Cargando…

Optimization of parameters for coverage of low molecular weight proteins

Proteins with molecular weights of <25 kDa are involved in major biological processes such as ribosome formation, stress adaption (e.g., temperature reduction) and cell cycle control. Despite their importance, the coverage of smaller proteins in standard proteome studies is rather sparse. Here we...

Descripción completa

Detalles Bibliográficos
Autores principales: Müller, Stephan A., Kohajda, Tibor, Findeiß, Sven, Stadler, Peter F., Washietl, Stefan, Kellis, Manolis, von Bergen, Martin, Kalkhof, Stefan
Formato: Texto
Lenguaje:English
Publicado: Springer-Verlag 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990009/
https://www.ncbi.nlm.nih.gov/pubmed/20803007
http://dx.doi.org/10.1007/s00216-010-4093-x
_version_ 1782192423104937984
author Müller, Stephan A.
Kohajda, Tibor
Findeiß, Sven
Stadler, Peter F.
Washietl, Stefan
Kellis, Manolis
von Bergen, Martin
Kalkhof, Stefan
author_facet Müller, Stephan A.
Kohajda, Tibor
Findeiß, Sven
Stadler, Peter F.
Washietl, Stefan
Kellis, Manolis
von Bergen, Martin
Kalkhof, Stefan
author_sort Müller, Stephan A.
collection PubMed
description Proteins with molecular weights of <25 kDa are involved in major biological processes such as ribosome formation, stress adaption (e.g., temperature reduction) and cell cycle control. Despite their importance, the coverage of smaller proteins in standard proteome studies is rather sparse. Here we investigated biochemical and mass spectrometric parameters that influence coverage and validity of identification. The underrepresentation of low molecular weight (LMW) proteins may be attributed to the low numbers of proteolytic peptides formed by tryptic digestion as well as their tendency to be lost in protein separation and concentration/desalting procedures. In a systematic investigation of the LMW proteome of Escherichia coli, a total of 455 LMW proteins (27% of the 1672 listed in the SwissProt protein database) were identified, corresponding to a coverage of 62% of the known cytosolic LMW proteins. Of these proteins, 93 had not yet been functionally classified, and five had not previously been confirmed at the protein level. In this study, the influences of protein extraction (either urea or TFA), proteolytic digestion (solely, and the combined usage of trypsin and AspN as endoproteases) and protein separation (gel- or non-gel-based) were investigated. Compared to the standard procedure based solely on the use of urea lysis buffer, in-gel separation and tryptic digestion, the complementary use of TFA for extraction or endoprotease AspN for proteolysis permits the identification of an extra 72 (32%) and 51 proteins (23%), respectively. Regarding mass spectrometry analysis with an LTQ Orbitrap mass spectrometer, collision-induced fragmentation (CID and HCD) and electron transfer dissociation using the linear ion trap (IT) or the Orbitrap as the analyzer were compared. IT-CID was found to yield the best identification rate, whereas IT-ETD provided almost comparable results in terms of LMW proteome coverage. The high overlap between the proteins identified with IT-CID and IT-ETD allowed the validation of 75% of the identified proteins using this orthogonal fragmentation technique. Furthermore, a new approach to evaluating and improving the completeness of protein databases that utilizes the program RNAcode was introduced and examined. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00216-010-4093-x) contains supplementary material, which is available to authorized users.
format Text
id pubmed-2990009
institution National Center for Biotechnology Information
language English
publishDate 2010
publisher Springer-Verlag
record_format MEDLINE/PubMed
spelling pubmed-29900092010-12-15 Optimization of parameters for coverage of low molecular weight proteins Müller, Stephan A. Kohajda, Tibor Findeiß, Sven Stadler, Peter F. Washietl, Stefan Kellis, Manolis von Bergen, Martin Kalkhof, Stefan Anal Bioanal Chem Original Paper Proteins with molecular weights of <25 kDa are involved in major biological processes such as ribosome formation, stress adaption (e.g., temperature reduction) and cell cycle control. Despite their importance, the coverage of smaller proteins in standard proteome studies is rather sparse. Here we investigated biochemical and mass spectrometric parameters that influence coverage and validity of identification. The underrepresentation of low molecular weight (LMW) proteins may be attributed to the low numbers of proteolytic peptides formed by tryptic digestion as well as their tendency to be lost in protein separation and concentration/desalting procedures. In a systematic investigation of the LMW proteome of Escherichia coli, a total of 455 LMW proteins (27% of the 1672 listed in the SwissProt protein database) were identified, corresponding to a coverage of 62% of the known cytosolic LMW proteins. Of these proteins, 93 had not yet been functionally classified, and five had not previously been confirmed at the protein level. In this study, the influences of protein extraction (either urea or TFA), proteolytic digestion (solely, and the combined usage of trypsin and AspN as endoproteases) and protein separation (gel- or non-gel-based) were investigated. Compared to the standard procedure based solely on the use of urea lysis buffer, in-gel separation and tryptic digestion, the complementary use of TFA for extraction or endoprotease AspN for proteolysis permits the identification of an extra 72 (32%) and 51 proteins (23%), respectively. Regarding mass spectrometry analysis with an LTQ Orbitrap mass spectrometer, collision-induced fragmentation (CID and HCD) and electron transfer dissociation using the linear ion trap (IT) or the Orbitrap as the analyzer were compared. IT-CID was found to yield the best identification rate, whereas IT-ETD provided almost comparable results in terms of LMW proteome coverage. The high overlap between the proteins identified with IT-CID and IT-ETD allowed the validation of 75% of the identified proteins using this orthogonal fragmentation technique. Furthermore, a new approach to evaluating and improving the completeness of protein databases that utilizes the program RNAcode was introduced and examined. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00216-010-4093-x) contains supplementary material, which is available to authorized users. Springer-Verlag 2010-08-28 2010 /pmc/articles/PMC2990009/ /pubmed/20803007 http://dx.doi.org/10.1007/s00216-010-4093-x Text en © The Author(s) 2010 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
spellingShingle Original Paper
Müller, Stephan A.
Kohajda, Tibor
Findeiß, Sven
Stadler, Peter F.
Washietl, Stefan
Kellis, Manolis
von Bergen, Martin
Kalkhof, Stefan
Optimization of parameters for coverage of low molecular weight proteins
title Optimization of parameters for coverage of low molecular weight proteins
title_full Optimization of parameters for coverage of low molecular weight proteins
title_fullStr Optimization of parameters for coverage of low molecular weight proteins
title_full_unstemmed Optimization of parameters for coverage of low molecular weight proteins
title_short Optimization of parameters for coverage of low molecular weight proteins
title_sort optimization of parameters for coverage of low molecular weight proteins
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2990009/
https://www.ncbi.nlm.nih.gov/pubmed/20803007
http://dx.doi.org/10.1007/s00216-010-4093-x
work_keys_str_mv AT mullerstephana optimizationofparametersforcoverageoflowmolecularweightproteins
AT kohajdatibor optimizationofparametersforcoverageoflowmolecularweightproteins
AT findeißsven optimizationofparametersforcoverageoflowmolecularweightproteins
AT stadlerpeterf optimizationofparametersforcoverageoflowmolecularweightproteins
AT washietlstefan optimizationofparametersforcoverageoflowmolecularweightproteins
AT kellismanolis optimizationofparametersforcoverageoflowmolecularweightproteins
AT vonbergenmartin optimizationofparametersforcoverageoflowmolecularweightproteins
AT kalkhofstefan optimizationofparametersforcoverageoflowmolecularweightproteins