ISSN 10619348, Journal of Analytical Chemistry, 2012, Vol. 67, No. 13, pp. 1014–1025. © Pleiades Publishing, Ltd., 2012. Published in Russian in MassSpektrometria, 2011, Vol. 8, No. 4, pp. 281–292.
ARTICLES
Inversion of Chromatographic Elution Orders of Peptides and Its Importance for Proteomics1 I. A. Tarasovaa, *, T. Yu. Perlovaa, M. L. Pridatchenkoa, A. A. Goloborod’koa, L. I. Levitskya, V. V. Evreinovb, V. Gurycac, d, C. D. Masselonc, d, A. V. Gorshkovb, and M. V. Gorshkova a
Institute for Energy Problems of Chemical Physics RAS, Leninsky pr. 38, bld. 2, Moscow, 119334 Russia *email:
[email protected] b Semenov’s Institute of Chemical Physics, RAS, ul. Kosygina 4, Moscow, 119991 Russia c CEA, Universite Joseph Fourier, 17 Avenue des Martyrs, Bat. C3, 38054 Grenoble Cedex 9, France d INSERM, U880, Grenoble, F38054, France Received August 4, 2011; in final form, October 10, 2011
Abstract—Inversion of the order of peptide elution in reversedphase liquid chromatography under changing separation conditions, such as gradient slope has been considered. Using a sixprotein proteolytic peptide standard and available literature data, the occurrence frequency and importance of this phenomenon in pro teomic studies utilizing methods of shotgun proteomics and accurate mass and time tags have been evaluated. Feasibility of qualitative and quantitative description of peptide elution order inversion has been demon strated using a model of critical liquid chromatography. Existing approaches to predict peptide separation and directions of the shifts of chromatographic peaks when the gradient profile changes have been compared. Keywords: proteomics, mass spectrometry, inversion of chromatographic elution orders of peptides, accurate mass and retention time tags, critical liquid chromatography DOI: 10.1134/S1061934812130102 1
INTRODUCTION
2
Inversion of peptide elution order in reversed phase liquid chromatography under changing separa tion parameters, e.g. solvent flow rate, column size and gradient profile, has been widely discussed in lit erature since 1980s [1–3]. Due to the growing interest in utilization of liquid chromatography in proteomic studies, discussion of this phenomenon still retains the importance [4–7]. Both the phenomenological description and the prediction of the inversion phe nomenon are challenging issues that force the researchers to employ the same peptide separation conditions for preserving the reproducibility of the chromatographic data. For example, the Accurate Mass and Time tags (AMT) method is increasingly used in proteomics. It is based on generation and uti lization of databases of accurate masses and retention times of peptides that are protein markers [6–10]. These AMT databases allow rapid identification of proteins based on liquid chromatography/mass spec trometry (HPLCMS) analysis without employing the tandem mass spectrometry. In the AMT approach, the parent mass measured with high accuracy and nor malized peptide retention time are used for unambig uous peptide identifications. Normalization is usually performed using linear (RTnorm = aRTexp + b) or qua 1 The article was translated by the authors.
