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Analysis of Biologics Molecular Descriptors towards Predictive Modelling for Protein Drug Development Using Time-Gated Raman Spectroscopy

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Pharmaceutical proteins, compared to small molecular weight drugs, are relatively fragile molecules, thus necessitating monitoring protein unfolding and aggregation during production and post-marketing. Currently, many analytical techniques take offline measurements, which cannot directly assess protein folding during production and unfolding during processing and storage. In addition, several orthogonal techniques are needed during production and market surveillance. In this study, we introduce the use of time-gated Raman spectroscopy to identify molecular descriptors of protein unfolding. Raman spectroscopy can measure the unfolding of proteins in-line and in real-time without labels. Using K-means clustering and PCA analysis, we could correlate local unfolding events with traditional analytical methods. This is the first step toward predictive modeling of unfolding events of proteins during production and storage.
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Citation: Itkonen, J.; Ghemtio, L.;
Pellegrino, D.; Jokela (née Heinonen),
P.J.; Xhaard, H.; Casteleijn, M.G.
Analysis of Biologics Molecular
Descriptors towards Predictive
Modelling for Protein Drug
Development Using Time-Gated
Raman Spectroscopy. Pharmaceutics
2022,14, 1639. https://doi.org/
10.3390/pharmaceutics14081639
Academic Editors: David Barlow and
Tihomir Tomašiˇc
Received: 18 February 2022
Accepted: 3 August 2022
Published: 5 August 2022
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4.0/).
pharmaceutics
Article
Analysis of Biologics Molecular Descriptors towards Predictive
Modelling for Protein Drug Development Using Time-Gated
Raman Spectroscopy
Jaakko Itkonen 1, , Leo Ghemtio 1, , Daniela Pellegrino 1, Pia J. Jokela (née Heinonen) 1,2, Henri Xhaard 3
and Marco G. Casteleijn 4, *
1
Drug Research Program, Division of Pharmaceutical Biosciences, Faculty of Pharmacy, University of Helsinki,
00100 Helsinki, Finland
2Orion Pharma, 02101 Espoo, Finland
3Drug Research Program, Division of Pharmaceutical Chemistry and Technology, Faculty of Pharmacy,
University of Helsinki, 00100 Helsinki, Finland
4VTT Technical Research Centre Finland, 02150 Espoo, Finland
*Correspondence: marco.casteleijn@vtt.fi; Tel.: +358-40-120-7303
These authors contributed equally to this work.
Abstract:
Pharmaceutical proteins, compared to small molecular weight drugs, are relatively fragile
molecules, thus necessitating monitoring protein unfolding and aggregation during production and
post-marketing. Currently, many analytical techniques take offline measurements, which cannot
directly assess protein folding during production and unfolding during processing and storage. In
addition, several orthogonal techniques are needed during production and market surveillance. In
this study, we introduce the use of time-gated Raman spectroscopy to identify molecular descriptors
of protein unfolding. Raman spectroscopy can measure the unfolding of proteins in-line and in real-
time without labels. Using K-means clustering and PCA analysis, we could correlate local unfolding
events with traditional analytical methods. This is the first step toward predictive modeling of
unfolding events of proteins during production and storage.
Keywords:
pharmaceutical proteins; Raman spectroscopy; PCA; K-means clustering; in-line
measurement; biologics; protein unfolding; CD; DLS; tryptophan fluorescence
1. Introduction
Biotechnological drugs and their development started in the early 1980s and they play
an increasingly important role in the treatment of many diseases, such as anemia, cystic
fibrosis, cancer, and neurological diseases [
1
3
]. Protein drugs are produced by the use
of recombinant DNA technologies in expression hosts and may have post-translational
modifications [
4
,
5
]. Protein-based drugs have different characteristics compared to non-
biologics; they have a higher specificity resulting in greater efficacy and reduced adverse
effects [
6
]. On the other hand, proteins are relatively fragile molecules and prone to unfold-
ing and forming aggregates [
7
,
8
], which are a major problem in terms of efficacy, limited
solubility, and increased viscosity but may also represent the main cause of immunological
responses [
9
,
10
]. Their presence, nature, and amounts are thus often considered critical
quality attributes [11].
To assess the incidence of unfolding problems, sensitive analytical techniques are
necessary to monitor and quantify protein aggregate levels [
12
]. Most techniques, however,
require offline measurements of (intermediate) product(s), such as:
(i) The detection and characterization of (sub)visible particles (e.g., visual inspection, opti-
cal microscopy, light obscuration, flow imaging, fluorescence microscopy, conductivity-
based particle counter, laser diffraction, dynamic light scattering (DLS), nanoparticle
tracking analysis, MALLS, turbidimetry, and nephelometry);
Pharmaceutics 2022,14, 1639. https://doi.org/10.3390/pharmaceutics14081639 https://www.mdpi.com/journal/pharmaceutics
Pharmaceutics 2022,14, 1639 2 of 18
(ii)
the use of separation techniques for the detection and characterization of aggre-
gates, i.e., (denaturing/reducing) size exclusion chromatography, SDS/Native PAGE,
capillary-SDS electrophoresis, and AF4 [13];
(iii)
other techniques, e.g., electron/atomic force microscopy, mass spectrometry, macro-
ion mobility spectrometry, and AUC [
14
]. Label-free methods for evaluating protein
folding states, such as infrared spectroscopy, Raman spectroscopy, UV/VIS absorption
spectroscopy, fluorescence spectroscopy, and circular dichroism spectroscopy, are
relevant to mention as they are utilized as in-line analytical techniques due to their
non-invasive nature. A recent review outlines the importance of Raman spectroscopy
to biopharmaceuticals in greater detail [
15
]. However, the sensitivity, robustness, and
the ability of these label-free techniques for quantification need to be improved for
protein applications.
One of the first reports to evaluate protein production within living cells was using
fluorescent dyes combined with fluorescence spectroscopy to detect antibody aggregates in
CHO cell lysates [
16
]. We recently evaluated the production of CNTF in living E. coli cells
without dyes or tags using time-gated Raman spectroscopy [
17
]. However, the resulting
spectra are complex and multivariate. Consequently, the raw data produced can be difficult
to interpret.
In recent years, multivariate data analysis and preprocessing methods have consid-
erably increased the ability to identify relevant information contained in Raman-, and
other electromagnetic-spectra, for a better qualitative and quantitative analysis of biological
samples [
18
20
]. Principal component analysis (PCA) and data K-means clustering are
well-established techniques and allow the identification of the spectral features with the
highest degree of variability [
21
23
]. K-means clustering allows the spectra to be grouped
based on spectral similarity and, therefore, identify similar features and distributions. The
clustering uses information contained in the individual spectra, and the results are reported
as dendrograms to show the classes available. PCA is a powerful approach widely used to
discriminate different Raman spectra using scores plots and enables to derive information
regarding the basis of the spectral variability. These methods are suitable for handling large
multidimensional data sets and exploring the complete spectral information.
