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Citation: Michael K, Gorden R, Martin E, Anke S, Helmut EM, etal. (2008) Automated Calculation of Unique Peptide Sequences for Unambiguous
Identification of Highly Homologous Proteins by Mass Spectrometry. J Proteomics Bioinform 1: 006-010.
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Copyright: © 2008 Michael K, etal. This is an open-access article distributed under the terms of the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and
source are credited.
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Abstract
In regular proteomics approaches proteases are used to digest the proteome into a set of peptides. Unfortunately, determining proteins
on the basis of peptides implies some uncertainty since a peptide may be part of different proteins. Therefore, a targeted detection of
unique peptides of particular proteins is a promising task for an unambiguous identification of a specific protein. Here we present a
software solution that offers the possibility for a highly efficient and simple detection of such unique peptides. In a first step a SQL-based
database of theoretically digested peptides from a given FASTA file formatted protein database is generated by choosing a protease. In a
second step, in silico generated peptides from a pre-defined protein sequence are compared to this peptide database in order to identify
unique peptides. Amongst others, possible applications are identification of proteins when only sparse peptide information is available
or advanced proteomics techniques that require information about the uniqueness of peptides such as Multiple Reaction Monitoring
(MRM).
Keywords
Multiple reaction monitoring; Unique peptides; in silico digest; SQL database; Java
Abbreviations
AUC: Area under the curve; AQUA: Absolute quantification;
DBMS: Database management system; IPI: International Protein
Index; JDK: Java development kit; JVM: Java virtual machine;
MPC: Medizinisches Proteom-Center; MRM: Multiple reaction
monitoring; MS: Mass spectrometry; UPF: Unique peptide finder
Introduction
Proteomics is a powerful methodology to investigate
protein expression in cells, tissues, organs or whole organisms.
One fundamental idea of proteomic approaches is the expression
analysis of thousands of proteins at the same time. Proteome
analysis more and more appears into the spotlight of classical
fundamental as well as clinical research. Differential quantitative
proteome analysis allows direct comparison of proteomes of different
cellular states whereas descriptive qualitative approaches
provide an insight in the protein composition of a given cell, organelle,
tissue etc. Protein identification usually is done by mass
spectrometry (MS). MS can be either performed in the form of
whole-protein analysis (“top-down” approach) or by investigation
of enzymatically produced peptides (“bottom-up” approach).
In bottom-up proteomics approaches trypsin or other
proteases are used to digest the proteins into a set of peptides
prior to their mass spectrometric analysis (Kocher and Superti-
Furga, 2007). Substantial advantages when working with peptides
instead of proteins such as superior solubility of peptides in
a wide variety of solvents as well as their lower nonspecific adsorption
to surfaces make the bottom-up approach very attractive
for comprehensive proteome analysis. On the other hand,
after protein digestion the number of molecular species increases
dramatically, which complicates the analysis (Lohaus et al., 2007).
Unfortunately, all information about a particular intact protein is
lost. Therefore, determination of proteins on the basis of detected
peptides implies some uncertainty since a peptide may be part of
different proteins.
The detection of unique peptides is especially important,
when doing targeted proteomics. In this (special) case the
proteins that shall be analyzed by mass spectrometry are already
known before the analysis.
As an example, multiple reaction monitoring (MRM)
turned out to be a fast and efficient technique, which allows quantifying
proteins either relatively or absolutely by means of triggering
a set of specific peptides (Janecki et al., 2007; Le Blanc et
al, 2003; Mayya et al., 2006; Wolf-Yadlin et al., 2007). Analysis of
very similar proteins usually deals with only a small set of unique
peptides that needs to be calculated for unambiguous identification
of these proteins.
Manually this can for example be done by theoretic digestion
of the sequences of interest and blasting of all resulting
peptides (http://www.ncbi.nlm.nih.gov/blast/Blast.cgi). However,
performing BLAST searches manually in order to identify unique
peptides is both tedious and time-consuming.
Nowadays sample preparation, nanoHPLC, mass spectrometry
and protein identification in modern proteomics can be
automated (Alterovitz et al., 2006) , but to our knowledge no software
is available for automated exact calculation of unique peptides
for unambiguous identification of proteins. Here we report
the construction of a software tool, which is called Unique Peptide
Finder (UPF). UPF is used to calculate sets of unique peptides
from protein sequences. Furthermore, UPF provides possibilities
to calculate the frequency of peptides and to retrieve information
from which proteins a particular peptide can be derived.
The automation of this process requires the theoretic
digestion of sequences of interest and a comparison with a pre-digested protein sequence database avoiding the discussed problems
caused by using the BLAST function against a non-digested
sequence database.
