Monitoring the glycosylation status of proteins using Raman spectroscopy

Convolutional Neural Networks Guided Raman Spectroscopy as a Process Analytical Technology (PAT) Tool for Monitoring and Simultaneous Prediction of Monoclonal Antibody Charge Variants

BACKGROUND

Charge related heterogeneities of monoclonal antibody (mAb) based therapeutic products are increasingly being considered as a critical quality attribute (CQA). They are typically estimated using analytical cation exchange chromatography (CEX), which is time consuming and not suitable for real time control. Raman spectroscopy coupled with artificial intelligence (AI) tools offers an opportunity for real time monitoring and control of charge variants.

OBJECTIVE

We present a process analytical technology (PAT) tool for on-line and real-time charge variant determination during process scale CEX based on Raman spectroscopy employing machine learning techniques.

METHOD

Raman spectra are collected from a reference library of samples with distribution of acidic, main, and basic species from 0-100% in a mAb concentration range of 0-20 g/L generated from process-scale CEX. The performance of different machine learning techniques for spectral processing is compared for predicting different charge variant species.

RESULT

A convolutional neural network (CNN) based model was successfully calibrated for quantification of acidic species, main species, basic species, and total protein concentration with R2 values of 0.94, 0.99, 0.96 and 0.99, respectively, and the Root Mean Squared Error (RMSE) of 0.1846, 0.1627, and 0.1029 g/L, respectively, and 0.2483 g/L for the total protein concentration.

CONCLUSION

We demonstrate that Raman spectroscopy combined with AI-ML frameworks can deliver rapid and accurate determination of product related impurities. This approach can be used for real time CEX pooling decisions in mAb production processes, thus enabling consistent charge variant profiles to be achieved.

Mechanisms of SARS-CoV-2 Inactivation Using UVC Laser Radiation

Severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) has had a tremendous impact on humanity. Prevention of transmission by disinfection of surfaces and aerosols through a chemical-free method is highly desirable. Ultraviolet C (UVC) light is uniquely positioned to achieve inactivation of pathogens. We report the inactivation of SARS-CoV-2 virus by UVC radiation and explore its mechanisms. A dose of 50 mJ/cm2 using a UVC laser at 266 nm achieved an inactivation efficiency of 99.89%, while infectious virions were undetectable at 75 mJ/cm2 indicating >99.99% inactivation. Infection by SARS-CoV-2 involves viral entry mediated by the spike glycoprotein (S), and viral reproduction, reliant on translation of its genome. We demonstrate that UVC radiation damages ribonucleic acid (RNA) and provide in-depth characterization of UVC-induced damage of the S protein. We find that UVC severely impacts SARS-CoV- 2 spike protein's ability to bind human angiotensin-converting enzyme 2 (hACE2) and this correlates with loss of native protein conformation and aromatic amino acid integrity. This report has important implications for the design and development of rapid and effective disinfection systems against the SARS-CoV-2 virus and other pathogens.

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

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.

A New Alternative Tool to Analyse Glycosylation in Monoclonal Antibodies Based on Drop-Coating Deposition Raman imaging: A Proof of Concept

Glycosylation is considered a critical quality attribute of therapeutic proteins as it affects their stability, bioactivity, and safety. Hence, the development of analytical methods able to characterize the composition and structure of glycoproteins is crucial. Existing methods are time consuming, expensive, and require significant sample preparation, which can alter the robustness of the analyses. In this context, we developed a fast, direct, and simple drop-coating deposition Raman imaging (DCDR) method combined with multivariate curve resolution alternating least square (MCR-ALS) to analyze glycosylation in monoclonal antibodies (mAbs). A database of hyperspectral Raman imaging data of glycoproteins was built, and the glycoproteins were characterized by LC-FLR-MS as a reference method to determine the composition in glycans and monosaccharides. The DCDR method was used and allowed the separation of excipient and protein by forming a "coffee ring". MCR-ALS analysis was performed to visualize the distribution of the compounds in the drop and to extract the pure spectral components. Further, the strategy of SVD-truncation was used to select the number of components to resolve by MCR-ALS. Raman spectra were processed by support vector regression (SVR). SVR models showed good predictive performance in terms of RMSECV, R²_CV.

