J. Phys. Chem. B 2009, 113, 6109–6118
`
`6109
`
`Increasing IgG Concentration Modulates the Conformational Heterogeneity and Bonding
`Network that Influence Solution Properties
`
`Tim J. Kamerzell,* Sonoko Kanai, Jun Liu, Steven J. Shire, and Y. John Wang
`Department of Late Stage Pharmaceutical and Processing DeVelopment, Genentech, Inc., 1 DNA Way, South
`San Francisco, California 94080
`ReceiVed: January 7, 2009; ReVised Manuscript ReceiVed: March 6, 2009
`
`Multiple molecular driving forces mediate protein stability, association, and recognition in concentrated
`solutions. Here we investigate the interactions that modulate the nonideal solution behavior of two
`immunoglobulins (IgG1s) in highly concentrated solutions using two-dimensional vibrational correlation
`spectroscopy (2D-COS) and principal components analysis (PCA). A specific sequence of changes is observed
`in the concentration-dependent vibrational spectra of the highly viscous IgG solution that deviates from ideality,
`whereas that sequence is reversed for all other conditions examined. The asynchronous spectra reveal variation
`in (cid:1)-sheet and turn regions occur before intensity variations in disordered and R-helical regions as the
`concentration is increased for the highly viscous regime. This is in contrast to the sequence observed for all
`other conditions studied and to the idea that (cid:1)-sheet regions are resistant to concentration-dependent affects.
`Finally, we show that increased hydrogen bonding and electrostatics primarily modulate the intermolecular
`association and nonideal behavior. Specifically, 2D-COS and PCA analysis of the amide II region suggests
`that Glu and Asp residues trigger the change resulting in increased viscosity and association of one IgG.
`
`Introduction
`
`Investigating protein and solution behavior in “crowded” or
`highly concentrated conditions is critical to our understanding
`of physiological and cellular function as well as the stability,
`therapeutics.1-4 In vivo,
`safety, and efficacy of biological
`proteins function in highly crowded environments where the
`concentration of molecules approximates 400 g/L or up to 40%
`of the total cellular volume.4,5 Often homogeneous systems are
`used to build models that describe functionally complex systems.
`These studies of increasing macromolecular concentration have
`been used to describe association rates,6-8 association equilibria,8,9
`protein conformational changes,8,10 protein activity,2,8 and protein
`stability.3,8,11 Similarly, the association state of proteins and
`model solutes in crowded and concentrated environments have
`been linked to nonideal solution behavior of which models are
`beginning to emerge.2,12-19 Our understanding of the effects of
`highly concentrated and volume occupied solutions on protein
`structure and stability, however, is incomplete.
`Recently, the effects of increasing protein concentration on
`the stability and safety of biological therapeutics have gained
`significant attention from the biotechnology industry and the
`Food and Drug Administration (FDA).13,20-27 The physicochem-
`ical stability of biological
`therapeutics may be negatively
`affected simply by increasing protein concentration. Chemical
`instability typically follows first-order kinetics with regard to
`concentration; however, physical
`instability may result
`in
`complex higher order processes. It has been shown that
`increasing immunoglobulin (IgG) concentration increases self-
`association of these molecules resulting in increased nonideal
`solution properties and significantly affects the viscosity and
`rheological behavior.13,14,24-26
`The purpose of this work is to better understand the molecular
`level processes that govern protein self-association and how
`
`these details modulate the solution properties of two previously
`studied IgGs.12,13 Two-dimensional correlation vibrational spec-
`troscopy and principal components analysis was used to monitor
`changes in the vibrational characteristics of these IgG molecules
`as a function of concentration and solution condition. The
`synchronous and asynchronous correlation spectra reveal im-
`portant molecular properties that influence self-association and
`various solution behaviors.
`
`Experimental Procedures (Materials and Methods)
`
`Two IgG1 full length monoclonal antibodies (MAb1 and
`MAb2) comprised of κ-light chains constructed from identical
`human frameworks were used in this study. The difference in
`amino acid composition is localized to CDR regions. These
`antibodies were cloned, expressed in Chinese hamster ovary
`cell lines, and purified at Genentech (South San Francisco, CA).