dratic (RTnorm = aR T exp + bRTexp + c) transforms [9– 11]. This approach to normalization is highly accurate when applied under identical or close separation parameters [9, 11]. However, it does not allow correct conversion of peptide retention times to selected nor malized time scale if the peptides have changed elu tion order due to a change in the experimental condi tions. Another target approach to fast protein identifica tion is LCdependent Selected/Multiple Reaction Monitoring (SRM/MRM) [12]. SRM/MRM method implements targeted search through specified “precursor ion product ion” transitions followed by the conclusion on the presence or absence of the peptide in the analyzed mixture. In the MRMbased strategy, three transitions per peptide are typically detected, and not more than 100 peptides per elution peak can be specifically searched under typical HPLCMS conditions [12]. Knowing the retention times allows separating the peptide search over the respective time intervals of the chromatogram and, thus, enhancing the analysis throughput by reducing the search space or the reliability of the quantitative assessment as the number of identifications per pro tein increases. However, the inability to correctly pre dict changes in the order of peptide elution under changing separation conditions can lead to the loss of
1014
INVERSION OF CHROMATOGRAPHIC ELUTION ORDERS OF PEPTIDES
a peptide tag in the corresponding search time inter val, as well as a false negative protein identification. Until recently, the only theory describing the change in the elution order of peptides when changing the gradient slope was the empirically derived Linear SolventStrength Theory (LSS) by L.R. Snyder [13]. One of the basic equations of the theory postulates a linear relation between the logarithm of the distribu tion coefficient logKd and the volume fraction of the organic solvent θ: (1) log K d = log K d0 – Sθ, where Kd0 is the distribution coefficient in pure water, and S is the proportionality coefficient. It is assumed that S is a complex function, which depends on phys icochemical properties of the biomolecule. For example, it was shown that it depends on the molecu lar mass [2], as well as length, charge, hydrophobicity, and amino acid sequence [14]. Snyder’s theory allows prediction of the inversion of peptide elution order when the gradient slope changes provided that both S and Kd0 values in Eq. (1) are determined. These values can be measured experimentally using the dependence of the distribution coefficient on the gradient slope for each peptide, although this approach is not feasible on the large scale as these coefficients should be deter mined for hundreds of thousands of peptides in this case. An empirical model for calculation of the S coeffi cients of tryptic peptides was proposed recently. Free parameters in the suggested model were obtained by optimization procedure applied to statistically large datasets [14, 15]. In particular, the following equation was proposed for prediction of the signs of the shifts in chromatographic peaks relative to a chosen tag peptide when the gradient profile changes [14]: (2) Δ = 100 log ( g 0 /g 1 ) ( 1/S – 1/S' ), in which g0 and g1 are gradient slopes, S and S ' are cal culated coefficients for a pair of peptides, one of which changes its position relative to the tag peptide when the gradient changes. The value of Δ in Eq. (2) is a change in the distance between two peaks after the gra dient slope has changed, and is expressed in percent age units of the organic eluent in the portion of the sol vent within which the peptides are eluted: (3) Δ = g 0 ( RT g0 – RT 'g0 ) – g 1 ( RT g1 – RT 'g1 ), in which RT 'g0 and RT 'g1 are retention times of the tag peptide, which is used as a reference for determination of the change in retention of another peptide with retention times of RTg0 and RTg1 in g0 and g1 gradients, respectively. The sign of Δ defines the direction of the relative shift of the pair of peptides. It is important to note that knowing Δ does not allow conclusion on whether the inversion of the elution order actually occurs or not, when the peaks are closing to each other. JOURNAL OF ANALYTICAL CHEMISTRY
Vol. 67
1015
Earlier, we proposed a model of separation of biomacromolecules—Liquid Chromatography at Critical Conditions for Biomacromolecules (BioLCCC) for the phenomenological description of peptide sepa ration which is based on statistical physics of macro molecules [16–18]. The model allows calculation of the chromatographic distribution coefficient of a bio molecule with a known amino acid sequence for the particular experimental conditions. In BioLCCC, the distribution coefficient, Kd, which depends on a change in the free energy, ΔG, is determined by the partition function, Zp, of the biomacromolecule inside the pore [19]: Kd = exp(–ΔG) ~ Zp. (4) Currently, there are two approaches for calculation of the partition function in the BioLCCC model. These approaches are based on different assumptions about the flexibility of biomolecules. In the first approach, the molecule is represented as a rigid rod, which better describes short molecules, such as pep tides [20]. In the other approach the model of free walk flexible chain is used. It can be applied to describe long molecules, such as denatured proteins [16–18, 20, 21]. The aim of the present work was experimental study on the inversion of peptide elution order under changing gradient elution profile, revealing the under lying mechanism, and the estimation of the scale of this phenomenon in proteomic studies. Also, the fea sibility to predict elution order inversion using differ ent models was evaluated. EXPERIMENTAL Samples Two commercially available peptide standards were used a tryptic digest of cytochrome c and a tryptic digest of six proteins, containing BSA (Bos tau rus), αgalactosidase (Escherichia coli), lysozyme (Gallus gallus), alcohol dehydrogenase (Saccharomy ces cerevisiae), cytochrome c (Bos taurus), and apo transferrin (Bos taurus). Both standards were pur chased from Dionex/LCPacking (Amsterdam, The Netherlands). Chromatography Chromatographic separation of peptide mixtures was performed using Ultimate 3000 gradient HPLC system (Dionex, Amsterdam, The Netherlands), cou pled to a hybrid ion cyclotron resonance LTQFT mass spectrometer (ThermoFisher, Bremen, Ger many). The samples were injected in 200– 500 fmol/μL concentrations. Mobile phases were water/acetonitrile (ACN) mixtures (Merck, Darms tadt, Germany) with addition of formic acid (VWR International, London, England) in the following vol ume ratios: A—acetonitrile/water/formic acid (2/98/0.1); B—acetonitrile/water/formic acid No. 13
2012
1016
TARASOVA et al.
