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Theoretical evaluation of shotgun proteomic analysis strategies;

Peptide observability and implication of choices in enzymes, technologies and their combinations

Summary Proteomics is a powerful high-throughput technique to study thousands of proteins.

Despite the improvements, shotgun proteomics approach is susceptible to sample complexity. The limited dynamic range and heavily overlapping peptides in LC-MS/MS reduce the efficiency and probability of peptide identification. Although widely used, such approaches are not completely understood. There is a lack of studies addressing the characteristics of protein digests and the efficiencies of their separations under various conditions. In this study we examine the observability of peptides as well as the separation profile of peptides generated by proteolysis under 2D-LC-MS/MS or peptide IEF-LC-MS/MS approach in conjunction with different proteases to better understand overall properties of proteomic peptides.

First, mouse shotgun MS raw data was obtained from a publicly available repository. The identified peptides and proteins were utilized to optimize amino acid hydrophobicity coefficients, to predict retention time of peptides, and to build a peptide observability function. Theoretical peptides by in Silico digestion of mouse proteome with virtual enzymes including trypsin, chymotrypsin, V8, Lys-C, and Asp-N are applied to peptide observability function to evaluate the observability of peptides, and the separation profiles by three different separations techniques such as SAX, SCX, and IEF followed by RP-HPLC coupled with MS/MS analyses.

The application of peptide observability function to the theoretical tryptic digests of mouse proteins achieved high correlation (R=0.995) to experimentally observed tryptic digests of mouse proteins by LC-MS/MS, demonstrating that observability function predicts peptide observability by LC-MS/MS analyses accurately. The evaluation of the theoretical peptides with observability function suggests SAX/trypsin, IEF/Trypsin as favorable combinations of enzymes and separation methods. Despite the difference in proteins’ nature in subcellular components, all observable sub-proteomes showed identical pattern for theoretical separations by methods evaluated. Overall, our theoretical evaluation of peptides observability and separation profile of digested peptides provides a valuable foundation for future direction. Introduction

Proteomics, the experimental investigation of the proteome (PROTEins expressed by the genOME) is a rapidly developing field of research. Proteomics studies large collection of proteins which define specific biological systems at a given time. Recent advances in technology allow researchers to apply proteomics techniques to understand the changes in a broad range of biological systems such as pathlogical disease states and, stress treatment, as well as to monitor the efficiency of therapeutic interventions (1-3). Currently there are two fundamental strategies used in proteomics studies, top-down and bottom-up.

The top-down approach separates and quantifies proteins at the intact protein level. Most frequently used method for top-down approach is the two-dimensional gel electrophoresis (2D gel) analysis followed by mass spectrometry to identify protein spots. Recently, mass spectrometry alone was also utilized to analyze intact proteins as a top-down strategy. In the bottom-up approach, protein complexes are first subjected to chemical or enzymatic digestion. The digested peptides are then separated usually by chromatography followed by mass spectrometry to identify peptide and protein sequences. This is also known as the shotgun approach.

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In the shotgun approach, trypsin is widely used to digest proteins to peptides. Trypsin, a serine protease, cleaves polypeptides immediately after an arginine (R) or a lysine (K). The cleaved peptides are usually fractionated using strong cation exchange (SCX) column to reduce the complexity and to allow the identification of low abundant proteins before applying reverse phase LC-MS/MS(4, 5). Recently, isoelectric focusing was utilized to fractionate tryptic peptides as a first dimensional separation instead of SCX prior to LC-MS/MS(6-8). The combined analyses of all fractions represent hundreds or thousands of proteins. With a rapid development in mass spectrometry techniques, it is expected that proteomics will be utilized routinely to identify the changes or biomarkers in various patho-physiologic proteomic samples in the future. However, being able to quantify an individual protein in a complex proteome will require more effort.

Despite improvements in bottom-up proteomics studies, shotgun proteomics approach still has known susceptibility to sample complexity. The limited dynamic range of peptide amounts and heavily overlapping peptide distribution in final LC-MS/MS analysis reduce the efficiency and probability of peptide identification. These limitations more severely affect the less abundant proteins that may be mostly functionally important species. There are many approaches used to address this problem. The pre-fractionation and multi-dimensional separation are most widely and successfully used techniques. Although widely used, lack of studies addressing fundamental understanding of digestion of proteins and separation of digested peptides under such approaches hinder improvements in these technologies.

Therefore, this study was undertaken to examine the observability of peptides as well as the separation profile of peptides generated by proteolysis under 2D-LC-MS/MS or peptide IEF-LC-MS/MS approach in conjunction with different proteases to understand better overall properties of proteomic peptides.

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Experimental procedure MS/MS data analysis of mouse shotgun proteomics data

The raw shotgun MS/MS data of 10 identical runs for tryptic digests of mouse breast tissue were acquired from public proteomic data repository (FHCRC proteomics Repository, http://proteomics.fhcrc.org/CPL/home.html)(9). The raw MS dataset of 10 runs for normal breast tissue(10) were analyzed using Mascot v.2.1.03(11) to identify the peptide and protein sequences as separate entries or a single combined .mgf (Mascot generic format) file. Searches were performed against rodent Swiss-Prot database with carbamidomethylation of cystein, with partial oxidation of methionine, with 1 missed cleavage allowed, and with mass tolerance of 1.5 Da and of 0.8 Da for MS and MS/MS, respectively. We have used relatively stringent cutoff ion score of 50 for peptides using Swiss-Prot/UniProt(12) Rodents database (50.3). Ion score 50 was calculated as follows.

The Mascot score is calculated with formula S = , where S = 50 means probability = 10-5. The probability for peptide to be observed by chance is database size dependent, so the following calculations are necessary.

Database size Ds = 2.2x105 entries Average length of protein ln = 360 residues Average K/R frequency in the sequence fR/fK = 5.9/5.5%, respectively Total Tryptic peptides N = Ds x ln x (fR+fK) = 2.2x105x3.6x102*1.1x10-1 ! 107 Average length of peptides Lav = Ds x ln / N = 9 residues

Suppose we have a peptide with 9 residues identified by a database search. Possible sequences for same amino acid composition with 9 residues : 9! ! 3.6x105 With consideration of amino acid frequency such as Leu/Ala ~10% in sequence,

Total number of possible unique sequences Sq = roughly 2x105 Probability to observe this peptide by chance c = 1/ Sq = (2x105)-1 =5x10-4 Occurrence of this peptide in this database Oc = N x c =107x5x10-4=5x103 Significance level (p-value 0.05) : Oc x p = 5x10-2

Thus, necessary probability p = 0.05/ Oc = 5x10-2/5x103 = 10-5 This above is a rough calculation for probability p, but as S is logarithmic, estimation of

order is meaningful. As this calculation is exactly same for M.W., with error range depending on mass spectrometer, this cutoff score is rather stringent enough. In addition, false discovery rate for each run calculated using decoy database search was below 0.2%.

