Volume

Dagstuhl Seminar Proceedings, Volume 5471

Publication Details

  • published at: 2006-05-03
  • Publisher: Schloss Dagstuhl – Leibniz-Zentrum für Informatik

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Document
DOI: 10.4230/DagSemProc.05471.1
05471 Abstract Collection – Computational Proteomics

Authors: Christian G. Huber, Oliver Kohlbacher, and Knut Reinert

Abstract
From 20.11.05 to 25.11.05, the Dagstuhl Seminar 05471 ``Computational Proteomics'' was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available.
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Christian G. Huber, Oliver Kohlbacher, and Knut Reinert. 05471 Abstract Collection – Computational Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{huber_et_al:DagSemProc.05471.1,
  author =	{Huber, Christian G. and Kohlbacher, Oliver and Reinert, Knut},
  title =	{{05471 Abstract Collection – Computational Proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--15},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.1},
  URN =		{urn:nbn:de:0030-drops-5569},
  doi =		{10.4230/DagSemProc.05471.1},
  annote =	{Keywords: Proteomics, mass spectrometry, MALDI, HPLC-MS, differential expression, clinical proteomics, quantitation, identification}
}
Document
DOI: 10.4230/DagSemProc.05471.2
05471 Executive Summary – Computational Proteomics

Authors: Christian G. Huber, Oliver Kohlbacher, and Knut Reinert

Abstract
The Dagstuhl Seminar on Computational Proteomics brought together researchers from computer science and from proteomics to discuss the state of the art and future developments at the interface between experiment and theory. This interdisciplinary exchange covered a wide range of topics, from new experimental methods resulting in more complex data we will have to expect in the future to purely theoretical studies of what level of experimental accuracy is required in order to solve certain problems. A particular focus was also on the application side, where the participants discussed more complex experimental methodologies that are enabled by more sophisticated computational techniques. Quantitative aspects of protein expression analysis as well as posttranslational modifications in the context of disease development and diagnosis were discussed. The seminar sparked a number of new ideas and collaborations and resulted in joint grant applications and publications.
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Christian G. Huber, Oliver Kohlbacher, and Knut Reinert. 05471 Executive Summary – Computational Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{huber_et_al:DagSemProc.05471.2,
  author =	{Huber, Christian G. and Kohlbacher, Oliver and Reinert, Knut},
  title =	{{05471 Executive Summary – Computational Proteomics}},
  booktitle =	{Computational Proteomics},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.2},
  URN =		{urn:nbn:de:0030-drops-5406},
  doi =		{10.4230/DagSemProc.05471.2},
  annote =	{Keywords: Proteomics, mass spectrometry, MALDI, HPLC-MS, differential expression, clinical proteomics, quantitation, identification}
}
Document
DOI: 10.4230/DagSemProc.05471.3
A machine learning approach for prediction of DNA and peptide HPLC retention times

Authors: Marc Sturm, Sascha Quinten, Christian G. Huber, and Oliver Kohlbacher

Abstract
High performance liquid chromatography (HPLC) has become one of the most efficient methods for the separation of biomolecules. It is an important tool in DNA purification after synthesis as well as DNA quantification. In both cases the separability of different oligonucleotides is essential. The prediction of oligonucleotide retention times prior to the experiment may detect superimposed nucleotides and thereby help to avoid futile experiments. In 2002 Gilar et al. proposed a simple mathematical model for the prediction of DNA retention times, that reliably works at high temperatures only (at least 70°C). To cover a wider temperature rang we incorporated DNA secondary structure information in addition to base composition and length. We used support vector regression (SVR) for the model generation and retention time prediction. A similar problem arises in shotgun proteomics. Here HPLC coupled to a mass spectrometer (MS) is used to analyze complex peptide mixtures (thousands of peptides). Predicting peptide retention times can be used to validate tandem-MS peptide identifications made by search engines like SEQUEST. Recently several methods including multiple linear regression and artificial neural networks were proposed, but SVR has not been used so far.
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Marc Sturm, Sascha Quinten, Christian G. Huber, and Oliver Kohlbacher. A machine learning approach for prediction of DNA and peptide HPLC retention times. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{sturm_et_al:DagSemProc.05471.3,
  author =	{Sturm, Marc and Quinten, Sascha and Huber, Christian G. and Kohlbacher, Oliver},
  title =	{{A machine learning approach for prediction of DNA and peptide HPLC retention times}},
  booktitle =	{Computational Proteomics},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.3},
  URN =		{urn:nbn:de:0030-drops-5484},
  doi =		{10.4230/DagSemProc.05471.3},
  annote =	{Keywords: High performance liquid chromatography, mass spectrometry, retention time, prediction, peptide, DNA, support vector regression}
}
Document
DOI: 10.4230/DagSemProc.05471.4
An Algorithm for Feature Finding in LC/MS Raw Data