Protein aggregation can, in part, be induced due to changes in secondary protein
structures. Here, we evaluate protein unfolding events
in vitro
in a buffered, controlled en-
vironment with fluorescence spectroscopy, circular dichroism spectroscopy, and time-gated
Raman spectroscopy to identify molecular descriptors of protein unfolding within time-
gated Raman spectra. This first step
in vitro
is needed before future evaluation of protein
production using Raman spectroscopy within cells has meaning. Protein aggregation was
determined with DLS. The proteins taken into consideration were divided based on the
characteristics of their secondary structure: α-helix, β-sheet, and α/β-mix.
2. Materials and Methods
2.1. Chemical, Reagents, and Protein Samples
Calcium chloride, 2-(N-morpholino)-ethanesulfonic acid (MES), sodium chloride, glucose,
ethylene diaminetetraacetic acid (EDTA), sodium azide, silver nanoparticles—40 nm particle
size (AgNP; #730807) and potassium dihydrogen phosphate were obtained from Merck Sigma-
Aldrich (Darmstadt, Germany). Potassium chloride was obtained from Honeywell Riedel
de Haën (Seelz, Germany) and disodium hydrogen phosphate was obtained from Fisher
Scientific (Hampton, VA, USA). Buffer A (20 mM phosphate buffer saline (pH 7.6), buffer B
(20 mM MES150 mM NaCl (pH 7.4), and the silver nanoparticle solution were prepared as
described before [17].
In this study, several proteins were evaluated (Table 1). BSA (Bovine Serum Albumin
(7% solution; SRM 927e)) was obtained from NIST (Gaithersburg, MD, USA); F
ab
, F
(ab0)2
, IgG
glycosylated (Immunoglobulin G), pepsin, ovalbumin (OVA), and ScTIM (triosephosphate
isomerase) were obtained from Merck Sigma-Aldrich (Darmstadt, Germany); IgG non-
glycosylated was obtained from AntibodyGenie (Dublin, IRL). CNTF (ciliary neurotrophic
Pharmaceutics 2022,14, 1639 3 of 18
factor) was prepared as earlier described [
24
,
25
] using buffer B; LmTIM
E65Q
was prepared
as described earlier [26] and diluted with buffer A.
Table 1. Overview of the proteins used in this study.
Protein (α,β,α/β) DLS Tryptophan Fluorescence CD Time-Gated a
BSA (α) yes yes yes yes
CNTF (α)no byes yes no c
Fab (β) yes yes yes no
F(ab0)2 (β) yes no no no
IgGglycosylated (β) yes yes yes yes
IgGnon-glycosylated (β) yes no yes yes d
Pepsin A (EC 3.4.23.1) (α/β) yes no yes yes
Ovalbumin (α/β) yes yes yes yes
ScTIM (EC 5.3.1.1) (α/β) yes yes yes no
LmTIME65Q (EC 5.3.1.1) (α/β) no yes yes yes
a
Samples as prepared for tryptophan fluorescence;
b
the DLS evaluation of hCNTF was recently published in
Itkonen et al., 2020 [
27
];
c
the time-gated evaluation of hCNTF protein aggregates was recently published in
Kögler et al., 2020 [17]; dSample was heated with a custom-built heat unit (supplementary data Figure S1).
All protein powders, except the CNTF and LmTIM
E65Q
samples, were dissolved and
diluted in buffer A prior to analysis with CD spectroscopy, DLS, tryptophan fluorescence,
and time-gated Raman spectroscopy. Molecular descriptors derived from each technique
are listed in Table 2.
Table 2. Molecular descriptors per analytical technique.
Technique Parameter 1 Parameter 2 Parameter 3
DLS Z-average Hydrodynamic diameter Polydispersity index
Tryptophan fluorescence Fluorescence intensity
(internal) quenching Red/blue shift Tryptophan oxidation
(peak at 515 nm)
CD Melting temperature (C) Van’t Hoff enthalpy (kJ/mol) -
Time-gated Raman
spectroscopy
Raman spectra similarity
clustering according to
temperature
Relevant time-gated
Raman peaks a-
aAccording to PCA analysis.
2.2. Dynamic Light Scattering
The dynamic light scattering (DLS) was performed using a Zetasizer APS (Malvern
Pananalytic, UK) with a 96-well plate autosampler at a 90
fixed angle and using an
internal heat-controlled measuring cuvette. All protein samples (60
µ
L; final concentration
0.2 mg/mL) were filtered with a 0.45
µ
m syringe filter and diluted in buffer A and kept
on ice prior to measurements. The pepsin solution was milky prior to dilution. Thus, a
1 mg/mL pepsin solution was spin-filtered prior to dilution with a 0.22
µ
m filter (Merck-
Millipore) at 12,000
×
gfor 4 min to remove additional impurities. To assess the aggregation
behavior of protein samples upon heating, Rh measurements with DLS were carried out
during thermal ramping (2–80
C). Samples were heated from 2 to 80
C with a 1
C step
size to determine the point of aggregation. The measured data were collected and analyzed
with the Zetasizer Software (NANO,
µ
V, APS) v6.02 (Malvern Panalytical; Malvern, UK).
Three parameters were evaluated: the Z-average of the hydrodynamic diameter (automatic
evaluation; n= 3), the lowest value of the hydrodynamic diameter (manual evaluation;
n= 1), and the polydispersity index (PDI).
2.3. Tryptophan Fluorescence
Protein samples were diluted in buffer A to 0.2 mg/mL and each sample (100
µ
L in
triplicate) was heated for 10 min in a water bath (VWR, PA, USA) at 25, 30, 37, 40, 45, 50, 52,
55, 57, 60, 65, 67, 70, 75, and 85
C and placed on ice prior to measurement. The LmTIM
E65Q
was subjected to higher temperatures due to its known higher thermostability (25, 50, 55,
Pharmaceutics 2022,14, 1639 4 of 18
60, 65, 70, 75, 78, 80, 83, 86, 90, 95, and 99
C. The samples were analyzed in white closed-
bottom microtiter plates (Hamilton, Reno (NV), USA) with a Thermo Scientific Varioskan
LUX (ThermoFisher Scientific, Waltham (MA), USA). IgG, BSA, OVA, and F
ab
samples
were excited at 295 nm and the emission spectrum were recorded between
314–550 nm
,
while LmTIME65Q was excited at 280 nm and the emission was recorded at 290–550 nm.
2.4. Circular Dichroism (CD) Spectroscopy
Samples were diluted to 0.1 mg/mL with deionized, sterile water from 1 mg/mL
solutions. A Chirascan CD spectrometer (Applied Photophysics, Leatherhead, UK) was
used to collect CD-spectroscopy data between 22
C and 90 at 280 nm using a 0.1 cm
path-length quartz cuvette. Data were collected every 1 nm utilizing 1 s as the integration
time. Each measurement was performed in triplicate with baseline correction. Pro-Data
Viewer software SX v2.5.0 (Applied Photophysics, Leatherhead, UK) was used to analyze
the spectra. The melting temperature was determined via thermal unfolding of the protein
sample between 190 and 260 nm with a 2
C step size at 1
C/min ramp rate with
±
0.2
C
tolerance and subsequently analyzed with the Global3 software package v3.1(Applied
Photophysics, Leatherhead, UK).