The identified peptide and protein lists from total of 10 runs are subjected to in-house program to extract information such as observed scan numbers, sequences, and protein IDs and to remove redundant peptides entries. The entries with best peptide probability were taken among the overlapped peptide entries with same sequence and protein ID. This procedure was necessary to have single entry for each peptide for parameter optimization. Raw data were also converted to dta files with header using ReAdW (13) in order to retrieve scan number/retention time relations.

Optimization of intrinsic amino acid hydrophobicity coefficients

Elution time/scan number information from non-redundant peptide list was obtained from dta header file of each run. The initial values for hydrophobicity coefficients measured by Kovacs, JM et al. using synthetic peptides(14) are utilized as starting values for optimization to make sure convergence of optimized coefficients to be around experimentally determined values under reversed-phase HPLC conditions. (Note: the values derived from reference literature are in arbitrary unit which are relative values to poly-glycine. Although unit is arbitrary, they are only used for further calculations as internal, and intermediate parameters.) The amino acid compositions of peptides, and observed scan numbers are used to optimize hydrophobicity

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coefficients of amino acid side-chains. The code is written in MATLAB® using function lsqnonlin. Charged amino acids are split into two entries (for the cases charged residues are located next to oppositely charged residues including amino and carboxyl-termini) to compensate effects of nearby charged residues effect. Thus, total 25 amino acid entries are used for optimization. Following is brief explanation for optimization process. Amino acid composition matrix for n peptides with m amino acid components is:

= ,

!

! c o= ,

!

! I dx=

!

! c o : Hydrophobicity coefficient vector, : peptide hydrophobicity index vector

!

! c o= +

!

! " ,

!

! c o- =

!

! " (

!

! " is error vector)

Suppose linear correlations between peptide hydrophobicity index and retention time/scan number and between scan number and retention time. (

!

! S c : scan number,

!

! R t(obs) ,

!

! R t(pred ) : observed/predicted retention time )

=a+b

!

! S c ,

!

! S c =k

!

! R t(obs) , set k as 1 for simplification, set an error vector

!

! R t(pred )

!

! R t(obs)+ ,

!

! R t(pred )=

!

! I dx " a

b, =

!

! R t(pred ) -

!

! R t(obs) =

!

! R t(pred ) -

!

" •! c o

b - -

!

! " b

Define overall error vector

!

! O +

!

! " b

=

!

! R t(pred ) -

!

" •! c o

b- ,

Thus, minimizer is |

!

! O |2=| =

( is i th component of vector

!

! R t(pred ) and is i th row of matrix )

The vector

!

! c o and scalar a, b are optimized by minimizing |

!

! O |2

Modeling of peptide observability function for LC-MS/MS

Sequences of all proteins identified among 10 runs of LC-MS/MS using Mascot search with peptides ion score greater than 50 are theoretically digested by in-house program with trypsin activity (cleaved at the C-terminal side of Lys and Arg). Hydrophobicity index of each theoretical peptides are calculated by summing up optimized amino acid side-chain hydrophobicity coefficients. Theoretical distribution of all peptides generated from observed protein is then filtered with function of peptide hydrophobicity index with two terms that are designed to indicate “C18 column interaction probability” and “peptide observability by MS” since probability density functions are probability to start interacting with C18 column or being observed by MS. It is designed around the error function as it is a good model for cumulative probability distribution function(15). The function has five parameters as it is described below (Equation 1),

, Equation 1

: hydrophobicity index, : Error function,

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Peptides observed and theoretically digested are binned by hydrophobicity index interval of 10. The sum of squares for difference between observed distribution and theoretical distribution at the center value of bins is used as minimizer with 5 parameters (A is amplitude or overall probability, m1,2 are center of sigmoid and d1,2 are width factor of distribution). The optimization is performed with MATLAB® using function lsqnonlin as well. Minimizer: |

!

! F |2 = = ,

!

! p : theoretical values at the center of bins,

!

! y (obs) : observed numbers in each bin. Collection of mouse whole proteome and location specific protein information LOCATE Subcellular Localization Database (16) is utilized to acquire the sequences of proteins in various cell compartments. The current released version of LOCATE contains 58128 unique proteins of the mouse. First, proteins are separated into 30 bins by their localization information. Each cellular compartment is also divided into five classes including cytoplasmic proteins, secreted proteins, type I membrane proteins, type II membrane proteins, and multipass transmembrane proteins. In total, 118 (out of 150 possible) subcellular protein localization sets are formed. Generation and classification of theoretical proteome digests

Each whole or sub-proteome is subjected to theoretical digestion by 5 virtual enzymes (Asp-N, V8 protease (V8), Lysyl endopeptidase (Lys-C), Trypsin and Chymotrypsin; table 1). As occurrence of sequences such as KP, RP are not high, we did not implement precise activities such as KP, RP rules of trypsin which Lys-Pro and Arg-Pro bonds are rarely cleaved by trypsin. Exclusion of these rules does not have statistical significance for analysis.

In order to compare separation profiles of peptides under different first dimension separation techniques, calculation of number of peptide digests as well as theoretical digestions were performed by in-house programs. The pI values were calculated using an algorithm based on David L. Tabb (17). The varying pKa values of N-terminal amino and C-terminal carboxyl groups are used for particular terminal residues unlike calculations for proteins as shorter peptides terminal pKa can be affected significantly by presence of charges on those terminal residues. Hydrophobicty index of peptides were calculated using optimized coefficients described in previous section (modeling of peptide observability function). Both low and neutral pH conditions have been used for calculations of number of positive or negative charges. The results at low pH (pH~5) have been shown in this study as it shows better characteristics for ion exchange separation than neutral pH does. Number of positive and negative charges was calculated by counting N-termini/Lys/Arg/His residues and C-termini/Asp/Glu residues of peptide digests, respectively. In this study, we do not consider hydrophobic interactions between ion exchange bed resins and peptides as it is dependent to column. Moreover, inclusion of organic solvents would affect the results. We assume that the column is packed with perfect material that does not have hydrophobic interaction with peptides at all.