Raman based chemometric model development for glycation and glycosylation real time monitoring in a manufacturing scale CHO cell bioreactor process

The Quality by Design (QbD) approach to the production of therapeutic monoclonal antibodies (mAbs) emphasizes an understanding of the production process ensuring product quality is maintained throughout. Current methods for measuring critical quality attributes (CQAs) such as glycation and glycosylation are time and resource intensive, often, only tested offline once per batch process. Process analytical technology (PAT) tools such as Raman spectroscopy combined with chemometric modeling can provide real time measurements process variables and are aligned with the QbD approach. This study utilizes these tools to build partial least squares (PLS) regression models to provide real time monitoring of glycation and glycosylation profiles. In total, seven cell line specific chemometric PLS models; % mono-glycated, % non-glycated, % G0F-GlcNac, % G0, % G0F, % G1F, and % G2F were considered. PLS models were initially developed using small scale data to verify the capability of Raman to measure these CQAs effectively. Accurate PLS model predictions were observed at small scale (5 L). At manufacturing scale (2000 L) some glycosylation models showed higher error, indicating that scale may be a key consideration in glycosylation profile PLS model development. Model robustness was then considered by supplementing models with a single batch of manufacturing scale data. This data addition had a significant impact on the predictive capability of each model, with an improvement of 77.5% in the case of the G2F. The finalized models show the capability of Raman as a PAT tool to deliver real time monitoring of glycation and glycosylation profiles at manufacturing scale.

Potential of Raman spectroscopic techniques to study proteins

Proteins are large, complex molecules responsible for various biological processes. However, protein misfolding may lead to various life-threatening diseases. Therefore, it is vital to understand the shape and structure of proteins. Despite numerous techniques, a mechanistic understanding of the protein folding process is still unclear. Therefore, new techniques are continually being explored. In the present article, we have discussed the importance of Raman spectroscopy, Raman Optical Activity (ROA) and various other advancements in Raman spectroscopy to understand protein structure and conformational changes based on the review of our earlier work and recent literature. A Raman spectrum of a protein provides unique signatures for various secondary structures like helices, beta-sheets, turns, random structures, etc., and various amino acid residues such as tyrosine, tryptophan, and phenylalanine. We have shown how Raman spectra can differentiate between bovine serum albumin (BSA) and lysozyme protein based on their difference in sequence and structure (primary, secondary and tertiary). Although it is challenging to elucidate the structure of a protein using a Raman spectrum alone, Raman spectra can be used to differentiate small changes in conformations of proteins such as BSA during melting. Various new advancements in technique and data analyses in Raman spectroscopic studies of proteins have been discussed. The last part of the review focuses on the importance of the ROA spectrum to understand additional features about proteins. The ROA spectrum is rich in information about the protein backbone due to its rigidity compared to its side chains. Furthermore, the ROA spectra of lysozyme and BSA have been presented to show how ROA provides extra information about the solvent properties of proteins.

Conformational fingerprinting of tau variants and strains by Raman spectroscopy

Tauopathies are a group of disorders in which the deposition of abnormally folded tau protein accompanies neurodegeneration. The development of methods for detection and classification of pathological changes in protein conformation are desirable for understanding the factors that influence the structural polymorphism of aggregates in tauopathies. We have previously demonstrated the utility of Raman spectroscopy for the characterization and discrimination of different protein aggregates, including tau, based on their unique conformational signatures. Building on this, in the present study, we assess the utility of Raman spectroscopy for characterizing and distinguishing different conformers of the same protein which in the case of tau are unique tau strains generated in vitro. We now investigate the impact of aggregation environment, cofactors, post-translational modification and primary sequence on the Raman fingerprint of tau fibrils. Using quantitative conformational fingerprinting and multivariate statistical analysis, we found that the aggregation of tau in different buffer conditions resulted in the formation of distinct fibril strains. Unique spectral markers were identified for tau fibrils generated using heparin or RNA cofactors, as well as for phosphorylated tau. We also determined that the primary sequence of the tau monomer influenced the conformational signature of the resulting tau fibril, including 2N4R, 0N3R, K18 and P301S tau variants. These results highlight the conformational polymorphism of tau fibrils, which is reflected in the wide range of associated neurological disorders. Furthermore, the analyses presented in this study provide a benchmark for the Raman spectroscopic characterization of tau strains, which may shed light on how the aggregation environment, cofactors and post-translational modifications influence tau conformation in vivo in future studies.