`All reagents were ACS grade. The buffer solutions used in this
`study were 30 mM histidine, pH 6.0, (150 mM NaCl.
`Fourier Transform Infrared Spectroscopy. Infrared spectra
`were collected at 25 °C and a resolution of 4 cm-1 using an
`attenuated total internal reflectance (ATR) accessory mounted
`in a Nicolet FTIR spectrometer (Thermo Scientific, Waltham,
`MA). The spectrometer and chamber enclosing the sample
`trough were continuously purged with dry air, which was
`controlled using two flow meters. The concentrations of samples
`were compared before and after spectral collection to ensure
`all sample concentrations remained constant. Approximately 256
`scans were coaveraged per spectrum using a zinc selenide
`(ZnSe) crystal with a 45° incidence angle.
`In this study, various pretreatment methods were applied to
`each individual spectrum. A series of pretreatment steps were
`performed according to Czarnik-Matusewicz et al.28 First, the
`spectra were normalized for the minimum of penetration depth
`and dependence of penetration depth on concentration. Next,
`* To whom correspondence should be addressed. Tel.: 650-225-6630.
`the contribution of water was subtracted using a second-order
`Fax: 650-225-3613. E-mail: tkamer@gene.com.
`10.1021/jp9001548 CCC: $40.75  2009 American Chemical Society
`Published on Web 04/03/2009
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`6110 J. Phys. Chem. B, Vol. 113, No. 17, 2009
`
`least-squares fit and baseline correction.29 Finally, all spectra
`were normalized for concentration using the intensity integral
`in the range 1720-1220 cm-1.
`Two-Dimensional Correlation Spectroscopy. In this work,
`FTIR spectroscopy was used to monitor the concentration
`dependence of an IgG and the subsequent spectral intensity
`variations along the concentration and frequency axis. Matrices
`of m rows of spectral traces and n columns of spectral intensity
`variations were created from the concentration-dependent FTIR
`spectra. From each matrix, the synchronous and asynchronous
`spectra were calculated according to Noda30-33 using routine
`functions in Matlab R2007a. The discrete data collected from
`the IR measurements were represented in matrix notation as
`previously described by Noda:34
`
`Y )[y˜(ν1, c1)
`
`]
`
`... y˜(νn, c1)
`y˜(ν2, c1)
`y˜(ν1, c2)
`... y˜(νn, c2)
`y˜(ν2, c2)
`...
`...
`...
`...
`y˜(ν1, cm) y˜(ν1, cm)
`... y˜(νn, cm)
`wherey˜(ν,c) is the set of dynamic spectra, from which the
`synchronous 2D correlation spectrum is defined as the inner
`product where each column of the matrix Y is a vector:
`Φ(ν1, ν2) ) 1
`orΦ ) 1
`m - 1
`m - 1
`(covariance matrix)
`
`y˜(ν1)T y˜(ν2)
`
`YTY
`
`It has also been shown that the synchronous spectrum is similar
`to the covariance matrix if the measurements are recorded at
`fixed intervals.34 The covariance matrix (C) ofn sets of variates
`(X1),..., (Xn), was calculated and defined as
`mn ) 〈(xiCij
`- µj)n〉
`- µi)m(xj
`
`where µi is the mean. The diagonal elements of the covariance
`matrix represent the autocorrelation of intensity variations with
`time at a given wavenumber, and the cross-peaks indicate the
`simultaneous change of intensity between wavenumbers. The
`matrix R of statistical correlation coefficients is related to the
`covariance matrix C(i,j) and was calculated from the following
`well-known relationship:
`
`R(i, j) )
`
`C(i, j)
`√C(i, i)C(j, j)
`where C(i,i) and C(j,j) are elements of the covariance matrix.
`In addition, a matrix of p-values for testing the hypothesis of
`no correlation was calculated. Each p-value is the probability
`of getting a correlation as large as the observed value by random
`chance, when the true correlation is zero.