Authors: Clemens Gröpl

Abstract
Liquid chromatography coupled with mass spectrometry is an established method in shotgun proteomics. A key step in the data processing pipeline is to transform the raw data acquired by the mass spectrometer into a list of features. In this context, a emph{feature} is defined as the two-dimensional integration with respect to retention time (RT) and mass-over-charge (m/z) of the eluting signal belonging to a single charge variant of a measurand (e.g., a peptide). Features are characterized by attributes like average mass-to-charge ratio, centroid retention time, intensity, and quality. We present a new algorithm for feature finding which has been developed as a part of a combined experimental and algorithmic approach to absolutely quantify proteins from complex samples with unprecedented precision. The method was applied to the analysis of myoglobin in human blood serum, which is an important diagnostic marker for myocardial infarction. Our approach was able to determine the absolute amount of myoglobin in a serum sample through a series of standard addition experiments with a relative error of 2.5\%. It compares favorably to a manual analysis of the same data set since we could improve the precision and conduct the whole analysis pipeline in a small fraction of the time. We anticipate that our automatic quantitation method will facilitate further absolute or relative quantitation of even more complex peptide samples. The algorithm was implemented in the publicly available software framework OpenMS (www.OpenMS.de)
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Clemens Gröpl. An Algorithm for Feature Finding in LC/MS Raw Data. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{gropl:DagSemProc.05471.4,
  author =	{Gr\"{o}pl, Clemens},
  title =	{{An Algorithm for Feature Finding in LC/MS Raw Data}},
  booktitle =	{Computational Proteomics},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.4},
  URN =		{urn:nbn:de:0030-drops-5341},
  doi =		{10.4230/DagSemProc.05471.4},
  annote =	{Keywords: Computational Proteomics, Quantitative Analysis, Liquid Chromatography, Mass Spectrometry, Algorithm, Software}
}
Document
DOI: 10.4230/DagSemProc.05471.5
Combinatorial Approaches for Mass Spectra Recalibration

Authors: Sebastian Böcker and Veli Mäkinen

Abstract
Mass spectrometry has become one of the most popular analysis techniques in Proteomics and Systems Biology. With the creation of larger datasets, the automated recalibration of mass spectra becomes important to ensure that every peak in the sample spectrum is correctly assigned to some peptide and protein. Algorithms for recalibrating mass spectra have to be robust with respect to wrongly assigned peaks, as well as efficient due to the amount of mass spectrometry data. The recalibration of mass spectra leads us to the problem of finding an optimal matching between mass spectra under measurement errors. We have developed two deterministic methods that allow robust computation of such a matching: The first approach uses a computational geometry interpretation of the problem, and tries to find two parallel lines with constant distance that stab a maximal number of points in the plane. The second approach is based on finding a maximal common approximate subsequence, and improves existing algorithms by one order of magnitude exploiting the sequential nature of the matching problem. We compare our results to a computational geometry algorithm using a topological line-sweep.
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Sebastian Böcker and Veli Mäkinen. Combinatorial Approaches for Mass Spectra Recalibration. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{bocker_et_al:DagSemProc.05471.5,
  author =	{B\"{o}cker, Sebastian and M\"{a}kinen, Veli},
  title =	{{Combinatorial Approaches for Mass Spectra Recalibration}},
  booktitle =	{Computational Proteomics},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.5},
  URN =		{urn:nbn:de:0030-drops-5455},
  doi =		{10.4230/DagSemProc.05471.5},
  annote =	{Keywords: Mass spectrometry recalibration computational geometry}
}
Document
DOI: 10.4230/DagSemProc.05471.6
Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins

Authors: Nathanaël Delmotte, Bettina Mayr, Andreas Leinenbach, Knut Reinert, Oliver Kohlbacher, Christoph Klein, and Christian G. Huber