Raman Spectroscopy with 2D Perturbation Correlation Moving Windows for the Characterization of Heparin-Amyloid Interactions

It has been shown extensively that glycosaminoglycan (GAG)-protein interactions can induce, accelerate, and impede the clearance of amyloid fibrils associated with systemic and localized amyloidosis. Obtaining molecular details of these interactions is fundamental to our understanding of amyloid disease. Consequently, there is a need for analytical approaches that can identify protein conformational transitions and simultaneously characterize heparin interactions. By combining Raman spectroscopy with two-dimensional (2D) perturbation correlation moving window (2DPCMW) analysis, we have successfully identified changes in protein secondary structure during pH- and heparin-induced fibril formation of apolipoprotein A-I (apoA-I) associated with atherosclerosis. Furthermore, from the 2DPCMW, we have identified peak shifts and intensity variations in Raman peaks arising from different heparan sulfate moieties, indicating that protein-heparin interactions vary at different heparin concentrations. Raman spectroscopy thus reveals new mechanistic insights into the role of GAGs during amyloid fibril formation.

High-Throughput Raman Spectroscopy Combined with Innovate Data Analysis Workflow to Enhance Biopharmaceutical Process Development

Raman spectroscopy has the potential to revolutionise many aspects of biopharmaceutical process development. The widespread adoption of this promising technology has been hindered by the high cost associated with individual probes and the challenge of measuring low sample volumes. To address these issues, this paper investigates the potential of an emerging new high-throughput (HT) Raman spectroscopy microscope combined with a novel data analysis workflow to replace off-line analytics for upstream and downstream operations. On the upstream front, the case study involved the at-line monitoring of an HT micro-bioreactor system cultivating two mammalian cell cultures expressing two different therapeutic proteins. The spectra generated were analysed using a partial least squares (PLS) model. This enabled the successful prediction of the glucose, lactate, antibody, and viable cell density concentrations directly from the Raman spectra without reliance on multiple off-line analytical devices and using only a single low-volume sample (50–300 μL). However, upon the subsequent investigation of these models, only the glucose and lactate models appeared to be robust based upon their model coefficients containing the expected Raman vibrational signatures. On the downstream front, the HT Raman device was incorporated into the development of a cation exchange chromatography step for an Fc-fusion protein to compare different elution conditions. PLS models were derived from the spectra and were found to predict accurately monomer purity and concentration. The low molecular weight (LMW) and high molecular weight (HMW) species concentrations were found to be too low to be predicted accurately by the Raman device. However, the method enabled the classification of samples based on protein concentration and monomer purity, allowing a prioritisation and reduction in samples analysed using A280 UV absorbance and high-performance liquid chromatography (HPLC). The flexibility and highly configurable nature of this HT Raman spectroscopy microscope makes it an ideal tool for bioprocess research and development, and is a cost-effective solution based on its ability to support a large range of unit operations in both upstream and downstream process operations.

Mutated Human P-Selectin Glycoprotein Ligand-1 and Viral Protein-1 of Enterovirus 71 Interactions on Au Nanoplasmonic Substrate for Specific Recognition by Surface-Enhanced Raman Spectroscopy

Protein tyrosine sulfation is a common post-translational modification that stimulates intercellular or extracellular protein-protein interactions and is responsible for various important biological processes, including coagulation, inflammation, and virus infections. Recently, human P-selectin glycoprotein ligand-1 (PSGL-1) has been shown to serve as a functional receptor for enterovirus 71 (EV71). It has been proposed that the capsid viral protein VP1 of EV71 is directly involved in this specific interaction with sulfated or mutated PSGL-1. Surface-enhanced Raman spectroscopy (SERS) is used to distinguish PSGL-1 and VP1 interactions on an Au nanoporous substrate and identify specific VP1 interaction positions of tyrosine residue sites (46, 48, and 51). The three tyrosine sites in PSGL-1 were replaced by phenylalanine (F), as determined using SERS. A strong phenylalanine SERS signal was obtained in three regions of the mutated protein on the nanoporous substrate. The mutated protein positions at (51F) and (48F, 51F) produced a strong SERS peak at 1599–1666 cm−1, which could be related to a binding with the mutated protein and anti-sulfotyrosine interactions on the nanoporous substrate. A strong SERS effect of the mutated protein and VP1 interactions appeared at (48F), (51F), and (46F, 48F). In these positions, there was less interaction with VP1, as indicated by a strong phenylalanine signal from the mutated protein.