2.5. Time-Gated Raman Spectroscopy
The time-resolved measurements were performed as described earlier [
17
,
28
,
29
], with
minor alterations. The time-gated Raman measurements (time-gated) were performed
with a reduced laser power of approximately 20mW (checked with an Ophir Nova II laser
power meter, Ophir Optronics Solutions Inc., Jerusalem, Israel) at the sample to avoid photo-
bleaching. Data acquisition software and setup control were carried out by Timegated
instruments software (Timegate Instruments Oy, Oulu, Finland). Protein samples were
prepared as described in Section 2.3, except for the IgG non-glycosylated sample. All protein
measurements, except for IgG non-glycosylated, were performed at ambient temperature
and humidity, and spectra were the sum of 11 repeats at an acquisition time of 14 min.
Two separated detector ranges (1900–900 and 1400–400 cm
1
) were measured in such a
way. Protein samples (50
µ
L) were measured in a custom-made aluminum round-bottom
well [
17
], positioned on top of a 3D-printed plastic holder within the Timegated Instruments
Sample-Cube using the Timegated a common BWTek sampling probe.
The analysis of the IgG non-glycosylated sample was performed using a 3D-printed
heat-ramping prototype (Figure S1) attached to a Julabo’s SL-26 heat circulating water bath.
Between the 3D-printed plastic, its aluminum cover, and the time-gated sampling area,
420
µ
L of distilled water was covered with a 0.13-0.16 mm thick cover glass (Paul Marien-
feld GmbH & Co., Lauda-Königshofen, Germany), sealed with pivot grease (Eppendorf,
Hamburg, Germany) to ensure optimal heat transfer. The sample (40
µ
L) was applied in
an aluminum crucible (40
µ
L ME-51119870; Mettler Toledo, Switzerland) and sealed with
0.13–0.16 mm thick cover glass (Paul Marienfeld GmbH & Co., K, Lauda-Königshofen,
Germany) and pivot grease (Eppendorf, Hamburg, Germany) and then placed on the Time-
gated sampling area. Heat ramping was performed according to Table S2 (supplementary
data). The time-gated spectra were the sum of 6 repeats (1811–555 cm
1
) at an acquisi-
tion time of 6.05 min per temperature. The temperature in the crucible in the time-gated
sampling area was checked with a FLIR TG165 imagining infrared thermometer prior to
Time-gated sampling to ensure the correct temperature at the Time-gated sampling site.
The thermometer had an error of
±
1.5%, or a minimum of 1.5
C, and the emittance was set
to 0.25 according to the manufacturer’s recommendations and was in line with a roughened
aluminum surface [30].
2.6. Data Preprocessing
The implementation of multivariate data analysis methods requires pretreatment of
the raw data. The preprocessing helps to eliminate unwanted signals and to enhance the
discrimination of structural features [20].
Pharmaceutics 2022,14, 1639 5 of 18
All spectral data (Table 2) was collected as text comma-separated values files to
facilitate the manipulation and provide a graphical representation of the data. Data analysis
was performed using R (R-Studio, Boston (MA), USA). Before statistical analysis, the data
sets have been corrected for baseline and vector normalized to facilitate comparison.
2.7. Data Analysis
K-means clustering analysis is an unsupervised learning algorithm widely used for
spectral image analysis [
18
]. In summary, it partitions the observations into clusters, with
the cluster centroid representing the whole cluster. The pre-processed spectra are grouped
according to their spectral similarity, forming clusters for particular temperature points,
each characterizing regions of the image with similar molecular properties. The dissemina-
tion of similarity can be visualized over the sample image or as a dendrogram showing the
hierarchical relationship between classes. Along with the spectra, additional parameters
like the number of clusters (k) and the initial cluster centers are calculated. The centroids
are set by shuffling the dataset and randomly selecting K points, and then each point of
the dataset is associated with the nearest centroid. The process is repeated until there is
no change in the centroids. Eventually, k clusters with the most similar spectra present
themselves and the centroids are determined by taking the average of all data points that
belong to each group.
PCA is a multivariate analysis broadly used to reduce the dimensionality of large data
sets [
18
]. It is mainly used to represent a multivariate data table as a smaller set of variables
to identify trends, jumps, clusters, and outliers. PCA identifies a new coordinate system
in the K-dimensional space that maximizes variation in the data space. This reduction
help discover relationships between observations and variables and among the variables.
Important information is extracted from the data and expressed as a set of indices called
principal components. The importance of each PC is identified by ordering the Eigenvalues
in descending order, corresponding to the descending order of variance, and denotes
their importance to the dataset. The PCs contribute less in decreasing order; the first PCs
contain the most information. The loading of a PC provides information on the source of
the variability inside the spectra, derived from variations in the molecular components
recorded from different experiments. The pre-processed dataset was evaluated by PCA,
with the covariance matrix to represent the dataset by eigenvectors accounting for most of
the variance and identifying the spectra’ similarities. Usually, the first two or three PCs
represent the highest variance present in the data sets [19].
K-means clustering and PCA were performed on the preprocessed time-gated Raman
spectral data of each protein (graphs are shown in the supplementary data). The dendro-
grams (panel 1 in the supplementary data) show the different classes available, grouped by
temperature. The cluster means (panels 2 in the supplementary data) depict the variance
and the contribution of the first components. The Scree plots (panels 3 in the supplementary
data) corresponding to each PC obtained by PCA have peaks that can be attributed to the
protein constituents and show the region of the Raman spectra where the main differences
occur (panels 4 in the supplementary data). Their respective negative and positive loadings
contribute substantially to the differentiation of the protein structure. This enables one
to derive information regarding the basis of the spectral variability. PCA provides thus
insight into the source of the spectral variability and, therefore, the differentiation of the
protein’s structural components.
3. Results
3.1. Alpha Helical Proteins
CNTF is a small
α
-helical, dimeric protein of 22.8 kDa and comprises 4
α
-helical
bundles per monomer [
24
]. The aggregation and unfolding of CNTF, studied by DLS
and CD (Figure S2), is described in a recent publication [
27
], and starts at 38
C, with an
estimated T
m
of 53
C (Tables 35). Our CD data is in line with this earlier observation
and displays a typical band at 220 nm (Figure S2), which is due to the peptide n
π
*
Pharmaceutics 2022,14, 1639 6 of 18
transitions and is indicative of
α
-helical structures [
31
]. In the tryptophan fluorescence
spectra, upon unfolding, CNTF exhibits a redshift of approximately 10 nm, indicating the
movement of the polar groups from a hydrophobic environment to a hydrophilic environ-
ment. In addition, the fluorescence spectra showed a lowering trend in the intensities as
the temperature increased.
BSA comprises 3
α
-helical domains with a molecular weight of 66.5 kDa [
32
]. The
α
-
helical structure is evident from the CD spectra, where the negative band at 208 nm, present
due to the exciton splitting of the lowest peptide
ππ
* transitions, is more prominent
due to 3
10 α
- helix structures (Figure S2) [
31
]. BSA aggregation, as evaluated with DLS,
starts at ~58
C, where we observed an initial increase in intensity (data not shown) and
size (Figure S3). After a short plateau, the particle size of the aggregates increased swiftly,
which is even observable in the sample polydispersity (Figure S3).