Abstract
The serum complexity makes the absolute quantitative analysis of medium to low-abundant proteins very challenging. Tens of thousands proteins are present in human serum and dispersed over an extremely wide dynamic range. The reliable identification and quantitation of proteins, which are potential biomarkers of disease, in serum or plasma as matrix still represents one of the most difficult analytical challenges. The difficulties arise from the presence of a few, but highly abundant proteins in serum and from the non-availability of isotope-labeled proteins, which serve to calibrate the method and to account for losses during sample preparation. For the absolute quantitation of serum proteins, we have developed an analytical scheme based on first-dimension separation of the intact proteins by anion-exchange high-performance liquid chromatography (HPLC), followed by proteolytic digestion and second-dimension separation of the tryptic peptides by reversed-phase HPLC in combination with electrospray ionization mass spectrometry (ESI-MS). The potential of mass spectrometric peptide identification in complex mixtures by means of peptide mass fingerprinting (PMF) and peptide fragment fingerprinting (PFF) was evaluated and compared utilizing synthetic mixtures of commercially available proteins and electrospray-ion trap- or electrospray time-of-flight mass spectrometers. While identification of peptides by PFF is fully supported by automated spectrum interpretation and database search routines, reliable identification by PMF still requires substantial efforts of manual calibration and careful data evaluation in order to avoid false positives. Quantitation of the identified peptides, however, is preferentially performed utilizing full-scan mass spectral data typical of PMF. Algorithmic solutions for PMF that incorporate both recalibration and automated feature finding on the basis of peak elution profiles and isotopic patterns are therefore highly desirable in order to speed up the process of data evaluation and calculation of quantitative results. Calibration for quantitative analysis of serum proteins was performed upon addition of known amounts of authentic protein to the serum sample. This was essential for the analysis of human serum samples, for which isotope-labeled protein standards are usually not available. We present the application of multidimensional HPLC-ESI-MS to the absolute quantitative analysis of myoglobin in human serum, a very sensitive biomarker for myocardial infarction. It was possible to determine myoglobin concentrations in human serum down to 100-500 ng/mL. Calibration graphs were linear over at least one order of magnitude and the relative standard deviation of the method ranged from 7-15%.
Cite as

Nathanaël Delmotte, Bettina Mayr, Andreas Leinenbach, Knut Reinert, Oliver Kohlbacher, Christoph Klein, and Christian G. Huber. Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{delmotte_et_al:DagSemProc.05471.6,
  author =	{Delmotte, Nathana\"{e}l and Mayr, Bettina and Leinenbach, Andreas and Reinert, Knut and Kohlbacher, Oliver and Klein, Christoph and Huber, Christian G.},
  title =	{{Evaluation of LC-MS data for the absolute quantitative analysis of marker proteins}},
  booktitle =	{Computational Proteomics},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.6},
  URN =		{urn:nbn:de:0030-drops-5397},
  doi =		{10.4230/DagSemProc.05471.6},
  annote =	{Keywords: RP-HPLC, monolith, Mascot, Myoglobin, Absolute quantitation, Serum}
}
Document
DOI: 10.4230/DagSemProc.05471.7
Future Challenges in Proteomics

Authors: Hartmut Schlüter

Abstract
The benefits of the present proteomic approaches for the life science community are limited by 3 major problems. 1. The identification of proteins, regardless whether the peptide mass fingerprint or the shot-gun approach is used, usually is based on an incomplete set of peptides leaving large parts of the full amino acid sequence and other structural details of the original protein species in the dark; 2. Missing validation of many of the identified proteins by orthogonal biological experiments thus risking false positive results; 3. Missing standardization in nomenclature. In sum these problems may hinder progress in life science projects employing proteomic strategies and may be especially risky for system biology approaches since the ambiguities resulting from the above mentioned problems may cause wrong models. It is recommended to guide future proteome analytical investigations by a hypothesis and to focus to a smaller number of proteins which should ideally be analyzed in detail covering 100 % sequence coverage as well as all posttranslational modifications and will allow validation by additional biological experiments.
Cite as

Hartmut Schlüter. Future Challenges in Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{schluter:DagSemProc.05471.7,
  author =	{Schl\"{u}ter, Hartmut},
  title =	{{Future Challenges in Proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--5},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.7},
  URN =		{urn:nbn:de:0030-drops-5449},
  doi =		{10.4230/DagSemProc.05471.7},
  annote =	{Keywords: Protein species, proteome, identification}
}
Document
DOI: 10.4230/DagSemProc.05471.8
Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools

