**8. Prediction of transmembrane domains (TMDs)**

The identified proteins were categorized based on their cellular locations and biological processes according to Gene Ontology (GO) information obtained at http://www.ebi.ac.uk/pub/databases/GO/goa/mouse and ftp://ftp.geneontology.org/pub/go/ [2]. The TMHMM (www.cbs.dtu.dk/services/TMHMM/) algorithm was used to predict transmembrane domains (TMDs) [15, 26].

The average hydrophobic values and transmembrane domains of the identied proteins were calculated using the SOSUI system that is available at http://bp.nuap.nagoya-u.ac.jp/sosui/ [11]. The proteins exhibiting positive GRAVY values were recognized as hydrophobic and those with negative values were hydrophilic [13].

theoretical spectra generated from known protein sequences [7].

There are a number of different methods for identifying the proteins in the sample, and the most frequently used is the searching of the uninterpreted MS/MS data. The FASTA formatted protein sequences from National Center for BioTechnology Information (NCBI) and UniProtKB/Swiss-Prot databases are collected for proteins identied or identification by each MS experiment. Searching uninterpreted MS/MS data from a single peptide or from a complete nanoLC-MS/MS run was automatically analyzed with a non-redundant protein database by the program SEQUEST, which allows the correlation of experimental data with

The precursor mass is used as a lter to nd a list of candidate peptide sequences from the theoretical digest of the database. A variety of different systems are used to score the experimental MS/MS spectrum against spectra predicted from the candidate peptide sequences. For protein identication, experimental data were searched against the NCBInr and Swiss-Prot mouse protein database using Mascot v1.8 software in which the criteria were based on the manufacturer's denitions (Matrix Science Ltd, London, UK) [20]. The parameters were set as follows: enzymatic cleavage with trypsin; 1 potential missed cleavage; a peptide and fragment mass tolerance of ± 0.25 Da, and xed modication of carbamidomethyl (cysteine); variable modication of oxidation (methionine); 1+, 2+, and 3+ peptide charge. Protein identications were performed using a Mowse scoring algorithm with a condence level of 95% and at least two matched peptides, showing a

For further verication, proteins might be validated by MSQuant software [1, 4, 24] available at http://msquant.sourceforge.net. The MSQuant software is used as a validation and quantitation tool that produces the Mascot peptide identications (HTLM les) and allows manual verication against the raw MS data (QSTAR XL raw les). The MSQuant software will pick up signicant and veried hits from the Mascot output le and export information of identied proteins into an .xls le, including the GI (genInfo identifier)

The identified proteins were categorized based on their cellular locations and biological processes according to Gene Ontology (GO) information obtained at http://www.ebi.ac.uk/pub/databases/GO/goa/mouse and ftp://ftp.geneontology.org/pub/go/ [2]. The TMHMM (www.cbs.dtu.dk/services/TMHMM/) algorithm was used to predict

The average hydrophobic values and transmembrane domains of the identied proteins were calculated using the SOSUI system that is available at http://bp.nuap.nagoya-u.ac.jp/sosui/ [11]. The proteins exhibiting positive GRAVY values were recognized as hydrophobic and

**7. Protein identication and validation** 

high score [12].

number and molecular-mass values.

transmembrane domains (TMDs) [15, 26].

those with negative values were hydrophilic [13].

**8. Prediction of transmembrane domains (TMDs)** 

**Figure 5.** Illustration of 2D-nanoLC-ESI-MS/MS spectra: (a) The total ion current (TIC) profile tryptic digest of membrane proteins (band 6) at the concentration of 100 mM ammonium acetate for run time 0- 50 min; (b) TOF-MS spectrum at 16.054 min; (c) TOF product spectrum of a peptide ion with m/z = 510.45.


2D-NanoLC-ESI-MS/MS for Separation and Identification of Mouse Brain Membrane Proteins 75

**Figure 7.** An example of hydropathy profile and transmembrane regions/domains of an identied

Identification and characterization of membrane proteins is a crucial challenge in proteomics research. Thus, we have designed a strategy of gel-based approach in combination with comprehensive two-dimensional nano liquid chromatography (2DnanoLC) that is robust and offers high separation capacity and high analysis throughput for mouse brain membrane proteins. By using this system, mixtures of in-gel trypsin-digested mouse brain membrane proteins were injected, desalted, separated and analyzed in complete automatization. The workflow started by the extraction and purification of the membrane fractions, then the SDS-PAGE was carried out as a useful preparative separation step. After staining, the gel slides with protein bands were cut, reduced, alkylated and

mouse brain membrane protein calculated using the SOSUI system that is available at

http://bp.nuap.nagoya-u.ac.jp/sosui/ [11].

**9. Conclusion** 

**Figure 6.** An example of Mascot search result shows list of the identified mouse brain membrane proteins isolated from band 6 (see figure 2) and their accession numbers, using SwissProt database (533049 sequences, 189064225 residues)

2D-NanoLC-ESI-MS/MS for Separation and Identification of Mouse Brain Membrane Proteins 75

74 Chromatography – The Most Versatile Method of Chemical Analysis

**Figure 6.** An example of Mascot search result shows list of the identified mouse brain membrane proteins isolated from band 6 (see figure 2) and their accession numbers, using SwissProt database

(533049 sequences, 189064225 residues)


**Figure 7.** An example of hydropathy profile and transmembrane regions/domains of an identied mouse brain membrane protein calculated using the SOSUI system that is available at http://bp.nuap.nagoya-u.ac.jp/sosui/ [11].

## **9. Conclusion**

Identification and characterization of membrane proteins is a crucial challenge in proteomics research. Thus, we have designed a strategy of gel-based approach in combination with comprehensive two-dimensional nano liquid chromatography (2DnanoLC) that is robust and offers high separation capacity and high analysis throughput for mouse brain membrane proteins. By using this system, mixtures of in-gel trypsin-digested mouse brain membrane proteins were injected, desalted, separated and analyzed in complete automatization. The workflow started by the extraction and purification of the membrane fractions, then the SDS-PAGE was carried out as a useful preparative separation step. After staining, the gel slides with protein bands were cut, reduced, alkylated and

trypsin-digested. The peptide mixtures extracted from each gel slice were fractionated by 2D-nanoLC coupled online with tandem mass spectrometry analysis (nanoESI-Q-TOF-MS/MS). The proteins were identified by MASCOT search against mouse protein database using a peptide and fragment mass tolerance of ±0.25 Da. Protein identification was carried out using a MOWSE scoring algorithm with a confidence level of 95% and processed by MSQuant software for further validation. In total, 298 identified membrane proteins from mouse brain tissues were verified by UniProt database, SOSUI and TMHMM prediction algorithms. Of these, 129 (43.3%) proteins have at least one transmembrane domain according to SOSUI and TMHMM. Furthermore, the function, subcellular location, molecular weight, post-translational modifications, transmembrane domains (TMD) and average of hydrophobicity of the identified membrane proteins might be categorized and analysed.

2D-NanoLC-ESI-MS/MS for Separation and Identification of Mouse Brain Membrane Proteins 77

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