Theoretical digests are then binned by different properties and organized into two-dimensional array form to see correlations among properties. In this study, SCX, SAX,(18, 19) and peptide IEF followed by RP-HPLC were evaluated by analyzing the hydrophobicity index and other properties (number of positively/negatively charged residues, pI) for classifying and analyzing data. Results 1. MS/MS data analysis of mouse shotgun proteomics data

Total 286 proteins were identified from tryptic digests of mouse breast tissue, which are applied to LC-MS/MS in 10 separate runs (10) and then to Mascot database search to identify peptides and protein sequences. Each single run identified around 500 tryptic peptides and 150

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proteins in which an average 45% of proteins are identified with a single peptide (Table 1). By combining 10 runs, the number of identified peptides and proteins increased to 1107 and 286, respectively, compared to averages of single LC-MS/MS runs, 526 and 148, respectively. The proteins identified with a single peptide decreased slightly to 40% by combining 10 single runs compared to the single runs ranging 41% to 54 %. The data of combined 10 runs that 60% of the proteins (173 out of 286 identified proteins) are identified with multiple peptides and sufficiently high Mascot score (above 50) were utilized to build an optimizer and peptide observability function (20). 2. Optimization of amino acid side-chain hydrophobicity coefficients

In order to estimate separation of peptide by RP-HPLC, hydrophobicity coefficients were optimized using data from observed peptides under the condition for RP-HPLC separation (pH ~2 and changing organic concentration) since other interactions such as ion-pair formation (LC runs are performed with 0.1% formic acid for this data set) in addition to hydrophobic interaction attribute to retention of peptides in the column (21). The sum of hydrophobicity coefficients of amino acids represents peptide hydrophobicity. The interaction of peptide with C18 column can be estimated from these coefficients with relatively good accuracy (22-24).

All identified peptides with Mascot ion scores 50 or above and the best peptide probabilities if observed multiple times, are used in the dataset for coefficient optimization using least-square non-linear minimization. The residue hydrophobicity coefficients are computed along with linear correlation coefficients a and b. The distribution of observed and predicted scan numbers for our dataset has R2, 0.87, meaning that 87% of the variability in predicted scan number was explained by observed scan number (Figure 1). In addition, error estimation demonstrates that it is good enough for proceeding to further calculations as propagated error throughout process still remains up to 10% level (R=0.93 with mean error 0.1 and standard error 0.003; supplemental text: error analysis). 3. Building peptide “observability” function

Computed hydrophobicity coefficients were used to calculate hydrophobicity indexes of observed and theoretical peptides that are derived from identified mouse proteins by LC-MS/MS analyses as described in Materials and Methods. The parameter optimization for function was performed with observed scan numbers of identified peptides and with scan numbers of theoretical peptides calculated with optimized hydrophobicity coefficients. The non-linear least square minimization between observed distribution and theoretical-filtered distribution was performed and optimized parameters are computed (Figure 2. See also equation 1 in Materials and Methods. A=0.265, m1=47.7, m2=173.8, d1=24.1, d2=25.6). The peptide observability function used for filtering theoretical peptides is composed of two terms. The first term, “C18 column interaction” is supposed to be a right-up sigmoidal function; conversely, “MS observability” term is a left-up sigmoidal function. Rational for this design is the following. The interaction term is a right-up curve as more hydrophobic peptides interact stronger with C18 column. At a certain point in index, all peptides interact strongly enough with column, thus probability of interaction is 1. Sigmoidal error function is chosen as it indicates cumulative probability density function, i.e. a probability function of possibly “starting an interaction”. Also, second term, “MS observability” is designed in opposite way as low index region has high, and high index has low probability to be observed. The low index region is set as 1 for this term since these low index regions are influenced more by peptide interaction with column, rather than by factors contributing MS observability. Probability of observation by LC-MS/MS decreases as indices become high due to factors such as large average size of peptides, which can be out of scan range, and low fragmentation/identification efficiencies by MS/MS. Peptides with very high hydrophobicity index may be insoluble in aqueous solvent or hard to elute from column. Interestingly, the two terms of functions reach a plateau and start descending at almost

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same place. As a result, the filtered distribution becomes Poisson distribution-like with no apparent plateau. The distribution of theoretical peptides filtered by peptide observability function shows a bell-shaped function slightly tailed to higher index direction, which is similar to the distribution of observed non-redundant peptides combined from 10 LC-MS/MS runs (Figure 2). Filtering of theoretical peptides by peptide observability function results in high correlation between hydrophobicity indexes of observed peptides and filtered theoretical peptides as it shows the correlation coefficient r=0.995.

Digestion of theoretical mouse proteins by listed enzymes (Asp-N, V8, Lys-C, chymotrypsin, and trypsin) generates peptides ranging from 734,936 to 2,916,283 peptides and the application of peptide observability function filtered out approximately 90% of peptides generated by Asp-N, V8, Lys-C, or trypsin and 95% of peptides generated by chymotrypsin (Table 2). Chymotrypsin generates 2,916,283 peptides with numerous small peptides (average length of peptides and m/z, 4.7 and 382, table 2), which were filtered out by peptide observability function.

The filtration by peptide observability function resulted in similar percentage of observable peptides of both multiple-span transmembrane proteins (MTMP) and whole proteome digested by typsin. A 93% of peptides of MTMP digests were filtered out while 94% of peptides of whole proteome digests were filtered out by peptide observability function. Tryptic peptides that are filtered by peptide observability function without MS observability term (figure 6) represents population that interact with C-18 column (low hydrophobicity index peptides are filtered out) and potentially observed by LC-MS/MS. These populations for both whole proteome and MTMP sub-proteome have 14 % and 42% of peptides that are filtered out by MS observability term (18997 out of 130793 for whole proteome vs. 4462 out of 10538 for MTMP). These represent that MTMP has more percentages of peptides with high hydrophobicity index than whole proteomes.

