conference review enabling proteomics: the need for an

Comparative and Functional GenomicsComp Funct Genom 2004; 5: 52–55.Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/cfg.372

Conference Review

Enabling proteomics: the need foran extendable ‘workbench’ foruser-configurable solutions

Robert J. Beynon*Department of Veterinary Preclinical Sciences, University of Liverpool, Crown Street, Liverpool L69 7ZJ, UK

*Correspondence to:Robert J. Beynon, Department ofVeterinary Preclinical Sciences,University of Liverpool, CrownStreet, Liverpool L69 7ZJ, UK.E-mail: [email protected]

Received: 13 November 2003Revised: 18 November 2003Accepted: 26 November 2003

AbstractProteomics has the capability to generate overwhelming quantities of data in relativelyshort timescales, and it is not uncommon to see experimenters investing substantiallymore time in data analysis than in data gathering. Although several sophisticatedtools for data reduction and analysis are available, they lack the flexibility to copewith increasingly innovative experimental strategies and new database resources thatencode both qualitative and quantitative data. I will outline a specification of aflexible proteomics tool that could address many current bottlenecks and deficiencies.Copyright 2004 John Wiley & Sons, Ltd.

Keywords: proteomics; mass spectrometry; bioinformatics

Introduction

The science of proteomics is often seen as a large-scale exercise in systems biology. The sequentialprocesses in proteomics are; simplification of theproteome (organelle purification, 2D gels, proteinchromatography, 1D and 2D peptide chromatog-raphy); mass spectrometric analysis of the ensu-ing peptides (using one or more of the impressivevariety of the current generation of mass spectrom-eters); and bioinformatic analysis of the resultantmass spectrometric data. One of the major chal-lenges in proteomics is that of identification andquantification of every protein in a complex mix-ture. This goal has been a major driver in the evolu-tion of high-throughput systems, in novel strategiesfor proteome simplification and in the constructionof powerful bioinformatics platforms.

Yes, there are many studies in proteomics thathave a rather different perspective. The focus canbe constrained to a single protein or a small groupof proteins. It is possible to entertain the ideaof proteomics experiments without a single gene,cDNA, EST or protein sequence being present inany database. Some experimenters seek to extendproteomics by the use of clever chemical or stable

isotope-based methods that greatly enhance sim-plification or subsequent analysis of the analyte.Whether such studies are all deserving of the titleof ‘proteomics’ is irrelevant; they represent cutting-edge thinking in analytical and preparative pro-tein chemistry. Imaginative as such studies are, Isuggest that a major constraint on such develop-ments is the absence of software tools to analysethe subsets and modifications to the analyte. I willpresent a case, based on the perspective of a pro-tein chemist and an end-user of such software tools,for a configurable proteomics platform that allowsindividual experimenters to define the nature oftheir analyses.

Current needs and frustrations

My research group is motivated by a range of pro-teomics studies that cover such diverse areas ofbiology as chemical communication, copper toxic-ity, cross-species matching and proteome dynam-ics. Of these studies, relatively few are driven bythe need for global protein identification or by com-parative proteomics. In all of the studies, I havebeen a little surprised by the investment of time in

Copyright 2004 John Wiley & Sons, Ltd.

Enabling proteomics: the need for an extendable ‘workbench’ 53

data analysis relative to the duration of the biolog-ical experiment. Whilst simple database searches(e.g. for peptide mass fingerprinting against knownproteomes) are rapid and efficient, other analysesrequire a high degree of manual intervention and,indeed, time-consuming visual inspection of thedata.

As an illustrative example, we are motivated toextend the description of a proteome to include anunderstanding of proteome dynamics, defined asthe rates of synthesis and degradation of any pro-tein within the proteome. Comparative proteomicsis set firmly in the arena of changing proteomes,but an understanding of changes in the amountof any protein in the cell requires definition ofboth the rate of synthesis and the rate of break-down. This acquires particular significance whenattempts are made to correlate proteome and tran-scriptome data, as such studies implicitly assumethat an increase in the level of protein reflects,through an increase in the mRNA level, a corre-sponding increase in the rate of synthesis of theproteins. However, a protein can also increase inconcentration if the rate of degradation is reduced.Parameterization of proteome changes into rate ofsynthesis and degradation is of fundamental biolog-ical importance. Changes in rates of synthesis arevery likely to correlate directly with transcriptomechanges, whilst changes in degradation may reflectinteractions at the substrate level, and may thusconnect more immediately with the metabolome.

