Liquid Chromatography at Critical Conditions:
Comprehensive Approach to Sequence-Dependent
Retention Time Prediction
Alexander V. Gorshkov,
†
Irina A. Tarasova,
‡
Victor V. Evreinov,
†
Mikhail M. Savitski,
§
Michael L. Nielsen,
§
Roman A. Zubarev,
§
and Mikhail V. Gorshkov*
,‡
Institute for Energy Problems of Chemical Physics, Russian Academy of Sciences, Leninsky Prosp. 38, bld.2,
Moscow 119334, Russian Federation, N.N. Semenov’s Institute of Chemical Physics, Russian Academy of Sciences,
Kosygina 4, Moscow 119991, Russian Federation, and Biological and Medical Center, Laboratory for Biological and Medical
Mass Spectrometry, Uppsala University, Box 583, S-75 123 Uppsala, Sweden
An approach to sequence-dependent retention time pre-
diction of peptides based on the concept of liquid chro-
matography at critical conditions (LCCC) is presented.
Within the LCCC approach applied to biopolymers (Bi-
oLCCC), the specific retention time corresponds to a
particular sequence. In combination with mass spectrom-
etry, this approach provides an efficient tool to solve
problems wherein the protein sequencing is essential. In
this work, we present a theoretical background of the
BioLCCC concept and demonstrate experimentally its
feasibility for sequence-dependent LC retention time
prediction for peptides. BioLCCC model is based on three
notions: (a) a random walk model for a macromolecule
chain; (b) an entropy and energy compensation for the
macromolecules within the adsorbent pore; and (c) a set
of phenomenological parameters for the effective interac-
tion energies of interactions between the amino acid
residues and the adsorbent surface. In this work, the
phenomenological parameters have been obtained for C
18
reversed-phase HPLC. Note, that contrary to alternative
additive models for retention time prediction based on
summation of the so-called “retention coefficients”, the
BioLCCC approach takes into account the location of
amino acids within the primary structure of a peptide and,
thus, allows the identification of the peptides having the
same composition of amino acids but differing by their
arrangement. As a result, this new approach allows
prediction of retention time for any possible amino acid
sequence in particular HPLC experiments. In addition,
the BioLCCC model lacks of main drawbacks of additive
approaches that predict retention time for sequences of
limited chain lengths and provide information about
amino acid composition only. The proposed BioLCCC
approach was characterized experimentally using LTQ FT
LC-MS and LC-MS/MS data obtained earlier for Es-
cherichia coli. The HPLC system calibration was per-
formed using peptide retention standards. The results
received show a linear correlation between predicted and
experimental retention times, with a correlation coef-
ficient, R
2
, of 0.97 for a peptide standard mixture and
0.9 for E. coli data, respectively, with the standard error
below 1 min. The work presents the first description of a
BioLCCC approach for high-throughput peptide charac-
terization and preliminary results of its feasibility tests.
A method of liquid chromatography at critical conditions
(LCCC) applied to synthetic oligomers was developed in the early
1980s.
1-4
A basic feature of the LCCC mode is that macromol-
ecules are separated at the so-called critical LC parameters
corresponding to adsorption-phase transition
5,6
that can be de-
scribed by renormalization group theory.
7
In critical conditions,
the molar mass distribution of synthetic polymers “disappears”
and separation takes place in accordance with other types of chain
heterogeneity, e.g., a number and the types of functional or
modified groups, their positions in the chain, and a chain topology.
The existence of the LCCC mode is based on the consideration
of polymer adsorption as a second-order phase transition that takes
place in a system consisting of monomers connected into a flexible
chain. The chain is characterized by the entropy losses, ∆S, due
to restrictions implied on the monomer degrees of freedom near
the pore wall and the free energy changes due to monomer
interactions with the pore surface. The interaction between a
macromolecule and a surface is characterized by an interaction
energy, ∆E
ads
, which depends, at fixed temperature T, on a binary
solvent composition, N
B
, i.e., ∆E
ads
)∆E
ads
(N
B
). At certain critical
solvent composition, N
c
, one can achieve an exact compensation
* Corresponding author. Phone: +7(495) 1371007. Fax: +7(495) 1378258.
E-mail: gorshkov@chph.ras.ru.
†
N.N. Semenov’s Institute of Chemical Physics.
‡
Institute for Energy Problems of Chemical Physics.
§
Uppsala University.
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1983, 272, 632-635.