`The asynchronous correlation spectrum Ψ(ν1,ν2), which
`represents the dissimilarity of spectral variations as a function
`of concentration, can be represented in matrix notation and
`defined as
`orΨ ) 1
`Ψ(ν1, ν2) ) 1
`YTNY
`y˜(ν1)TNy˜(ν2)
`m - 1
`m - 1
`where N is the Hilbert-Noda transformation matrix.32 The
`asynchronous spectrum is antisymmetric, and cross-peaks arise
`only if the spectral intensity variations change out of phase with
`each other. Thus, the asynchronous spectrum is useful for
`interpreting the sequential order of spectral intensity variations.
`The ratio of asynchronous to synchronous functions F(ν1,ν2)
`has been determined following the method proposed by Buchet
`et al. 35 using the following relationship:
`
`Kamerzell et al.
`F(ν1, ν2) ) Ψ(ν1, ν2) ⁄Φ(ν 1, ν2)
`This ratio was used to confirm correlation peaks and has been
`suggested to be a measure of the degree of coherence similar
`to the global correlation phase angle proposed by Noda.36 All
`calculations were performed and plotted using routine functions
`in Matlab R2007a.
`Central Moments. The central moments of matrix X of
`various orders (k) were calculated using the following notation,
`) E(x - µ)k
`mk
`where E(x) is the expected value of x. The first central moment
`is zero, and the second moment is the variance, while higher
`moments describe asymmetry and shape parameters or peaked-
`ness of a distribution. Higher moments magnify the importance
`of those points which deviate from the sample mean or normal
`distribution. These parameters may be used to further emphasize
`changes in specific vibrational band frequencies with increasing
`protein concentration.
`Principal Component Analysis. Principal component analy-
`sis (PCA) is a well-established method used in statistics and
`chemometrics to reduce the dimensionality of a data set while
`retaining much of the variation present.37 PCA is used as an
`independent data analysis method and compliments the 2D
`correlation approach. For example, it has been shown that the
`autopower spectrum across the diagonal of a synchronous
`spectrum is similar to the first PCA loading vector.38,39 All PCA
`analysis was performed using Matlab.
`
`Results
`The concentration-dependent (20-120 mg/mL) ATR-FTIR
`spectra are shown in Figure 1 for MAb1 without NaCl. After
`pretreatments and normalization, the spectra of MAb1 and
`MAb2 appear similar. Little to no change was observed in
`percent secondary structure or the number of component bands
`and positions as a function of protein concentration using
`standard curve fitting procedures.
`Covariance Matrices and Synchronous Spectra. The
`development of perturbation-based 2D correlation spectroscopy
`was first described by Noda in 1986 30-32 and later extended to
`generalized 2D correlation spectroscopy.33,34 The use of two
`dimensions facilitates the deconvolution of complex overlapping
`spectral bands. Herein, the synchronous correlation spectra
`describe the simultaneous change in intensity variation between
`two wavenumbers along the perturbation axis of concentration.
`
`Figure 1. Concentration-dependent FTIR spectra prior to mean
`centering and variance scaling. The numbers correspond to protein
`concentration in units of milligrams per milliliter.
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`Molecular Forces That Mediate IgG Nonideality
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`J. Phys. Chem. B, Vol. 113, No. 17, 2009 6111
`
`Figure 2. Wavenumber power spectra (cm-1) along the diagonal line in the amide I synchronous spectra for (A) MAb1 in the absence of 150 mM
`NaCl, (B) MAb1 + 150 mM NaCl, (C) MAb2 in the absence of 150 mM NaCl, and (D) MAb2 + 150 mM NaCl.
`
`Correlation peaks arise along the diagonal and off-diagonal
`(cross-peaks) positions of the synchronous spectrum. Math-
`ematically, the diagonal is equivalent to the autocorrelation or
`variance of spectral intensity.
`The amide I autopower spectra along the diagonal line of
`the synchronous matrix with indicated peak positions are shown
`for MAb1 and MAb2 in the presence and absence of 150 mM
`NaCl (Figure 2). The power spectrum is composed of well-
`resolved bands and represents the overall intensity variation of
`components potentially unidentified in the broadened one-
`dimensional spectrum. Two bands are observed at similar
`positions (1637 and 1653 cm-1) in the amide I region under all
`solution conditions tested. The bands at higher wavenumbers,
`>1655 cm-1 arise at distinctly different positions. Interestingly,
`peaks arise at 1622 cm-1 in the power spectrum of MAb1 in
`solution without 150 mM salt, while a slightly higher wave-
`number is observed for MAb2 in solution without 150 mM
`NaCl. This peak is well-resolved for MAb1 and may be
`attributed to associated molecules, extended structure and
`increased intermolecular hydrogen bonding.40,41 The synchro-
`nous maps (not shown) were used to identify the position (ν1,ν2)
`and sign (() of potentially relevant auto- and cross-peaks.