Table 1. Results of the prediction of peptide elution order inversion for different gradient slopes. “+” and “–” signs mark the cases of presence or absence of elution order inversion for MIFAGIK and TGPNLHGLFGR peptides from cyto chrome c when the gradient slope changes from 0.23 to 1.3% ACN/min. Calculations of the distribution coefficient were performed using two different approaches: freewalk chain BioLCCC model (chain) and rigid rod BioLCCC model (rod)
No.
Column
Column diameter, µm
Column Particle size, length, cm µm
Pore size, nm
Inversion, experiment
Inversion, BioLCCC, chain
Inversion, BioLCCC, rod
1
PepMap C18, LC Packing
75
15
5
10
+
+
–
2
Gemini C18, Phenomenex
75
15
3
10
+
+
–
3
Atlantis C18, Waters
75
15
3.5
11
–
+
–
4
Chromolith C18, monolith, Merck
100
15
–
10
–
+
–
5
PepMap C18, LC Packing
75
25
3
10
–
+
–
6
PLRP–S C18, LC Packing
75
15
5
30
–
–
–
(80/20/0.08). Inversion of peptide elution order was studied for two gradient slopes: “steep” (0–50% B in 30 min, gradient slope of 1.3% ACN/min) and “shal low” (0–35% B in 120 min, gradient slope of 0.23% ACN/min). In these studies the separations of tryptic peptides of cytochrome c were performed using differ ent reversedphase columns as summarized in Table 1. The results shown in Table 1 were obtained for pep tides MIFAGIK and TGPNLHGLFGR from cyto chrome c for the gradient slope changed from 0.23 to 1.3% ACN/min. The distribution coefficient was calculated using both BioLCCC free chain and BioLCCC rigid rod models mentioned above. The column No. 1 was used for separation of sixprotein digest. Before each experiment, the columns were equilibrated for 30 minutes. Reproducibilities of the experiments with “steep” and “shallow” gradients between replicas were 0.1 and 0.2 minutes, respectively. The solvent flow rate was kept constant in all experiments and equal to 300 nL/min. MS Detection and Peptide Identification Mass measurement accuracy for parent ions was 10 ppm. Peptides were fragmented by collision induced dissociation in the linear quadrupole trap of the mass spectrometer, prior to the measurement of product ion masses with the accuracy of 0.8 Da. Data base search and processing of the results were made using TransProteomic Pipeline (TPP)
software (http://tools.proteomecenter.org/wiki/index. php?title=Software:TPP) and X!Tandem [22] inte grated in TPP as a search engine for peptide identifi cation. The database for the search consisted of taxon omies corresponding to the proteins under study, and its reversed database was used for “decoybased” esti mation of False Discovery Rate (FDR) [23]. Experimental Data Processing and Search for Peptides with Inverted Elution Order Four replicas of each of the experimental runs under identical conditions were used. Peptides identi fied with FDR less than 2.5% were used in the analysis. All routines including selection of identifications meeting the FDR ≤ 2.5% criterion and presented in all experiments, generation of the list of top proteins, extraction of retention times at the apex of a chro matographic peak, and searching for peptide pairs that changed the elution order were automated using Python programming language. The number of pep tide pairs that changed the elution order was calcu lated using the criterion that the distance between the peaks was at least 0.2 min. Validations of peptide and protein identifications were performed using Pep tideProphet and ProteinProphet tools integrated in TPP, respectively.