4. Evaluation of 2D-LC-MS/MS and peptide IEF-LC-MS/MS strategies 4.1. Consideration for evaluation of the enzymes and strategies

To analyze separation profile of peptides under different strategies, the theoretical distribution of peptides digested by several different enzymes such as ones specific to negatively charged residues (Asp-N, and V8), positively charged residues (trypsin and Lyc-C), or hydrophobic residues (chymotrypsin) with several 1st dimensional separation methods (SCX, SAX, IEF) and 2nd dimensional separation (RP-HPLC) were evaluated. Particularly, first dimension in multi-dimensional Protein Isolation Technology (MudPIT) is critical for reducing complexity of sample in LC-MS/MS analysis. Accordingly, the wider and flatter distribution over the characteristics used in 1st dimensional technique may result in better separation in 1st dimention separation and eventually reduce the complexity in LC-MS/MS. Charge distribution (positive and negative charges) was analyzed to evaluate ion exchange separation (SCX and SAX, respectively) (25). For peptide IEF, pI distribution of peptides was used as it indicates separation by this method. The distribution of hydrophobicity is analyzed with all 1st dimension separation methods as it indicates separation by RP-HPLC in 2nd dimension.

Another indirect but also important characteristic is the charge state distribution by MS (assuming ESI ionization). This impacts MS/MS data quality, coverage of fragments ions and accordingly database search results. Large population of singly charged peptides is not favorable as it gives ion series mostly only b-series and lacking y-series. The singly charged peptides with MS/MS spectra in which only b-series ions, give significantly lower scores than both b- and y-series which are always observed with doubly or multiply charged peptides (26).

Also multiple charges (more than +2) are not favorable in general. Firstly, there is the MW scan range issue. Usually scans for MS is not set to very high mass range due to scan speed, and number of MS/MS scans. Suppose the scan range is set to 2,000 and MS send the

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ion with +3 charge and m/z 1200, singly charged peptide would be 3,600 Da. Depending on the distribution of charged residues within the sequence, it is likely to lose almost half of fragment ions because m/z values are out of scan range. Secondly, fragmentation efficiency does not favor highly charged peptides. The observed peptides with high number of charged residues tend to have more residues (longer) and efficiency of CID goes down by length of peptide (reduced probability of cleavage at particular bond). Thus, we may see intense MS signal but poor MS/MS spectrum. Thirdly, there is a bias in database search results in Mascot (26). SEQUEST handles triply charged ions in the same way as doubly charged peptides but Mascot scores triply charged ions as low as singly charged ions. Although we do not have data for X! Tandem, it is clear that one of the most widely used database search engine has bias against multiply charged peptides. In addition to biased search scores of search engines, the complicated charge states of fragments makes interpretation difficult. Thus, multiply charged peptides are not considered favorable. 4.2. Protein digestion by protease

Chymotrypsin and trypsin generated a lot of peptide digests (143866 and 130808, respevtively) compared to Asp-N, V8, and Lyc-C (90067, 104500, and 82487, respectively). Even though chymotrypsin produces a large number of peptides, 75 % of generated peptides are singly charged which compete with other peptides but it will not observed with good MS/MS fragment coverage due to lack of complementary series within spectra (26). Asp-N and V8 produce 20~25% of peptides (19102 out of 90061 and 26777 out of 104493 observable peptides digested by Asp-N and V8 respectively) with single charge which are not favorable for analyses. On the other hand, small population of tryptic and Lys-C peptides that are C-terminal peptides (~2%) are singly charged. 4.3. Peptide IEF-LC-MS/MS approach

The peptide IEF on whole mouse proteome was reported as a good method for separation of peptides prior to regular reversed-phase LC-MS/MS analysis (7, 27, 28). Although Asp-N and V8 produce peptides widely distributed in both hydrophobicity index and pI, as shown in inset of Figure 3, these enzymes produce 20~25% of peptides with single charge which are not favorable for analyses. The number of peptides generated by trypsin is about 60% more compared to ones generated by Lys-C, however, those 60 % tryptic peptides are distributed in acidic and neutral pI as Figure 3 indicates. 28%, 16% and 9 % of typtic digest and 20%, 13%, and 13% of Lys-C peptides are distributed to most populated pI ranges of 4.0~4.5, 7.0~7.5 and 9.5~10.0, respectively.

4.4. SCX-LC-MS/MS approach

The SCX is a technique widely used for multi-dimensional LC-MS/MS. The number of positive charges is a major factor for SCX peptide separation (29). The distribution for number of positively charged residues is a good indication of the peptide separation efficiency by SCX approach. After filtering by peptide observability function, we classified digests by hydrophobicity index and number of positive charges including amino terminus and Arg/Lys/His residues. Tryptic digest shows very poor distribution for positive charges as 2%, 79%, 12% and 5% of peptides are distributed to +1,+2,+3 and +4 charges, respectively. As shown in Figure 4, digested peptides by enzymes specific to negatively charged residue such as Asp-N and V8 and to basic residue, Lys-C show relatively wide distribution over charges and hydrophobic index. 4.5. SAX-LC-MS/MS approach

All enzymes except chymotrypsin produce a wide distribution for number of negative charges (Figure 5). Lys-C and trypsin digests show wide and uniform charge distribution (15%,

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21% 20%, 15% and 11% of Lys-C digests and generates 24%, 27%, 21% 13% and 7% of trypsin over -1 to -5 charges) while peptides digested by Asp-N and V8 have only 1.1 % of peptides with -1 charge. Actual numbers of tryptic peptides are almost double for -1 to -4 chargeed peptides compared to Lys-C peptides (1.85 times more for trypsin) however, tryptic and Lys-C peptides with -5 and more charges have similar numbers of peptides and similar distribution (total 23090 and 20487 for Lys-C and trypsin, respectively, Figure 6). Overall, trypsin has good charge distribution with more peptides while Lys-C generates fewer amounts of peptides with wide and uniform distribution. 5. Evaluation of sub-fractionation with MudPIT techniques

To evaluate proteome analyses for sub-cellular components and membrane proteins, we have examined the three techniques widely available to proteomic analyses in conjunction with choice of enzymes. These include the peptide IEF with Lys-C or trypsin, SCX with Lys-C or Asp-N, and SAX with Lys-C or trypsin, which were demonstrated to have relatively good separation of peptide digests (refer to Section 4). The proteins are classified by cellular localizations such as cytoplasm, ER, lysosome, mitochondria, and nucleus, which are functionally important for biological viewpoint and its cellular separation has been utilized for numerous studies. The proteins are also classified in cytoplasmic, type-I, type-II, secretome and multiple-span transmembrane proteins (MTMP).