`The autopower spectra of the amide II region for both mAb’s
`are shown in Figure 3. Many of the same peaks that arise from
`charged residues are observed with differing intensity and
`resolution for all conditions studied, suggesting the degree of
`backbone hydration and/or molecular environments of charged
`residues are noticeably affected upon increasing protein con-
`
`centration. Peaks of interest include bands at 1529, 1549, and
`1514 cm-1 for MAb1 without NaCl and 1558 and 1533 cm-1
`for MAb1 in the presence of 150 mM NaCl. Similar bands near
`1558 and 1533 cm-1 appear to dominate the MAb2 autopower
`spectra in the presence and absence of NaCl. The asynchronous
`spectra describe the sequence of events leading to these changes
`(see below). That is, a description of the residues which trigger
`significant changes when the concentration is increased. The
`significance of these peaks in the autopower spectra is discussed
`below.
`The covariance matrix is a symmetric square matrix math-
`ematically equivalent to the synchronous correlation spectra.
`The diagonal elements (autopeaks) represent the variance of
`spectral signal fluctuations as a function of time for the
`representative spectral variable, while the cross-peaks correspond
`to the covariance between spectral signal fluctuations at two
`separate spectral variables. The covariance matrices (not shown)
`were nearly identical to the synchronous spectra for all condi-
`tions studied.
`Asynchronous Correlation Spectra and Sequence of
`Spectral Variation. Asynchronous correlation spectroscopy is
`a powerful method which allows the deconvolution of overlap-
`ping spectral bands while providing detailed information regard-
`ing the sequence of events that give rise to changes in the
`spectra. The asynchronous amide I spectra are shown in Figure
`4. Using the signs of the asynchronous cross-peaks,
`the
`following sequence of events was determined for MAb1 without
`NaCl according to Noda’s rules: 1639 > 1647, 1657 cm-1. This
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`6112 J. Phys. Chem. B, Vol. 113, No. 17, 2009
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`Kamerzell et al.
`
`Figure 3. Power spectra along the diagonal line in the amide II synchronous spectra for (A) MAb1 in the absence of 150 mM NaCl, (B) MAb1
`+ 150 mM NaCl, (C) MAb2 in the absence of 150 mM NaCl, and (D) MAb2 + 150 mM NaCl.
`
`sequence suggests that changes in concentration affect MAb1
`(cid:1)-sheet/turn structures before disordered and helix regions.
`Interestingly, the sequence of events is reversed (1670-1680,
`1641, 1657 > 1653 > 1637 cm-1) for MAb1 in the presence
`of 150 mM NaCl indicating changes in turn, disordered, and
`helix structures before (cid:1)-sheet structure. The sequence of amide
`I changes for MAb2 in the presence and absence of 150 mM
`NaCl is disordered > disordered/helix > (cid:1)-sheet. In addition,
`the change observed at 1622-1624 wavenumbers is observed
`before all other changes for MAb1 without 150 mM NaCl and
`is not observed with MAb1 + NaCl or MAb2 ( NaCl.
`The amide II sequence of events follow a similar order
`compared to amide I vibrational band changes. For MAb1 in
`the absence of 150 mM NaCl, changes at 1549 and 1560 cm-1
`occur prior to molecular variations reflected near 1529 cm-1.
`Interestingly, the sequence of spectral changes is reversed for
`MAb1 in the presence of salt and MAb2 ( 150 mM NaCl. A
`discussion of the significance of these changes is described
`below.
