Patent Publication Number: US-7910372-B2

Title: Absolute quantitation of protein contents based on exponentially modified protein abundance index by mass spectrometry

Description:
This application is a national stage application of PCT International Application No. PCT/JP2005/012705, filed Jul. 4, 2005. This application also claims the benefit of US Provisional Application No. 60/591,963, filed Jul. 29, 2004. 
    
    
     TECHNICAL FIELD 
     The present invention generally relates to analysis of protein contents by means of mass spectrometry, and more particularly to a method and a computer program for executing quantitation of the protein contents based on protein abundance index by mass spectrometry in proteomics. 
     BACKGROUND OF ART 
     Proteomic liquid chromatography-mass spectrometry (hereinafter referred to as “LC-MS”) approaches today combined with genome-annotated database allow to identify thousands of proteins from a protein mixture solution [1]. These approaches have been also applied to relative quantitation using stable isotope labeling [2-4]. Recently, not only comprehensive quantitation studies between two states [5,6], but also interaction analysis between protein-protein [7,8], protein-peptides [9] and protein-drug [10] have been extensively reported. So far, however, a comprehensive approach for protein contents in one sample solution has not been established yet. Protein concentrations are one of the most basic and important parameters in quantitative proteomics because the kinetics/dynamics of cellular proteins are described as changes in concentrations of proteins in particular regions. In addition, protein concentrations in a sample can be also used for relative quantitation between two samples even when the difference in concentration is too large to perform isotope-based relative; quantitation. So far, isotope-labeled synthetic peptides were used as internal standards for absolute quantitation of particular proteins of interest [11,12]. This approach would be applicable to comprehensive analysis but the cost of isotope-labeled peptides as well as the difficulty to do quantitative digestion of proteins in gel would cause a problem [13]. 
     Even a single analysis of nanoLC-MS/MS generates a long list of identified proteins easily with the help of database searching, and additional information is extracted from this list with raw data, such as hit ranking in identification, the probability score, the number of identified peptides per protein and ion counts of identified peptides, LC retention times, and so on. Qualitatively, some parameters such as the hit rank, the score and the number of peptides per protein [14] would be a kind of indicators for protein abundance in the analyzed sample. Among them, ion counts of peptides would be the most direct parameter to describe the abundance and were used for protein expression at different states [15]. However, a mass spectrometer as a detector is not so versatile as an absorbance detector in terms of the limited linearity and the ionization suppression effect with background [16]. Therefore, it is required to normalize these parameters to obtain reliable quantitative information. The first approach along this strategy was, as far as the present inventor knows, to use the number of peptides per proteins normalized by theoretical number of peptides, which was named protein abundance index (hereinafter referred to as “PAI”), and was applied to human spliceosome complex analysis [17]. Similar concept was recently reported that the number of peptides or spectra counts in LC/LC-MS/MS analysis were used for relative quantitation [18]. The present inventor also developed normalized ion counts-based approach, where at least three peptides are used to calculate the average ion counts of each protein [19]. This approach has been used for relative quantitation in peptide correlation profiling [20]. 
     DISCLOSURE OF INVENTION 
     However, the applicability of this approach was limited because it needs three peptides at least to keep the accuracy. Here the present inventor explores the PAI strategy to determine protein abundance from nanoLC-MS/MS experiments. 
     It is an object of the present invention to provide a method for executing quantitation of protein contents based on an exponentially modified PAI (hereinafter referred as to “EMPAI”) in a sample of biological material. 
     In an embodiment of the present invention, there is also provided a computer program product, for example, a computer readable medium which can be read by a computer and stores a computer program for executing quantitation of protein contents based on the above EMPAI in a sample of biological material. 
     In another embodiment of the present invention, there is also provided a computer program for executing quantitation of protein contents based on the above EMPAI in a sample of biological material. 
     In further embodiment of the present invention, there is also provided an analytical apparatus for executing quantitation of protein contents based on the above EMPAI in a sample of biological material. 
     In order to attain the above object, the present invention provides a method for executing quantitation of protein content in a sample of biological material, said method comprising the steps of: (a) identifying a protein to be quantified by mass spectrometry; (b) measuring the number of observed peptides per the protein (N obsd ); (c) calculating the number of observable peptides per protein (N obsbl ); and (d) computing the following equation to obtain EMPAI:
 
 EMPAI= 10 Nobsd/Nobsbl −1.
 
     In one preferred aspect of the method according the present invention, the method further comprises calculating protein contents (mol %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     mol   ⁢           ⁢   %     )       =       EMPAI     ∑     (   EMPAI   )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins.
 
     In another preferred aspect of the method according to the present invention, the method further comprises calculating protein contents (weight %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     weight   ⁢           ⁢   %     )       =         EMPAI   ×   MW       ∑     (     EMPAI   ×   MW     )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins and MW represents molecular weight of each protein identified.
 
     In further preferred aspect of the method according to the present invention, the mass spectrometry comprises a liquid chromatography-mass spectrometry. 
     The present invention also provides a computer program product for executing quantitation of protein content in a sample of biological material, said program product comprising: a computer readable storage medium having a computer program stored there on for performing the steps: (a) identifying a protein to be quantified by mass spectrometry; (b) measuring the number of observed peptides per the protein (N obsd ); (C) calculating the number of observable peptides per protein (N obsbl ); and (d) computing the following equation to obtain EMPAI
 
 EMPAI= 10 Nobsd/Nobsbl −1.
 
     In one preferred aspect of the computer program product according to the present invention, the program comprises performing of calculating protein contents (mol %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     mol   ⁢           ⁢   %     )       =       EMPAI     ∑     (   EMPAI   )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins.
 
     In another preferred aspect of the computer program product according to the present invention, the program comprises performing of calculating protein contents (weight %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     weight   ⁢           ⁢   %     )       =         EMPAI   ×   MW       ∑     (     EMPAI   ×   MW     )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins and MW represents molecular weight of each protein identified.
 
     In further aspect of the computer program product according to the present invention, the product is a computer readable recording medium which can be read by a computer. 
     The present invention also provides a computer program which executes quantitation of protein content in a sample of biological material, said program comprising performing the steps of: (a) identifying a protein to be quantified by mass spectrometry; (b) measuring the number of observed peptides per the protein (N obsd ); (c) calculating the number of observable peptides per protein (N obsbl ); and (d) computing the following equation to obtain EMPAI
 
 EMPAI= 10 Nobsd/Nobsbl −1.
 
     In one aspect of the computer program according to the present invention, the program further comprises performing of calculating protein contents (mol %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     mol   ⁢           ⁢   %     )       =       EMPAI     ∑     (   EMPAI   )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins.
 
     In another aspect of the computer program according to the present invention, the program further comprises performing of calculating protein contents (weight %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     weight   ⁢             ⁢             ⁢   %     )       =         EMPAI   ×   MW       ∑     (     EMPAI   ×   MW     )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins and MW represents molecular weight of each protein identified.
 
     The computer program according to the present invention is characterized in that this program causes the respective steps of the method for quantifying protein content according to the present invention to be performed by a computer. The computer program can also be provided in the form of a storage medium where the program is stored as well as can be supplied via a transmission medium, such as the Internet. 
     The present invention also provides an analytical apparatus for executing quantification of protein content in a sample of biological material, comprising: identifying means for receiving information as to a mass spectrometric data of proteins obtained by mass spectrometry and identifying a protein to be quantified by the mass spectrometry; measuring means for measuring the number of observed peptides per the protein (N obsd ); calculating means for calculating the number of observable peptides per protein (N obsbl ); and computing means for computing the following equation to obtain EMPAI
 
 EMPAI= 10 Nobsd/Nobsbl −1.
 