Label-free spectrochemical probe for determination of hemoglobin glycation in clinical blood samples

Glycated hemoglobin, HbA1c, is an important biomarker that reveals the average value of blood glucose over the preceding 3 months. While significant recent attention has been focused on the use of optical and direct molecular spectroscopic methods for determination of HbA1c, a facile test that minimizes sample preparation needs and turnaround time still remains elusive. Here, we report a label-free approach for identifying low, mid and high-HbA1c groups in hemolysate and in whole blood samples featuring resonance Raman (RR) spectroscopy and support vector machine (SVM)-based classification of spectral patterns. The diagnostic power of RR measurements stems from its selective enhancement of hemoglobin-specific features, which simultaneously minimizes the blood matrix spectral interference and permits detection in the native solution. In this pilot study, our spectroscopic observations reveal that glycation of hemoglobin results in subtle but reproducible changes even when detected in the whole blood matrix. Leveraging SVM analysis of the principal component scores determined from the RR spectra, we show high degree of accuracy in classifying clinical specimen. We envisage that the promising findings will pave the way for more extensive clinical specimen investigations with the ultimate goal of translating molecular spectroscopy for routine point-of-care testing.

Characterization of Soybean Protein Adhesives Modified by Xanthan Gum

The aim of this study was to provide a basis for the preparation of medical adhesives from soybean protein sources. Soybean protein (SP) adhesives mixed with different concentrations of xanthan gum (XG) were prepared. Their adhesive features were evaluated by physicochemical parameters and an in vitro bone adhesion assay. The results showed that the maximal adhesion strength was achieved in 5% SP adhesive with 0.5% XG addition, which was 2.6-fold higher than the SP alone. The addition of XG significantly increased the hydrogen bond and viscosity, as well as increased the β-sheet content but decreased the α-helix content in the second structure of protein. X-ray diffraction data showed significant interactions between SP molecules and XG. Scanning electron microscopy observations showed that the surface of SP adhesive modified by XG was more viscous and compact, which were favorable for the adhesion between the adhesive and bone. In summary, XG modification caused an increase in the hydrogen bonding and zero-shear viscosity of SP adhesives, leading to a significant increase in the bond strength of SP adhesives onto porcine bones.

Real-time monitoring of antibody glycosylation site occupancy by in situ Raman spectroscopy during bioreactor CHO cell cultures

The glycosylation of therapeutic monoclonal antibodies (mAbs), a known critical quality attribute, is often greatly modified during the production process by animal cells. It is essential for biopharmaceutical industries to monitor and control this glycosylation. However, current glycosylation characterization techniques involve time- and labor-intensive analyses, often carried out at the end of the culture when the product is already synthesized. This study proposes a novel methodology for real-time monitoring of antibody glycosylation site occupancy using Raman spectroscopy. It was first observed in CHO cell batch culture that when low nutrient concentrations were reached, a decrease in mAb glycosylation was induced, which made it essential to rapidly detect this loss of product quality. By combining in situ Raman spectroscopy with chemometric tools, efficient prediction models were then developed for both glycosylated and nonglycosylated mAbs. By comparing variable importance in projection profiles of the prediction models, it was confirmed that Raman spectroscopy is a powerful method to distinguish extremely similar molecules, despite the high complexity of the culture medium. Finally, the Raman prediction models were used to monitor batch and feed-harvest cultures in situ. For the first time, it was demonstrated that the concentrations of glycosylated and nonglycosylated mAbs could be successfully and simultaneously estimated in real time with high accuracy, including their sudden variations due to medium exchanges. Raman spectroscopy can thus be considered as a promising PAT tool for feedback process control dedicated to on-line optimization of mAb quality. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 34:486-493, 2018.