`Central Moments. Central moments have been used histori-
`cally in physics and probability theory to better understand
`diffusion, polymer chain conformations, statistical thermody-
`namics, visual pattern recognition, and shape discrimination,
`and for the characterization of chromatographic peaks.42-48 The
`central moments describe various peak characteristics including
`the center of gravity, width, asymmetry, and flattening. In this
`work,
`the first
`through fourth central moments have been
`calculated to probe the changes in vibrational spectra as a
`function of solution condition and mAb concentration. Indeed
`the central moments confirm the results obtained from two-
`
`dimensional analysis regarding the wavenumber position and
`peak resolution of the most relevant spectroscopic changes (data
`not shown).
`Statistical Correlation Coefficients and Coherence. Cor-
`relation coefficient mapping was first introduced by Barton et
`al. to explore the correlation between near-IR and mid-IR
`regions,49 and numerous variations of this method soon fol-
`lowed.50 The matrix of statistical correlation coefficients is
`related to the covariance matrix and is a normalized measure
`of the strength of the relationships between two variables. Thus,
`all possible scalar products between vectors will capture values
`between -1 and +1, indicating absolute correlation at these
`two values. In contrast, 0 represents the absence of correlation.
`The matrix of correlation coefficients was calculated and the
`relevant values listed in Table 1. In addition, a matrix of p-values
`for testing the hypothesis of no correlation was calculated (Table
`1). The ratio of asynchronous to synchronous correlation
`functions, Ψ(ν1,ν2)/Φ(ν1,ν2), was also calculated to identify false
`peaks according to the method proposed by Buchet et al.35 These
`values support
`the results observed from the analysis of
`synchronous and asynchronous maps.
`Principal Component Analysis. Principal component analy-
`sis is a well-established method used in statistics and chemo-
`metrics to reduce the dimensionality of a data set while retaining
`much of the variation present. Furthermore, PCA has been
`successfully combined with 2D-COS to better understand many
`complex systems.34,51-54 Mutually independent events are dis-
`tinguished using PCA because all the principal components are
`orthogonal, so there is no redundant information, thus making
`this method powerful for our purpose.
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`Molecular Forces That Mediate IgG Nonideality
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`J. Phys. Chem. B, Vol. 113, No. 17, 2009 6113
`
`Figure 4. Asynchronous correlation spectra for (A) MAb1 in the absence of 150 mM NaCl, (B) MAb1 + 150 mM NaCl, (C) MAb2 in the absence
`of 150 mM NaCl, and (D) MAb2 + 150 mM NaCl.
`
`Herein, the principal components (PC) and the percent total
`variability explained by each principal component have been
`calculated as a function of varying protein concentration for
`MAb1-2 ( 150 mM NaCl. The first three PCs describe greater
`than 95% of the amide I and II spectral variation for both MAb’s
`with and without salt (Figures 5 and 6). The position of the
`peaks from the first loadings plot is similar to the autopower
`spectra of the synchronous spectra for MAb1-2 ( salt, although
`some new peaks are observed in the synchronous spectra (Table
`2). The shape and symmetry of the peaks from the loadings
`plot, however, are distinctive compared to the autopower spectra
`in all cases. New peaks are identified in the second loadings
`plot for MAb1-2 only in the presence of 150 mM NaCl (Figures
`5 and 6 and Table 2).
`
`Discussion
`
`The importance of macromolecular crowding and confine-
`ment affects on protein structure, stability, and function have
`
`been exhaustively and unquestionably demonstrated.4,5,8,21,55
`Indeed, our understanding of these processes has increased
`dramatically over the past 2 decades.2,8,17,56,57 For example,
`general qualitative effects of crowding and volume occupation
`on the rates and equilibria of interactions involving macro-
`molecules can now be reasonably well predicted using
`statistical thermodynamic models.8,55 Similarly, the associa-
`tion state of proteins and model solutes in crowded and
`concentrated environments have been linked to nonideal
`solution behavior of which models are beginning to
`emerge.2,12-19 Our understanding of the affects of highly
`concentrated and volume occupied solutions on protein
`structure and stability, however, is incomplete. This work
`aims to understand the molecular level interactions at high
`concentrations that modulate the self-association of two
`MAbs with similar sequences but widely differing solution
`behavior. Herein, the affects of increasing protein concentra-
`tion and confinement on the molecular properties of two well-
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`Kamerzell et al.