JOURNAL OF ANALYTICAL CHEMISTRY
Vol. 67
No. 13
2012
INVERSION OF CHROMATOGRAPHIC ELUTION ORDERS OF PEPTIDES
Retention Time Calculation Retention times for different gradient slopes were theoretically predicted using the BioLCCC model, implemented by the authors in C++ and Python programming languages as an opensource library pyBioLCCC (http://www.theorchromo.ru/ lib/). The rigid rod model (rpAcnFaRod) [20] with the standard set of phenomenological parameters (inter action energies between amino acid residues and the stationary phase) was used in the predictions. These phenomenological parameters were previously deter mined for a reversedphase Magic AQ C18 20 nm col umn (Michrom BioResources, Auburn, USA) and a wateracetonitrile mobile phase with addition of iso propanol and formic acid (pH 3.5) [24]. It should be noted that the properties of Magic AQ C18 phase dif fer from the commonly used reversedphase C18: it is chemically modified phase for enhanced retention of the hydrophilic substances yet provides similar separa tion selectivity for the hydrophobic substances [25]. When comparing the BioLCCC model to alternative approaches for prediction of the direction of peak shifts for different gradients, the absolute retention times calculated by pyBioLCCC were converted to relative shifts as follows: ΔBioLCCC = theor ' theor theor ' theor g ( RT – RT ) – g 1 ( RT g1 – RT g1 ). The sign 0
g0
g0
of ΔBioLCCC was compared to the experimental value, Δexp. The value of Δexp was determined using Eq. (3) [14]: exp exp exp exp Δ = g ( RT – RT ' ) – g ( RT – RT ' ). (4) exp
0
g0
g0
1
g1
g1
Alternatively, the direction of peptide retention time shifts and S coefficients of the Snyder’s theory, were calculated using Sequence Specific Slope Reten tion Calculator (SSSRCalc) (http://hs4.pro teome.ca/SSRCalc/Slope/). In this approach the the oretical values of peak shifts for different gradients were determined using Eq. (2) [14]: ΔSSSRC = 100log(g0/g1)(1/S – 1/S'), (5) followed by comparison between theoretically pre dicted and experimentally measured shifts in retention times. RESULTS AND DISCUSSION Analysis of Inverting Peptide Pairs Table 2 shows peptide pairs from 6protein stan dard that changed the elution order after the gradient slope was changed. These pairs were determined by the analysis of 47 unmodified peptides identified with FDR ≤ 2.5%. 19 pairs with inversion were found. As shown in Table 2, the longer peptide in each of the pairs was eluting first under the “steep” gradient, and vise versa for the “shallow” gradient. One pair of pep tides with equal sequence lengths was also found. We also analyzed the literature data for different gradient slopes [14] and searched for peptides that JOURNAL OF ANALYTICAL CHEMISTRY
Vol. 67
1017
changed the elution order. The first dataset contained 247 peptides, among which 567 pairs with inversion were found. In these pairs, peptide with largest sequence length was eluting first in the “steep” gradi ent (with the gradient slope of 0.75% ACN/min) in most of the cases, as was observed for the 6protein standard. However, there are “exceptions” in which the shorter peptides were eluting first in the “steep” gradient, for example:
Peptide
Elution time, min, at slope 0.75 Mass, Da Length (0.187)% ACN/min
HQTVPQNTG 1975.15 GKNPDPWAK NPSSAGSWNSG 1693.95 SSGPSTGNR
18
20.72 (54.52)
21
21.03 (53.7)
Yet, the molecular weights of these peptides were not always higher:
Peptide YQISVNK QDGSVDFGR
Mass, Da 850.97 980.00
Elution time, min, at slope 0.75 Length (0.187)% ACN/min 7 9
21.78 (54.15) 22.1 (53.47)
Among the peptide pairs changing the elution order there are cases that peptides have the same amino acid sequence length, as shown below:
Peptide ECCEKPLLEK LGGGGGGDFR RHPYFYAPE LLFFAK DSDWPFCSD EDWNYK
Elution time, min, at slope 0.75 Mass, Da Length (0.187)% ACN/min 1191.42 891.94 1899.23
10 10 15
19.8 (48.37) 20.27 (47.14) 38.32 (124.65)
1906.95
15
38.86 (123.95)
In this case, either the lighter or the heavier peptide can be eluted first in the “steep” gradient. These exceptional pairs constituted 4.5% of the total number of inverted pairs found in the 247peptide set. The second analyzed literature dataset contained 504 tryptic peptides [14]. When the gradient slope was changed from 0.75% ACN/min to 0.187% ACN/min, 1392 peptide pairs with changed elution order were found. Among these pairs, 4.0% of “exceptions” were also found, in which the shorter (or the lighter) pep tide was eluting first under the “steep” gradient. The considered experimental data show that in gra dient chromatography a change in the gradient slope No. 13
2012
1018
TARASOVA et al.