Applications of Raman Spectroscopy in Biopharmaceutical Manufacturing: A Short Review

The production of active pharmaceutical ingredients (APIs) is currently undergoing its biggest transformation in a century. The changes are based on the rapid and dramatic introduction of protein- and macromolecule-based drugs (collectively known as biopharmaceuticals) and can be traced back to the huge investment in biomedical science (in particular in genomics and proteomics) that has been ongoing since the 1970s. Biopharmaceuticals (or biologics) are manufactured using biological-expression systems (such as mammalian, bacterial, insect cells, etc.) and have spawned a large (>€35 billion sales annually in Europe) and growing biopharmaceutical industry (BioPharma). The structural and chemical complexity of biologics, combined with the intricacy of cell-based manufacturing, imposes a huge analytical burden to correctly characterize and quantify both processes (upstream) and products (downstream). In small molecule manufacturing, advances in analytical and computational methods have been extensively exploited to generate process analytical technologies (PAT) that are now used for routine process control, leading to more efficient processes and safer medicines. In the analytical domain, biologic manufacturing is considerably behind and there is both a huge scope and need to produce relevant PAT tools with which to better control processes, and better characterize product macromolecules. Raman spectroscopy, a vibrational spectroscopy with a number of useful properties (nondestructive, non-contact, robustness) has significant potential advantages in BioPharma. Key among them are intrinsically high molecular specificity, the ability to measure in water, the requirement for minimal (or no) sample pre-treatment, the flexibility of sampling configurations, and suitability for automation. Here, we review and discuss a representative selection of the more important Raman applications in BioPharma (with particular emphasis on mammalian cell culture). The review shows that the properties of Raman have been successfully exploited to deliver unique and useful analytical solutions, particularly for online process monitoring. However, it also shows that its inherent susceptibility to fluorescence interference and the weakness of the Raman effect mean that it can never be a panacea. In particular, Raman-based methods are intrinsically limited by the chemical complexity and wide analyte-concentration-profiles of cell culture media/bioprocessing broths which limit their use for quantitative analysis. Nevertheless, with appropriate foreknowledge of these limitations and good experimental design, robust analytical methods can be produced. In addition, new technological developments such as time-resolved detectors, advanced lasers, and plasmonics offer potential of new Raman-based methods to resolve existing limitations and/or provide new analytical insights.

Detection of glycosylation and iron-binding protein modifications using Raman spectroscopy

We have used Raman spectroscopy and chemometrics to determine protein modification as a result of glycosylation and iron binding.

Epigenetic changes in cancer by Raman imaging, fluorescence imaging, AFM and scanning near-field optical microscopy (SNOM). Acetylation in normal and human cancer breast cells MCF10A, MCF7 and MDA-MB-231

This paper examines epigenetic changes in breast cancer by Raman imaging, fluorescence imaging, AFM and SNOM and discusses how they contribute to different aspects of tumourigenesis in malignant human breast epithelial cell lines MCF7 and MDA-MB-231 compared with non-malignant MCF10A cell lines. The paper focuses on information that can be extracted from Raman microscopy and Raman imaging for the biological material of nucleoli contained within the cell nucleus and lipid droplets within the cell cytoplasm. The biochemical composition of the nuclei and lipid droplets in the non-malignant and malignant human breast epithelial cell lines has been monitored. The potential of Raman microspectroscopy to monitor acetylation processes and a prognostic value of Raman biomarkers in breast cancer have been discussed.

Two-dimensional Raman correlation spectroscopy reveals molecular structural changes during temperature-induced self-healing in polymers based on the Diels–Alder reaction

For the first time two-dimensional Raman correlation analysis has been used to study self-healing polymers based on the Diels–Alder reaction.

Monitoring antibody aggregation in early drug development using Raman spectroscopy and perturbation-correlation moving windows

In this study, we demonstrate the sensitivity of two-dimensional perturbation-correlation moving windows (PCMW) to characterize conformational transitions in antibodies. An understanding of how physiochemical properties affect protein stability and instigate aggregation is essential for the engineering of antibodies. In order to establish the potential of PCMW as a technique for early identification of aggregation mechanisms during antibody development, five antibodies with varying propensity to aggregate were compared. Raman spectra were acquired, using a 532 nm excitation wavelength as the protein samples were heated from 56 to 78 °C and analyzed with PCMW. Initial principal component analysis confirmed a trend between the observed spectral variations and increasing temperature for all five samples. Analysis using PCMW revealed that when spectral variations were directly related to temperature, distinct differences in conformational changes could be determined between samples related to protein stability, providing a greater understanding of the aggregation mechanisms of problematic antibody variants.