The software will help on the interpretation of regular
proteomics data and the generation of peptide lists for targeted
proteomics especially when it comes to a desired differentiation
between very similar proteins.
Material & Methods
Software Requirements
The developed tool is a JavaTM -based software solution
(JDK 6, Sun Microsystems Inc., Santa Clara, CA, USA). Therefore,
the user needs to install the Java Virtual Machine (JVM) on
his computer. Since JavaTM provides platform independent technology,
UPF can be used on various computer systems (e.g.
MicrosoftTM WindowsTM or Linux operating systems).
In order to identify unique peptides with respect to a
given set of proteins, it is necessary to generate databases containing
all peptides from a whole set of proteins. Therefore, the
UPF software uses the relational database model for storing peptide
information obtained from pre-digested protein sequences.
Users need to have access to an adequate database management
system (DBMS). During the development of the software,
MicrosoftTM SQL Server 2000 (MicrosoftTM Corp., Redmond, WA,
USA) was used as database management system. However, for
providing the use of UPF free of charge, the open source database
software MySQL can be utilized as well. Adequate database
drivers are supplied with UPF for both DBMSs.
Conceptual Design of the UPF Software
In general the software readily generates an exact list of peptide
related information. Counting the peptides from an in silico-digested
protein of interest in a pre-digested protein database results
in a frequency being of particular importance. This holds
true, because then it is quite simple to detect peptides that are
unique for a particular protein when considering the frequency
information.
The presented software consists of two modules:
Module 1 - The first module is used to generate databases,
which contain comprehensive information about peptides
obtained from enzymatic digestion of a set of proteins. An
important objective during the development was to provide easy
access to current and common protein databases. Currently,
designated input can be IPI and UniProtKB/Swiss-Prot databases
in FASTA file format. Current IPI databases are available from ftp:/
/ftp.ebi.ac.uk/pub/databases/IPI/current/ and the UniProtKB/
Swiss-Prot database is available from ftp://ftp.ebi.ac.uk/pub/
databases/uniprot/current_release/knowledgebase/complete/.
Processing of the NCBInr database will be integrated into UPF in
the near future.
For each considered enzymatic digestion a particular peptide
database has to be created. Each of those databases consists of
two tables. The first table stores information retrieved from the
headers of the protein entries of the FASTA file (e.g. alias accession
numbers, taxonomic identification, etc.) Great importance is
attached on retrieving a maximum of information. The second table
stores sequences and masses of the peptides obtained from an in
silico digestion. However, no masses are calculated for the peptides
when their sequences contain any wildcards (symbols B, X
and Z).
The second table is linked to the first table. Therefore, starting
from a specific peptide sequence ‘A’, which occurs in the second
table, it is possible to track any protein from the first table that
contains peptide sequence ‘A’.
This database generating tool (Module 1) is a command line
tool to allow integration into scheduled batch processes. Hence,
tool generated databases can be easily synchronized with current
protein databases, that are available from the internet.
Module 2 - The major task of the second module is an automatic
generation of database queries and the presentation of the results.
Users simply need to provide a protein or peptide sequence
of interest as input for module 2. An in silico digest is performed
in order to get all possible peptides from this input sequence.
Then, frequencies of occurrence are computed for this set of peptides
with respect to the database that was generated by module
1. In order to allow intuitive applicability, the second module was
designed with an easy to use graphical user interface (Figs. 1, 2).
Further Notes
UPF is currently designed for usage of both IPI and the
UniProtKB/Swiss-Prot database. Since the UniProtKB/Swiss-Prot
database offers manual validated entries we recommend the use
of this database. However, usage of other databases may be
indispensible for finding proteins not included in the UniProtKB/
Swiss-Prot. It is planned to provide opportunity for incorporating
a wide range of different databases into future versions of the
UPF software. Therefore, maximum freedom of decision regarding
the choice of an appropriate database remains in the hands of the
user.
The following proteins were used to evaluate the software
(Swissprot accession in parentheses): CYP2C8 (P10632),
CYP2C9 (P11712), CYP2C18 (P33260), CYP2C19 (P33261).
UPF can be downloaded as a zip formatted file from the
website (software section) of the ‘Medizinisches Proteom-Center’
(MPC), Ruhr-Universitaet Bochum (http://www.medizinischesproteom-
center.de/). The zip file additionally includes a detailed
user manual.
Results and Discussion
Mass spectrometry is the standard tool for protein identification,
which is prevalently performed on peptide level after
digestion. Unique peptides are exclusive for a single protein. Therefore,
such peptides are essential for unambiguous identification
of a particular protein.