     In one aspect of the analytical apparatus according to the present invention, the computing means comprises performing of calculating protein contents (mol %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     mol   ⁢           ⁢   %     )       =       EMPAI     ∑     (   EMPAI   )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins.
 
     In another aspect of the analytical apparatus according to the present invention, the computing means comprises performing of calculating protein contents (weight %) based on a value of EMPAI as follows: 
                 protein   ⁢           ⁢     contents   ⁢             ⁢             (     weight   ⁢             ⁢             ⁢   %     )       =         EMPAI   ×   MW       ∑     (     EMPAI   ×   MW     )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins and MW represents molecular weight of each protein identified.
 
     In further aspect of the analytical apparatus according to the present invention, the mass spectrometry comprises a liquid chromatography-mass spectrometry. 
     An advantage of the present invention is that the scale for absolute protein abundance, namely exponentially modified protein abundance index is established, which can use for absolute quantitation of protein contents in proteomics. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above object and features of the present invention will be more apparent from the following description of the preferred embodiments with reference to the accompanying drawings, wherein: 
         FIG. 1  shows a drawing of the hardware structure for the computer executing quantitation of protein contents based on the above EMPAI according to the present invention; 
         FIG. 2  shows a block diagram which is used to illustrate the construction of an analytical apparatus for executing quantitation of protein contents based on the EMPAI according to the present invention; 
         FIG. 3  shows a flowchart for executing quantitation of protein contents based on the EMPAI according to the present invention; 
         FIG. 4  shows one example of a flowchart for calculating the number of observable peptides per the protein; 
         FIG. 5  illustrates dependence of the number of peptides and peak area on the injected amounts of human serum albumin (HSA).  FIG. 5A  shows peak area and the number of unique parent ions of peptides versus injection amounts of (HSA).  FIG. 5B  shows three different numbers of peptides versus injection amounts of HSA; 
         FIG. 6  shows the relationship between protein concentration and different parameters for 47 proteins in neuro2a cells.  FIG. 6A  shows protein concentrations versus PAI.  FIG. 6B  shows protein concentration versus the number of peptides divided by molecular weight of proteins.  FIG. 6C  shows protein concentration versus Mascot score.  FIG. 6D  shows protein concentration versus the number of observed peptides (unique parent ions); 
         FIG. 7  shows the influence of MS measurement conditions on linear relationship between PAI and log [protein].  FIG. 7A  shows time-of-flight type mass spectrometry (QSTAR) with slower scans.  FIG. 7B  shows ion trap type mass spectrometry (LCQ) with slower scans; 
         FIG. 8  shows the relationship between protein concentrations and EMPAI for 47 proteins in neuro2a cells; 
         FIG. 9  shows the results of absolute quantitation of 47 proteins in neuro2a using EMPAI according to the present invention; and 
         FIG. 10  shows the comparison between gene and protein expression in HCT116 cells according to one embodiment of the present invention. 
     
    
    
     BEST MODE FOR CARRYING OUT THE INVENTION 
     The present invention is explained in detail using examples including the method for executing quantitation of the protein contents and the computer program for carrying out the method. 
       FIG. 1  shows a drawing of the hardware structure for the computer carrying out quantitation of protein contents based on the above EMPAI by the LC-MS according to the present invention. An analytical apparatus  10  for executing quantitation of protein contents based on the EMPAI according to the present invention comprises a central processing unit  12  (hereinafter abbreviated as “CPU”), a memory  14 , a display device  16 , a user interface  18 , and a communication interface  22 , all mutually connected via a bus  24  with the CPU  12 . The apparatus  10  further comprises an external storage (not shown in  FIG. 1 ), such as a CD-ROM or a magnetic medium, connected to an external storage medium drive unite  20 . The apparatus  10  can be connected to the external data base, such as NCBlnr (http://www.ncbi.nlm.nih.gov/) and so on, through the communication interface  22 . The apparatus  10  can also be connected to the mass spectrometric device via the communication interface  22 , which carries out analysis of the proteins. 
       FIG. 2  shows a block diagram which is used to illustrate the construction of the analytical apparatus  10  for executing quantitation of protein contents based on the EMPAI according to the present invention. As is shown in  FIG. 2 , the apparatus  10  comprises IF (interface) means  30  and control means  40 . The apparatus  10  is constructed so that these means  30 ,  40  receive input, for example, a mass spectrometric data, from the user utilizing this apparatus and/or a mass spectrometry, and output information to this user and/or the mass spectrometry. An ordinary personal computer can be used as the apparatus  10 . Examples of the mass spectrometric data include a mass spectrum, a mass chromatogram and MSMS data and so on. 
     The IF means  30  is constructed so that information can be input and output with respect to input device such as a keyboard, the mass spectrometry or the like and output device such as a display, printer or the like. Via the IF means  30 , the mass spectrometric data to be analyzed is transmitted to the control means  40 . 
     The control means  40  comprises identifying means  42 , measuring means  44 , calculating means  46  and computing means  48 . In the present invention, the identifying means  42  can receive information as to mass spectrometric data of proteins via the IF means  30  and identifies a protein to be quantified by mass spectrometry. Next, based on the mass spectrometric data, the measuring means  44  measures the number of observed peptides per the protein (N obsd ) which has been identified by the identifying means  42 . On the other hand, based on identification of the protein to be quantified, calculating means  46  calculates the number of observable peptides per protein (N obsbl ). Here, the term “the number of observed peptides per protein” used herein means that the number of peptides per protein to be quantified which was actually observed by the mass spectrometry. The term “the number of observable peptides per protein” used herein means that a theoretical number of peptides per the protein. It should be noted that these numbers are defined in the document [17]. 
     In the method according to the present invention, based on N obsd  and N obsbl  values, the computing means  48 , which receives information as to N obsd  and N obsbl , computes the following equation to obtain EMPAI:
 
 EMPAI= 10 Nobsd/Nobsbl −1
 
     As a result, according to the present invention, it is established that the exponentially modified protein abundance index (“EMPAI”), which is proportional to protein contents in the protein mixture, to determine the protein contents. 
     In addition, the computing means  48  calculates protein contents (mol %) and protein contents (weight %) in accordance with the two equations as follows: 
                 protein   ⁢           ⁢   contents   ⁢           ⁢     (     mol   ⁢           ⁢   %     )       =       EMPAI     ∑     (   EMPAI   )         ×   100       ,     
     ⁢       protein   ⁢           ⁢     contents   ⁢             ⁢             (     weight   ⁢             ⁢             ⁢   %     )       =         EMPAI   ×   MW       ∑     (     EMPAI   ×   MW     )         ×   100       ,         
wherein Σ(EMPAI) is the summation of EMPAI values for all identified proteins and MW represents molecular weight of each protein identified.
 