All sub-proteomes by cellular localization and whole proteome showed almost identical distributions regardless the combinations of an enzyme and a technique (supplemental Figure 1). Same combinations of a technique and an enzyme are used to evaluate the membrane proteins (supplemental Figure 2). The results indicate the same results as sub-cellular components. MTMP show relatively flat distributions among pI ranges by peptide IEF compared to the other acidic peptide dominant distributions.

Overall, we do not see any major difference among different subcellular components or classes of membrane/cytosomal proteins. As a proteome, if there is enough number of proteins, observable proteomes are all alike in terms of peptides generated from them. As we have covered whole mouse proteome in this study, it is reasonable to deduce that this strategy may work for all eukaryote proteomes. Discussion Modeling of peptide observability function for LC-MS/MS

The constructed model efficiently demonstrated prediction of MS observability of theoretical peptides as the theoretical peptides show high correlation to actually observed peptides with correlation coefficient R=0.995. Theoretical tryptic peptides that are derived from 286 proteins that are identified with Mascot ion score >50 have very high population between observability index value -30 and -20 due to short basic peptides such as single Lys/Arg or Xaa-Lys, Xaa-Arg (Figure 2, distribution of theoretical peptides). These short peptides may not interact with C-18 column and may pass through the column. They could skew the overall result, if they remain in the dataset. Thus, filtering the peptides with peptide observability function is an important process to see the un-skewed results or more likely to be observed, real-world results.

The modeling of peptide observability function in this study, do not use any quantitative information, as it is ambiguous to assign “quantity” information to each protein entry without actual quantitative data. Thus, amplitude parameter for filtering function is assumed to be an average for observed protein. Low abundant proteins may not be accounted in this study. However, the construction of filtering function is based on observed peptides by real LC-MS/MS analyses and although there is no actual quantitative information, the datasets themselves have

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the information that observed peptides are abundant enough. As shown in results, this study demonstrates that sub-proteomes and classes of membrane protein behave same way as long as enough number of proteins is sampled. Also, there is no reason to believe that the proteome from other species (at least eukaryotes) would behave differently. Thus, it is reasonable to consider the filtering function proposed as universally applicable to any proteome. Evaluation of 2D-LC-MS/MS and peptide IEF-LC-MS/MS strategies

As HPLC has non-comparable high separation power for peptides and extremely high compatibility with MS platform, it is natural choice to use for separation technique directly coupled to MS analysis. Charge-based separations such as ion exchanges and IEF are ideal orthogonal, complementary technologies to be combined with hydrophobicity-based HPLC separation as they are based on different physicochemical principles for separation.

Although the importance and wide usage of shotgun proteomics approaches, there is no thorough evaluation of technologies. SCX-trypsin approach is taken so frequently as first choice for MudPIT. Peng et al. have clearly shown that number of charges of peptides (His and miss-cleavages) is clearly the most important factor for separating peptides using SCX approach (25, 29). Conversely, if there are peptides with different number of charges, the separation efficiency of peptides using ion exchange column will increase. It is confirmed recently by Taouatas et al. by using Lys-N with SCX(30). Although the development of mixed-bed approach for ion exchange column, use of pH steps, Lys-N with low pH SCX and mixed-mode hydrophilic/cation exchange technique (30-34) improves the separation by SCX, the subtle difference between the pKa of Lys/Arg with same number of charges fails to separate the peptide efficiently, resulting in peptide re-sampling in different fractions (34, 35). For SCX-trypsin strategy, pH step would be a better strategy as pI of the peptide plays a role in elution and makes it a hybrid approach between regular SCX and peptide IEF. For conventional strategies, this study suggests that SAX/trypsin, SCX/Asp-N, SCX/Lys-C or peptide IEF/trypsin gives relatively good separation profile of digested peptides among all combinations analyzed as first dimension separation of peptides and peptidase. When the charge states of peptides were considered, SCX/Asp-N may have potential setback since as many as 30% of observable peptides are singly charged. On the other hand, the distribution of peptides over positive charges is obviously superior with Asp-N and Lys-C (Figure 4). So far, we did not consider properties of proteases. It is well known that Lys-C has extremely strict specificity and robustness of activity. Complex proteomes would need a “complete” digest to acquire maximum number of peptides and also to generate the simplest population. The miss-cleavages would not improve the coverage but would increase complexity. In terms of enzyme activity, Lys-C is far superior to Asp-N. Thus, overall effectiveness of these two enzymes in combination with SCX should be experimentally evaluated. Also, the result would be dependent on the search engine used as shown by Kapp et al. SEQUEST handles triply charged ions same as doubly charged ions. On the other hand, Mascot scores triply charged peptides are as low as singly charged ions. Thus, it is important to note that the peptides with SCX/Lys-C and SCX/Asp-N are distributed from 1 to 10 and majority of peptides (79% and 67% for Lys-C and Asp-N), which are possible charge states over +2, will be scored with bias (Figure 6, top panel).

The SAX has not employed for 2D-LC-MS/MS so commonly even though anion exchange resin such as DEAE, Mono-Q are one of the most frequently used resins for protein purification. Isobe’s group reported separation of full proteome by anion exchange for first dimension. Full proteomes of C. elegans(36) and mouse embryonic stem cells(37) were subjected to proteomic analysis utilizing SAX for 1st dimension separation and LC-MS/MS for 2nd dimension. The group reported 1616 and 1790 proteins (p<0.005), half of them were identified with a single peptide. Both reports utilized 70 min linear gradient (5-40% acetonitrile) and identified 2000~4000 unique peptides with p-value below 0.05 from total 4 and 6 runs while SCX/trypsin typically identifies 500~2000(38-40). Our analysis indicats SAX/trypsin as a good

11

combination for proteomics analysis and it is supported by the study performed by Nakamura et al. The study reports that more than twice as much peptides were observed by SAX compared to SCX (~3500 and ~1500 by single run, respectively) with less fraction overlap, significantly better separation of standard BSA digests(41). Trypsin is quite robust and has high specificity. Tryptic peptides always have +2 charges except small population of C-terminal and His-containing peptides. Hence, tryptic peptides always have a high probability to give good MS/MS identification and simpler interpretation compared to multiply charged peptides. The distribution of negative charge is as wide as distribution of positive charge for Asp-N peptides so that the separation by SAX is expected to be excellent. With combination of completely volatile buffer system such as hexafuloroaceton-ammonium system (or more conventional ammonium bicarbonate) and neutral and volatile salts such as ammonium chloride or ammonium formate, SAX/trypsin system may be very effective, MS-compatible first dimension system.

