Nucleic Acids Research Advance Access published online on November 6, 2006
Nucleic Acids Research, doi:10.1093/nar/gkl805
© 2006 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.
TMBETA-GENOME: database for annotated ß-barrel membrane proteins in genomic sequences
M. Michael Gromiha1,*,
Yukimitsu Yabuki1,
Srinesh Kundu2,
Sivasundaram Suharnan2 and
Makiko Suwa1
1 Computational Biology Research Center (CBRC), National Institute of Advanced Industrial Science and Technology (AIST) AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, Tokyo 135-0064, Japan
2 Axiohelix Pvt Limited Tokyo, Japan
*To whom correspondence should be addressed. Tel: +81 3 3599 8046; Fax: +81 3 3599 8081; Email: michael-gromiha{at}aist.go.jp
Received August 7, 2006. Revised September 20, 2006. Accepted September 29, 2006.
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ABSTRACT
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We have developed the database, TMBETA-GENOME, for annotated
ß-barrel membrane proteins in genomic sequences using
statistical methods and machine learning algorithms. The statistical
methods are based on amino acid composition, reside pair preference
and motifs. In machine learning techniques, the combination
of amino acid and dipeptide compositions has been used as main
attributes. In addition, annotations have been made using the
criterion based on the identification of ß-barrel
membrane proteins and exclusion of globular and transmembrane
helical proteins. A web interface has been developed for identifying
the annotated ß-barrel membrane proteins in all known
genomes. The users have the feasibility of selecting the genome
from the three kingdoms of life, archaea, bacteria and eukaryote,
and five different methods. Further, the statistics for all
genomes have been provided along with the links to different
algorithms and related databases. It is freely available at
http://tmbeta-genome.cbrc.jp/annotation/.
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INTRODUCTION
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The ß-barrel membrane proteins perform a variety of
functions, such as pore formation, membrane anchoring, enzyme
activity, bacterial virulence, mediating non-specific, passive
transport of ions and small molecules, selectively passing the
molecules such as maltose and sucrose and are involved in voltage-dependent
anion channels (
1). The annotation of ß-barrel membrane
proteins in genomic sequences will be helpful for understanding
their functions. In our earlier works, we have developed statistical
methods and machine learning techniques for discriminating transmembrane
ß-barrel proteins (TMBs) from globular and transmembrane
helical (TMH) proteins (
2–
5) and we obtained the accuracy
in the range of 89–95% in discriminating TMBs. These methods
are mainly based on amino acid composition, residue pair preference/dipeptide
composition, motifs and the combinations of them. On the other
hand, different methods, such as hidden Markov models, neural
networks and nearest neighbor algorithms, have been proposed
for discriminating TMBs and screening them in genome sequences
(
6–
12). However, there is no electronically available
database for the annotated ß-barrel membrane proteins
in genomes.
In this work, we have developed a database, TMBETA-GENOME, which has the annotated ß-barrel membrane proteins for all the completed genomes. The annotation has been carried out with several statistical methods and machine learning techniques along with a new approach based on detecting ß-barrel membrane proteins and eliminating other folding types of globular and membrane proteins. The users have the feasibility of selecting the method and the genome. The database is freely available at http://tmbeta-genome.cbrc.jp/annotation/.
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CONTENTS OF THE DATABASE
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TMBETA-GENOME contains the annotated ß-barrel membrane
proteins for 275 completed genomes, including 23 genomes from
archaea, 237 from bacteria and 15 from eukaryote. It may be
noted that very few TMBs are annotated experimentally in the
eukaryote proteomes. Further, for a genome that have different
chromosomes (e.g. human genome has 24 chromosomes) the data
for each chormosome is given individually. This increased the
total number of entries into 24, 254 and 149, respectively,
for archaea, bacteria and eukaryote. The total number of proteins
in these three kingdoms of life is 52 241, 686 562 and 165 186,
respectively, with the total of 903 989 sequences. The amino
acid sequences of all the genomes have been taken from the NCBI
database (
http://www.ncbi.nih.gov/). In addition, we have provided
the statistics for the annotated ß-barrel membrane
proteins in all the genomes by different discrimination methods
(see below).
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DISCRIMINATION METHODS
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The annotation results are accumulated for different statistical
methods and machine learning techniques. The statistical methods
include the composition of amino acid residues (
2), residue
pair preference (
3) and motifs (
4). In these methods the compositions
of amino acid residues/residue pairs/residue pairs with a gap
(motif) have been computed for a training set of 674 globular
and 377 TMB proteins obtained from Protein Data Bank (PDB) and
PSORT database, respectively (
2–
4,
13,
14). For a new protein
X, we have calculated the deviations of the amino acid composition
between protein X and globular/TMBs. The protein is said to
be a TMB if the deviation is the lowest for TMB and vice versa
(
2). For residue pair preference and motif, the compositional
difference between globular and TMBs have been calculated (
TMB-glob).
The weighted average of
TMB-glob with the dipeptide/motif composition
of protein X discriminates the TMB and globular protein (
3,
4).