     According to need, the computed and/or calculated data can be stored in memory means, which is not shown in  FIG. 2 . 
       FIG. 3  shows a flowchart for executing quantitation of protein contents based on the EMPAI according to the present invention. In the step S 10 , a protein of interest is identified by the identifying means  42  after performing the mass spectrometry of samples of the biological materials by a MS method. This MS method includes peptide mass fingerprinting method and MS/MS method. It is understood by those skilled in the art that as disclosed in the following documents (Proc. Natl. Acad. Sci. USA. 1993, 90, 5011-5015, J. Curr. Biol. 1993, 3, 327-332, Biol. Mass. Spectrom. 1993, 22, 338-345, Nat Genet. 1998: 20, 46-50; J Cell Biol. 1998:141, 967-977; J Cell Biol, 2000:148, 635-651; Nature. 2002: 415.141-147; Nature, 2002: 415, 180-183; Curr Opin Cell Biol. 2003: 15, 199-205; Curr Opin Chem Biol. 2003: 7, 21-27, which are incorporated by reference in their entirety). In the next step S 11 , the number of the observed peptides per the protein identified above is measured by the measuring means  44  with use of the MS data. Then, the number of observable peptides per the protein is calculated by the calculating means  46  based on the structure of the protein identified above (as shown in step S 12 ). It is possible to calculate the number of observable peptides per the protein prior to measurement of the observed peptides per the protein. 
       FIG. 4  shows one example of a flowchart for calculating the number of observable peptides per the protein, which is used in the present invention. In the step S 121 , the mass range is determined by using the observed peptides and the scan range of mass spec. In the next step S 122 , the predicted retention times in each observed peptides are calculated based on Meek&#39;s equation (as described in [23]). Note that amino acid sequence of each observed peptides can be determined, for example, by the MS/MS method and according to the Meek&#39;s equation, there is the relationship between the known amino acid sequence and the retention time in liquid chromatography. Then, in the step S 123 , the predicted retention time range is determined by using a retention time of observed peptides, based on the above Meek&#39;s equation. Then, the digested tryptic peptides without missed cleavage are calculated in silico (in step S 124 ). More specifically, since trypsin is famous protease by which the peptide bond can selectively be cleaved at the carboxylic side of lysine residue and arginine residue in the protein, there is determined amino acid sequences of the digested tryptic peptides without missed cleavage. Thus, in this step S 124 , molecular weight (MW) and predicted retention time of the digested tryptic peptides are calculated in silico. In the next step S 125 , the observable peptides are sorted according to the MW and the predicted retention time. Finally, in the step S 126 , the number of observable peptides per protein is counted, on the basis that MW and the predicted retention time of the observable peptides fall both within the mass range (in S 121 ) and retention time range (in S 123 ). 
     It should be noted that in the present invention, there is no limitation on calculation of the number of observable peptides which is carried out according to the flowchart of  FIG. 4 . 
     By use of the number of the observed peptides per the protein (N obsd ) and the number of observable peptides per the protein (N obsbl ), an EMPAI is calculated by the computing means  48  as follows (in step S 13 ):
 
 EMPAI= 10 Nobsd/Nobsbl −1
 
     Using the value of EMPAI, protein contents in molar and weight percentage are expressed as follows: 
                     Protein   ⁢           ⁢   contents   ⁢           ⁢     (     mol   ⁢           ⁢   %     )       =       EMPAI     ∑     (   EMPAI   )         ×   100                   Protein   ⁢           ⁢     contents   ⁢             ⁢             (     weight   ⁢             ⁢             ⁢   %     )       =         EMPAI   ×   MW       ∑     (     EMPAI   ×   MW     )         ×   100                 
wherein MW is the molecular weight of each protein identified, and Σ(EMPAI) is the summation of EMPAI values for all identified proteins (as shown in Steps 14 and 15).
 
     A program which executes the flow of the analysis of protein contents illustrated in  FIG. 3 , which is discussed above, is stored in the memory  14  or the external storage via the external storage medium drive unit  20  or is directly transferred to the CPU  12  in case where the program is stored in the external storage medium, such as the CD-ROM. 
     It should be noted that prior to the quantitation according to the present invention, the protein is identified by the reference to the external database, such as NCBlnr database, through the communication interface  22 . According to need, the display device  16  displays results of the quantitation according to the present invention via the user interface  18 . 
     In this way, the present invention provides the method for executing quantitation of the protein contents based on the EMPAI and computer program for performing the above method. 
     The following will be given of typical examples according to aspects of the present invention, however, the scope of the present invention is not intended to be limited thereto. 
     MATERIALS AND METHODS 
     Preparation of Cell Lysate. 
     RPMI-1640 media (Gibco BRL, Grand Island, N.Y.) containing  13 C 6 -Leu (Cambridge Isotope Laboratories, Andover, Mass.) were prepared according to SILAC protocol by Ong et al. [4]. Mouse neuroblastoma neuro2a cells were cultured for  13 C 6 -Leu labeling in this medium. Whole proteins were lysed using ultrasonication with protease inhibitor cocktail (Roche Diagnostics, Basel, Switzerland). HCT116-C9 cells were grown in a normal RPMI 1640 culture medium as described [10]. Whole proteins were extracted with 5 mL of M-PER (Pierce, Rockford, Ill., USA) containing protease inhibitor cocktail and 5 mM dithiothreitol. 
     Preparation of Peptide Mixtures for LCMSMS. 
     Proteins from cells were dried down and re-suspended by 50 mM Tris-HCl buffer (pH 9.0) containing 8M urea. These mixtures were subsequently reduced, alkylated, and digested by Lys-C (Wako, Osaka, Japan) and trypsin (Promega, Madison, Wis., USA) as described [6]. Digested solutions were acidified by TFA, and were desalted and concentrated by C18-StageTips[21], which were prepared by a fully-automated instrument (Nikkyo Technos, Tokyo, Japan) with Empore C18 disks (3M, Minn., USA). Candidates for peptide synthesis containing at least one leucine and one tyrosine were selected considering the sequences of tryptic peptides from proteins expressed in neuro2a cells. Peptides containing methionine and tryptophan were removed to avoid the oxidation problems during sample preparation. In addition, peptides with double basic residues were removed considering the frequent missed cleavage by trypsin. The selected 54 peptides were synthesized using a Shimadzu PSSM8 (Kyoto, Japan) with F-moc chemistry and were purified by preparative HPLC. Amino acid analysis, peptide mass measurement and HPLC-UV were carried out for purity and structure elucidation. Different amounts of these peptides were spiked to the peptide mixtures from neuro2a cells and purified by StageTip as described above. 
     NanoLC-MS/MS Analysis 
     All samples were analyzed by nanoLC-MS/MS using a QSTAR Pulsar i (ABI/MDS-Sciex, Toronto, Canada) or Finnigan LCQ advantage (Thermoelectron, San Jose, Calif., USA) equipped with a Shimadzu LC10A gradient pump, and an HTC-PAL autosampler (CTC Analytics AG, Zwingen, Switzerland) mounting Valco C2 valves with 150 μm ports. ReproSil C18 materials (3 μm, Dr. Maisch, Ammerbuch, Germany) were packed into a self-pulled needle (100 μm ID, 6 μm opening, 150 mm length) with a nitrogen-pressurized column loader cell (Nikkyo) to prepare an analytical column needle with “stone-arch” frit [22]. A Teflon-coated column holder (Nikkyo) was mounted on. Proxeon x-y-z nanospray interface (Odense, Denmark) and a Valco metal connector with magnet was used to hold the column needle and to adjust the appropriate spray position. The injection volume was 3 μL and the flowrate was 250 nL/min after a tee splitter. The mobile phases consisted of (A) 0.5% acetic acid and (B) 0.5% acetic acid and 80% acetonitrile. The three-step linear gradient of 5% B to 10% in 5 min, 10% to 30% in 60 min, 30% to 100% in 5 min and 100% in 10 min was employed through this study. Spray voltage of 2400 V was applied via the metal connector as described [22]. For QSTAR with faster scan mode, MS scans were performed for 1 second to select three intense peaks and subsequent three MSMS scans were performed for 0.55 seconds each. An Information Dependent Acquisition (IDA) function was active for three minutes to exclude the previously scanned parent ions. For slower scan mode, four MSMS scans (1.5 s each) per one MS scan (1 s) were performed. For LCQ, two MSMS scans per one MS scan were performed with AGC mode. The average scan cycle was 1.19 s for one MS and 1.17 s for one MSMS in average, respectively. The scan range was m/z 300-1400 for both QSTAR and LCQ. 
     DATA Analysis 
     Custom-made software called Spice (Mitsui Knowledge Industry, Tokyo, Japan) was used to extract all peaks from raw data files of both LCQ and QSTAR, and the resultant peak files were submitted to Mascot ver1.9 database searching engine (Matrix Sciences, London, UK; D. M. Perkins, D. J. Pappin, D. M. Creasy, J. S. Cottrell, Electrophoresis 20 (1999) 3551, which is incorporated by reference in its entirety.) for protein identification against Swiss-Prot protein database. The allowed number of missed cleavage set to be 1, and peptide scores to indicate identity was used for peptide identification without manual inspection of MSMS spectra. MSQuant ver1.4a was downloaded from http://msquant.sourceforge.net/, and was customized for  13 C 6  Leu SILAC in order to determine the ion counts in chromatograms for absolute concentration of proteins using the known amounts of the synthetic peptides. 
     Protein Abundance Determination 
     To calculate the number of observable peptides per protein, proteins were digested in silico and the obtained peptide mass was compared with the measurement scan range of mass spectrometers. In addition, the retention times under our nanoLC condition were calculated according to the procedure by Meek [23] and Sakamoto et al.[24] with our own coefficients based on approximately 3000 peptides, and peptides with too hydrophilic or hydrophobic properties were eliminated. An in-house PHP program based on the following equations (1) to (4) was written to calculate the peptide number and was used to export all data to Microsoft Excel. Regarding the number of observed peptides per protein, three counting ways were employed, such as (1) to count unique parent ions, (2) to count unique sequences, and (3) to count unique sequences without partial modification and the overlap caused by missed cleavage. These numbers were exported from Mascot html files to Excel spreadsheets using an “Export All Peptides” function of MSQuant. 
     PAI is Defined As 
                   PAI   =       N   obsd       N   obsbl               (   1   )               
wherein N obsd  and N obsbl  are the number of observed peptides per protein and the number of observable peptides per protein, respectively[17]. Then, EMPAI is defined as
 