To analyse proteome dynamics, we use stableisotope-labelled amino acids, which are incorpo-rated into proteins as they are synthesized de novo.Note that we design experiments to avoid completelabelling of the proteins, as this would eliminateany information about the relative rates of synthe-sis. The proteins are then separated and analysedmost simply by MALDI-TOF mass spectrometryof a tryptic peptide mixture. The relative amountsof ‘heavy’ and ‘light’ peptides define the rate ofreplacement of that particular protein in the sys-tem. The ‘heavy’ and ‘light’ variants of the peptideare clearly identifiable, and the mass offset coinci-dentally informs about the number of the labelledamino acid in the peptide. However, to determinethe rate of turnover we need to calculate the areasunder the ‘heavy’ and ‘light’ peptide peaks. Fur-ther, we would ideally have a tool that would scana MALDI-TOF mass spectrum, identify those pep-tides that exist as heavy–light pairs and calculate

the rate of turnover automatically [1]. No such toolexists in any readily accessible form.

I suggest that the lack of such simple and targetedtools is an obstacle to the development of noveland imaginative approaches to the developmentof proteomics. It is unlikely that any one groupwould produce a full range of such tools, and I ampersuaded by the opportunities for construction ofan open, extensible platform onto which new toolsand modules can be bolted as required, and offeredas a service to the entire proteomics community.I refer to this concept as a ‘workbench’ to assistproteomics and, rather than coin another acronym(there are already too many of these in proteomics),will refer to ‘workbench’ throughout this article.

One of the better analogous systems that mightserve as a model for the workbench is the AVSpackage (http://www.avs.com) designed for visu-alization of scientific data and presentation ofimage data. This comprises a core product ontowhich can be introduced task-specific tools, writtenby the scientific community. The tools are assem-bled in a modular format, with defined connectiv-ities to the core and to other tools. Modules areassembled in a graphical environment where mod-ules, represented as building blocks, are linked todefine complete analyses. I am intrigued by thepossibility that proteomics, or protein mass spec-trometry, might also benefit from the availability ofsuch an open environment into which new tools canbe slotted without the added overhead of the needto write a complete application. The behaviour ofeach tool or module is either inflexible, performinga single invariant function, or is modified by a setof parameters that are adjusted by the user throughcontrol panels (using such visual devices as sliders,dials and check boxes) or through text commands.The workbench would have a scripting languageunderlying each module, and it might be possibleto dispense with the visual metaphor and cast ananalytical process as a script.

A proteomics workbench

The workbench would not be intended to replaceor compete with other developments for manage-ment of complete proteomics experiments, includ-ing PEDRO (http://pedro.man.ac.uk [2]) and theHuman Proteome Organisation (HUPO) propos-als (http://psidev.sourceforge.net/ [3,4]). Rather,

Copyright 2004 John Wiley & Sons, Ltd. Comp Funct Genom 2004; 5: 52–55.

http://www.avs.com

http://pedro.man.ac.uk

http://psidev.sourceforge.net/

54 R. J. Beynon

I see it as a set of tools that at least in part canprecede experimental design, encourage an analyt-ical approach to the development of novel strate-gies and provide customizable modules for analysisof novel, and sometimes unique, proteomics data(Table 1).

The two major sources of data for the work-bench are sequence databases and mass spectro-metric data. Each covers a range of specific datatypes. Sequence databases can be protein sequences(SWISSPROT, TREMBL), untranslated gene orcDNA sequences (EMBL-Bank, GenBank) or ESTresources (dbEST). In all instances, these datasetshave utility in proteomics studies. They differ inthe degree of error that they manifest (e.g. single-pass vs. multiple-pass sequencing of DNA) but,with appropriate tools, can generate a search spaceagainst which proteomics data can be matched.Typical tools that might fall under the aegis ofdatabase manipulation include extraction of a sub-set of sequences from multiple data sources tocreate a local, private database or subproteome, orgeneration of a summary analysis of the membersof a proteome or subproteome.

Table 1. Some modules that might form part of a coreproteomics workbench

Filters• Selective recovery of entries from protein or DNA databases

according to user-specified criteria to form a subproteome• Filtering of a proteome according to use-defined criteria, such

as presence of specific pairs of amino acids, post-translationalmodification sites

Processes• Six-frame translation and recovery of all putative ORFs

according to pre-defined criteria• Scanning of mass spectra for stable isotope-labelled duplexes

or multiplexes• Summary statistics pertaining to a local subproteome• Chemical modification strategies• Proteolytic digestion to generate a database of fragments• Detailed queries of proteome, subproteomes or fragments

sets• Shotgun sequencing assembly of overlapping MS/MS data• Searching private databases using experimentally derived data

(possibly externally computed using GRID-like capabilities)

Outputs• Plot a distribution of a range of parameters, define a

proteome, subproteome• Presentation of detailed mass spectrometric coverage

diagrams• Tabulate and export database sets in text or XML format

Mass spectrometric data would be more prob-lematical, because several instrument manufac-turers use proprietary data formats that are notas readily accessible. There is a need for someintermediate mass spectrometric data format towhich all instrument suppliers adhere, at least asan exportable format. This is a topic of activedebate and development and which can be builtupon existing programmes in analytical science(http://psidev.sourceforge.net/ms/docs/030611PSI ASMS.pdf).