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Anal. Chem. 2006, 78, 7770-7777
7770 Analytical Chemistry, Vol. 78, No. 22, November 15, 2006 10.1021/ac060913x CCC: $33.50 2006 American Chemical Society
Published on Web 10/21/2006

of entropy losses by energy gains so as the total change in free
energy, ∆G, of this system becomes zero, i.e., ∆G ) ∆E
ads
(N
c
) -
kT∆S ) 0. These conditions correspond to a chain transition from
a solution to a localized state for the macromolecule inside the
pore. Following this consideration, the LCCC theory describes
the chain as having three states: (i) a nonadsorbed 3d coil in a
solution at kT∆S . ∆E
ads
; (ii) completely adsorbed 2d coil at the
surface (when each of the monomers binds to a surface) at kT∆S
, ∆E
ads
; and (iii) a coil, which undergoes the phase transition
between the nonadsorbed and the adsorbed states. These ther-
modynamic states correspond to the size exclusion chromatog-
raphy (SEC), the liquid adsorption chromatography (LAC), and
the LCCC modes of chromatographic separation. For a given chain
length, N, and an interaction energy, ∆E
ads
, the particular mode
of separation can be realized. It is quite clear that for the coils in
3d (nonadsorbed) and 2d (fully adsorbed) states there is no
retention time dependence on the macromolecule sequence and
the elution will be sensitive to either a size of a chain (SEC) or
the chain composition (LAC). Near the critical conditions, the
adsorption properties become sensitive to smaller details, such
as the chain sequence or the presence of certain chemical groups.
Using the LCCC theory, it is possible to calculate the interaction
energies between the monomers and the surface for a known
chemical structure, or sequence, and, thus, to predict LC retention
times. Earlier, this approach has been successfully applied to
characterize synthetic oligomers
8-12
and copolymers.
13,14
Recently,
the LCCC approach to chromatographic separation of complex
polymer mixtures becomes more broadly accepted
15,16
with new
developments including the combination of LCCC with mass
spectrometry.
17-19
The BioLCCC approach in combination with tandem mass
spectrometry (MS/MS) for the characterization of biopolymers
has been proposed recently, and the feasibility of this approach
to predict LC retention time based on peptide amino acid sequence
and, in combination with MS/MS, to facilitate de novo sequencing
has been demonstrated. Note, that the developments of LC
retention time prediction models related to peptide separation have
gained renewed interest in recent years as a way to improve
efficiency of protein identification. Indeed, LC data are compli-
mentary to the MS and MS/MS data in respect to the peptide
structure and, thus, bring additional information about the
structure that can be missed or overlooked during routine MS
experiments. Among recently developed approaches to LC peptide
retention time prediction are the artificial neural network (ANN)
approach,
22
the model based on sequence-specific correction
factors,
23
and the model based on quantitative structure-retention
relationships.
24
In this work, we present the BioLCCC concept and first
experimental results of its application for sequence-dependent LC
retention time prediction.
THEORY
Separation in chromatography is defined by the distribution
coefficient K
d
, associated with a change in free energy, ∆G, when
the macromolecule passes from a mobile phase into the pores of
a stationary phase. Consider the general expression for chro-
matographic distribution coefficient K
d
as follows:
in which ∆G ) ∆E
ads
- kT∆S is the energy difference, ∆S is the
change in entropy, and ∆E
ads
is the adsorption energy that
characterizes interaction of a macromolecule with the surface. The
particular mode of separation will be SEC, when ∆G > 0(K
d
<
1), or LAC, when ∆G < 0(K
d
> 1). Finally, at critical conditions
(LCCC), when ∆G ) 0, the distribution coefficient equals 1, K
d
)
1, and is independent of the molecular size or the polymerization
degree.
The retention volume is defined as
in which V
0
, V
p
are the interstitial and total pore volumes,
respectively.
Under the conditions of gradient elution, a binary solvent
composition changes and becomes dependent on a volume, V )
V(t), passing through a column, i.e., N
B
) N
B
(V), where N
B
is a
molar fraction of a solvent component B (e.g., acetonitrile) in a
component A (e.g., water). Respectively, the distribution coefficient
changes with time, K
d
) K
d
(V). The corresponding equation for
the retention volume in a gradient elution becomes the follow-
ing:
25
Equation 3 is a general equation, which can be used to find
retention volume in a gradient mode of separation that further
allows determination of the retention volume for particular
macromolecule and specific gradient conditions, N
B
) N
B
(V), and
column parameters.
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Nashville, TN, 2004; MPX447.
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M. V. 53rd ASMS Conference, San Antonio, TX, 2005; THP263.
(22) Petritis, K.; Kangas, L. J.; Ferguson, P. L.; Anderson, G. A.; Pasaˇ-Tolic’, L.;
Lipton, M. S.; Auberry, K. J.; Strittmatter, E. F.; Shen Yu.; Zhao, R.; Smith,
R. D. Anal. Chem. 2003, 75, 1039-1048.
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C.; Wilkins, J. A. Mol. Cell. Proteomics 2004, 3.9, 908-919.
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Grzonka, Z. Proteomics 2005, 5, 409-415.
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K
d
) exp
-∆G/kT
(1)
V
R
) V
0
+ K
d
V
p
(2)
∫
0
V
R
- V
0 dV
V
P
K
d
(V)
) 1 (3)
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