Table 2. Peptide pairs from 6protein standard which changed the elution order under the change of gradient slopes from 0.23 to 1.3% ACN/min
No.
Peptide pairs with inverted elution order
1 HSTVFDNLPNPEDRK DKPDNFQLFQSPHGK 2 DQTVIQNTDGNNNEAWAK RHPEYAVSVLLR 3 KVPQVSTPTLVEVSR GYLAVAVVK 4 KTYDSYLGDDYVR GYLAVAVVK 5 HSTVFDNLPNPEDR TGPNLHGLFGR 6 TSDANINWNNLK SISIVGSYVGNR 7 TSDANINWNNLK GTDVQAWIR 8 TSDANINWNNLK LVVSTQTALA 9 TSDANINWNNLK LVNELTEFAK 10 TYDSYLGDDYVR LVNELTEFAK 11 LPLVGGHEGAGVVVGMGENVK LVNELTEFAK 12 VLGIDGGEGKEELFR LVNELTEFAK 13 AISNNEADAVTLDGGLVYEAGLKPNNLKPVVAEF HGTK GEADAMSLDGGYLYIAGK 14 AISNNEADAVTLDGGLVYEAGLKPNNLKPVVAEF HGTK IVSDGNGMNAWVAWR 15 SANLMAGHWVAISGAAGGLGSLAVQYAK AIQAAFFYLEPR 16 SANLMAGHWVAISGAAGGLGSLAVQYAK TAGWNIPMGLLYSK 17 GITWGEETLMEYLENPKK GPTLTEILEGLK 18 GITWGEETLMEYLENPKK TAGWNIPMGLLYSK 19 EDVIWELLNHAQEHFGK TAGWNIPMGLLYSK
Mass, Da
Gradient, % ACN/min Num ber of 0.23 1.3 amino acid RTexp, RTBioLCCC, RTexp, RTBioLCCC, residues min min min min
1768.90 1757.92 2018.08 1439.68 1639.91 919.13 1594.70 919.13 1640.73 1168.32 1389.49 1251.40 1389.49 1045.16 1389.49 1002.18 1389.49 1163.34 1466.52 1163.34 2019.35 1163.34 1618.81 1163.34 3953.42
15 15 18 12 15 9 13 9 14 11 12 12 12 9 12 10 12 10 12 10 21 10 15 10 38
54.28 53.99 56.57 55.30 58.30 57.18 58.35 57.18 62.52 61.31 65.98 63.59 65.98 62.93 65.98 63.09 65.98 65.70 67.51 65.70 70.01 65.70 68.81 65.70 87.26
31.96 41.91 51.42 74.98 56.61 55.48 51.02 55.48 42.95 40.43 53.40 51.33 53.40 53.06 53.40 71.46 53.40 65.82 52.92 65.82 76.90 65.82 71.03 65.82 98.60
20.03 20.26 20.47 20.72 20.81 21.32 20.86 21.32 21.71 21.93 22.01 22.29 22.01 22.44 22.01 22.48 22.01 23.38 22.88 23.38 22.90 23.38 22.90 23.38 25.81
9.97 12.17 13.44 20.94 15.07 16.75 14.53 16.75 12.39 13.77 15.58 14.42 15.58 17.41 15.58 19.45 15.58 18.11 14.94 18.11 17.32 18.11 18.06 18.11 19.77
1831.03 3953.42
18 38
86.62 87.26
95.77 98.60
26.38 25.81
21.63 19.77
1675.88 2701.10 1425.65 2701.10 1550.84 2138.42 1270.49 2138.42 1550.84 2065.27 1550.84
15 28 12 28 14 18 12 18 14 17 14
85.96 98.66 97.48 98.66 98.37 99.80 99.19 99.80 98.37 101.24 98.37
99.75 111.64 109.28 111.64 110.05 90.26 86.07 90.26 110.05 86.51 110.05
26.53 28.55 28.76 28.55 29.14 28.78 29.04 28.78 29.14 28.88 29.14
24.07 22.77 25.66 22.77 25.04 20.19 21.54 20.19 25.04 20.19 25.04
Note: Calculation parameters correspond to the experimental conditions used in the study (gradient profile, mobile phase composition, flow rate, column parameters). For the rest of parameters the default values were used, including the dead volume equal to zero. JOURNAL OF ANALYTICAL CHEMISTRY
