Nucleic Acids Research Advance Access published online on April 29, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn241
© 2008 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.
MassNet: a functional annotation service for protein mass spectrometry data
Daeui Park1,
Byoung-Chul Kim1,
Seong-Woong Cho1,
Seong-Jin Park1,
Jong-Soon Choi2,
Seung Il Kim2,
Jong Bhak1 and
Sunghoon Lee1,*
1Korean BioInformation Center, KRIBB, Daejeon 305-806 and 2Proteomics Research Team, Korea Basic Science Institute, Daejeon 305-333, Korea
*To whom correspondence should be addressed. Tel: +82 42 879 8530; Fax: +82 42 879 8519; Email: ishoon{at}kribb.re.kr Correspondence may also be addressed to Jong Bhak. Tel: +82 42 879 8500; Fax: +82 42 879 8519; Email: jong{at}kribb.re.kr
Received February 23, 2008. Revised April 4, 2008. Accepted April 15, 2008.
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ABSTRACT
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Although mass spectrometry has been frequently used to identify
proteins, there are no web servers that provide comprehensive
functional annotation of those identified proteins. It is necessary
to provide such web service due to a rapid increase in the data.
We, therefore, introduce MassNet, which provides (i) physico-chemical
analysis information, (ii) KEGG pathway assignment (iii) Gene
Ontology mapping and (iv) protein–protein interaction
(PPI) prediction for the data from MASCOT, Prospector and Profound.
MassNet provides the prediction information for PPIs using both
3D structural interaction and experimental interaction deposited
in PSIMAP, BIND, DIP, HPRD, IntAct, MINT, CYGD and BioGrid.
The web service is freely available at
http://massnet.kr or
http://sequenceome.kobic.re.kr/MassNet/.
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INTRODUCTION
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Mass spectrometry (MS) is the key method for proteomics (
1).
MS is widely used to study complex cellular proteomes and low
abundance proteins (
1–3). With it we can rapidly identify
proteins and obtain information for protein complexes and posttranslational
modification (
3). MS data are used to produce genome-scale data
(
4). Presently, the functional annotation of MS data often requires
researchers to navigate numerous web-accessible primary data
servers. In order to analyze large-scale data, one approach
is to provide access to an integrated web server that contains
rich bio-information with graphic interfaces (
5). Several MS
data processing systems have been developed to handle these
challenges. They are MASCOT (
http://www.matrixscience.com) (
2),
Prospector (
http://prospector.ucsf.edu) and Profound (
http://prowl.rockefeller.edu)
(
6). These systems provide protein identification data using
public databases such as SwissProt (
http://www.ebi.ac.uk/swissprot)
and NCBInr (
http://www.ncbi.nlm.nih.gov). These web services
do not include the functional annotation of MS data and do not
supply the latest version of the analysis tools. To provide
an easy and automated pipeline for functional annotation of
given MS results, we constructed a web-based server, MassNet.
The use of MassNet does not require any application installation
and it is easy to use.
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METHODS
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To analyze MS data, various protein annotation resources are
required. Therefore, we integrated major protein sequence databases,
protein–protein interaction (PPI) databases, Gene Ontology
(GO) (
http://www.geneontology.org) (
7), KEGG pathway (
http://www.genome.jp/kegg)
(
8) and bioinformatics analysis tools such as SignalP. This
system has four major parts: (i) a nonredundant protein database,
(ii) a physico-chemical property analysis module, (iii) a function
annotation module and (iv) a PPI prediction module. A schematic
workflow of MassNet is shown in
Figure 1.
Construction of the nonredundant protein database
In order to identify proteins from MS data, researchers use
various protein sequence databases such as NCBInr, SwissProt
and trEMBL. However, there can be confusion among protein identifiers.
Because of this problem, all protein identifiers were relationally
linked. We integrated the protein sequence databases (Swiss-Prot,
trEMBL, NCBInr, RefSeq, Ensembl and IPI) using only perfect-matching
sequences. The database unifies protein IDs of the same sequence,
summarizes annotations and descriptions of proteins from a range
of organisms representing all three major kingdoms of life:
eukaryotes, prokaryotes and viruses. Therefore, the root identifier
(Sequenceome_ID) can contain several protein identifiers from
all available databases. The Sequenceome_ID database is a nonredundant
sequence database of 6 856 434 proteins (April 2008).
Analysis of physico-chemical properties of proteins
The physico-chemical properties of MS data are important to understand biological functions. Especially, the prediction of hydropathy and subcelular localization of MS data is closely related to find membrane proteins which are involved in cellular processes and protein classes as drug targets (9). We used modules from Biopython (http://biopython.org) (10) to calculate hydropathy profile, GRAVY score (the average hydropathy score for all the amino acids), protein length, molecular weight, amino acid distribution, isoelectric point and protein instability index (11). For the subcellular localization prediction, we predicted transmembrane helices and signal peptides using Phobius (http://phobius.sbc.su.se) (12) and SignalP 3.0 (http://www.cbs.dtu.dk/services/SignalP) (13) programs. In order to provide physico-chemical information without any time delay, we provide precalculated physico-chemical properties for all nonredundant protein sequences. Whole proteins’ physico-chemical properties are also provided as summary tables or figures. If a set of proteins was input, the user can acquire information on the protein set's physico-chemical distribution against whole-protein distribution of the organism. If the identified protein set was from the membrane fraction of an organism, the user compares the relative transmembrane protein abundances between the organism's whole-protein set and the identified protein set. Therefore, this summary information can be used to evaluate the input data quality.