Authors: Hansjörg Toll, Peter Berger, Andreas Hofmann, Andreas Hildebrandt, Herbert Oberacher, Hans Peter Lenhof, and Christian G. Huber

Abstract
Due to their extensive structural heterogeneity, the elucidation of glycosylation patterns in glycoproteins such as the subunits of chorionic gonadotropin (CG), CG-alpha and CG-beta remains one of the most challenging problems in the proteomic analysis of posttranslational modifications. In consequence, glycosylation is usually studied after decomposition of the intact proteins to the proteolytic peptide level. However, by this approach all information about the combination of the different glycopeptides in the intact protein is lost. In this study we have, therefore, attempted to combine the results of glycan identification after tryptic digestion with molecular mass measurements on the intact glycoproteins. Despite the extremely high number of possible combinations of the glycans identified in the tryptic peptides by high-performance liquid chromatography-mass spectrometry (> 1000 for CG-alpha and > 10.000 for CG-beta), the mass spectra of intact CG-alpha and CG-beta revealed only a limited number of glycoforms present in CG preparations from pools of pregnancy urines. Peak annotations for CG-alpha were performed with the help of an algorithm that generates a database containing all possible modifications of the proteins (inclusive possible artificial modifications such as oxidation or truncation) and subsequent searches for combinations fitting the mass difference between the polypeptide backbone and the measured molecular masses. Fourteen different glycoforms of CG-alpha, including methionine-oxidized and N-terminally truncated forms, were readily identified. For CG-beta, however, the relatively high mass accuracy of ± 2 Da was still insufficient to unambiguously assign the possible combinations of posttranslational modifications. Finally, the mass spectrometric fingerprints of the intact molecules were shown to be very useful for the characterization of glycosylation patterns in different CG preparations.
Cite as

Hansjörg Toll, Peter Berger, Andreas Hofmann, Andreas Hildebrandt, Herbert Oberacher, Hans Peter Lenhof, and Christian G. Huber. Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{toll_et_al:DagSemProc.05471.8,
  author =	{Toll, Hansj\"{o}rg and Berger, Peter and Hofmann, Andreas and Hildebrandt, Andreas and Oberacher, Herbert and Lenhof, Hans Peter and Huber, Christian G.},
  title =	{{Glycosylation Patterns of Proteins Studied by Liquid Chromatography-Mass Spectrometry and Bioinformatic Tools}},
  booktitle =	{Computational Proteomics},
  pages =	{1--6},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.8},
  URN =		{urn:nbn:de:0030-drops-5431},
  doi =		{10.4230/DagSemProc.05471.8},
  annote =	{Keywords: Liquid chromatography, mass spectrometry, glycoproteins, glycosylation, peak annotation}
}
Document
DOI: 10.4230/DagSemProc.05471.9
High-accuracy peak picking of proteomics data

Authors: Eva Lange, Clemens Gröpl, Oliver Kohlbacher, and Andreas Hildebrandt

Abstract
A new peak picking algorithm for the analysis of mass spectrometric (MS) data is presented. It is independent of the underlying machine or ionization method, and is able to resolve highly convoluted and asymmetric signals. The method uses the multiscale nature of spectrometric data by first detecting the mass peaks in the wavelet-transformed signal before a given asymmetric peak function is fitted to the raw data. In an optional third stage, the resulting fit can be further improved using techniques from nonlinear optimization. In contrast to currently established techniques (e.g. SNAP, Apex) our algorithm is able to separate overlapping peaks of multiply charged peptides in ESI-MS data of low resolution. Its improved accuracy with respect to peak positions makes it a valuable preprocessing method for MS-based identification and quantification experiments. The method has been validated on a number of different annotated test cases, where it compares favorably in both runtime and accuracy with currently established techniques. An implementation of the algorithm is freely available in our open source framework OpenMS (www.open-ms.de).
Cite as

Eva Lange, Clemens Gröpl, Oliver Kohlbacher, and Andreas Hildebrandt. High-accuracy peak picking of proteomics data. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{lange_et_al:DagSemProc.05471.9,
  author =	{Lange, Eva and Gr\"{o}pl, Clemens and Kohlbacher, Oliver and Hildebrandt, Andreas},
  title =	{{High-accuracy peak picking of proteomics data}},
  booktitle =	{Computational Proteomics},
  pages =	{1--9},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.9},
  URN =		{urn:nbn:de:0030-drops-5358},
  doi =		{10.4230/DagSemProc.05471.9},
  annote =	{Keywords: Mass spectrometry, peak detection, peak picking}
}
Document
DOI: 10.4230/DagSemProc.05471.10
MALDI Mass Spectrometry for Quantitative Proteomics – Approaches, Scopes and Limitations