Peptide IEF is potentially a good technology although current implementation or achieved technical level does not match with the other two techniques. IPG-peptide IEF can achieve-high resolution separation(7) and has good potential although extraction of peptides from IPG by equilibrium would lose most of low abundant proteins with high volume of extraction buffer. The biggest problem stems from bulky apparatus that impose huge amount of initial sample amount and also loss of sample during the process. If technology is further improved with micro-scale apparatus(28) with pI range of 0.5 for separation, it has highest separation capability with descent to good peptide distribution among pI ranges.

We have examined a full proteome with various strategies. Analysis suggests SAX/trypsin or peptide-IEF/trypsin as a good combination of techniques that can be used. Although both techniques have good quality of first dimensional separation, however, each fraction may still contain more peptides than that can be separated by both combinations (for example, the number of -2 charged peptides are 35041 in SAX/trypsin even after filtering). It would be necessary to implement a technique such as a pH step to facilitate separation among same charge states in order to achieve better separation. Overall, some factors such as charge state/search engine bias, low fragment ion observability due to the size of fragment ions that is exceeding m/z values beyond scan range, or decreased fragmentation efficiency needs to be considered before experiments are designed. Evaluation of sub-fractionation with MudPIT techniques

The results suggest that there is no need to use different strategies among different subcellular components or the classes of membrane proteins. This also implies that the protein abundance is the major factor for proteins to be observed. The dynamic range is one of the problems for shotgun proteomics. The peptides from highly abundant proteins within the proteome hinders detection of low abundance proteins not only by overlapping/covering them with the peptides from low abundance proteins but also limiting actual amount of low abundance proteins per loading amount. The collection of subcellular components or membrane fractions will only contain its own sub-proteome without highly abundant proteins such as cytoskeletal proteins or enzymes for respiratory system and fractionation of these components will simply increase the amount of proteins per loading. As our data indicate that there is no difference among the distributions of observable peptides generated from different subcellular components, observable peptides will increase as their amounts increase to enough for LC-MS/MS detections.

Subcellular fractionation serves as an enrichment technique and allows detection of low abundance proteins specifically expressed in particular organelles. Although loading sample amount and number of peptides uniquely identified have positive correlations, at a certain point, number of identified peptides declines even with higher amount of loaded sample(42). It is thought due to matrix effects as peptides that do not show correlation between observability and loading amount are observed in clouded area in chromatogram. Simply increasing loading

12

amount does not improve peptide/protein identification but sample complexity needs to be addressed to solve matrix effects and co-elution in MS analysis. Thus, fractionation eases both issue of sample loading amount per protein and reduction of sample complexity.

Hydrophobic long peptides are highly abundant in membrane proteins and these highly hydrophobic peptides show low “observability” in LC-MS/MS identification (Figure 7). These peptides may be too hydrophobic to be eluted or insoluble. In our experiences, these peptides tend to stay in column and eluted for prolonged time with leaky manner, contaminating the LC system. Even regular peptides can be problematic in this regard and there is absolute necessity of blank wash run for column after every single run of analysis, this may pose major problem of run-to-run contamination if not handled carefully.

The proteome analyses could be direct determination techniques of localization if applied to subcellular components although careful validation would be necessary to assess contaminations from whole cell proteome. It is a discovery-based technique and unlike tagging techniques such as GFP-tag, no prior knowledge for targets is necessary. In that way, we can access rich protein localization information. As we are approaching to systems biology as a mainstream research field, protein localization data are also extremely important. In this study, we acquired the protein localization information from the LOCATE database. In this dataset, some components are extracted from literature or gene ontology. However, some entries were ambiguous. Increase of available experimental data for protein localization improves the accuracy of databases such as LOCATE. For building interaction networks and other systems approaches, such system wide databases are quite useful(43). Thus, more accurate determination of subcellular localization is an important task to be completed.

Conclusions

Proteomics is a powerful technique to study thousands of proteins. However, large-scale proteomic analyses are extremely human/machine labor intensive and expensive. The theoretical evaluation of peptides’ observability as well as different separation techniques allows researchers to choose the most appropriate methods suited for the analysis of the biological system studied. In addition, this can save time and money, because invaluable experimental proteomics data available through various public repositories can be reused (9, 44, 45). Overall, our theoretical evaluation of peptides’ observability and separation using several combinations of currently available separation techniques captures the current state-of-the-art proteomics and provides a valuable foundation for future directions though it remains experimental validation of these study.