 EMPAI= 10 PAI −1  (2)
 
Thus, the protein contents in molar and weight percentages are described as
 
                     Protein   ⁢           ⁢   contents   ⁢           ⁢     (     mol   ⁢           ⁢   %     )       =       EMPAI     ∑     (   EMPAI   )         ×   100             (   3   )                 Protein   ⁢           ⁢     contents   ⁢             ⁢             (     weight   ⁢           ⁢   %     )       =         EMPAI   ×   MW       ∑     (     EMPAI   ×   MW     )         ×   100             (   4   )               
wherein MW is the molecular weight of each protein identified, and Σ EMPAI is the summation of EMPAI values for all identified proteins.
 
DNA Microarray Analysis.
 
     HCT116-C9 cells were plated at 5.0×10 6  cells/dish in 10-cm diameter dishes with 10 mL of the culture medium. After 24-h preincubation, the cells were treated for 12 h with 0.015% DMSO. Duplicate experiments were performed using Affymetrix HuGene FL arrays according to established protocols. Affymetrix GeneChip software was used to extract gene signal intensities, and two sets of data were grouped and averaged based on gene symbol. 
     The Number of Identified Peptides from Single Protein with Different Concentrations. 
     Different amounts of human serum albumin (HSA) tryptic peptides were analyzed by nanoLC-ESI-MS/MS and the number of identified peptides was counted. As shown in  FIG. 5A , both peak area and the number of identified peptides increased as the injection amounts increased although both curves were saturated at higher concentration of HSA. However even at the region where the peak area is linear, the number of peptides does not have linear relationship to the protein amount. Interestingly, the number of peptides shows linear relationship to logarithm of the injected amount from 3 fmol to 500 fmol ( FIG. 5B ). The same data was obtained from LCQ with slower scan. It means that each peak was well separated in time and the influence of “random sampling” caused by the slower scan did not happen under this condition. In this case, three ways were used to count peptides, i.e., (1) all parent ions including different charge states from the same peptide sequences, (2) all peptides excluding different charge states, partial modification such as methionine oxidation, (3) peptides with unique sequence excluding peptides overlapped by missed cleavage. Among them, the number of peptides based on unique parent ions gives the best correlation to the logarithm of protein abundance. It is believed that the results were not under the particular conditions, but more general phenomena. Recently, two independent groups presented the similar curves between the number of peptides and the concentration of proteins. Although both of them did not analyze the logarithm of proteins, but in our hand, their data also looked the linear relationship between logarithm of protein concentration and the number of peptides. The reason why the logarithm of protein concentration correlates to the number of digested peptides is not clear, but it might be explained by the fact that chemical potential is proportional to the logarithm of concentration and the required energy for ionization of peptides is linearly increased as the chemical potential increased. 
     PAI of 47 Proteins in Highly Complex Mixture Solutions 
     Next, the present inventor investigated 54 proteins with known amounts in a whole cell lysate. Tryptic peptides from mouse neuroblastoma neuro2a cell labeled with  13 C 6 -Leu were measured by a single LC-MS/MS run with QSTAR, and 336 proteins were identified based on 1462 peptides. The present inventor spiked 54 synthetic peptides containing  12 C 6 -Leu to this sample solution and quantified the corresponding tryptic peptides containing  13 C 6 -Leu. Seven peptides were not quantified because they gave overlapped peaks in the extracted ion chromatograms (XIC). As a result, 47 proteins with 13K-229 KDa in molecular weight were quantified in the range from 30 fmol to 1.8 pmol/μL in the sample solution as listed in Tables 1A and 1B. 
     
