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Computational Molecular Biology 2014, Vol. 4, No. 7, 1-17
http://cmb.biopublisher.ca
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subcellular locations would greatly enhance
comparative analysis of subcellular proteomes in
different species. However, as the protein sequence
data were obtained from the UniProtKB and some
duplicated entries are present, thus for proteome-wide
analysis for a given species the non-redundant
reference or complete proteome dataset needs to be
used and that can be downloaded at UniProt
(http://www.uniprot.org/taxonomy/complete-proteome
s). It also should be noted that for a given species in
the list if no specific strain or sub-genotype is
specified, the entries for that specific species included
all available proteins from the species.
We also provided the BLAST tool to allow users to
search all fungal protein data or the predicted fungal
secreted protein data with their own protein sequences.
This utility facilitates identifying protein homologs
with their potential subcellular locations. Otherwise,
for any anonymous protein sequence users can predict
protein subcelluar locations using the tools we have
used in this work. Other available tools for prediction
of secretomes and other protein subcellular locations
were summarized by Meinken and Min (2012) and
Caccia et al. (2013). Recently Cortázar et al. (2014)
implemented a webserver, named SECRETOOL,
which integrated several tools for predicting fungal
secretomes. As some of the tools implemented in the
server are the same tools as we used, we expect the
server generates fairly reliable results for fungal
secretome prediction, thus, it is particularly useful for
newly generated proteomes (Cortázar et al., 2013;
Lum and Min, 2011). In addition, another available
database, named the fungal secretome database (FSD),
which was constructed using a slightly different suite
of tools, may provide extra subcellular location
information for these fungal proteins (Choi et al., 2010).
Fungal species have a secretome adapted to their
environment and the selection pressure exerted by
environmental constrains led to the species with
varying complexity in their secretome compositions
(Girad et al., 2013; Alfaro et al., 2014). Depending on
the lifestyle, fungal species which belong to
saprotrophs mainly have degrading hydrolases in their
secretomes, biotrophic species have both degrading
hydrolases and compatibility effectors, mycorrhiza
species have degrading hydrolases, compatibility
effectors, and exchange effectors, and necrotrophic
species have degrading hydrolases and killing
effectors (Girad et al., 2013, Alfaro et al., 2014). The
basal secretome contains generally two pools of
proteins: a large proportion represented by the
polysaccharide degrading enzymes, i.e. hydrolases
acting on glycosyl bonds, and a minor part including
the proteases, lipases, and oxidoreductases, etc. (see
Table 3). In this work, the secretome identification
was limited to classical secreted proteins, i.e., signal
peptide containing proteins, and curated proteins
which may include both classical and leadless
secreted proteins (LSP). SecretomeP was a tool
implemented for predicting these LSPs in bacteria and
mammals (http://www.cbs.dtu.dk/services/SecretomeP/)
(Bendtsen et al., 2004a). Because the tool has not been
trained with fungal data and the prediction accuracy
could not be evaluated, we did not include this tool in
our data processing. We would like to request the
fungal research community to submit fungal protein
subcellular locations, particularly LSPs, with
experimental evidence traceable from literature to the
database. Genome-wide computational prediction of a
secretome for a species provides the first step for
experimental validation and characterization of
secreted proteins under various changing environments
or culture conditions (Alfaro et al., 2014). Along with
our published plant secretome and subcellular
proteome knowledgebase (PlantSecKB) (Lum et al.,
2014), we expect that FunSecKB2 will serve the
community a useful resource for genome-wide
comparative analysis and for further exploring the
potential applications of fungal secreted proteins in biofuel
production, environmental remediation, and prevention
and treatment of plant and human fungal pathogens.
Authors' contributions
JM implemented the database, DA collected the lifestyle data,
KA and GZ participated in method development, XJM and CC
conceived of the study, designed the procedure of data
processing. XJM, JM, DA and CC analyzed the data and
prepared the manuscript. All authors read and approved the
final manuscript.
Acknowledgements
We thank Gengkon Lum and Dr. Feng Yu for their assistance in
maintaining the server and Jessica Orr and Stephanie Frazier
for manually curating secreted proteins.