Authors: Andreas Tholey

Abstract
The determination of absolute protein amounts and the quantification of differentially expressed proteins belong to the most important goals in proteomics. Despite being one of the key technologies for the identification of proteins, the application of matrix assisted laser desorption/ionization (MALDI) mass spectrometry (MS) for quantitative analyses is hampered by several inherent factors. The goal of the present paper is to outline these difficulties but also to present some selected approaches which enable MALDI MS to be used for the quantification of biomolecules. In particular, methods for the improvement of the homogeneity of MALDI samples and the use of internal standards for the relative quantification are discussed. Strategies for in-vivo and in-vitro labelling of peptides and proteins with stable isotopes are presented. The need for guidelines for the presentation and evaluation of data as well as for bioinformatical approaches for the interpretation of quantitative data will be addressed.
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Andreas Tholey. MALDI Mass Spectrometry for Quantitative Proteomics – Approaches, Scopes and Limitations. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{tholey:DagSemProc.05471.10,
  author =	{Tholey, Andreas},
  title =	{{MALDI Mass Spectrometry for Quantitative Proteomics – Approaches, Scopes and Limitations}},
  booktitle =	{Computational Proteomics},
  pages =	{1--11},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.10},
  URN =		{urn:nbn:de:0030-drops-5367},
  doi =		{10.4230/DagSemProc.05471.10},
  annote =	{Keywords: MALDI, mass spectrometry, quantification, ionic liquid matrices}
}
Document
DOI: 10.4230/DagSemProc.05471.11
MULTIDIMENSIONAL PEPTIDE/PROTEIN ANALYSIS AND IDENTIFICATION BY SEQUENCE DATABASE SEARCH USING MASS SPECTROMETRIC DATA

Authors: Christian Schley, Matthias Altmeyer, Rolf Müller, and Christian G. Huber

Abstract
In order to generate proteomics data that are suitable to validate protein identification in complex mixtures using multidimensional liquid-chromatography-mass spectrometry approaches, we implemented an offline two-dimensional liquid chromatography method combining strong cation-exchange- and ion-pair reversed-phase chromatography followed by electrospray ionization tandem mass spectrormetry (ESI-MS/MS) for the analysis of a bovine serum albumin digest. The fragment ion spectra generated by ESI-MS/MS were subsequently analyzed via MASCOT database search. The obtained identification data were evaluated in terms of quality of protein/peptide identification by means of score values, reproducibility of identification in replicate measurements, distribution of tryptic peptides among different fractions, and overall number of unique identified proteins/peptides. Finally, we improved the trapping conditions in the second dimension by using a more hydrophobic amphiphile in the loading buffer. The improvement was demonstrated by comparison of the obtained identification data, such as number of identified peptides, cumulative mowse scores and reproducibility of identification.
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Christian Schley, Matthias Altmeyer, Rolf Müller, and Christian G. Huber. MULTIDIMENSIONAL PEPTIDE/PROTEIN ANALYSIS AND IDENTIFICATION BY SEQUENCE DATABASE SEARCH USING MASS SPECTROMETRIC DATA. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{schley_et_al:DagSemProc.05471.11,
  author =	{Schley, Christian and Altmeyer, Matthias and M\"{u}ller, Rolf and Huber, Christian G.},
  title =	{{MULTIDIMENSIONAL PEPTIDE/PROTEIN ANALYSIS AND IDENTIFICATION BY SEQUENCE DATABASE SEARCH USING MASS SPECTROMETRIC DATA}},
  booktitle =	{Computational Proteomics},
  pages =	{1--8},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.11},
  URN =		{urn:nbn:de:0030-drops-5389},
  doi =		{10.4230/DagSemProc.05471.11},
  annote =	{Keywords: Multidimensional chromatography, proteome analysis, monolithic column, tandem mass spectrometry}
}
Document
DOI: 10.4230/DagSemProc.05471.12
New statistical algorithms for clinical proteomics