We have also applied support vector machines (
5) for discriminating
TMBs, which uses the combinations of amino acid composition
and residue pair preference. Further, the program, SOSUI (
15)
has been used for finding the TMH in genomic sequences. In the
new approach we have used the following criteria: (i) identification
of TMBs using the preference of residue pairs in globular, TMH
and TMBs, (ii) elimination of globular/TMH proteins that show
the sequence identity of >70% for the coverage of 80% residues
with known structures in PDB, (iii) elimination of globular/TMH
proteins that have the sequence identity of >60% with known
sequences in SWISS-PROT, and (iv) exclusion of TMH proteins
using SOSUI, a prediction system for TMH proteins. This method
also showed good agreement with experimental observations.
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FEATURES OF TMBETA-GENOME
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TMBETA-GENOME includes several features, such as, the service
for detecting TMBs in genomic sequences using various methods,
related references, statistics for the detected TMBs by different
methods for each genome, details about all algorithms used to
detect TMBs, relative links to other databases and a help page.
The ‘help’ section illustrates the details to perform
the search and to obtain the results.
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ACCESS TO TMBETA-GENOME
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TMBETA-GENOME can be directly accessed through the web at
http://tmbeta-genome.cbrc.jp/annotation/.
The users have the feasibility to select the method of annotation
and name of the genome. This server provides the annotated TMBs
using our previous methods, such as, statistical, dipeptide,
motif and SVM as well as the ‘New Approach’. The
new approach considerably reduced the number of false positives
and it has the ability of picking up most of the real TMBs.
An example is shown in
Figure 1. In this figure the results
are shown for
Escherichia coli K12 genome. This can be obtained
by clicking on the button + Bacteria and selecting the name
of the genome. It is also possible to get the data by entering
the name of the genome.
View larger version (54K):
[in this window]
[in a new window]
[Download PowerPoint slide]
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Figure 1 Illustration for searching the database. The arrows indicate the selected items, (i) name of the genome (Escherichia coli K12), (ii) method (New Approach) and (iii) annotation (set as default). Each change can be taken into account by clicking on the ‘Submit’ button. The displayed results are also shown in this figure.
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We have selected the method, ‘New Approach’ for
obtaining the annotated TMBs. This search picked up 87 entries
and the TMBs identified by the new approach are shown with the
identification number. In addition, the results obtained with
other methods are also given for comparison. The method SVM
yielded 337 TMBs and the combination of ‘Amino acid’
and ‘Dipeptide’ showed 501 TMBs. It is noteworthy
that several discrimination methods including statistical, dipeptide
and motifs have a tendency of providing many false positives.
The new approach results are reasonable that it identified just
2.05% of the proteins as TMBs. Further, the comparison between
identified TMBs and experimentally known TMBs revealed that
the new approach could correctly pick up all the 11 TMBs of
known three-dimensional structures obtained from
E.coli and
representative proteins in all the families of Transport Classification
Database (TCDB,
16). Hence, we suggest to use the new approach
for obtaining TMBs in genomic sequences.
Few other methods have also been proposed for detecting TMBs and these methods have their own advantages and shortcomings (6–12). Zhai and Saier (10) developed a program, ß-barrel finder, based on secondary structure, hydropathy and amphipathicity for identifying ß-barrel membrane proteins in prokaryotic genomes. This method detected 118 TMBs in E.coli genome and it could identify representative proteins from nine among 15 families available in TCDB. The method based on k-nearest neighbor missed few TMBs with the E-value > 3 although these proteins have been used in the training set to develop the method (9). The BOMB server based on (i) C-terminal pattern typical of many integral ß-barrel proteins and (ii) integral ß-barrel score based on the extent to which the sequence contains stretches of amino acids typical of transmembrane ß-strands missed eight proteins (11). The profile based hidden Markov model (12) identified TMBs belonging to 12 families in TCDB. This analysis reveals that the results obtained with new approach are better than other methods in the literature.
For more details about the protein the appropriate links to NCBI protein sequences have been provided for each annotated protein. Further, the users have the feasibility of downloading the annotated TMBs in FASTA format, which can be used for further analysis. The complete sequences of the genome have been obtained by selecting the ‘Sequence’ button. It is also possible to download all the protein sequences of the specific genome in FASTA format.
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LINKS TO OTHER DATABASES
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Each protein in the specified genome as well as the annotated
TMBs are directly linked with NCBI protein sequences. Further,
TMBETA-GENOME is linked with several related genome, structure
and sequence databases, such as, Genome Online Database [GOLD;
(
17)], NCBI (
http://www.ncbi.nlm.nih.gov/Genomes/), KEGG (
http://www.genome.jp/),
PDB (
13), SWISS-PROT (
http://www.expasy.org/sprot/), Protein
Information Resource (PIR;
http://pir.georgetown.edu), Uniprot
(
18), Protein Data Bank of Transmembrane proteins [PDBTM; (
19)],
Transport Classification Database [TCDB; (
16)] etc.
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AVAILABILITY AND CITATION OF TMBETA-GENOME
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The database can be freely accessible at
http://tmbeta-genome.cbrc.jp/annotation/.
If this database is used as a tool in your published research
work, please cite this article including the URL. Suggestions
and comments are welcome and should be sent to
michael-gromiha{at}aist.go.jp.
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ACKNOWLEDGEMENTS
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We thank Dr Yutaka Akiyama for encouragement. Funding to pay
the Open Access publication charges for this article was provided
by the National Institute of Advanced Industrial Science and
Technology (AIST).
Conflict of interest statement. None declared.
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