This is especially important, when very similar proteins
with only few unique peptides - e.g. highly homolog protein
isoforms - need to be differentiated by mass spectrometry.
Other important fields of application are functional
proteomics approaches where the proteins of interest are already
known before starting the experimental studies (targeted proteome
studies).
One common procedure to find unique peptides for a
protein of interest would be a protein BLAST search after performing
a theoretical digest against a defined protein sequence
database. Indeed, this approach occupies much time as every
peptide has to be aligned and results have to be manually evaluated.
The aim of our work was to develop the UPF software
solution as a tool that automates the detection of unique peptides.
In the next section both program features and the basic
application of the UPF software will be briefly described and discussed.
Description of the UPF Software
Basic Features of the Command Line Tool (Module 1)
The command line tool (Module 1) can be used to generate
up-to-date databases for storing peptide related information
with respect to an in silico digest as part of a scheduled batch process.
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Figure 1: Screenshot of the query tool. The text field on the left hand side shows the sequence of the human Cytochrome P450 (CYP)
isoform 2C8. The results in the summary table show peptides obtained from the theoretical digest. A filter was applied in order to restrict
the output to peptides with sequence lengths between 6 and 20 amino acids.
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Figure 2: Screenshot of the query tool showing the results of a database query. The text field on the left hand side shows the sequence of
the human Cytochrome P450 (CYP) isoform 2C8. Results concerning both peptide and related protein information are given in the
summary table on the right hand side. Thereby, the output is restricted to peptides with sequence lengths between 6 and 20 amino acids.
The highlighted peptide information lines were further discussed in the text.
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Basically, peptide sequences are computed for each protein
entry of the input FASTA file considering specific enzyme
properties. Both monoisotopic and average masses are calculated
for each peptide and additionally the protein mass is given.
Basic Features of the Query Tool (Module 2)
The query tool (Module 2) basically consists of two
different areas and a tool bar (Figs. 1, 2). Input sequences can be
supplied by copy and paste on the left hand side. Sequence
information must be given in one letter code. Both the enzyme
and one of the databases generated by the first module can be
chosen interactively from the tool bar.
Output is given in the summary table. After pressing the
digest button in the panel on bottom left, the summary table
shows all theoretic peptides of this protein that are generated
specifically for the predefined protease (Fig. 1). This list now
serves for comparison with a predigested protein database, which
was generated with module 1. An adequate database can be
preselected in the tool bar. Furthermore, a peptide length restriction
can be used for excluding peptides which might not be monitored
with the applied experimental setup, e.g. very large or short
peptides, respectively. Exclusion of very short peptides significantly
reduces the time needed for the database search.
After pressing the ‘find unique’ button a database query
is performed and the results are displayed in the summary table.
The frequency is always displayed in the frequency column showing
the number of protein entries of the designated database in
which the peptide sequence was found. Peptides with frequency
values of one are unique with respect to the sequence given as
user input. If the frequency is higher than one, the peptide is
homolog in several database entries. The experimentalist of course
has to evaluate if different database entries can be grouped to
one protein class. Therefore additional information, which can
be displayed in the summary table, should be considered. Beside
the monoisotopic and average peptide mass and the corresponding
protein mass, the accession number, the protein name and
the
peptide sequences (Fig. 2) are given.
Results can be marked within the summary table and
transferred into style sheet programs via the clipboard. Customizing
the UPF software
In order to facilitate customizing both modules use configuration
files. The configuration files were designed in the userfriendly
WindowsTM ini-file style, i.e. the files contain lines of key and
values pairs, e.g. the line ‘cleavageSites=KR’ assigns the value‘KR’ to the key ‘cleavageSites’. This special key and value pair
defines the amino acids where enzymatic cleavage occurs. The
configuration files store properties necessary for establishing
the database connection as well as specific parameters, which
are needed to perform the theoretical digest, e.g. the cleavage
sites of a particular proteases.
The UPF configuration files support tryptic digestion
per default. However, the configuration files can easily be extended
for the use of other proteases. Therefore, the user simply
must attach another section and specify the given parameter
values in order to add the functionality of any new enzymatic
digest.
Regular expressions, which were also included in the
configuration files, provide a succinct and flexible means for identifying
patterns of characters and can therefore be used for parsing the header of IPI and UniProtKB/Swiss-Prot databases in
order to obtain protein related information. From the supported
databases (IPI and UniProtKB/Swiss-Prot) accession numbers,
taxonomic identifiers and protein names are extracted. Additionally,
the header of IPI databases contains cross references, which
are stored in the generated database as well.
Application of UPF to a Concrete Question – CYP Subfamilies
To comment on the usability of UPF several isoforms
from the human Cytochrome P450 (CYP) family have been selected
and their sequences have been analyzed.