Vol. 67
No. 13
2012
INVERSION OF CHROMATOGRAPHIC ELUTION ORDERS OF PEPTIDES Kd* 0.7 0.6
(a)
1019
(b)
0.6
ATVILAHTIK
KVPQVSTPTLVEVSR
0.5
0.5
0.4
0.4 0.3 0.2
ASVESNFALR
0.3
GYLAVAVVK
ϕ
0.2
ϕ
0.1
0.1
0 0.40
0 0.7
(c)
(d)
0.6 AVVMDHDANIISVSQR
0.35 0.30
0.5 KQDVMLA
0.25
0.4 ϕ
0.20
ϕ
0.3
0.15 QKDAVML
0.10
0.2 ILILHADHEQNASTSTVR
0.1
0.05 0
10
20
30
40
50 0
10
20
30
40
50 % ACN
Fig. l. Dependencies of the fraction of desorbed molecules, K *d , on acetonitrile percentage concentration, % ACN, in a binary solvent for different peptide pairs with elution order inversion predicted theoretically. (Calculation parameters correspond to the experimental conditions used in present study for separation of 6protein standard digest.)
(with all the other parameters fixed) leads to a signifi cant change in the interaction between peptides and the stationary phase because of their physical and chemical properties. The BioLCCC model allows pre diction of these changes by calculating the distribution coefficient Kd of the peptides as a function of percent age of acetonitrile in the mobile phase and plotting the adsorption curves Kd (% ACN). Because of the differ ence in the rate of Kd (% ACN) change during the gra dient for peptides of different lengths, the adsorption curves of closely eluting peptides may intercept that results in the observed inversion of peptide elution order. Figure 1 shows theoretical dependencies of the fraction of desorbed molecules K *d on the percentage of acetonitrile in the binary solvent K *d = (1 + VpKd/V0)–1, JOURNAL OF ANALYTICAL CHEMISTRY
(6) Vol. 67
where Vp and V0 are the pore and interstitial volumes, respectively. Acetonitrile concentrations in the por tion of the solvent in which the peptides are eluting are shown as dots and triangles for “shallow” and “steep” gradients, respectively. Acetonitrile percentage in this portion of the solvent was calculated as % ACN = peptide gradient_slope ( RT theor – t0), where t0 = V0/flow_rate is the time required for the nonretained component to pass through the interstitial volume of V0 at given flow rate. Figure 1a shows typical example (~95% of all inversion cases), when the longer peptide is retained stronger compared with the shorter one for “shallow” gradient, and vise versa for “steep” gradient. The adsorption curve for peptide KVPQVSTPTLVEVSR exhibits higher inclination angle ϕ of the sharply rising part of the curve. This angle is smaller for the shorter peptide GYLAVAVVK. According to BioLCCC No. 13
2012
1020
TARASOVA et al.