Integration of annotation information
MassNet provides biological function information by using KEGG pathways and GO. The KEGG pathway database and GO represent an attempt to assign known proteins into known biological pathways and are updated regularly (8). MassNet assigns proteins to KEGG pathways thorough ID mapping and shows color-coded proteins in the context of biochemical pathway maps using KEGG API. In order to find significant associations of GO terms with queried proteins, we assigned proteins into GO categories and GO-slim (14) through ID mapping. In order to gain more accurate statistical test results of KEGG and GO assignment, we added Fisher's exact test algorithm (P-value).
Prediction of PPI
The prediction of PPI is based on PSIMAP (protein structural interactome MAP) (http://psimap.com, http://psibase.kobic.re.kr) (15,16) and PEIMAP (protein experimental interactome MAP) (17). The basic algorithm of PSIMAP infers interactions among proteins by using their homologs. Interactions among domains or proteins for known PDB (Protein Data Bank) (http://www.rcsb.org/pdb) structures are the basis of the predictions. If an unknown protein has a homolog to a domain, PSIMAP assumes that the query tends to interact with its homolog's partners. Its concept is called ‘homologous interaction’ (18–20). The original interaction between two proteins or domains is based on the Euclidean distance. Therefore, PSIMAP gives a structure-based interaction prediction (15). On the other hand, PEIMAP is a well-established method that uses public resources of experimentally confirmed protein interaction information such as BIND (http://bond.unleashedinformatics.com) (21), DIP (http://dip.doe-mbi.ucla.edu) (22), IntAct (http://www.ebi.ac.uk/intact) (23), MINT (http://mint.bio.uniroma2.it/mint) (24), HPRD (http://www.hprd.org) (25), CYGD (http://mips.gsf.de/genre/proj/yeast) (26) and BioGrid (http://www.thebiogrid.org) (27). We constructed a nonredundant PPI database from the source databases. We carried out a redundancy check to remove identical protein sequences from the source interaction databases using PERL (http://www.perl.org). Now, it contains 116 773 proteins and 229 799 interactions. The accuracy of PEIMAP is dependent on the confidence of each resource. In order to reduce the false positive rate of PEIMAP, we computed the final ‘combined score’ for each pair of proteins which were predicted by PEIMAP and PSIMAP algorithms. This scoring methodology has been proposed by published articles including the STRING server (http://string.embl.de) (28). Users can easily predict PPI for queried proteins in a list and can examine PPIs with a network viewer.
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USER INTERFACE
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Input
The query interface allows the user to submit an HTML file from
the mass spectrometry or a TAB-delimited text file. The tab-delimited
file must contain protein names in the first column. Detailed
information about the TAB-delimited file format is described
on the ‘HOW TO USE’ page. MassNet can use four types
of MS data formats, i.e. MASCOT, Prospector, Profound and TAB-delimited
file.
Output
After uploading the query file, users can obtain the annotation information as in Figure 2a. The annotation results consist of five parts: (i) a protein list page, (ii) the physico-chemical property of each protein, (iii) a PPI prediction page, (iv) a KEGG pathway page and (v) a GO page.
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Figure 2. Screenshots of MassNet annotation results. (a) Panel in the middle is the protein list table. KEGG Pathway tab shows KEGG pathway assignment and metabolic pathway graph (right panels). Gene Ontology tab shows proteins assigned to GO categories (left top panel). Chemical Statistics tab shows the input protein set's physico-chemical distribution against whole protein distribution of the organism (left bottom panels). (b) Protein-protein interactions of user-selected proteins are visualized by a network viewer. Rectangular shapes are protein nodes. The black connecting lines indicate interactions among the nodes. The two red rectangular nodes are proteins that are selected by the users through the right hand side panel. When users select the right pull down menus in the right panel, the left drawing canvas shows highlighted protein nodes.
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The protein list page shows a table describing protein names
and scores, which are parsed from the query file. The KEGG pathway
and the GO pages show the number of proteins, which belong to
the categories of KEGG pathways and GO. By clicking the ‘Run
PPI Prediction’ button at the top of the protein list
table, the user can acquire the PPI information for selected
proteins. The PPI page shows PEIMAP and PSIMAP (see Methods
section) data at two separated tables.
By clicking Sequenceome_IDs at all pages, users can access two pages, i.e. a Same IDs page and a Chemical Property page. The same IDs page shows the identical sequences at various protein sequence databases and provides the hyperlinks to original database web pages. In order to provide clear information, MassNet provides a viewer for PPI networks as in Figure 2b.
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IMPLEMENTATION
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The MassNet web server runs on a Linux server. It combines a
MySQL (
http://www.mysql.com) database with a dynamic web interface
using Java Server Pages (
http://java.sun.com/products/jsp).
Data preprocessing is implemented in Perl and Python, and the
network viewer for PPI was constructed using Java.
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CONCLUSION
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The functional analysis and interpretation of the large-scale
MS data are still a challenging task. An automatic approach
is necessary for tens of thousands of MS data collected throughout
the world. MassNet is the first web server that provides various
kinds of functional information, such as physico-chemical properties,
biological pathways, gene ontology and PPI, for MS data. MassNet
is easy to use and provides information through an automatic
annotation for queried proteins.
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ACKNOWLEDGEMENTS
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We thank Maryana Bhak for editing the article and Suan Cho for
making the figures. This work was supported by a grant from
the KRIBB Research Initiative Program of Korea, by the Korea
Science and Engineering Foundation (KOSEF) grant funded by the
Korean government (MOST) (No. M10508040002-07N0804-00210) and
supported by a Korea Basic Science Institute K-Mep grant (T28021)
to J-.S.C. Funding to pay the Open Access publication charges
for this article was provided by MOST (No. M10508040002-07N0804-00210).
Conflict of interest statement. None declared.
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Footnotes
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The authors wish it to be known that, in their opinion, the
first two authors should be regarded as joint First Authors.
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ABSTRACT
INTRODUCTION