Authors: Tim Conrad

Abstract
Background: Mass spectrometry based screening methods have been recently introduced into clinical proteomics. This boosts the development of a new approach for early disease detection: proteomic pattern analysis. Aim: Find, analyze and compare proteomic patterns in groups of patients having different properties such as disease status or epidemio-logical parameters (e.g. sex, age) with a new pipeline to enhance sensitivity and specificity. Problems: Mass data acquired from high-throughput platforms frequently are blurred and noisy. This extremely complicates the reliable identification of peaks in general and very small peaks below noise-level in particular. Approach: Apply sophisticated signal preprocessing steps followed by statistical analyzes to purge the raw data and enable the detection of real signals while maintaining information for tracebacks. Results: A new analysis pipeline has been developed capable of finding and analyzing peak patterns discriminating different groups of patients (e.g. male/female, cancer/healthy). First steps towards distributed computing approaches have been incorporated in the design.
Cite as

Tim Conrad. New statistical algorithms for clinical proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-2, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{conrad:DagSemProc.05471.12,
  author =	{Conrad, Tim},
  title =	{{New statistical algorithms for clinical proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--2},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.12},
  URN =		{urn:nbn:de:0030-drops-5427},
  doi =		{10.4230/DagSemProc.05471.12},
  annote =	{Keywords: MS, Mass Spectrometry, MALDI-TOF, Fingerprinting, Proteomics}
}
Document
DOI: 10.4230/DagSemProc.05471.13
OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics

Authors: Knut Reinert, Oliver Kohlbacher, Clemens Gröpl, Eva Lange, Ole Schulz-Trieglaff, Marc Sturm, and Nico Pfeifer

Abstract
In the talk we describe the freely available software library OpenMS which is currently under development at the Freie Universität Berlin and the Eberhardt-Karls Universität Tübingen. We give an overview of the goals and problems in differential proteomics with HPLC and then describe in detail the implemented approaches for signal processing, peak detection and data reduction currently employed in OpenMS. After this we describe methods to identify the differential expression of peptides and propose strategies to avoid MS/MS identification of peptides of interest. We give an overview of the capabilities and design principles of OpenMS and demonstrate its ease of use. Finally we describe projects in which OpenMS will be or was already deployed and thereby demonstrate its versatility.
Cite as

Knut Reinert, Oliver Kohlbacher, Clemens Gröpl, Eva Lange, Ole Schulz-Trieglaff, Marc Sturm, and Nico Pfeifer. OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{reinert_et_al:DagSemProc.05471.13,
  author =	{Reinert, Knut and Kohlbacher, Oliver and Gr\"{o}pl, Clemens and Lange, Eva and Schulz-Trieglaff, Ole and Sturm, Marc and Pfeifer, Nico},
  title =	{{OpenMS - A Framework for Quantitative HPLC/MS-Based Proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--7},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.13},
  URN =		{urn:nbn:de:0030-drops-5463},
  doi =		{10.4230/DagSemProc.05471.13},
  annote =	{Keywords: Proteomics, C++, Differential expression}
}
Document
DOI: 10.4230/DagSemProc.05471.14
Software platforms for quantitative proteomics

Authors: Ole Schulz-Trieglaff

Abstract
In recent years, it has become obvious that mRNA expression does not always correlate with protein expression. It seems that a full understanding of the complexity of life can only be obtained by examining abundances of proteins under varying conditions. Accurate measurements of these expression values is crucial. This field of research also requires new computational efforts since the data, often from mass spectrometry experiments, is very complex. We present two academic software platforms that offer means to reduce, analyse and compare protein expression data gained from liquid chromatography coupled with mass spectrometry. We outline their methodology and compare them to our own project, OpenMS, which is currently developed in our research group at the Free University Berlin in collaboration with the Kohlbacher group at Tuebingen University.
Cite as

Ole Schulz-Trieglaff. Software platforms for quantitative proteomics. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{schulztrieglaff:DagSemProc.05471.14,
  author =	{Schulz-Trieglaff, Ole},
  title =	{{Software platforms for quantitative proteomics}},
  booktitle =	{Computational Proteomics},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.14},
  URN =		{urn:nbn:de:0030-drops-5376},
  doi =		{10.4230/DagSemProc.05471.14},
  annote =	{Keywords: Proteomics, mass spetrometry, quantitative measurements}
}
Document
DOI: 10.4230/DagSemProc.05471.15
The Peptide MS/MS-Fragmentome: A Set of Predictable Fragment Ions with Highly Redundant Sequence Information