The development of surface-enhanced Raman scattering as a detection modality for portable in vitro diagnostics: progress and challenges

This perspective provides an overview of the diverse surface-enhanced Raman scattering (SERS)-based sensor platforms that have been developed for in vitro diagnostic applications. To provide focus, protein and nucleic acid detection assays based on the principle of extrinsic SERS sensing are emphasized, as well as their potential for translation to fully integrated point-of-care (POC) test platforms. The development of intrinsic SERS sensors, which are predicated on the direct detection of analytes by laser excitation, entails unique opportunities and challenges deserving of their own attention. As the robust sensing of disease pathogens and cancers in both clinical facilities and limited resource settings is the targeted objective of many next-generation biosensors, the majority of the research progress summarized here centers on SERS sensors developed for the rapid, sensitive and selective detection of disease-causing pathogens and biomarkers. In our effort to communicate a realistic assessment of the progress that has been made and the challenges that lie ahead, we avoid an overtly optimistic appraisal of the current status of SERS diagnostics that does not tacitly acknowledge the difficulties inherent in aligning SERS-based technologies alongside ELISA and PCR technologies as a complementary method for bioanalyte detection possessing unique advantages.

Understanding glycoprotein behaviours using Raman and Raman optical activity spectroscopies: characterising the entanglement induced conformational changes in oligosaccharide chains of mucin

We illustrate the great potential of Raman and ROA spectroscopies for investigating the structure and organisation of glycoproteins and the complex matrices they can form. In combination these spectroscopic techniques are sensitive to changes in conformation revealing details of secondary and tertiary structures, probing hydrogen bonding interactions, as well as resolving side chain orientation and the absolute configuration of chiral substructures. To demonstrate this potential we have characterised the structural changes in a complex glycoprotein, mucin. Spectral changes were observed during the entanglement transition as the mucin concentration was increased. By applying two-dimensional correlation analysis (2DCos) to the ROA and Raman concentration-dependent spectral sets delicate transitions in mucin conformation could also be determined. From ~20-40 mg/ml conformational transitions assigned mainly to the sugar N-acetyl-d-galactosamine (GalNAc), which is the linking saccharide unit to the protein backbone, were monitored. Further changes in local oligosaccharide conformation above 40 mg/ml were also monitored, together with other structural transitions observed in the protein core, particularly β-structure formation. Consequently, these spectral techniques were shown to monitor the formation of transient entanglements formed by brush-brush interactions between oligosaccharide combs of mucin molecules identifying changes in both carbohydrate and protein moieties. This work clearly shows how these methods can be used to elucidate fresh insights into the complex behaviour of these large complex molecules.

Proteins behaving badly: emerging technologies in profiling biopharmaceutical aggregation

Over recent decades biotechnology has made significant advances owing to the emergence of powerful biochemical and biophysical instrumentation. The development of such technologies has enabled high-throughput assessment of compounds, the implementation of recombinant DNA technology, and large-scale manufacture of monoclonal antibodies. Such innovations have ultimately resulted in the current experienced biopharmaceutical stronghold in the therapeutic market. Yet aggregate prediction and profiling remains a challenge in the formulation of biopharmaceuticals due to artifacts associated with each analytical method. We review some emerging trends and novel technologies that offer a promising potential for accurately predicting and profiling protein aggregation at various stages of biopharmaceutical product design.

Monitoring guanidinium-induced structural changes in ribonuclease proteins using Raman spectroscopy and 2D correlation analysis