From the tryptophan fluorescence spectra (Figure S4), we observed a redshift of
approximately 10 nm, indicating the movement of the polar groups from a hydrophobic
environment to a hydrophilic environment, even though the intensities of the BSA spectra
were relatively low at 25–30
C and with intensities highest at 45–50
C, to then come down
to the same level of intensity slowly as 25 C at temperatures above 70 C.
The time-gated Raman spectra of BSA showed a clear drop in intensity between 55 and
57
C (Figure S5), especially around 1650 cm
1
. In addition, the spectra grouped according
to their similarity shown in Figure S6A,B panel 1, showed that the spectra recorded at
25–55 C
differed from the spectra recorded at 57–85
C. This difference correlates with the
start of aggregation seen in DLS. The two groups were well discriminated in the cluster
means, as shown in panel 2 (Figure S6A,B). PC1 and PC2 contributed to most of the
explained variance and allowed the discrimination between the two groups. Raman peaks
were identified that contribute to the PC scores. The positive and negative correlation of
PC1 and PC2 is depicted in Figure S6A,B (panel 4), where zero is the dashed line. At Raman
peaks, where the spectral differences between data exist (i.e., the correlation of PC1 and
PC2 is in the opposite direction), the corresponding physical changes in protein bonds were
relevant changes due to thermal unfolding. The significant differences in the secondary
structure of BSA due to the increase in temperature are summarized in Table S3.
3.2. Beta Sheet Proteins
One example of a
β
-sheet protein is pepsin A, an archetypal aspartic proteinase
belonging to the class of endopeptidases. The aspartic proteinases display a predominant
β
-fold with only a few short helical segments [
33
], though it has been classified by Rygula
et al. (2013) [
34
] as
β
-sheet protein. This was evident from the CD-spectra (Figure S7), as
the spectrum was a mix of mainly random coil and
β
-sheets. At the same time, the protein
appeared to be disordered at pH 7.0. Pepsin started aggregating at this pH at about 64
C
(Figure S8B). The main observation from the tryptophan fluorescence spectra at different
temperatures (Figure S9) was that the intensities were very low for all spectra, and as such,
no conclusions can be drawn.
The time-gated Raman spectra of pepsin showed no clear drop in intensity due
to the increase in temperature (Figure S10). As such, the spectra grouped according to
their similarity at different temperature points shown in Figure S13A (panel 1) did not
show significant differences. This corresponded to the observations in the CD-spectra that
the protein, in this particular low-quality sample, was already partly unfolded at room
temperature. PC1 and PC2 contributed to only 50% of the explained variance. Raman
peaks were identified that contributed to the PC scores. However, the large changes in the
relative intensities observed in the grouping in Figure S13B panel 1 show that the spectra at
clusters 25, 30, 40, 45, 75, and 85
C and at clusters 37 and 50–70
C show similarities. These
observations did not correspond with the aggregation temperature of 64
C, indicating
further that the pepsin protein solution used in this study was of poor quality.
The positive and negative correlation of PC1 and PC2 is depicted in Figure S13A,B
(panel 4), where zero is the dashed line. At Raman peaks where the spectral differences
Pharmaceutics 2022,14, 1639 7 of 18
between data exist (i.e., the correlation of PC1 and PC2 is in the opposite direction), the
corresponding physical changes in protein bonds are relevant changes due to thermal
unfolding. Changes in the secondary structure following the increase in temperature can
be observed in Figure 1, Figure S10, and Figure S13A,B, and summarized in Table S4.
Pharmaceutics 2022, 14, x FOR PEER REVIEW 8 of 20
Figure 1. Averaged (N = 11) and normalized time-gated spectra of pepsin at 25 °C (blue) and 65 °C
(red). (A,B) correspond to the individual, non-processed, and non-normalized Time-gated spectra
as presented in the left and right panes in Figure S10. (i.e., raw data). Raman values of significance,
as presented in Table S4 (supplementary data), are depicted in bold and red.
When directly comparing the relative time-gated Raman spectra of pepsin at ambient
temperature and 65 °C (Figure 1), we observed changes in the secondary structure upon
aggregation. It is also evident that at ambient temperature, pepsin was already partly un-
folded, indicated by the peak at 1245 cm
1
, typical of random coil structures. The fermi
doublet ratio of ~1 showed that the buried tryptophans were more exposed to the hydro-
philic matrix. The ratio of the tyrosine peaks at 850/830 cm
1
= 0.65 at 25 °C to 0.43 at 65 °C
showing tyrosines act as a strong H-bond donor with little change between these temper-
atures.
Another class of β-sheet proteins are antibodies or their fragments [35,36], which dur-
ing the last decades, have proven themselves as a highly effective and specific class of
biological drugs if they maintain a high thermostability and low aggregation propensity
[37]. Their structures are very well characterized and proteolytic cleavage to remove the
Fc tail results in either F
ab
or F
(ab)2
fragments [35]. In addition, IgG antibodies are naturally
Figure 1.
Averaged (N = 11) and normalized time-gated spectra of pepsin at 25
C (blue) and 65
C
(red). (
A
,
B
) correspond to the individual, non-processed, and non-normalized Time-gated spectra as
presented in the left and right panes in Figure S10. (i.e., raw data). Raman values of significance, as
presented in Table S4 (supplementary data), are depicted in bold and red.
When directly comparing the relative time-gated Raman spectra of pepsin at ambient
temperature and 65
C (Figure 1), we observed changes in the secondary structure upon
aggregation. It is also evident that at ambient temperature, pepsin was already partly
unfolded, indicated by the peak at 1245 cm
1
, typical of random coil structures. The
fermi doublet ratio of ~1 showed that the buried tryptophans were more exposed to the
hydrophilic matrix. The ratio of the tyrosine peaks at 850/830 cm
1
= 0.65 at 25
C to
0.43 at 65
C showing tyrosines act as a strong H-bond donor with little change between
these temperatures.
Pharmaceutics 2022,14, 1639 8 of 18
Another class of
β
-sheet proteins are antibodies or their fragments [
35
,
36
], which
during the last decades, have proven themselves as a highly effective and specific class of
biological drugs if they maintain a high thermostability and low aggregation propensity [
37
].
Their structures are very well characterized and proteolytic cleavage to remove the Fc tail
results in either F
ab
or F
(ab0)2
fragments [
35
]. In addition, IgG antibodies are naturally
modified by the decoration of glycan sugars [
38
]; however, deglycosylated IgG has its place
as a therapeutic as well [
39
]. The
β
-sheet secondary structure was well characterized by
the CD spectra of both the F
ab
fragment and the full IgGs (Figure S7). We derived the
clear right twisted anti-parallel
β
-sheet formation [
40
] as expected in the F
ab
fragment and
the non-glycosylated IgG spectra [
41
] as they consist of seven
β
-strands with four strands
forming one
β
-sheet and three strands forming a second sheet. However, glycosylated
IgG appeared to be more relaxed or exhibited left twisted anti-parallel
β
-sheets (Figure S7).