       
         
           
               
               
               
               
               
               
               
               
               
             
               
                 TABLE 1A 
               
               
                   
               
               
                   
                   
                 MASCOT 
                   
                 INJECTION 
                 THE NUMBER 
                 MASCOT 
                   
                   
               
               
                 Acc NO 
                 NAME 
                 HIT# 
                 MW 
                 AMOUNT (fmol) 
                 OF PEPTIDES 
                 SCORE 
                 PAI 
                 EMPAI 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 P19378 
                 Heat shock cognate 71 kDa protein 
                 1 
                 70989 
                 2055 
                 29 
                 1235 
                 0.88 
                 6.56 
               
               
                 P07901 
                 Heat shock protein HSP 90-alpha 
                 2 
                 85003 
                 2351 
                 31 
                 1047 
                 0.86 
                 6.26 
               
               
                 P20152 
                 Vimentin 
                 5 
                 53581 
                 840 
                 27 
                 1080 
                 0.82 
                 5.58 
               
               
                 P58252 
                 Elongation factor 2 (EF-2) 
                 6 
                 96091 
                 295 
                 24 
                 830 
                 0.53 
                 2.41 
               
               
                 Q03265 
                 ATP synthase alpha chein, mitochondrial 
                 8 
                 59830 
                 371 
                 18 
                 635 
                 0.56 
                 2.65 
               
               
                   
                 precursor 
               
               
                 P17182 
                 Alpha enolase 
                 9 
                 47322 
                 1491 
                 21 
                 828 
                 0.88 
                 6.50 
               
               
                 P15331 
                 Peripherin 
                 10 
                 54349 
                 209 
                 13 
                 556 
                 0.39 
                 1.48 
               
               
                 P48975 
                 Actin, cytoplasmic 1 (Beta-actin) 
                 12 
                 42053 
                 3015 
                 22 
                 894 
                 1.22 
                 15.66 
               
               
                 P05213 
                 Tubulin alpha-2 chain (Alpha-tublin 2) 
                 14 
                 50818 
                 4455 
                 24 
                 862 
                 1.14 
                 12.89 
               
               
                 P52480 
                 Pyruvate kinase, M2 isozyme 
                 16 
                 58289 
                 539 
                 17 
                 643 
                 0.49 
                 2.06 
               
               
                 P20001 
                 Elongation factor 1-alpha 1 
                 24 
                 50424 
                 2176 
                 17 
                 647 
                 1.00 
                 9.00 
               
               
                 P08113 
                 Endoplasmin presursor 
                 25 
                 92703 
                 225 
                 10 
                 379 
                 0.23 
                 0.71 
               
               
                 O35501 
                 Stress-70 protein, mitochodrial precursor 
                 27 
                 73970 
                 488 
                 15 
                 495 
                 0.38 
                 1.42 
               
               
                 P14869 
                 60S acidic ribosomal protein P0 
                 34 
                 34336 
                 255 
                 9 
                 379 
                 0.50 
                 2.16 
               
               
                 P03975 
                 igE-binding protein 
                 35 
                 63221 
                 306 
                 10 
                 368 
                 0.33 
                 1.15 
               
               
                 Q9CZD3 
                 Glycyl-tRNA synthetase 
                 37 
                 82624 
                 193 
                 9 
                 341 
                 0.21 
                 0.62 
               
               
                 P35215 
                 14-3-3 protein zeta/delta 
                 40 
                 27925 
                 952 
                 12 
                 401 
                 0.75 
                 4.62 
               
               
                 P42932 
                 T-complex protein 1, theta, subunit 
                 42 
                 60088 
                 240 
                 9 
                 281 
                 0.26 
                 0.81 
               
               
                 P51881 
                 ADP, ATP carrier protein, fibrobiast 
                 46 
                 33138 
                 660 
                 8 
                 294 
                 0.42 
                 1.64 
               
               
                   
                 isoform 
               
               
                 Q9JIK5 
                 Nucleolar RNA helicase II 
                 48 
                 94151 
                 75 
                 8 
                 268 
                 0.16 
                 0.45 
               
               
                 P14148 
                 60S ribosomal protein L7 
                 52 
                 31457 
                 300 
                 9 
                 227 
                 0.53 
                 2.38 
               
               
                 Q9WVA4 
                 Transgelin 2 
                 65 
                 23810 
                 324 
                 8 
                 258 
                 0.62 
                 3.12 
               
               
                 P14211 
                 Calreticulin precursor 
                 72 
                 48136 
                 285 
                 8 
                 246 
                 0.33 
                 1.15 
               
               
                 P16858 
                 Glyceraldehyde 3-phosphate dehydrogenase 
                 87 
                 35941 
                 1000 
                 8 
                 270 
                 0.53 
                 2.41 
               
               
                   
               
            
           
         
       
     
     
       
         
           
               
               
               
               
               
               
               
               
               
             
               
                 TABLE 1B 
               
               
                   
               
               
                   
                   
                 MASCOT 
                   
                 INJECTION 
                 THE NUMBER 
                 MASCOT 
                   
                   
               
               
                 Acc NO 
                 NAME 
                 HIT# 
                 MW 
                 AMOUNT (fmol) 
                 OF PEPTIDES 
                 SCORE 
                 PAI 
                 EMPAI 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
               
               
               
            
               
                 P29314 
                 40S ribosomal protein S9 
                 88 
                 22418 
                 338 
                 6 
                 201 
                 0.46 
                 1.89 
               
               
                 Q60932 
                 Voltage-dependent anion-selective 
                 89 
                 32502 
                 180 
                 6 
                 261 
                 0.38 
                 1.37 
               
               
                   
                 channel protein 
               
               
                 P17080 
                 GTP-bilding nuclear protein RAN 
                 97 
                 24579 
                 638 
                 5 
                 181 
                 0.45 
                 1.85 
               
               
                 P17008 
                 40S ribosomal protein S16 
                 98 
                 16418 
                 1140 
                 6 
                 174 
                 0.60 
                 2.98 
               
               
                 Q60930 
                 Voltage-dependent anion-selective 
                 99 
                 32340 
                 135 
                 4 
                 161 
                 0.27 
                 0.85 
               
               
                   
                 channel protein 
               
               
                 P11983 
                 T-complex protein 1, alpha subunit B 
                 100 
                 60867 
                 90 
                 6 
                 143 
                 0.18 
                 0.52 
               
               
                 P05064 
                 Fructose-bisphosphate aldolase A 
                 103 
                 39656 
                 525 
                 6 
                 260 
                 0.33 
                 1.15 
               
               
                 P09058 
                 40S ribosomal protein S8 
                 109 
                 24344 
                 240 
                 6 
                 186 
                 0.60 
                 2.98 
               
               
                 Q01320 
                 DNA topoisomerase II, alpha isozyme 
                 143 
                 173567 
                 90 
                 4 
                 125 
                 0.05 
                 0.12 
               
               
                 Q8VEM8 
                 Phosphate carrier protein, mitochondrial 
                 149 
                 40063 
                 120 
                 4 
                 122 
                 0.19 
                 0.55 
               
               
                   
                 precursor 
               
               
                 P19253 
                 60S ribosomal protein L13a 
                 150 
                 23432 
                 120 
                 4 
                 123 
                 0.33 
                 1.15 
               
               
                 P08526 
                 60S ribosomal protein L27 
                 157 
                 15657 
                 214 
                 3 
                 113 
                 0.50 
                 2.16 
               
               
                 P47961 
                 40S ribosomal protein S4 
                 160 
                 29666 
                 405 
                 4 
                 109 
                 0.21 
                 0.62 
               
               
                 Q06647 
                 ATP synthase ollgomycin sensitivity 
                 179 
                 23440 
                 195 
                 3 
                 98 
                 0.23 
                 0.70 
               
               
                   
                 conferral protein 
               
               
                 Q9CPR4 
                 60S ribosomal protein L17 
                 182 
                 21506 
                 225 
                 3 
                 96 
                 0.30 
                 1.00 
               
               
                 P39026 
                 60S ribosomal protein L11 
                 186 
                 20337 
                 270 
                 3 
                 92 
                 0.33 
                 1.15 
               
               
                 Q9D1R9 
                 60S ribosomal protein L34 
                 204 
                 13381 
                 240 
                 3 
                 83 
                 0.50 
                 2.16 
               
               
                 O08807 
                 Peroxiredoxin 4 
                 206 
                 31261 
                 180 
                 4 
                 83 
                 0.27 
                 0.85 
               