Representative modules

The modules that could be built into the work-bench are limited only by the imagination of theinvestigator and the availability of appropriate pro-gramming skills. However, careful description ofthe scope and behaviour of some primitive toolsshould permit a hierarchical construction of task-specific tools that could be shared. Rather thandevise a tool to define proteome-relevant analy-sis of a single database, a generic tool should bedefined to operate on any global or local database.Perhaps the most appropriate way forward in defin-ing the functionalities of the modules is by directinteraction with end-users, who will be most ableto define their tasks in terms of natural languagespecifications.

Many workbench specifications could be initi-ated with three types of modules — filters, pro-cesses and outputs. The filters work on externaldata sources or on internally created local or privatedatasets, and offer rule-based simplification of thedata sources. A filter might, for example, supportSQL statements or allow more flexible user controlvia an intuitive control panel. Filters are equivalentto searches, and could be applied at an early stageor intermediate stage of any workbench applica-tion. In natural language, a filter might ‘prepare alocal, temporary proteome database of all proteinsin TREMBL or SwissProt that are derived fromchicken’. An investigator should be able to pose thetask ‘plot the distribution of masses of endopep-tidase LysC peptides from rodent skeletal mus-cle, irrespective of species, and split according towhether the peptides contain no, one, two or morethan two valine residues’, or ‘what percentage ofhuman liver proteins have a tryptic N-terminal pep-tide that is between 400 Da and 4000 Da?’. These


http://psidev.sourceforge.net/ms/docs/030611_PSI_ASMS.pdf

http://psidev.sourceforge.net/ms/docs/030611_PSI_ASMS.pdf

Enabling proteomics: the need for an extendable ‘workbench’ 55

might seem like arcane questions, but they are thetypes of problem that proteome research groups areposing all of the time, and are the sort of ques-tions that are needed to inform the developmentof new experimental strategies. More subtle ques-tions, such as ‘what percentage of proteins fromproteome X contain post-translational site Y andwhat is the size distribution of those peptides?’ arealso common, and require an element of sequencescanning of the proteome or subproteome dataset.

For mass spectrometric data, there are a numberof tasks that are not presently catered for. All high-quality mass spectra provide clear resolution of theall 12C and one 13C mass peaks at charge statesup to 5. Yet, the algorithms to reduce such datato the masses of the parent peptides (sometimesreferred to as ‘deisotoping’) is of variable effec-tiveness. A second task germane to our researchprogrammes would be a simple method to scan amass spectrum in m/z space for ([M + nH ]n+)/n ,([M + pX + nH ]n+)/n pairs, where M is the massof the parent peptide, n is the charge state (numberof protons) and X is the additional mass affordedby a stable isotope-labelled amino acid that occursp times in that peptide. From that scan, isotopicallylabelled pairs could be collated and used to enhancethe processes of protein identification [5], relativequantification or even calculation of the parametersof proteome dynamics. Another specialist applica-tion might be that of ‘shotgun protein sequencing’,where multiple peptides, derived from digests withproteases of different primary specificities, are usedto create substantial tracts of sequence informa-tion. At present, we can only perform this task bymanual overlapping of interpreted peptide sequencedata. There is considerable scope for a tool thatbuilds overlaps from uninterpreted tandem massspectrometric data and which ultimately enhancesconfidence in the final sequence call.

A final requirement is for high-quality datapresentational tools that can create visualizationsof the data using familiar and, hopefully, somenovel graphical modes. However, most users willprobably also require export of the data in XMLor ASCII files for import into other presentationaland analytical software. Any workbench should beexpected to adhere to emerging standards for XMLrepresentation of mass spectrometric data.

It is not clear that such a plan would ever berealized; there may be enough interested parties toagree on the common interface and core modulesthat such a workbench would require. Then, thecommunity will create the needs, from which thespecification of new modules can be drawn.

Acknowledgments

Research in my group is supported by BBSRC, NERCand the Wellcome Trust. The projects supported by thesefunding agencies have done much to help me identifybottlenecks and frustrations in a range of proteomicsexperiments.

References

1. Pratt JM, Robertson DH, Gaskell SJ, et al. 2002. Stable isotopelabelling in vivo as an aid to protein identification in peptidemass fingerprinting. Proteomics 2: 157–163.

2. Taylor CF, Paton NW, Garwood KL, et al. 2003. A systematicapproach to modelling, capturing and disseminating proteomicsexperimental data. Nature Biotechnol 21: 247–254.

3. Orchard S, Hermjakob H, Apweiler R. 2003. The proteomicsstandards initiative. Proteomics 3: 1374–1376.

4. Orchard S, Kersey P, Zhu W, et al. 2003. Progress inestablishing common standards for exchanging humanproteomics data. Comp Funct Genom 4: 203–206.

5. Pratt JM, Petty J, Riba-Garcia I, et al. 2002. Dynamics ofprotein turnover, a missing dimension in proteomics. Mol CellProteom 1: 579–591.


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