The stability of IgG, or its derived fragments, appeared similar based on their melting
temperatures (Table 3); however, individual F
ab
fragments appeared to aggregate at slightly
lower temperatures (Figure S8 and Table 5). Tryptophan fluorescence spectra comparing
the Fab fragments and the full IgG appeared very similar (Figure S9).
However, when overlaying the time-gated Raman data of glycosylated and non-
glycosylated IgGs, there were clear differences at 20
C (Figure 2A). Since aggregation
had its onset at ~64
C (Table 4), we also compared time-gated Raman spectra at 65
C
(Figure 2B), where the changes differed in response to the rise in temperature. We ob-
served a clear shift in the amide I peak from 1631 to 1646 cm
1
due to the loss of
β
-sheet
structures [
42
] and a reduction of the 1097 cm
1
peak. Yet, at ambient temperatures, both
spectra differed greatly. In non-glycosylated IgG time-gated spectra, we observed changes
at 954/986 cm
1
(likely the protein backbone) and the 1207 cm
1
peak (cysteine or the
ν
SO
4
peak) [
43
]. To our surprise, we observed the very characteristic carotenoid peaks at
1152 and 1517 cm
–1
due to C–C and conjugated C=C bond stretches [
44
], on which we can
only speculate them to be a remnant of the production process.
In the glycosylated IgG spectra (Figure 2A,B), we observed the reduction of the C-H
(def) peak at 1456 cm
1
, Trp C
α
-H (def) peak at 1455 cm
1
, and changes in 1045 cm
1
,
1097 cm
1
, all likely marker bands of aromatic side chains affected upon heating. The
relative Raman intensities between 600–900 cm
1
appeared to drop dramatically; however,
this could also be an artifact due to the detector shift between lower and higher wavenum-
bers (Raman shift) in this particular measurement. Despite these differences, the peak at
606 cm
1
, likely the CCC deformation in-plane vibration mode of the phenylalanine ring,
in the non-glycosylated spectra did not change due to heating [
45
]. Of interest is the ratio of
the Raman peak intensity seen in the tyrosine doublet Raman bands near 850 and 830 cm
1
of 0.89 at 20
C and 1.14 at 65
C. This shift indicates that upon heating, the 10–12 tyrosines
present in the IgG were more exposed to the solvent, albeit the shift is small.
In the non-glycosylated IgG sample, we observed the disappearance of the carotenoid
peaks, the likely protein back-bone peaks at 954/986 cm
1
, the 1207 cm
1
peak, and the
C–N peak at 1120 cm
1
. Overall, we saw fewer changes in the overall spectra compared to
glycosylated IgG. The Int
850
/Int
830
ratio of tyrosine shift from 0.89 at 20
C to 1.53 at 65
C
indicates a higher exposure of tyrosines to the solvent upon heating.
The time-gated Raman spectra of glycosylated IgG did not show a clear drop in inten-
sity in the amide I peak (1650–1660 cm
1
) due to the increase in temperature (Figure S11).
The intensity rose first and then dropped at the higher temperatures. As with pepsin, the
spectra grouped according to their similarity at different temperature points shown in
Figure S14A (panel 1) do not show any differences. However, there is no indication in
the CD-spectra that the protein was partly unfolded (Figure S7). The one high intensity
at 50
C at 880 cm
1
indicated a specific large change in the tryptophan environment. In
addition, the grouping in Figure S14B panel 1 shows that the spectra at clusters 25, 30, 40,
37, 40, 45, 60, 70, and 85
C and at clusters 50, 52, 55, 57, 65, and 75
C show similarities. Yet,
PC1 and PC2 contributed around 50% of the explained variance, so it does not allow the
discrimination between the two groups. These observations did not directly correlate with
Pharmaceutics 2022,14, 1639 9 of 18
the aggregation temperature of 64
C and CD melting temperatures (T
m
) of 65.5 and 72.1
C.
Upon closer inspection in Figure S14B panel 2, we observed the second cluster was grouped
due to changes around a Raman shift of 1460–1480 cm
1
(C–H and aliphatic side chains)
at these temperatures. PC1 and PC2 contribute around 60% of the explained variance.
Significant changes in the IgG secondary structure due to the increase in temperature can
be observed in Figures S14 and S15, summarized in Table S5.
Pharmaceutics 2022, 14, x FOR PEER REVIEW 10 of 20
Figure 2. Averaged and normalized Time-gated spectra of IgG (glycosylated; blue; N = 11) and IgG
(non-glycosylated; red; N = 6) at (A) 25 °C, (B) and at 65 °C. Individual, non-processed, and non-
normalized Time-gated spectra (i.e., raw data) are shown in Figures S11 and S12. Regions of signif-
icance in respect of the glycosylation status of proteins, as determined by Brewser et al. (2011)
[46]
are shown in green. Raman values of significance, as presented in Table S5, are depicted in bold and
red.
The time-gated Raman spectra of glycosylated IgG did not show a clear drop in in-
tensity in the amide I peak (1650–1660 cm
1
) due to the increase in temperature (Figure
S11). The intensity rose first and then dropped at the higher temperatures. As with pepsin,
the spectra grouped according to their similarity at different temperature points shown in
Figure S14A (panel 1) do not show any differences. However, there is no indication in the
CD-spectra that the protein was partly unfolded (Figure S7). The one high intensity at 50
°C at 880 cm
1
indicated a specific large change in the tryptophan environment. In addi-
tion, the grouping in Figure S14B panel 1 shows that the spectra at clusters 25, 30, 40, 37,
40, 45, 60, 70, and 85 °C and at clusters 50, 52, 55, 57, 65, and 75 °C show similarities. Yet,
PC1 and PC2 contributed around 50% of the explained variance, so it does not allow the
discrimination between the two groups. These observations did not directly correlate with
the aggregation temperature of 64 °C and CD melting temperatures (T
m
) of 65.5 and 72.1
Figure 2.
Averaged and normalized Time-gated spectra of IgG (glycosylated; blue; N = 11) and
IgG (non-glycosylated; red; N = 6) at (
A
) 25
C, (
B
) and at 65
C. Individual, non-processed, and
non-normalized Time-gated spectra (i.e., raw data) are shown in Figures S11 and S12. Regions
of significance in respect of the glycosylation status of proteins, as determined by Brewser et al.
(2011) [
46
] are shown in green. Raman values of significance, as presented in Table S5, are depicted in
bold and red.
Summarizing, for
β
-sheet proteins, an interesting observation is the relevance change
of C=O stretching peak between 1760–1840 cm
1
, most likely in the carbonyl groups upon
thermal unfolding. Overall, the intensity decrease in the amide I peak is likely due to
the increased interaction of amino acids with water, thus indicating the unfolding of the
β
-sheet proteins. In addition, significant changes observed both in IgG type structures
Pharmaceutics 2022,14, 1639 10 of 18
and pepsin are changes due to tryptophan in the fingerprint region. The latter observation
correlates with the major changes observed in the tryptophan fluorescence spectroscopy.