               
                 Q62188 
                 Dihydropyrimidinase related protein-3 
                 207 
                 62296 
                 75 
                 3 
                 82 
                 0.10 
                 0.27 
               
               
                 P50310 
                 Phosphoglycerate kinase 
                 213 
                 44776 
                 75 
                 3 
                 80 
                 0.13 
                 0.33 
               
               
                 Q9DBJ1 
                 Phosphoglycerate mutase 1 
                 223 
                 28797 
                 285 
                 3 
                 70 
                 0.25 
                 0.78 
               
               
                 P11442 
                 Clathrin heavy chain 
                 226 
                 193187 
                 137 
                 3 
                 69 
                 0.04 
                 0.09 
               
               
                 QPJLT0 
                 Myosin heavy chain, nonmuscle type B 
                 305 
                 229793 
                 87 
                 1 
                 45 
                 0.01 
                 0.02 
               
               
                   
               
            
           
         
       
     
     In this case, two additional factors should be considered. One is the influence of protein size on the number of peptides. Generally larger proteins generate more detectable peptides. Therefore, observable peptides were used for normalization as previously except the additional criteria on retention times. Another factor was the background. In this case, a huge number of peptides existed in the sample. Therefore, the number of observed peptides would be to some extent influenced by the ionization suppression effect as well as the random selection for MSMS events.  FIG. 6A  shows that there is also a linear relationship between log [protein] and the number of observed peptides normalized by the number of observable peptides per protein even when the different proteins were plotted into one graph. Other parameters such as Mascot score and the number of peptides do not correlate well to protein abundance, and the number of peptides divided by molecular weight of protein gives moderate correlation to logarithm of protein contents, as shown in  FIGS. 6B-D . 
     In this case, the present inventor used highest MSMS scan speed of QSTAR to minimize the background influence. When lower scan speed was used, the correlation was decreased (r=0.90 to 0.81). For example, refer to  FIG. 7A . This effect was more pronounced when iontrap instrument was used (r=0.77). This would be because the limited amount of trap capacity causes more biased peak selection for more abundant proteins, and actually the larger deviation was observed for higher abundant proteins in  FIG. 7B . Recent development of linear iontrap with higher capacity with faster scan would provide similar results to QSTAR with faster scan mode. 
     In addition, the influence of the sample complexity would be minimized by using multidimensional separation prior to MSMS analysis such as LC/LC-MS/MS [25] and GeLCMS (gel-enhanced LC-MS, 1D-gel followed by slicing, digesting and LCMS analysis) approaches [15]. 
     Example of EMPAI Calculation 
     The whole protocol to calculate EMPAI values is as follows:
     (1) Perform LC-MS/MS analysis;   (2) Identify proteins using search engines such as Mascot;   (3) Extract the number of unique parent ions per protein;   (4) Count the number of observable peptides per protein; and   (5) Calculate EMPAI value using (3) and (4).   

     The following is an example of EMPAI calculation by use of one typical example in Table 2. 
                                 TABLE 2                          Sample   human serum albumin, 150 fmol           Method   LC-MSMS           Search engine   Mascot           Protein database   SwissProt                        
(3) Extraction of Observed Unique Parent Ions
 
     The extraction of observed unique parent ions was performed following the above protocol (1) and (2). The results of the above extraction are tabulated in Table 3. In the column of “Accept or not” in Table 2, based on the results of Mascot score, the term “Yes” refers to extraction being carried out and the term “No” means that the observed parent ions was not extracted due to small Mascot score. 
                                                 TABLE 3                              Total                       ALBU_HUMAN   Mass:   71317   score:   1337   Peptides matched:   37                         P02768   Serum albumin precursor                                                     Pept           Missed               Accept           No   Observed   Mr(calc)   cleavage   Score   Rank   Peptide   or not   Comments                                                         1   395.2529   788.4643   0   35   1   LVTDLTK   Yes           2   440.7369   879.4337   0   27   1   AEFAEVSK   Yes       3   464.2663   926.4861   0   35   1   YLYEIAR   Yes       4   467.2726   932.5113   0   40   1   LCTVATLR   Yes       5   470.7521   939.441   0   60   1   DDNPNLPR   Yes       6   476.2422   950.4345   0   10   1   DLGEENFK   No   Score is less than the                                       threshold.       7   480.8003   959.5552   0   51   1   FQNALLVR   Yes       8   492.7642   983.4811   0   3   1   TYETTLEK   No   Score is less than the                                       threshold.       9   500.8268   999.5964   0   53   1   QTALVELVK   Yes       10   507.3224   1012.5916   0   20   2   LVAASQAALGL   No   Rank is not 1       11   509.2948   1016.5291   0   27   1   SLHTLFGDK   Yes       12   535.7427   1069.4386   0   16   1   ETCFAEEGK   Yes       13   537.7922   1073.5352   1   43   1   LDELRDEGK   Yes       14   376.9148   1127.6913   1   17   1   KQTALVELVK   Yes   Overlapped sequence with                                       pept 9, but different parent ion       15   564.8761   1127.6913   1   38   1   KQTALVELVK   Yes   Same sequence as Pept14, but                                       different charge       16   569.7725   1137.4906   0   65   1   CCTESLVNR   Yes       17   575.3179   1148.6077   0   60   1   LVNEVTEFAK   Yes       18   599.7489   1197.5335   1   36   1   ETCFAEEGKK   Yes       19   671.7959   1341.6274   0   86   1   AVMDDFAAFVEK   Yes       20   679.7764   1357.6223   0   33   1   AVMDDFAAFVEK +   Yes   Same sequence and charge as                               Oxidation(M)       Pept20, but different parent ion                                       because of modification       21   686.267   1370.5594   0   56   1   AAFTECCQAADK   Yes       22   717.7518   1433.5261   0   61   1   ETYGEMADCCAK   Yes       23   722.3062   1442.6347   0   60   1   YICENQDSISSK   Yes       24   749.7717   1497.5711   0   72   1   TCVADESAENCDK   Yes       25   756.4021   1510.8354   0   19   1   VPQVSTPTLVEVSR   Yes       26   820.4454   1638.9304   1   40   1   KVPQVSTPTLVEVSR   Yes       27   547.3489   1638.9304   1   63   1   KVPQVSTPTLVEVSR   Yes   Same sequence as Pept26, but                                       different charge       28   829.3597   1656.7453   0   61   1   QNCELFEQLGEYK   Yes       29   581.6665   1741.8867   0   17   1   HPYFYAPELLFFAK   Yes       30   955.9536   1909.9243   0   26   1   RPCFSALEVDETYVPK   Yes       31   955.95   1909.9243   0   20   1   RPCFSALEVDETYVPK   No   Same parent ion as pept 30       32   1023.0412   2044.088   0   39   1   VFDEFKPLVEEPQNLIK   Yes       33   696.2625   2085.8302   0   35   1   VHTECCHGDLLECADDR   Yes       34   1043.9149   2085.8302   0   60   1   VHTECCHGDLLECADDR   Yes   Same sequence as Pept33, but                                       different charge       35   522.4952   2085.8302   0   35   1   VHTECCHGDLLECADDR   Yes   Same sequence as Pept33-34,                                       but different charge       36   862.3713   2584.1104   1   18   1   VHTECCHGDLLECAD   Yes   Overlapped sequence with                               DRADLAK       pept 35, but different parent ion       37   884.0808   2649.2566   0   34   1   LVRPEVDVMCTAFHD   Yes                               NEETFLK               Observed unique parent ions 33            
(4) Counting of the Number of Observable Peptides Per Protein
 