`
`MAb1 + 150 mM NaCl
`
`TABLE 1: Cross-Peaks from Amide I Asynchronous Spectra
`IgG1((150 mM NaCl)
`ν1, ν2
`MAb1
`1622, 1637
`1637, 1645
`1637, 1653
`1603, 1637
`1645, 1655
`1645, 1622
`1622, 1655
`1637, 1641
`1641, 1653
`1637, 1653
`1637, 1657
`1653, 1657
`1653, 1672
`1653, 1626
`1672, 1637
`1672, 1626
`1635, 1645
`1635, 1624
`1624, 1645
`1624, 1653
`1662, 1653
`1662, 1645
`1662, 1635
`1662, 1624
`1635, 1653
`1637, 1653
`1637, 1684
`1637, 1626
`1653, 1684
`
`MAb2 + 150 mM NaCl
`
`MAb2
`
`0.98
`0.97
`0.97
`0.95
`0.96
`0.95
`0.91
`0.98
`0.70
`0.68
`0.89
`0.88
`0.78
`0.58
`0.85
`0.89
`0.81
`0.90
`0.68
`0.73
`0.91
`0.92
`0.63
`0.39
`0.89
`0.92
`-0.40
`0.97
`-0.72
`a r, correlation coefficient. b Each p-value is the probability of obtaining a correlation as large as the observed value by random chance.
`c F(ν1,ν2) ) Ψ(ν1,ν2)/Φ(ν1,ν2), ratio of asynchronous to synchronous.
`
`ra
`
`p-valueb
`
`0.0004
`0.001
`0.001
`0.03
`0.003
`0.005
`0.005
`0.0008
`0.12
`0.14
`0.01
`0.02
`0.07
`0.22
`0.03
`0.02
`0.10
`0.04
`0.21
`0.16
`0.03
`0.03
`0.25
`0.52
`0.04
`0.03
`0.51
`0.006
`0.17
`
`|Ψ(ν1,ν2)/Φ(ν1,ν2)| c
`0.51
`0.42
`0.32
`0.48
`0.0016
`0.036
`0.0022
`0.32
`0.24
`0.09
`0.22
`0.24
`0.19
`2.59
`0.22
`0.33
`0.11
`0.11
`0.23
`0.06
`0.11
`0.05
`0.06
`0.14
`0.06
`0.06
`0.14
`0.35
`0.49
`
`Figure 5. Percent variation described by each principal component for (A) MAb1 in the absence of 150 mM NaCl, (B) MAb1 + 150 mM NaCl,
`(C) MAb2 in the absence of 150 mM NaCl, and (D) MAb2 + 150 mM NaCl.
`
`important
`studied IgG’s have been explored. The most
`changes in MAb1 association appear to be correlated with
`hydrogen bonding and electrostatic effects.
`
`It has been shown that MAb1 self-associates at high
`concentrations, primarily through Fab-Fab interactions, result-
`ing in nonideal solution behavior and increased viscosity, while
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`Molecular Forces That Mediate IgG Nonideality
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`J. Phys. Chem. B, Vol. 113, No. 17, 2009 6115
`
`Figure 6. Concentration-dependent principal components for (A) MAb1 in the absence of 150 mM NaCl, (B) MAb1 + 150 mM NaCl, (C) MAb2
`in the absence of 150 mM NaCl, and (D) MAb2 + 150 mM NaCl. PC1 (red), PC2 (blue), and PC3 (black).
`
`the addition of 150 mM NaCl decreases MAb1 association and
`viscosity.12,13 In contrast, MAb2 does not appreciably associate
`in the presence or absence of NaCl and was used for comparison
`in this work. We have shown that significant protein confor-
`mational changes do not preclude or result from self-association,
`but rather conformational heterogeneity and bonding networks
`modulate association and increased viscosity with increasing
`mAb concentration.