13

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14

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32. Dai, J., Shieh, C. H., Sheng, Q. H., Zhou, H., and Zeng, R. (2005) Proteomic analysis with integrated multiple dimensional liquid chromatography/mass spectrometry based on elution of ion exchange column using pH steps. Anal Chem 77, Page. 33. Mant, C. T., and Hodges, R. S. (2008) Mixed-mode hydrophilic interaction/cation-exchange chromatography: separation of complex mixtures of peptides of varying charge and hydrophobicity. J Sep Sci 31, Page. 34. Dowell, J. A., Frost, D. C., Zhang, J., and Li, L. (2008) Comparison of two-dimensional fractionation techniques for shotgun proteomics. Anal Chem 80, Page. 35. Le Bihan, T., Duewel, H. S., and Figeys, D. (2003) On-line strong cation exchange micro-HPLC-ESI-MS/MS for protein identification and process optimization. J Am Soc Mass Spectrom 14, Page. 36. Mawuenyega, K. G., Kaji, H., Yamuchi, Y., Shinkawa, T., Saito, H., Taoka, M., Takahashi, N., and Isobe, T. (2003) Large-scale identification of Caenorhabditis elegans proteins by multidimensional liquid chromatography-tandem mass spectrometry. J Proteome Res 2, Page. 37. Nagano, K., Taoka, M., Yamauchi, Y., Itagaki, C., Shinkawa, T., Nunomura, K., Okamura, N., Takahashi, N., Izumi, T., and Isobe, T. (2005) Large-scale identification of proteins expressed in mouse embryonic stem cells. Proteomics 5, Page. 38. Prieto, J. H., Koncarevic, S., Park, S. K., Yates, J., 3rd, and Becker, K. (2008) Large-scale differential proteome analysis in Plasmodium falciparum under drug treatment. PLoS ONE 3, Page. 39. Gao, M., Deng, C., Yu, W., Zhang, Y., Yang, P., and Zhang, X. (2008) Large scale depletion of the high-abundance proteins and analysis of middle- and low-abundance proteins in human liver proteome by multidimensional liquid chromatography. Proteomics 8, Page. 40. Kirkland, P. A., Humbard, M. A., Daniels, C. J., and Maupin-Furlow, J. A. (2008) Shotgun proteomics of the haloarchaeon Haloferax volcanii. J Proteome Res 7, Page. 41. Nakamura, T., Kuromitsu, J., and Oda, Y. (2008) Evaluation of comprehensive multidimensional separations using reversed-phase, reversed-phase liquid chromatography/mass spectrometry for shotgun proteomics. J Proteome Res 7, Page. 42. Liu, K., Zhang, J., Wang, J., Zhao, L., Peng, X., Jia, W., Ying, W., Zhu, Y., Xie, H., He, F., and Qian, X. (2009) Relationship between Sample Loading Amount and Peptide Identification and Its Effects on Quantitative Proteomics. Anal Chem, Page. 43. Duan, X. J., Xenarios, I., and Eisenberg, D. (2002) Describing biological protein interactions in terms of protein states and state transitions: the LiveDIP database. Mol Cell Proteomics 1, Page. 44. Krogan, N. J., Cagney, G., Yu, H., Zhong, G., Guo, X., Ignatchenko, A., Li, J., Pu, S., Datta, N., Tikuisis, A. P., Punna, T., Peregrin-Alvarez, J. M., Shales, M., Zhang, X., Davey, M., Robinson, M. D., Paccanaro, A., Bray, J. E., Sheung, A., Beattie, B., Richards, D. P., Canadien, V., Lalev, A., Mena, F., Wong, P., Starostine, A., Canete, M. M., Vlasblom, J., Wu, S., Orsi, C., Collins, S. R., Chandran, S., Haw, R., Rilstone, J. J., Gandi, K., Thompson, N. J., Musso, G., St Onge, P., Ghanny, S., Lam, M. H., Butland, G., Altaf-Ul, A. M., Kanaya, S., Shilatifard, A., O'Shea, E., Weissman, J. S., Ingles, C. J., Hughes, T. R., Parkinson, J., Gerstein, M., Wodak, S. J., Emili, A., and Greenblatt, J. F. (2006) Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, Page. 45. Desiere, F., Deutsch, E. W., King, N. L., Nesvizhskii, A. I., Mallick, P., Eng, J., Chen, S., Eddes, J., Loevenich, S. N., and Aebersold, R. (2006) The PeptideAtlas project. Nucleic Acids Res 34, Page.

   Table  1  Protein/peptide  identification  by  Mascot  with  10  runs    

Peptides  /Protein    Run  

Identified  peptides  

Identified  Proteins   1   2   3   >3  

Protein  with  Single  peptide  

1   573   162   76   31   14   41   47%  2   568   151   71   24   12   44   47%  3   540   153   63   36   15   39   41%  4   549   151   60   44   11   36   40%  5   505   151   76   23   16   36   50%  6   564   144   63   23   16   42   44%  7   420   132   71   18   15   28   54%  8   465   138   67   22   13   36   49%  9   551   159   73   29   19   38   46%  10   528   144   59   39   13   33   41%  

Combined   1107   286   113   46   35   92   40%      Table  2    Peptides  generated  by  5  proteases    

 Nnumber  of  peptides  

average  length  

average  m/z  

Singly  charged  

Number  of  observable  peptide    

Asp-­N   789539   19.9   720.4   0.373   90061  V8   1098314   14   617.3   0.467   104493  Lys-­C   734936   21   728.2   0.017   82497  Trypsin   1261074   11.8   673   0.019   119350  Chymotrypsin   2916283   4.7   382.1   0.71   143866    Number  of  peptides  is  theoretical  calculation  of  whole  proteome  digests  without  filtering  by  peptide  observability  function.          

0 5000 10000 15000 20000 25000 30000

0

5000

10000

15000

20000

25000

Equation:  y  =  a  +  b*x

   R2=    0.86823

   a 48.97 ± 125.33

b 0.997 ± 0.012

Observed  scan#

Predicted  scan#

Residual

-5 5 25

hydrophobicity index

hydrophobicity index hydrophobicity index

hydrophobicity index

hydrophobicity index

Num

ber

of peptides

Num

ber

of peptides

Num

ber

of peptides

Num

ber

of peptides

Filtering theoretical calculation

with “observability” function

Column interactionterm

MS observabilityterm

Filtered

Theoretical Observed

( Observed - Filtered )

 

-­

After  

Figure 1. The hydrophobicity coe!cient optimizationThe least-square non-linear optimization of amino acid side-chain hydrophobicity coe!cients are performed by predicting power of coe!cients to the peptide retention time. After optimization, hydrophobicity coe!cientsand linear relation coe!cients results in the "tting of observed and predicted scan number with R2=0.87.

Figure 2. Modeling the peptide observability functionThe optimized hydrophobicity coe!cients are used to calculate the peptide hydrophobicity indexes of both observed and theoretical peptides. The hydrophobicity index distribution of observed and theoretical peptides were used to model "ltering function that de"nes “observability” of peptides as a function of hydrophobicity index (peptide observability function). The function is composed of amplitude parameter and two terms of error functions and achieved R2=0.99 to "lter the theoretical distribution to observed one. The theoretical peptides are derived from the proteins actually identi"ed by observed peptides with at least Mascot ion score 50.

