack predicted TM spans, whereas secretory pathway
signals may contain a TM domain. In accordance, the other proteins
(except 1g53660) are supposedly located in the plasma membrane or the
tonoplast or may be secreted.
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DISCUSSION |
Aims and Concept of the ARAMEMNON DB
A variety of bioinformatic tools are publicly available for the
analysis and interpretation of gene and protein sequence data that were
generated during the course of genome sequencing projects. For example,
several TM span prediction programs have been published (Table I).
However, in the past, TM protein identification frequently relied on a
single program, although the predictions greatly differ for many
proteins with respect to the number of predicted TM domains (Fig. 1)
and their relative location in the polypeptide sequence (Fig.
2B). To compare the predictions generated by these programs for a given
protein sequence, it is necessary to submit the sequence successively
to each URL. Because the output formats of the programs differ, direct
comparison is inconvenient. Similar inconveniences are encountered
regarding the comparison of predictions of the subcellular localization
of a specific protein or protein family.
A DB was created for Arabidopsis membrane proteins,
named ARAMEMNON, that simplifies the identification, classification,
and interpretation of membrane protein/gene sequences. The ARAMEMNON DB
collects sequence data, predictions of TM regions and subcellular localization, and if available, also bibliographical data from different sources and displays them in an integrated format. For example, the ARAMEMNON DB combines TM predictions derived from seven
different TM prediction programs and presents side-by-side the location
of TM spans along the polypeptide sequence for each protein in a
directly comparable, uniform graphical format. This feature is
especially helpful in recognizing cases where predictions by individual
programs deviate significantly. An evaluation of TM span prediction
programs has indicated that frequently, but not always, the programs
TmHMM and HmmTop, which are based on hidden Markov models, perform
better than other methods (Möller et al., 2001,
2002). However, it has also been reported that a consensus prediction by using several programs achieves the best reliability (Nilsson et al., 2000).
The individual predictions for Arabidopsis proteins containing TM spans
range from approximately 24% (TmHMM 2.0) to 65% (TmPred) of all
proteins (Table II). After eliminating false positive reading frames
(Table II) and open reading frames that contained a single TM domain at
the extreme N terminus coinciding with a secretory pathway signal
sequence, as is found in secreted soluble proteins, the ARAMEMNON DB
classifies 6,475 proteins or 25% of the proteome as putative membrane
proteins (Table II). This frequency is in the same range as had been
estimated for several eubacterial, archaean, and eukaryotic organisms
(Wallin and von Heijne, 1998; Mitaku et al.,
1999; Stevens and Arkin, 2000).
The direct comparison of signal sequence predictions by eight programs
implemented in the ARAMEMNON DB (Table III) shows that the prediction
of subcelluar targeting is usually more ambiguous than TM span
predictions. The predictions must occasionally fail, because some
proteins do not have an exclusive destination but are dually targeted
to plastids and mitochondria (Peeters and Small, 2001).
Therefore, no attempt was made to extrapolate a "consensus"
targeting information. Experimental data are presently the only
reliable information about protein targeting to subcellular compartments.
The family structure analysis function implemented in the ARAMEMNON DB
is superior to the simpler listing of similar paralogs that is also
available in the DB. By assembling clusters of related proteins and
determining the distance of the cluster to the query sequence, protein
family members are detected that are more than 28% similar to at least
one other member of the protein family, but not necessarily to the
member with that the search was initiated. The clustering method
implemented in ARAMEMNON DB is similar to the simple unweighted pair
group method, which is less suitable for concise tree building as
compared for example with NJ procedures on aligned protein sequences
(Huelsenbeck, 1995). However, for several protein
families, it was demonstrated that the results generated by the
ARAMEMNON DB through clustering are comparable with NJ trees calculated
from aligned protein sequences (see Figs. 3-5).
The ARAMEMNON DB will be further developed by incorporating new
features that enhance functionality and support. Additional TM
predictions will be incorporated in future ARAMEMNON versions (e.g.
PSORT II/ALOM2 [Nakai and Kanehisha, 1992], PHDhtm
[Rost et al., 1996]). The gene/protein models will be
regularly updated, links to publications will be extended, and
annotations will be improved.
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MATERIALS AND METHODS |
Sources of Sequence Information and Sequence Analysis
Programs
The complete set of predicted Arabidopsis pseudochromosomes
(genomic DNA, mRNA, and protein sequences) was downloaded from TIGR
(http://www.tigr.org/tdb/e2k1/ath1/). From NCBI GenBank
(http://www.ncbi.nlm.nih.gov), all Arabidopsis protein entries were
extracted. Synechocystis sp. sequences were obtained
from NCBI and the Kazusa DNA Research Institute
(http://www.kazusa.or.jp/cyano/). All predicted Arabidopsis genes were
subjected to TM span and subcellular targeting predictions using the
programs shown in Tables I and III. All information was translated into
a uniform, data-centric XML-vocabulary. XML data were compiled,
reorganized, and finally mapped into a relational database.
Similarity Clustering
All Arabidopsis protein sequences were aligned pairwise to each
other using the Smith-Waterman algorithm implemented in FASTA 3 (Pearson and Lipman, 1988; Pearson,
1996), yielding a table of pairwise distance values. For
clustering, different similarity levels were chosen empirically: The
maximal resolution is 70%, i.e. proteins with a higher degree of
similarity are not subclustered. A lower threshold of 28% was chosen
as the minimal similarity between two proteins to initiate a cluster,
and two intermediate levels were chosen at 40% and 50%, respectively.
Different groups with equal similarity levels, that share at least one
common protein, merge into a superordinate group (W. Martin, personal
communication). Relationships between clusters are determined based on
the distance between two clusters, which is defined as the average
distance between pairs of sequences from each cluster (Sokal and
Michener, 1958).
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ACKNOWLEDGMENTS |
We thank Prof. William Martin (Heinrich-Heine-University
Düsseldorf) for inspiring discussions and Jochen Wiedmann
(http://search.cpan.org/author/JWIED/) for providing EP, a flexible
flavor of an embedded Perl scripting language.
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FOOTNOTES |
Received July 23, 2002; returned for revision August 28, 2002; accepted October 14, 2002.
1
This work was supported by Kleinwanzlebener
Saatzacht AG (Einbeck, Germany), by Südzucker AG
(Mannheim, Germany), and by the German Ministery for Education and
Research-Genomanalyse in biologischen Systemen program.
*
Corresponding author; e-mail rainer.schwacke{at}uni-koeln.de;
fax 49-221-470 5039.
Article, publication date, and citation information can be found at
www.plantphysiol.org/cgi/doi/10.1104/pp.011577.
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S. M. Assmann
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S. M. Assmann
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B. N. KAISER, K. L. GRIDLEY, J. NGAIRE BRADY, T. PHILLIPS, and S. D. TYERMAN
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T. Werner, V. Motyka, V. Laucou, R. Smets, H. Van Onckelen, and T. Schmulling
Cytokinin-Deficient Transgenic Arabidopsis Plants Show Multiple Developmental Alterations Indicating Opposite Functions of Cytokinins in the Regulation of Shoot and Root Meristem Activity
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M. Ferro, D. Salvi, S. Brugiere, S. Miras, S. Kowalski, M. Louwagie, J. Garin, J. Joyard, and N. Rolland
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S. Knappe, U.-I. Flugge, and K. Fischer
Analysis of the Plastidic phosphate translocator Gene Family in Arabidopsis and Identification of New phosphate translocator-Homologous Transporters, Classified by Their Putative Substrate-Binding Site
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TOP
ABSTRACT
INTRODUCTION