3.3. Alpha/Beta Proteins
Ovalbumin is the main protein found in egg white (~55% of the total protein) and
consists of 385 amino acids, with a relative molecular mass of 42.7 kDa. Ovalbumin contains
several post-translational modifications, including N-terminal acetylation (G1), phosphory-
lation (S68, S344), and glycosylation (N292). Ovalbumin’s internal signal sequence (residues
21–47) is not cleaved off but remains as part of the mature protein. Ovalbumin displays
sequence and three-dimensional homology to the serpin superfamily, but unlike most
serpins, it is not a serine protease inhibitor.
The secondary structure, also supported by the CD-spectrum (Figure S16), was com-
prised of
α
-helices (12) and
β
-sheets (15). The
β
-sheets form the core of the protein, while
the α-helices form the outside of the protein, especially in its dimeric form [47].
Ovalbumin aggregation could not be evaluated with DLS in this study as the samples
seemed contaminated with larger molecules or were already partly aggregated prior to
heating in the DLS-cuvette (Figure S17A). Earlier reports indicated aggregation to start at
~71
C [
48
]. According to the tryptophan fluorescence spectra (Figure S18), the intensities of
the spectra were highest between 37–40
C and 50
C, with a lower intensity at the highest
temperatures (75–85
C). These results indicate that neighboring amino acids initially
reduced quenching, while the temperature-induced changes in the secondary structures
then increased quenching. In addition, we observed a blue shift, and since W149 and
W268 are buried at room temperature and bound to charged groups, water molecules may
create a blue shift in this environment [
49
]. W185 is buried and stabilized by hydrophobic
groups [47].
The time-gated Raman spectra (Figure S19) show a dramatic drop in intensity of the
amide I peak between 40–50
C, indicating denaturation [
50
], with some recovery at 60
C.
In addition, the spectra grouped according to their similarity at different temperatures
shown in Figure S21A,B panel 1, show that the spectra from 25–40
C were more similar
than the spectra from 55–85
C. Combined with the tryptophan fluorescence data, it appears
that our sample started denaturing at lower temperatures than earlier reported [48].
Significant changes in the secondary structure of ovalbumin following the increase in
temperature were observed in the time-gated Raman spectra (Figures 3and S19) and are
summarized in Table S6. Upon heating, we observed the reduction of
α
- helical structures,
as indicated by the amide I peak splitting and the rise of the tryptophan indole ring peak
at 1561 cm
1
(Figure 3A). Furthermore, due to the overall reduction of the peak intensity
of the amide I peak, the peak details seen in the normalized data appear enhanced in the
heated sample. PCA analysis of the time-gated Raman spectra in Figure S21A,B, panel 4,
identified the relevant changes due to a positive correlation in the PC1 components of
Raman peaks of phenylalanine (at 1015–1020 cm
1
), the amide II (at 1200 cm
1
) and C=O
stretching bond (at 1750–1860 cm
1
). Further modeling of the amide I peak during thermal
unfolding could give a better insight into the unfolding of ovalbumin.
Triosephosphate isomerase (TIM) comprises the classical
α
/
β
barrel [
51
] and the wild-
type exists as a dimeric protein. The Leishmania mexicana (Lm) mutant E65Q (LmTIM
E65Q
) is
a thermostable variant of the wild-type protein [
26
]. This mixed
α
-helix/
β
-sheet structure
is clearly observed in the CD-spectrum (Figure S16), and the LmTIM
E65Q
variant appears to
be better folded than Saccharomyces cerevisiae (Sc) TIM under the same conditions. ScTIM
aggregation evaluated with DLS in this study (Figure S17B) indicates aggregation to start
at ~58
C, which was in correlation with the CD-melting curve (Table 3). In the tryptophan
fluorescence spectra (Figure S18), the intensities of the spectra were highest between at
37–40 C
, with a lower intensity at the highest temperatures (75–85
C). These results
indicate that neighboring amino acids initially reduced quenching, while the temperature-
induced changes in the secondary structures then increased quenching. In addition, we
observe for both TIMs a redshift above 300 nm
1
, indicating exposure of the tryptophans
Pharmaceutics 2022,14, 1639 11 of 18
to the solvent. In ScTIM, both W89 and W156 are buried and stabilized by hydrophobic
groups [
52
], while in the LmTIM
E65Q
W11, W160, and W194 are buried and stabilized by
hydrophobic groups, while the buried W91 is bound to a polar group [
53
]. Both the W167
in ScTIM and W170 in LmTIM
E65Q
reside in the hinge of the catalytic loop and thus are
exposed to the solvent [54].
Pharmaceutics 2022, 14, x FOR PEER REVIEW 12 of 20
Figure 3. Averaged (N = 11) and normalized Time-gated spectra of (A) ovalbumin at 25 °C (blue)
and 50 °C (red), and (B) LmTIM
E65Q
at 25 °C (blue) and 86 °C (red) correspond to the individual, non-
processed, and non-normalized time-gated spectra as presented in Figures S19 and S20 (i.e., raw
data). Raman values of significance, as presented in Tables S6 and S7, are depicted in bold and red.
Triosephosphate isomerase (TIM) comprises the classical α/β barrel [51] and the
wild-type exists as a dimeric protein. The Leishmania mexicana (Lm) mutant E65Q
(LmTIM
E65Q
) is a thermostable variant of the wild-type protein [26]. This mixed α-helix/β-
sheet structure is clearly observed in the CD-spectrum (Figure S16), and the LmTIM
E65Q
variant appears to be better folded than Saccharomyces cerevisiae (Sc) TIM under the
same conditions. ScTIM aggregation evaluated with DLS in this study (Figure S17B) indi-
cates aggregation to start at ~58 °C, which was in correlation with the CD-melting curve
(Table 3). In the tryptophan fluorescence spectra (Figure S18), the intensities of the spectra
were highest between at 37–40 °C, with a lower intensity at the highest temperatures (75–
85 °C). These results indicate that neighboring amino acids initially reduced quenching,
while the temperature-induced changes in the secondary structures then increased
Figure 3.
Averaged (N = 11) and normalized Time-gated spectra of (
A
) ovalbumin at 25
C (blue)
and 50
C (red), and (
B
)LmTIM
E65Q
at 25
C (blue) and 86
C (red) correspond to the individual,
non-processed, and non-normalized time-gated spectra as presented in Figures S19 and S20 (i.e., raw
data). Raman values of significance, as presented in Tables S6 and S7, are depicted in bold and red.
The time-gated Raman spectra show a large reduction in relative intensity between
83 and 86
C for LmTIM
E65Q
(Figure S20), which is in accordance with the CD melting
curve and tryptophan fluorescence measurement (Tables S9 and S11). In addition, the
spectra grouped according to their similarity shown in Figure S22A panel 1 show that the
spectra from 25–86
C were more similar than the spectra from 90–99
C. However, spectra
Pharmaceutics 2022,14, 1639 12 of 18
grouped in Figure S22B, differ, as we observed earlier in the IgG spectra, due to changes
around Raman Shift of 1460–1480 cm
1
(C–H and aliphatic side chains) at 25, 30, 40, 45,
75, and 85
C. PC1 and PC2 contribute to most of the explained variance and allows the
discrimination between the two groups.