In human 57 CYP isoforms have been identified so far,
which are classified on their sequence homology [Lewis 2004].
CYPs are responsible for the oxidative metabolism of many
xenobiotics as well as organic endogenous compounds. Especially
members of the CYP3A* and CYP2C* families are highly
homologous; CYP2C9 and CYP2C19 for example show a sequence
homology of 91% (calculated by sequence alignment). Because
no antibodies are available for the same purpose, mass spectrometry
is the method of choice to differentiate between these protein
isoforms.
In the following a subset of the UniProtKB/Swiss-Prot
database, which considers only entries of human proteins, was
used in order to evaluate the software. The sequence of CYP2C8
(left, Fig. 2) was in silico digested with trypsin (toolbar at the top,
Fig. 2). With a peptide length restriction of six to twenty amino
acids, eighteen unique peptides can be found for CYP2C8 (peptides
showing a frequency of 1, Fig. 2).
In case a peptide is homolog in another or several other
proteins, the protein name information is very useful to evaluate
the results in more detail; the peptide SPCMQDR for example is
found to be present in all four CYP2C* isoforms (CYP2C8, CYP2C9,
CYP2C18, and CYP2C19). Furthermore the peptide FSLTTLR can
be used indeed to differentiate CYP2C8 from the other members of
the CYP2C* subfamily, but it is homolog in another CYP isoform,
namely CYP2E1. Finally the tool calculates eight unique peptides
for CYP2C9, 19 unique peptides for CYP2C18 and 15 unique peptides
for CYP2C19.
These results show that even for highly homologous
proteins in general enough theoretically generated unique peptides
exist. Nevertheless, by disfavored digestion or ionization of
some peptides or due to the low abundance of the protein in
general proteins often get identified by only few peptides. The
application of UPF tremendously reduces the time needed to argue
about the uniqueness of those peptides to minutes in comparison
to hours if a manual Blast search is done.
The application regarding to targeted proteomics is for
instance the detection as well as the quantification of a protein by
using multiple reaction monitoring (MRM), which is applied to
the measurement of specific peptides in complex mixtures
(Kuhn
et al., 2004). In the MRM approach the sensitive detection of
precursor-to-product ion transition is a diagnostic trigger for the
presence of a peptide. Because several triggering points are recorded
in a certain time window, the AUC (area under the curve)
can be used to relatively quantify the abundance of this peptide.
In the AQUA (absolute quantification) strategy (Gerber
et al., 2003; Barnidge et al., 2003), including MRM, a peptide can
be used as a stoichiometric representative of the protein from
which it is originated and related against a spiked stable isotopelabelled
internal standard to calculate the absolute protein amount
in a sample through the comparison of the AUCs. The uniqueness
of a peptide ensures the specificity of the quantification
approach for the targeted protein and therefore it is indispensable.
Applying UPF for the search of such peptides will considerably
facilitate and accelerate the workflow in the fields of targeted
proteomics.
Future Prospects
Different features are planned to be integrated into the
UPF software for the near future:
Efforts will be undertaken to provide UPF as a web service
in the future. Then, several pre-digested databases will be
hosted and maintained at the MPC.
Input is currently restricted to a single protein sequence.
However, in order to ensure a more flexible usability the query
module will be enhanced in order to accept many sequences or
accession numbers as input. Moreover a batch processing should
be integrated, i.e. multiple protein sequences or accession numbers
should be accepted at the same time. Currently, results can
be restricted to peptides with a particular sequence length. Because
it is desirable to filter peptides with respect to their mass,
such a selection will be integrated into UPF.
The configuration files will be extended for more predefined
sections considering other proteases and databases in
order to provide an easy access for users in most instances. Integration
of the NCBInr database into UPF will obtain priority during
the further work and may probably be accessible at the time
point of publication.
Moreover, for enhancing adaptability a type of ‘SQLParser’
will be integrated into the software, which adopts the output
of the query module to a specific SQL statement, which can be
given by the user.
It is planned to take a predefined number of missed cleavage
sites into account.
Conclusions
A software solution is presented for automatic calculation
of unique peptide sequences. It was shown that identification
of such peptides could be facilitated when using the software
in comparison with a multitude of manually performed BLAST
or alignment searches. Additionally, applying the software can
be of value in particular when conducting larger MRM or AQUA
experiments.
Acknowledgments
This work was funded by the HepatoSys network project of the
German Ministry of Education and Science (BMBF, Hepatosys,
grant 0313080J) and by ProDaC (European Commission project,
6th framework programme, project number LSHG-CT-2006-036814).
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