Assessing the stability of proteins by comparing their unfolding profiles is a very important characterization and quality control step for any biopharmaceutical, and this is usually measured by fluorescence spectroscopy. In this paper we propose Raman spectroscopy as a rapid, noninvasive alternative analytical method and we shall show this has enhanced sensitivity and can therefore reveal very subtle protein conformational changes that are not observed with fluorescence measurements. Raman spectroscopy is a powerful nondestructive method that has a strong history of applications in protein characterization. In this work we describe how Raman microscopy can be used as a fast and reliable method of tracking protein unfolding in the presence of a chemical denaturant. We have compared Raman spectroscopic data to the equivalent samples analyzed using fluorescence spectroscopy in order to validate the Raman approach. Calculations from both Raman and fluorescence unfolding curves of [D]50 values and Gibbs free energy correlate well with each other and more importantly agree with the values found in the literature for these proteins. In addition, 2D correlation analysis has been performed on both Raman and fluorescence data sets in order to allow further comparisons of the unfolding behavior indicated by each method. As many biopharmaceuticals are glycosylated in order to be functional, we compare the unfolding profiles of a protein (RNase A) and a glycoprotein (RNase B) as measured by Raman spectroscopy and discuss the implications that glycosylation has on the stability of the protein.

Detection of receptor-induced glycoprotein conformational changes on enveloped virions by using confocal micro-Raman spectroscopy

Conformational changes in the glycoproteins of enveloped viruses are critical for membrane fusion, which enables viral entry into cells and the pathological cell-cell fusion (syncytia) associated with some viral infections. However, technological capabilities for identifying viral glycoproteins and their conformational changes on actual enveloped virus surfaces are generally scarce, challenging, and time-consuming. Our model, Nipah virus (NiV), is a syncytium-forming biosafety level 4 pathogen with a high mortality rate (40 to 75%) in humans. Once the NiV attachment glycoprotein (G) (NiV-G) binds the cell receptor ephrinB2 or -B3, G triggers conformational changes in the fusion glycoprotein (F) that result in membrane fusion and viral entry. We demonstrate that confocal micro-Raman spectroscopy can, within minutes, simultaneously identify specific G and F glycoprotein signals and receptor-induced conformational changes in NiV-F on NiV virus-like particles (VLPs). First, we identified reproducible G- and F-specific Raman spectral features on NiV VLPs containing M (assembly matrix protein), G, and/or F or on NiV/vesicular stomatitis virus (VSV) pseudotyped virions via second-derivative transformations and principal component analysis (PCA). Statistical analyses validated our PCA models. Dynamic temperature-induced conformational changes in F and G or receptor-induced target membrane-dependent conformational changes in F were monitored in NiV pseudovirions in situ in real time by confocal micro-Raman spectroscopy. Advantageously, Raman spectroscopy can identify specific protein signals in relatively impure samples. Thus, this proof-of-principle technological development has implications for the rapid identification and biostability characterization of viruses in medical, veterinary, and food samples and for the analysis of virion glycoprotein conformational changes in situ during viral entry.

Glycoproteomics on the rise: established methods, advanced techniques, sophisticated biological applications

Glycosylation is the most complex form of protein PTMs. Affected proteins may carry dozens of glycosylation sites with tens to hundreds of glycan residues attached to every site. Glycosylated proteins have many important functions in biology, from cellular to organismal levels, being involved in cell-cell signaling, cell adhesion, immune response, host-pathogen interactions, and development and growth. Glycosylation, however, expands the biological functional diversity of proteins at the expense of a tremendous increase in structural heterogeneity. Aberrant glycosylation of cell surface proteins, as well as their detectable fingerprint in plasma samples, has been associated with cancer, inflammatory and degenerative diseases, and congenital disorders of glycosylation. Therefore, there are on-going efforts directed toward developing new technologies and approaches for glycan sequencing and high-throughput analysis of glycosylated proteins in complex samples with simultaneous characterization of both the protein and glycan moieties. This work is aimed primarily at pinpointing the challenges associated with the large-scale analysis of glycoproteins and the latest developments in glycoproteomic research, with focus on recent advancements (2011-2012) in microcolumn separations and MS detection.