Significant changes in the secondary structure due to the increase in temperature in
LmTIM
E65Q
were observed in time-gated Raman spectra (Figures S20 and S22; summarized
in Table S7). Upon heating, we observed the changes in the secondary structure of the
α
/
β
barrel, as indicated by the significance of changes in the amide II peaks at 1285 cm
1
(
α
helix/
β
-sheet) and 1230–1240 cm
1
(
β
-sheet) (Figure 3A). Furthermore, due to the
reduction of the peak intensity of the amide I peak, details in other regions of the data were
enhanced. The unfolding of this variant of LmTIM is well understood [
53
]. At neutral pH,
the active dimer unfolds into partially unfolded monomers, which are prone to aggregation.
Hence, the relatively small changes in the amide I peak, compared to ovalbumin, are due
to remaining partially folded monomers. Unlike in the ovalbumin spectra, we observed
significant changes in the peptide bonds in LmTIME65Q.
When comparing common features of unfolding in ovalbumin and TIM, we observed
that the K-clustering profiles correlated well with the tryptophan fluorescence. In both
cases, when the red shift occurs and the intensities drop, we also observed a reduction
in the amide I peaks in the time-gate Raman spectra. The relevant changes derived from
the PCA analysis observed for both proteins (Figures S21 and S22, and Table 6) are due to
the phenylalanine peak 980–1020 cm
1
, the amide II peak (1200–1205 cm
1
), and the C=O
stretching bond between 1820–1860 cm1.
4. Discussion
Pharmaceutical proteins have proven to be very important in the field of medicines
and vaccines [
1
]. The practice of pharmacovigilance has gained significant momentum since
1963, following the thalidomide tragedy [
55
]. As such, the evaluation of proteins during
biotechnological production, downstream processing, and storage are crucial. Since protein
function is linked directly to their three-dimensional shape or structural fold, the evaluation
of changes in their secondary structure during the above-mentioned processes sheds
important insights into their stability, which is directly linked to safety and efficacy [56].
Earlier, we addressed the notion that there is a need to directly monitor the intermedi-
ate products during protein production within the living cells [
17
]. Before we can utilize
Raman spectroscopy for this task, we first need to understand how protein Raman spectra
change in the function of unfolding and aggregation. In this study, we induced changes
in the secondary structure of several proteins in in-vitro conditions via thermal ramping.
Then we used time-gated Raman spectroscopy coupled with PCA analysis of the Raman
spectra to evaluate relevant molecular descriptors toward predictive modeling of unfolding
and aggregation of pharmaceutical proteins. Furthermore, we created a novel 3D-printed
heat-exchange Raman sample holder for expensive samples. We evaluated several proteins
to find common molecular descriptors within time-gated Raman spectra regarding the
thermal unfolding of proteins and compare the changes in Raman spectra with established
spectroscopic methods often used to evaluate changes in the secondary structure of proteins
and the formation of aggregates. Finally, we could identify different changes due to thermal
unfolding between the three protein classes (
α
,
β
,
α
/
β
). For each protein, a deeper analysis
using NMR unfolding studies and additional unfolding studies using different unfolding
methods would be very insightful for each specific protein; however, this was not the aim
of this study.
Overall, CD measurements are in accordance with earlier reports (references are listed
in Table 3), taking into account differences in concentrations or buffer. The DLS results, as
summarized in Table 4, show insight into the start of aggregation and cannot be compared
to CD melting curves; however, similar trends can be observed upon heating the different
proteins. The tryptophan-fluorescence measurements, summarized in Table 5, showed how
the local environment of tryptophan changes during unfolding and aggregation. Changes
Pharmaceutics 2022,14, 1639 13 of 18
in intensities are due to quenching events [
57
], while red and blue shifts are due to changes
in specific interactions with tryptophans within the protein [
49
]. We observe all these
effects, but not tryptophan oxidation [58].
Table 3. Summarized CD results.
Protein (α,β,α/β) Tm(C) Van’t Hoff Enthalpy (kJ/mol) Literature Value Tm(C)
BSA (α) 61.2 ±0.1 a256.6 ±8.4 63 [59]
CNTF (α)55.0 ±1.9 b360.0 ±34.3 53 [27]
Fab (β) 73.9 ±0.3 437.5 ±17.4 61–70 [60,61]
IgGglycosylated (β)c65.5 ±0.2
72.1 ±0.4 336.6 ±7.9
203.9 ±10.6 60–68
71–77 [60,62]
IgGnon-glycosylated (β) 71.5 ±0.2 265.2 ±12.6 62–66 [63]
Pepsin (β)49.9 ±0.2 d352.1 ±17.0 52 e[64]
Ovalbumin (α/β) 72.3 ±0.1 181.3 ±2.2 71–76 [65]
ScTIM (α/β) 55.3 ±1.7 360.0 ±13.4 ~58 [66]
LmTIME65Q (α/β) 81.0 ±0.3 a172.9 ±12.6 83 [26]
a
The protein was not totally unfolded at 92
C;
b
Tighter
α
-helical folding occurred between 30–40
C;
c
Additional
unfolding at 35 C; dProtein might be aggregated to disordered at pH 7 during analysis; eAt pH = 8.0.
Table 4. Summarized DLS results.
Protein (α,β,α/β)Hydrodynamic Diameter
at 20 C [nm] Taggregation [C] Literature Value Taggregation [C] c
BSA (α) 8.0 58 ~62 [67]
CNTF (α) ND ND 38 a[27]
Fab (β) 205.8 60 -
F(ab0)2 (β) 11.2 63 -
IgGglycosylated (β) 12.3 64 55–80 [68]
IgGnon-glycosylated (β) ND ND -
Pepsin (β) 50.9 64 -
Ovalbumin (α/β) 28.8 -b71 [48]
ScTIM (α/β) 68.4 58 -
LmTIME65Q (α/β) ND ND -
a
Hydrodynamic radius at 2
C is 2.42
±
0.50 nm and 2.95
±
0.22 nm (two different buffers were used [
27
]);
bCould not be determined (see Figure S12B); cTo best of our knowledge.
Table 5. Summarized tryptophan fluorescence results.
Protein (α,β,α/β)Maximum Fluorescence
Intensity at Temperature [C] Red/Blue Shift aTryptophan Oxidation
(Peak at 515)
BSA (α) 45 Red (10 nm) No
CNTF (α) 30 Red (12 nm) No
Fab (β) 85 Red (4 nm) No
F(ab0)2 (β)ND bND ND
IgGglycosylated (β) 85 Red (5 nm) No
IgGnon-glycosylated (β) ND ND ND
Pepsin (β) 37 No No
Ovalbumin (α/β)37
50 Blue (5 nm)
Blue (5 nm) No
ScTIM (α/β) 65 Red (10 nm) No
LmTIME65Q (α/β) 86 Red (6 nm) No
aComparing the maximum of the peaks at lowest temperatures to the highest; bND = Not Determined.