     As explained above in  FIG. 4 , this calculation was performed according to the below Steps 1 to 6; 
     Step 1 (see S 121  in  FIG. 4 ): Determination of the mass range using observed peptides and the scan range of mass spec. This step was carried out by using the actual observed mass spectrometry of the peptides, i.e., the observed peptides and the scan range of the mass spec. 
     Step 2 (see S 122  in  FIG. 4 ): Calculation of the predicted retention times in each observed peptides, based on Meek&#39;s equation; As explained in S 122  in  FIG. 4 , since it is appreciated that amino acid sequences of each observed peptides can generally be determined by the MS/MS method, retention time of the observed peptides could be calculated according to the Meek&#39;s equation in which there is the relationship between the known amino acid sequence and the retention time. 
     Step 3 (see S 123  in  FIG. 4 ): Determination of the retention time range using observed peptides; Similarly, this step was carried out by use of the Meek&#39;s equation. 
     Step 4 (see S 124  in  FIG. 4 ): Calculation of the digested tryptic peptides without missed cleavage in silico; As described in S 124  of  FIG. 4 , molecular weight (MW) and the predicted retention time of the digested tryptic peptides could be calculated from amino acid sequences of the digested tryptic peptides, which was determined in silico. 
     Step 5 (see S 125  in  FIG. 4 ): The observable peptides were sorted according to MW and the predicted retention time by use of results of S 124 . 
     Step 6 (see S 126  in  FIG. 4 ): The number of the observable peptides per protein was counted, on the basis that MW and the predicted retention time of the observable peptides fall both within the mass range (Step  121 ) and the retention time range (Step  123 ). 
     According to results using the sample in Table 2, mass range and retention time range were determined as in Table 4. 
     
       
         
           
               
             
               
                 TABLE 4 
               
             
            
               
                   
               
               
                 Mass range: 700-2800 
               
               
                 Retention time range: 40-150 
               
            
           
           
               
               
               
               
               
            
               
                 Pept 
                 Peptide 
                 Retention 
                   
                 Accept or 
               
               
                 No 
                 Mass 
                 time 
                 Peptide 
                 not 
               
               
                   
               
            
           
           
               
               
               
               
               
            
               
                 1 
                 277.14603 
                 35.47 
                 MK 
                 No 
               
               
                 2 
                 2036.0772 
                 166.9 
                 WVTFISLLFLFSSAYSR 
                 No 
               
               
                 3 
                 477.27 
                 49.91 
                 GVFR 
                 No 
               
               
                 4 
                 174.11169 
                 27.66 
                 R 
                 No 
               
               
                 5 
                 469.22852 
                 20.51 
                 DAHK 
                 No 
               
               
                 6 
                 697.35077 
                 29.75 
                 SEVAHR 
                 No 
               
               
                 7 
                 293.17396 
                 42.12 
                 FK 
                 No 
               
               
                 8 
                 950.43458 
                 61.1 
                 DLGEENFK 
                 Yes 
               
               
                 9 
                 2432.2563 
                 130.71 
                 ALVLIAFAQYLQQCPFEDHVK 
                 Yes 
               
               
                 10 
                 1148.6078 
                 77.64 
                 LVNEVTEFAK 
                 Yes 
               
               
                 11 
                 1383.5283 
                 43.9 
                 TCVADESAENCDK 
                 Yes 
               
               
                 12 
                 1016.5291 
                 64.57 
                 SLHTLFGDK 
                 Yes 
               
               
                 13 
                 875.48992 
                 67.93 
                 LCTVATLR 
                 Yes 
               
               
                 14 
                 1319.4833 
                 52.14 
                 ETYGEMADCCAK 
                 Yes 
               
               
                 15 
                 657.30824 
                 31.51 
                 QEPER 
                 No 
               
               
                 16 
                 1017.4702 
                 45.5 
                 NECFLQHK 
                 Yes 
               
               
                 17 
                 939.44106 
                 43.21 
                 DDNPNLPR 
                 Yes 
               
               
                 18 
                 2592.2354 
                 110.88 
                 LVRPEVDVMCTAFHDNEETFLK 
                 Yes 
               
               
                 19 
                 146.10554 
                 26.54 
                 K 
                 No 
               
               
                 20 
                 926.48621 
                 72.63 
                 YLYEIAR 
                 Yes 
               
               
                 21 
                 174.11169 
                 27.66 
                 R 
                 No 
               
               
                 22 
                 1741.8869 
                 114.7 
                 HPYFYAPELLFFAK 
                 Yes 
               
               
                 23 
                 174.11169 
                 27.66 
                 R 
                 No 
               
               
                 24 
                 309.16887 
                 33.88 
                 YK 
                 No 
               
               
                 25 
                 1256.5166 
                 52.52 
                 AAFTECCQAADK 
                 Yes 
               
               
                 26 
                 714.40988 
                 59.32 
                 AACLLPK 
                 Yes 
               
               
                 27 
                 644.34938 
                 61.04 
                 LDELR 
                 No 
               
               
                 28 
                 447.19656 
                 30.1 
                 DEGK 
                 No 
               
               
                 29 
                 462.24384 
                 31.3 
                 ASSAK 
                 No 
               
               
                 30 
                 302.17027 
                 26.46 
                 QR 
                 No 
               
               
                 31 
                 259.18961 
                 41.21 
                 LK 
                 No 
               
               
                 32 
                 648.32653 
                 41.34 
                 CASLQK 
                 No 
               
               
                 33 
                 507.24418 
                 45.11 
                 FGER 
                 No 
               
               
                 34 
                 364.21108 
                 44.19 
                 AFK 
                 No 
               
               
                 35 
                 672.37079 
                 59.13 
                 AWAVAR 
                 No 
               
               
                 36 
                 502.28637 
                 41.44 
                 LSQR 
                 No 
               
               
                 37 
                 390.22673 
                 42.47 
                 FPK 
                 No 
               
               
                 38 
                 879.43385 
                 58.42 
                 AEFAEVSK 
                 Yes 
               
               
                 39 
                 788.46441 
                 67.48 
                 LVTDLTK 
                 Yes 
               
               
                 40 
                 1914.766 
                 54.16 
                 VHTECCHGDLLECADDR 
                 Yes 
               
               
                 41 
                 516.2908 
                 47.04 
                 ADLAK 
                 No 
               
               
                 42 
                 1385.6133 
                 57.4 
                 YICENQDSISSK 
                 Yes 
               
               
                 43 
                 259.18961 
                 41.21 
                 LK 
                 No 
               
               
                 44 
                 1190.5676 
                 50.35 
                 ECCEKPLLEK 
                 Yes 
               
               
                 45 
                 2916.3159 
                 117.83 
                 SHCIAEVENDEMPADLPSLAADFVESK 
                 No 
               
               
                 46 
                 463.2101 
                 34.33 
                 DVCK 
                 No 
               
               
                 47 
                 694.32864 
                 38.77 
                 NYAEAK 
                 No 
               
               
                 48 
                 1622.7804 
                 124.55 
                 DVFLGMFLYEYAR 
                 Yes 
               
               
                 49 
                 174.11169 
                 27.66 
                 R 
                 No 
               