Authors: Wolf D. Lehmann, Junhua Wei, Juri Rappsilber, and Mojiborahman Salek

Abstract
Upon low energy collision induced dissociation (CID), multiply protonated peptides generate a set of interdependent fragment ions detectable by MS/MS, the '[peptide]n+-fragmentome'. In particular dynamic fragmentation of [peptide]n+ ions in a collision cell generates information-rich MS/MS spectra. Currently, database-supported annotations of peptide MS/MS spectra are mainly based on a combination of peptide molecular weight and y type fragment ions, leaving a considerable number of good-quality peptide MS/MS spectra in proteomics studies unannotated. This situation may be improved by a more complete use of the structural information present in the [peptide]n+-fragmentome. The presentation provides an overview on the fragment ions of multiply protonated peptides and their connectivity, comprising a ions, b ions, y ions, and neutral loss reactions from the N-, and C-terminus, and internal b ions. In the low-mass region, the unique set of 19 y1 ions and of the 190 b2 ions carries a particular message, since these ions define the N-or C-terminal amino acid(s). Further, the b1 ions of the basic residues K, H, W, and R carry a specific N-terminal information, which is redundant to that contained in the corresponding b2 ions and in the N-terminal neutral loss peaks. Redundant information is also found in b and y ion series and in complementary b/y ion pairs. The latter are particularly abundant when generated by proline- or aspartate-induced backbone cleavages. From complementary b/y ion pairs the molecular weight of the precursor ion can be reconstructed to confirm or determine its molecular weight. This procedure is helpful in case a mixture of precursor ions is isolated or in case a precursor ion of very low abundance is isolated. Information about the precursor ion charge state is also delivered by precursor ion reconstruction using MS/MS data. In the analysis of covalently modified peptides, reporter ions are of particular importance. These ions can be used for mining of MS/MS data sets for the occurrence of selected modifications. Examples are presented for selected modifications, such as acetylation and phosphorylation. In phosphorylation analysis neutral loss reactions are highly important, and may also carry redundant information, when observed both from the molecular ion and from fragment ions. Search tools, which fully incorporate the current knowledge about the [peptide]n+-fragmentome will increase the scores of peptide/protein identifications by MS/MS and thus will increase the fraction of automatically assigned MS/MS spectra in proteomics studies.
Cite as

Wolf D. Lehmann, Junhua Wei, Juri Rappsilber, and Mojiborahman Salek. The Peptide MS/MS-Fragmentome: A Set of Predictable Fragment Ions with Highly Redundant Sequence Information. In Computational Proteomics. Dagstuhl Seminar Proceedings, Volume 5471, pp. 1-4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2006)

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@InProceedings{lehmann_et_al:DagSemProc.05471.15,
  author =	{Lehmann, Wolf D. and Wei, Junhua and Rappsilber, Juri and Salek, Mojiborahman},
  title =	{{The Peptide MS/MS-Fragmentome: A Set of Predictable Fragment Ions with Highly Redundant Sequence Information}},
  booktitle =	{Computational Proteomics},
  pages =	{1--4},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2006},
  volume =	{5471},
  editor =	{Christian G. Huber and Oliver Kohlbacher and Knut Reinert},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.05471.15},
  URN =		{urn:nbn:de:0030-drops-5472},
  doi =		{10.4230/DagSemProc.05471.15},
  annote =	{Keywords: Peptides, collision-induced dissociation, tandem mass spectrometry, electrospray, peptide fragment ions}
}

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  • Authors
  • A
  • Altmeyer, Matthias
  • B
  • Berger, Peter
  • Böcker, Sebastian
  • C
  • Conrad, Tim
  • D
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  • G
  • Gröpl, Clemens
  • H
  • Hildebrandt, Andreas
  • Hofmann, Andreas
  • Huber, Christian G.
  • K
  • Klein, Christoph
  • Kohlbacher, Oliver
  • L
  • Lange, Eva
  • Lehmann, Wolf D.
  • Leinenbach, Andreas
  • Lenhof, Hans Peter
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  • Mayr, Bettina
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  • O
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  • Pfeifer, Nico
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  • R
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  • Reinert, Knut
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  • Salek, Mojiborahman
  • Schley, Christian
  • Schlüter, Hartmut
  • Schulz-Trieglaff, Ole
  • Sturm, Marc
  • T
  • Tholey, Andreas
  • Toll, Hansjörg
  • W
  • Wei, Junhua

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