0

1000

2000

3000

4000

-­50.0

0.0

50.0

100.0

150.0

200.0

250.0

Hydrophobicity  Index

0

500

1000

1500

2000

-­50.0

0.0

50.0

100.0

150.0

200.0

250.0

Hydrophobicity  Index

-­50.0

0.0

50.0

100.0

150.0

200.0

250.0

0

1000

2000

3000

4000

Hydrophobicity  Index

0

10000

20000

30000

-­50.0

0.0

50.0

100.0

150.0

200.0

250.0

Hydrophobicity  Index

0

500

1000

1500

2000

-­50.0

0.0

50.0

100.0

150.0

200.0

250.0

Hydrophobicity  Index

[  0.00-­  0.50]

[  0.50-­  1.00]

[  1.00-­  1.50]

[  1.50-­  2.00]

[  2.00-­  2.50]

[  2.50-­  3.00]

[  3.00-­  3.50]

[  3.50-­  4.00]

[  4.00-­  4.50]

[  4.50-­  5.00]

[  5.00-­  5.50]

[  5.50-­  6.00]

[  6.00-­  6.50]

[  6.50-­  7.00]

[  7.00-­  7.50]

[  7.50-­  8.00]

[  8.00-­  8.50]

[  8.50-­  9.00]

[  9.00-­  9.50]

[  9.50-­10.00]

[10.00-­10.50]

[10.50-­11.00]

[11.00-­11.50]

[11.50-­12.00]

[12.00-­12.50]

[12.50-­13.00]

[13.00-­13.50]

pI  range

Bas i c

Acidic

Bas i c

Acidic

Bas i c

Acidic

A ci d ic

B as i cp I

A ci d ic

B as i cp I

p I p I p I

Asp-­N V8

Lys-­C Trypsin

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

1 3 5 7 9 11 13 15 >15

AspN

V8

AspN  with  filter

V8  with  filter

Number  of  positive  charges

Relative  abundance

Chymotrypsin

Inset

   Charge  number  distribution

Figure 3. Hydrophobicity v.s. pI distribution of peptidesIn order to simulate the separation by peptide IEF followed by LC-MS/MS, theoretical peptides are generated by 5 virtual enzymes and binned by two parameters. The pI values are calculated with bin width of 0.5 although it seems too good for current separation. They have mainly 3 distinct populations in acidic, neutral and basic pI regions. Acidic for peptides with more negative charges than positive, neutral for same numbers and basic in more positive charges.

Asp-­N V8

Lys-­C TrypsinChymotrypsin

-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index

-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index

Number  of

Positive  Charges

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

>15

2500

5000

7500

10000

12500

15000

1000

2000

3000

4000

5000

10000

20000

30000

40000

50000

60000

0

5

10

15

0

5

10

15

0

5

10

15

0

5

10

15

0

5

10

15 Number  of  

positive  charge

Number  of  

positive  charge

Number  of  

positive  charge

Number  of  

positive  charge

Number  of  

positive  charge

1000

2000

3000

4000

5000

500

1000

1500

2000

2500

3000

Asp-­NV8

Lys-­C Trypsin Chymotrypsin

1000

2000

3000

4000

5000

6000

1000

2000

3000

4000

5000

1000

2000

3000

4000

10000

20000

30000

40000

50000

500

1000

1500

2000

2500

Number  of

Acidic  residues

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

>15

-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index

-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index-­50.0

0.0

50.0

100.0150.0

200.0250.0

Intrinsic  Hydrophobicity  Index

0

5

10

15

0

5

10

15

0

5

10

15

0

5

10

15

0

5

10

15 Number  of  

Negative  charge

Number  of  

Negative  charge

Number  of  

Negative  charge

Number  of  

Negative  charge

Number  of  

Negative  charge

Figure 4. Hydrophobicity v.s. positive charge distribution of peptidesThe SCX followed by LC-MS/MS scenario is evaluated by plotting the number of peptides binned by number of positive charges and hydrophobicity index of peptides. As Lys-C and trypsin cut at basic amino acids, they never have +1 charge except small population of C-terminal peptides. Chymotrypsin has high number of peptides but they are mostly singly charged and expected not be separated well. Most of Tryptic peptides have +2 charges and also expected not to be separated by this approach.

Figure 5. Hydrophobicity v.s. negative charge distribution of peptidesThe combination of SAX and LC-MS/MS is evaluated with negative charge and hydrophobicity index distributions. Chymotrypsin has poor separation with mostly -1 charge. Lys-C and trypsin show wide distributions in both charges and hydrophobicity. Asp-N and V8 also have descent distribution although lacking -1 charge.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 >150

10000

20000

30000

40000

50000

60000

70000

80000

90000

Number  of  peptides

number  of  negative  charges

Asp-­NLys-­CV8TrypsinChymotrypsin

0.00.51.01.52.02.53.03.54.04.55.05.56.06.5

 7.0 7.58.08.59.09.5

10.0

10.5

11.0

11.5

12.0

12.5

13.0

0

10000

20000

30000

40000

50000

60000

70000

Number  of  peptides

pI  range

Asp-­NLys-­CV8Tyrpsinchymotrypsin

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 >150

20000

40000

60000

80000

100000

120000

140000Number  of  peptides

Number  of  positive  charges

Asp-­NLys-­CV8TrypsinChymotrypsin

SCX  :  Expected  separtion SAX  :  Expected  separtion

IEF  :  Expected  separtion

-200 0 200 400

Whole proteome (Column interactant)

Whole proteome (Filtered)

MTMP (Column interactant) x12.8 #

MTMP (Filtered) x12.8

Stick to colum

but not to be observed

42% of observable peptides

14% of observable peptides

Hydrophobicity index

*

* The peptides are filtered with “column interaction” term with amplitude parameter only.

# Amplitude was adjusted to match Whole Proteome for clarity.

Figure 6. Summary of !rst dimensional separationsThe separations by three techniques are summarized. Panel 1: Separation by SCX. Negative charge speci!c enzymes have wide and relatively "at distributions. Lys-C also has relatively good separation except lack of +1 charged peptides. Trypsin and chymotrypsin have poor distribution. Panel 2: Separation by SAX. Except chymotrypsin, other four enzymes show excellent distributions. V8 and Asp-N do not have -1 charged peptides. Trypsin and Lys-C have wide distributions. Panel 3: Separation by peptide-IEF. Other than chymotrypsin, all enzymes produce well distributed peptides in pI ranges.

Figure 7. Hydrophobic MTMP peptidesThe peptides generated from MTMP are !ltered with column interaction !lter (peptide observability function without second MS observability term). The ratios of unobservable against observable peptides are computed for both whole and MTMP proteomes. While whole proteome has only 14% peptides are !ltered out due to MS observability term, MTMP peptides are !ltered out by 42%.