Table 3. Results of the data processing of different experimental datasets, which demonstrate the dependence of the number of inverted peptide pairs on the total number of peptides in the dataset and a change in gradient slopes. Results of prediction of peptide elution order inversions and the direction of the corresponding LC peak shifts are presented. Peak shifts were predicted using both the BioLCCC and SSSRCalc models
Sample
6Protein standard digest, Dionex, USA Literature dataset no. 1, “test peptide mixture”
Literature dataset no. 2, Clostridium Thermocellum
Prediction Fraction of Peak shift, of the inver peptides from Peak shift, sion, BioLCCC, SSSRCalc, the dataset Number Number BioLCCC, matches matches which Gradient change, of inverted of matches with the with the changed/ % ACN/min peptide pairs, peptides with the experiment* unchanged the experiment, experiment, experiment, % % elution order, % % 47
0.23
1.3
19
53.2/46.8
94.7
88.9
47.4
247
0.375
0.75
151
56.3/43.7
92.7
100.0
13.3
0.187
0.375
269
71.3/28.7
91.1
99.3
11.2
0.187
0.75
567
85.4/14.6
90.7
99.6
23.8
0.375
0.75
318
54.6/45.4
97.5
99.7
8.2
0.187
0.375
627
81.7/18.3
95.1
97.6
11.3
0.187
0.75
1392
93.1/6.9
94.9
98.4
22.2
504
* During the processing of the experimental data the number of inverted peptide pairs was counted for peptide pairs which were displaced from each other by more than 0.2 min.
model the inclination angle ϕ of the adsorption curve is affected not only by peptide length, but also by its amino acid composition and sequence as shown in Fig. 1: Fig. 1b shows dependence of the fraction of desorbed molecules on acetonitrile concentration K *d (% ACN) for a pair of peptides having the same sequence lengths but different compositions, ASVESNFALR and ATVILAHTIK; and Fig. 1c dem onstrates the influence of the sequence on the shape of the adsorption curve for the case of two peptides of the same composition and length, QKDAVML and KQDVMLA. The example presented in Fig. 1d for peptide pair ILILHADHEQNASTSTVR/AVVM DHDANIISVSQR shows further that BioLCCC model may predict a number of cases when the longer peptide is eluting earlier than the shorter peptide in the “shallow” gradient followed by elution order inversion when “steep” gradient is applied. Therefore, the examples shown in Figs. 1c⎯1d demonstrate that BioLCCC model accounts for inver sion of elution order for all types of “exceptions” when the shorter or the lighter peptide is eluted first in the “steep” gradients. These “exceptions” comprised 4.0 to 4.5% of all peptide pairs changing the elution order.
The Scale of Inversion Phenomenon in Proteomic Studies When different experimental datasets obtained under different HPLC conditions are compared, the scale of the inversion phenomenon and the subsequent information losses have to be estimated. Specifically, the following problems have to be addressed: (1) dependence of the number of inverting peptides on the composition and size of the peptide dataset; (2) range of the changes in the gradient profiles that leads to a number of inversions significant enough to affect the comparison of different chromatography datasets; and, finally; (3) a possibility for evaluation of the num ber of peptide pairs that change the elution order depending on the HPLC conditions using retention time prediction. Table 3 presents the results of analysis of our own and literature data [14] that address the above prob lems. It shows that with increasing number of peptides in the dataset the number of peptide pairs with invert ing elution order also increases up to 90% of the total number of peptides in the set. It is interesting to note that for all HPLC conditions used the number of pep tides involved in inversions when gradient changes exceeded 50% of the total. Consider further the dependence of the number of inverting pairs. Expect
JOURNAL OF ANALYTICAL CHEMISTRY
Vol. 67
No. 13
2012
INVERSION OF CHROMATOGRAPHIC ELUTION ORDERS OF PEPTIDES
edly, the highest number of these pairs corresponds to a change from the “steepest” to the most “shallow” gradient. However, a transition between two “shallow” gradients, e.g. 0.1875 0.375% ACN/min, results in twice larger number of inversions compared with transition 0.375 0.75 ACN/min. The percentage of acetonitrile in the peptide elution fraction was cal culated as discussed before: % ACN = peptide gradient_slope( RT theor – t0). The above results prove that even small variations in the gradient slopes result in significant number of elution order inversions in case of the “shallow” gradients (