`Multiple vibrational bands change as a function of protein
`concentration. The vibrational frequency and magnitude and
`sequence of those changes is distinctive to each condition and
`follows the solution behavior from previous studies.12,13 The
`asynchronous spectra reveal variation in (cid:1)-sheet and turn regions
`occur before intensity variations in disordered and R-helical
`regions as the concentration is increased for MAb1 in the
`absence of salt (SAS, self-associated state; HV, high-viscosity
`regime) (Figure 4). Interestingly, this sequence was reversed
`for MAb1 in the presence of salt and MAb2 ( salt (no
`association) and is comparable to that observed for (cid:1)-lactoglo-
`bulin.28 Also interesting is that,
`in all
`instances including
`
`(cid:1)-lactoglobulin, except the highly viscous self-associated state
`of MAb1, (cid:1)-sheet regions are more resistant to concentration-
`dependent changes. In addition,
`the presence of a strong
`component at 1622 cm-1 in the power spectra and loadings from
`PCA for MAb11 may suggest the formation of intermolecular
`extended chains or networks and increased intermolecular
`hydrogen bonding strength and/or number as a function of
`concentration (Figure 2, Table 2). This band has been often
`observed and associated with intermolecular (cid:1)-sheet formation
`and often irreversible protein association.40,58-63 It should be
`noted, however, that irreversible protein association was not
`observed in any of our studies. Increasing hydrogen bond
`strength and number typically decreases the frequency of
`stretching vibrations and increases the frequency of bending
`vibrations.64 A similar vibrational band was observed with
`increasing concentration for MAb2 in the absence of 150 mM
`NaCl; however, the peak is broadened and positioned at higher
`frequency indicating highly dynamic and weaker H-bonding,
`respectively. This data also suggest
`that MAb2 may self-
`associate in the absence of 150 mM NaCl. Previously, 2D-IR
`
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` Publication Date (Web): April 3, 2009 | doi: 10.1021/jp9001548
`
`Ex. 2040-0007
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`

`
`6116 J. Phys. Chem. B, Vol. 113, No. 17, 2009
`
`Kamerzell et al.
`
`TABLE 2: Amide I and II Band Positions (cm-1)
`amide I
`amide II
`PC2b
`PC2
`
`sample
`
`PC1a
`
`MAb1
`
`MAb1 + NaCl
`
`MAb2
`
`MAb2 + NaCl
`
`1622
`1637
`1645
`1655
`
`1637
`1645
`1653
`1672
`1624
`1635
`1643
`
`1662
`
`1637
`
`1622
`1637
`1645
`
`1610
`1626
`
`1657
`1678
`1626
`1639
`
`1653
`1662
`
`1626
`1633
`1641
`1653
`
`Sync
`
`PC1
`
`1603
`1622
`1637
`1645
`1655
`
`1626
`1637
`1645
`1653
`1672
`1624
`1635
`1645
`1653
`1662
`
`1637
`
`1514
`1529
`1549
`
`1558
`1572
`
`1508
`1524
`1543
`1558
`
`1502
`1506
`
`1506
`
`1522
`1549
`1560
`1576
`
`1506
`1533
`1549
`1558
`
`1493
`1512
`
`1543
`
`1566
`1578
`
`1533
`1549
`
`Syn
`
`1502
`1514
`1529
`1549
`1560
`1572
`
`1506
`1533
`1549
`1558
`1574
`1493
`1508
`1524
`1543
`1558
`1566
`
`1502
`1512
`1533
`1549
`
`of electrostatic interactions.67 It is reasonable to consider our
`system using similar logic. At high concentrations, MAb1 forms
`highly cooperative intermolecular hydrogen bonds through
`“peptide-like” exposed CDR regions resulting in extended
`structures or networks with an important contribution arising
`from electrostatics.