               
                 50 
                 1310.7347 
                 85.87 
                 HPDYSVVLLLR 
                 Yes 
               
               
                 51 
                 330.22673 
                 43.28 
                 LAK 
                 No 
               
               
                 52 
                 983.48118 
                 57.39 
                 TYETTLEK 
                 Yes 
               
               
                 53 
                 1380.5261 
                 33.61 
                 CCAAADPHECYAK 
                 No 
               
               
                 54 
                 2044.0882 
                 108.35 
                 VFDEFKPLVEEPQNLIK 
                 Yes 
               
               
                 55 
                 1599.724 
                 80.32 
                 QNCELFEQLGEYK 
                 Yes 
               
               
                 56 
                 959.5553 
                 79 
                 FQNALLVR 
                 Yes 
               
               
                 57 
                 410.21655 
                 35.26 
                 YTK 
                 No 
               
               
                 58 
                 146.10554 
                 26.54 
                 K 
                 No 
               
               
                 59 
                 1510.8356 
                 76.16 
                 VPQVSTPTLVEVSR 
                 Yes 
               
               
                 60 
                 430.25401 
                 39.13 
                 NLGK 
                 No 
               
               
                 61 
                 389.22746 
                 33.52 
                 VGSK 
                 No 
               
               
                 62 
                 352.12392 
                 24.44 
                 CCK 
                 No 
               
               
                 63 
                 580.29694 
                 21.52 
                 HPEAK 
                 No 
               
               
                 64 
                 174.11169 
                 27.66 
                 R 
                 No 
               
               
                 65 
                 2403.1638 
                 117.37 
                 MPCAEDYLSVVLNQLCVLHEK 
                 Yes 
               
               
                 66 
                 673.33954 
                 38.54 
                 TPVSDR 
                 No 
               
               
                 67 
                 346.22164 
                 35.07 
                 VTK 
                 No 
               
               
                 68 
                 1023.4478 
                 49.82 
                 CCTESLVNR 
                 Yes 
               
               
                 69 
                 1852.903 
                 78.52 
                 RPCFSALEVDETYVPK 
                 Yes 
               
               
                 70 
                 2201.994 
                 104.04 
                 EFNAETFTFHADICTLSEK 
                 Yes 
               
               
                 71 
                 303.15429 
                 30.01 
                 ER 
                 No 
               
               
                 72 
                 387.24819 
                 36.54 
                 QIK 
                 No 
               
               
                 73 
                 146.10554 
                 26.54 
                 K 
                 No 
               
               
                 74 
                 999.5965 
                 74.78 
                 QTALVELVK 
                 Yes 
               
               
                 75 
                 508.31219 
                 6.27 
                 HKPK 
                 No 
               
               
                 76 
                 318.19034 
                 29.99 
                 ATK 
                 No 
               
               
                 77 
                 516.29079 
                 42.36 
                 EQLK 
                 No 
               
               
                 78 
                 1341.6276 
                 92.87 
                 AVMDDFAAFVEK 
                 Yes 
               
               
                 79 
                 352.12392 
                 24.44 
                 CCK 
                 No 
               
               
                 80 
                 447.19656 
                 31.99 
                 ADDK 
                 No 
               
               
                 81 
                 1012.4172 
                 51.09 
                 ETCFAEEGK 
                 Yes 
               
               
                 82 
                 146.10554 
                 26.54 
                 K 
                 No 
               
               
                 83 
                 1012.5918 
                 95.44 
                 LVAASQAALGL 
                 Yes 
               
               
                   
               
               
                 Number of observable peptides 34 
               
            
           
         
       
     
     The results of the above counting are tabulated in Table 4. In the column of “Accept or not” in Table 2, based on the results of calculation of the observable peptides in silico, the term “Yes” refers to the observable peptides falling both within the mass range and the retention time range and the term “No” means that the observable peptides falling outside either the mass range or the retention time range. 
     (5) Calculation of EMPAI Value Using (3) and (4) 
     According to the above equations (1) and (2), EMPAI was calculated by using results of the above (3) and (4). The results of EMPAI are tabulated in Table 5. As can be seen in Table 5, a value of EMPAI using the sample in Table 2 was 8.345. 
                                     TABLE 5                          Number of observed   33               unique parent ions           Number of observable   34           peptides           EMPAI   8.345   EMPAI = 10 PAI  − 1                        
Absolute Quantitation Using EMPAI
 
     Although PAI can estimate the abundance relationship between proteins, it cannot express the molar fraction directly. Therefore, the present inventor derived a new parameter, EMPAI, from PAI, as described in Protein Abundance Determination section as the equation (2), which is directly proportional to protein contents as shown in  FIG. 8 . In order to calculate the absolute concentrations, total protein amounts were measured in weight by BCA assay and the weight fractions of 47 proteins among 336 neuro2a proteins were calculated using equation (4): As shown in  FIG. 9 , the EMPAI-based concentrations were highly consistent with the actual values (y=0.97x, r=0.93) to and the deviation percentage to the actual values ranged from 3% to 260%, and the average was 63%. Because the present inventor used BCA assay for total protein amounts, these values were easily changed. Nevertheless, this EMPAI approach provides quite accurate index for comprehensive absolute quantitation. 
     Application to Comprehensive Protein Expression Analysis 
     PAI is really convenient to produce protein expression data from just single LCMSMS run. The present inventor applies this approach to compare it to gene expression in HCT116 human cancer cells. DNA microarray provided expression data of 4971 genes, whereas single LCMS run provided 402 identified proteins based on 1811 peptides with unique sequences. Bridging gene symbols with protein accession numbers resulted in total 227 genes/proteins employed for the expression comparison study. As expected, slight correlation was observed as expected from previous studies on yeast [18,26]. Interestingly, most of outliers were ribosomal proteins (see  FIG. 10 ). It is well known that unlike prokaryotes such as  E. coli , mammalian cells regulate the expression levels of ribosomal proteins not only by transcription, but also transport of mRNA, translation, and the degradation of excess amounts of proteins unassociated with rRNA [27,28]. The present inventor also did comparison study between gene and protein expression using EMPAI for  E. coli  and did not find such a deviation of ribosomal proteins. Although both gene and protein expression data are not so accurate as to discriminate 10% difference for instance, it is quite helpful to obtain the brief overview as shown above. 
     INDUSTRIAL APPLICABILITY 
     According to the present invention, it is established the scale for absolute protein abundance named EMPAI. Because EMPAI is easily calculated from the output information of database search engines such as Mascot, it is possible to apply this approach to the previously measured or published dataset to add the quantitative information without any additional step. EMPAI can also use for relative quantitation, especially in the cases where isotope-based approaches cannot be applied because of quantitative changes that are too large for accurate measurements of ratios, because metabolic labelling is not possible or because sensitivity constraints do not allow chemical labelling techniques. In such cases, EMPAI values of proteins in one sample can compare to those in another sample, and the outliers from the EMPAI correlation between two samples can be determined as increasing or decreasing proteins. 
     This EMPAI approach can also apply to multidimensional separation-MSMS to extend the coverage of proteins. Further improvement would be possible to consider MS instrument-dependent parameters such as ionization dependence on m/z region. Since the EMPAI index can be calculated with a simple script and does not require further experimentation in protein identification experiments, we suggest its routine use in the reporting of proteomic results. 
     REFERENCES 
     The following references cited herein are hereby incorporated by reference in their entirety.
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