Pharmaceutics 2022,14, 1639 14 of 18
Table 6.
Summarized results of changes in the time-gated spectra identified with K-means clustering
and PCA analysis.
Protein Structural Class Most Relevant Changes
[cm1]Bond Type Relevant Temperature
Change Correlates with
α880–900 Trp Trp-fluorescence
910 Ser aDLS
940 N-Ca-C CD
950 N-Ca-C
970 Ser/His a
1180 Val/Arg/other amino acids a
1280 Amide II
1310 Phe, Tyr, Trp a
β1350–1390 Trp aTrp-fluorescence
1695–1760 Amide I/carbonyl stretch
α/β980–1020 Phe Trp-fluorescence
1200–1205 Amide II DLS
1820–1860 C=O CD
aAccording to De Gelder et al. (2007) [69].
Changes in the time-gated spectra are related to changes in the secondary structure of
the protein. While the intensity reduction of the peptide-bond (C–N) peak would clearly
indicate protein degradation and similar changes of the phenylalanine peak at ~1000 cm
1
would indicate chances in the protein concentration, other changes are more subtle and a
reflection of secondary structure changes. Table 6summarizes shared regions per protein
structural class, though only one α-helical protein was evaluated.
One drawback of the TimeGated
TM
device used in the study was a baseline artifact due
to switching the measuring window from a lower to a higher range. We chose this set-up to
maximize the number of datapoints and thus lower the deviation. We intentionally do not
correct for the baseline in this study as we were aiming for a method that can be utilized by
only rapidly evaluating the raw data. However, if changes in both regions were significant,
the PCA analysis isolated changes as significant. The difference in intensity between the
low and the higher wavenumber scans of the same sample is due to the higher intensity
from the Amide I peak during the second scan. In the non-glycosylated sample, we opted
for larger steps to cover the same spectrum in one scan, but here we have fewer repetitions
per scan and thus introduce a larger standard deviation.
When we compare time-gated Raman data of (partly) unfolded CNTF [
17
] with the
thermal unfolding of BSA, we observed
β
-sheet formation due to the rise of a peak at
1332 cm
1
and changes in the amide III peak of tyrosine. The most significant changes
in BSA are in the backbone, in the secondary C-N peak at 1130 cm
1
, and in the peak at
1180 cm
1
(Table 6). Unlike the other two protein classes, no common features can be
observed other than tyrosine peak changes at 1240 cm
1
(amide III (
β
-sheets); Table 6),
thus shifting away from α-helical structures.
Finally, we combined all time-gated Raman spectra in the PCA analysis to utilize the
noise filtering properties of the method on the lower-scanning region of the detector of
the TimeGated
TM
device used in the study (500–1300 cm
1
). The positive and negative
correlation of PC1 compared to PC2 for all the Raman peaks combined to show three
regions of particular interest (Figure S23). Changes in the peaks in, or close, to the amide III
region (1180–1300 cm
1
), tryptophan (880 cm
1
), and phenylaniline (~1000 cm
1
) were
present in all proteins due to thermal unfolding.
Rather than dissecting each protein structure in detail to identify the structural changes
during the unfolding and aggregation events as observed in the time-gated Raman spectra,
we aimed to reduce the raw data to smaller components to identify significant changes
pertaining to a protein structural class.
The data analysis used in this study is an unsupervised method; therefore, the resulting
components do not necessarily reveal the features directly linked to the classification but
Pharmaceutics 2022,14, 1639 15 of 18
represent the sources of variation and the representative properties of the raw data. Even
with the small data set presented here, relevant changes in secondary structures of proteins
correlate with other, more traditional, label-free methods such as DLS, CD-spectroscopy,
and tryptophan fluorescence. As such, identifying relevant changes is possible, but it will
require a larger dataset to create a model predicting relevant structural changes based on
changes in Raman spectra. Additionally, a higher resolution Raman dataset using more
advanced detectors would be beneficial in reducing the noise-to-signal ratio and to improve
the PCA analysis.
5. Conclusions
In combination with K-means clustering, PCA sheds further light on changes in the
structural elements of the Raman spectroscopy spectra. Principal component analysis can
be considered a noise filtering method. The relevant differences are captured in the first
components, while the higher components contain noise only. The spectra can be recon-
structed using only the first p components. The current study demonstrates the capabilities
of time-gated Raman spectroscopy in characterizing structural changes of proteins under
different experimental conditions without offline sampling and the addition of protein
labels. Time-gated Raman spectroscopy gives valuable insights into secondary protein
structures that correlate with observations with tryptophan fluorescence spectroscopy,
dynamic light scattering, and circular dichroism spectroscopy. Additional variations to
perturb proteins could be added as additional parameters to identify additional descrip-
tors of protein unfolding. Raman signals can then be translated into high-level structural
information of interest to derive statistical models from being used to predict the relative
folding states of unknown samples compared to fully folded proteins. We intend to create
additional data sets for building such models in the near future.
Supplementary Materials:
The following supporting information can be downloaded at: https://
www.mdpi.com/article/10.3390/pharmaceutics14081639/s1, Supplementary data file, which includes
Table S1–S7 and Figures S1–S23. References [27,34,6971] are cited in the Supplementary Materials.
Author Contributions:
Conceptualization, M.G.C. and L.G.; methodology, J.I., L.G., D.P., P.J.J. and
M.G.C.; data curation, L.G.; writing—original draft preparation, M.G.C.; writing—review and editing,
J.I., P.J.J., M.G.C. and L.G.; visualization, M.G.C. and L.G.; supervision, M.G.C. and H.X.; project
administration, M.G.C.; funding acquisition, M.G.C. and H.X. All authors have read and agreed to
the published version of the manuscript.
Funding:
This research was funded by the Academy of Finland project 303884 and VTT technical
research Centre of Finland. L.G. gratefully acknowledges the support of the Drug Discovery and
Chemical Biology Network of Finland. J.I. has also been funded by grants from the Finnish Cultural
Foundation, the Evald and Hilda Nissi Foundation, the Päivikki and Sakari Sohlberg Foundation,
and the Paulo Foundation. D.P. received Erasmus+ program funding from the Italian Ministry
of Education.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
The authors acknowledge the use of Instruct-HiLIFE Crystallization unit (Uni-
versity of Helsinki, Biocenter Finland, and Instruct-FI). The use of the facilities and expertise of the
Biocenter Oulu protein biophysical analysis core facility, a member of Biocenter Finland, is gratefully
acknowledged. We kindly thank Rik Wierenga for proving us with the LmTIM
E65Q
protein. We
also would like to thank Leena Pietilä for her kind laboratory assistance and Regina Casteleijn-
Osorno of Aalto University (Finland) for comments that greatly improved the manuscript. We
kindly like to thank M. Kögler of VTT (Finland) for his expert advice. We kindly acknowledge our
funding sources and support from the CSC IT Center for Science Ltd. is thanked for organizing
computational resources.
Pharmaceutics 2022,14, 1639 16 of 18
Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or
in the decision to publish the results.
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