`The increasing high wavenumber component (1672, 1684
`cm-1) observed in the autopower spectra and loadings plot for
`MAb1 + NaCl and MAb2 ( NaCl may be attributed to changes
`in (cid:1)-sheet or turn (Figure 2, Table 2). It is plausible that this
`component is the consequence of increased band splitting or
`exciton splitting resulting from transition dipole coupling (TDC)
`in all solutions except the associated, highly viscous MAb1
`solution. TDC is a resonant interaction between oscillating
`dipoles near one another similar to energy transfer in fluores-
`cence experiments and has been used to explain the splitting of
`the amide I band of proteins with high (cid:1)-sheet structure.68-73
`Both through space and through bond vibrational mode cou-
`plings arise from electrostatics and/or covalent contributions
`involving the amide backbone and depend on the correlation
`of electron density and nuclear position. In addition, coupling
`depends on the relative orientations of and distance between
`oscillating dipoles which intuitively would be greater for MAb1
`without 150 mM NaCl, unless the dipoles were oscillating with
`different frequencies or localized on one oscillator, the oscil-
`lating dipoles were out of phase, or the molecular geometry
`was not conducive to coupling. In fact, Kobayashi et al. have
`shown that for certain linear polymers the energy arising from
`dipole coupling is absent for extended chain polymer crystals
`but maximum for folded conformers.74 It is plausible that subtle
`conformational heterogeneity modulates the TDC effect for
`MAb11. Finally, increased solvation has also been shown to
`decrease the vibrational frequency of R-helices through forma-
`tion of hydrogen bonding with the solvent.75-77 Our results
`suggest solvation of MAb2 ( 150 mM NaCl is greater in
`volume occupied solutions compared to MAb1 in the absence
`of 150 mM NaCl since solvated helices absorb at
`lower
`frequencies compared to nonsolvated helices (Figures 2 and 6
`and Table 2).
`The amide II region was used to investigate changes in amino
`acid side chain absorption and backbone hydrogen bonding.40,78-81
`The side chain absorptions of specific residues may overlap with
`other residues or the protein backbone; however, only the amino
`acids with particularly strong absorption coefficients due to
`vibrations of polar groups have been assigned. Specifically,
`deprotonated Asp and Glu and protonated Lys residues strongly
`absorb near 1572-1579, 1550-1560, and 1526, respectively.40
`The asynchronous and autopower spectra of MAb1 in the
`absence of 150 mM NaCl suggest perturbation of the molecular
`environment near deprotonated Asp and Glu residues occur
`before those near protonated Lys or Tyr (asynchronous not
`shown, see Figure 2 and Table 2). The sequence of events was
`reversed for MAb1 NaCl and MAb2 ( 150 mM NaCl.
`Herein, we have further described the molecular level
`processes responsible for the increased self-association of a
`particular monoclonal antibody and the resulting changes in
`solution properties. Interestingly, increased intermolecular hy-
`drogen bonding and electrostatics appear to modulate the self-
`association and subsequent solution viscosity. Furthermore, the
`order of the concentration-dependent changes appears to be an
`important component of the driving force for the nonideal
`solution behavior.
`
`1541
`1653
`1651
`1558
`1684
`1684
`a PC1 ) first principal component. b PC2 ) second principal
`component. c Syn ) synchronous component.
`
`and near-IR COS was used to study the temperature-dependent
`spectral variations of self-associating N-methylacetamide.65 This
`work attributes oligomer and chain size to frequency, suggesting
`here that MAb1 in the absence of 150 mM NaCl forms slightly
`larger reversibly associated complexes consistent with previous
`observations.13
`Inherently, it is difficult to distinguish between increased
`intermolecular hydrogen bonding resulting from protein-protein
`interactions with that from increased protein-H2O hydrogen
`bonding. However, it is clear that this band is an important
`component that increases in this system. This band may be the
`result of increased intermolecular bonding, changes in hydrogen
`bonding, or some combination.
`The potential change in hydrogen bonding as a function of
`concentration for MAb11 may not be entirely surprising
`considering the well-documented relationship between viscosity
`and hydrogen bonding. This specific feature was not evident in
`the spectra of MAb1 in the presence of 150 mM NaCl or MAb2
`( 150 mM NaCl, all of which exhibit little or no self-association
`and low viscosity.13 Interestingly, the only differences in the
`amino acid sequence between the two MAbs studied occur in
`CDR regions which are highly solvent exposed loops with
`varying proportions of hydrogen bonding donors and acceptors.
`Perhaps not surprisingly the CDR of MAb1 is composed of
`significantly more strong H-bonding partners including His, Ser,
`Thr, Tyr, Asp, and Glu residues.
`Aggelli et al. designed peptides that self-assemble to form
`(cid:1)-sheet tapes with nonideal solution behavior. These peptides
`form highly cooperative intermolecular hydrogen bonds a