Sequential identification of model parameters by derivative double two-dimensional correlation spectroscopy and calibration-free approach for chemical reaction systems

A sequential identification approach by two-dimensional (2D) correlation analysis for the identification of a chemical reaction model, activation, and thermodynamic parameters is presented in this paper. The identification task is decomposed into a sequence of subproblems. The first step is the construction of a reaction model with the suggested information by model-free 2D correlation analysis using a novel technique called derivative double 2D correlation spectroscopy (DD2DCOS), which enables one to analyze intensities with nonlinear behavior and overlapped bands. The second step is a model-based 2D correlation analysis where the activation and thermodynamic parameters are estimated by an indirect implicit calibration or a calibration-free approach. In this way, a minimization process for the spectral information by sample-sample 2D correlation spectroscopy and kinetic hard modeling (using ordinary differential equations) of the chemical reaction model is carried out. The sequential identification by 2D correlation analysis is illustrated with reference to the isomeric structure of diphenylurethane synthesized from phenylisocyanate and phenol. The reaction was investigated by FT-IR spectroscopy. The activation and thermodynamic parameters of the isomeric structures of diphenylurethane linked through a hydrogen bonding equilibrium were studied by means of an integration of model-free and model-based 2D correlation analysis called a sequential identification approach. The study determined the enthalpy (ΔH = 15.25 kJ/mol) and entropy (TΔS = 13.20 kJ/mol) of C═O···H hydrogen bonding of diphenylurethane through direct calculation from the differences in the kinetic parameters (δΔ(‡)H, -TδΔ(‡)S) at equilibrium in the chemical reaction system.

Soft sensors in bioprocessing: a status report and recommendations

The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology - Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

Raman spectroscopy-based sensitive and specific detection of glycated hemoglobin

In recent years, glycated hemoglobin (HbA1c) has been increasingly accepted as a functional metric of mean blood glucose in the treatment of diabetic patients. Importantly, HbA1c provides an alternate measure of total glycemic exposure due to the representation of blood glucose throughout the day, including post-prandially. In this article, we propose and demonstrate the potential of Raman spectroscopy as a novel analytical method for quantitative detection of HbA1c, without using external dyes or reagents. Using the drop coating deposition Raman (DCDR) technique, we observe that the nonenzymatic glycosylation (glycation) of the hemoglobin molecule results in subtle but discernible and highly reproducible changes in the acquired spectra, which enable the accurate determination of glycated and nonglycated hemoglobin using standard chemometric methods. The acquired Raman spectra display excellent reproducibility of spectral characteristics at different locations in the drop and show a linear dependence of the spectral intensity on the analyte concentration. Furthermore, in hemolysate models, the developed multivariate calibration models for HbA1c show a high degree of prediction accuracy and precision--with a limit of detection that is a factor of ~15 smaller than the lowest physiological concentrations encountered in clinical practice. The excellent accuracy and reproducibility achieved in this proof-of-concept study opens substantive avenues for characterization and quantification of the glycosylation status of (therapeutic) proteins, which are widely used for biopharmaceutical development. We also envision that the proposed approach can provide a powerful tool for high-throughput HbA1c sensing in multicomponent mixtures and potentially in hemolysate and whole blood lysate samples.

Raman spectroscopy provides a powerful diagnostic tool for accurate determination of albumin glycation

We present the first demonstration of glycated albumin detection and quantification using Raman spectroscopy without the addition of reagents. Glycated albumin is an important marker for monitoring the long-term glycemic history of diabetics, especially as its concentrations, in contrast to glycated hemoglobin levels, are unaffected by changes in erythrocyte life times. Clinically, glycated albumin concentrations show a strong correlation with the development of serious diabetes complications including nephropathy and retinopathy. In this article, we propose and evaluate the efficacy of Raman spectroscopy for determination of this important analyte. By utilizing the pre-concentration obtained through drop-coating deposition, we show that glycation of albumin leads to subtle, but consistent, changes in vibrational features, which with the help of multivariate classification techniques can be used to discriminate glycated albumin from the unglycated variant with 100% accuracy. Moreover, we demonstrate that the calibration model developed on the glycated albumin spectral dataset shows high predictive power, even at substantially lower concentrations than those typically encountered in clinical practice. In fact, the limit of detection for glycated albumin measurements is calculated to be approximately four times lower than its minimum physiological concentration. Importantly, in relation to the existing detection methods for glycated albumin, the proposed method is also completely reagent-free, requires barely any sample preparation and has the potential for simultaneous determination of glycated hemoglobin levels as well. Given these key advantages, we believe that the proposed approach can provide a uniquely powerful tool for quantification of glycation status of proteins in biopharmaceutical development as well as for glycemic marker determination in routine clinical diagnostics in the future.

The importance of protonation in the investigation of protein phosphorylation using Raman spectroscopy and Raman optical activity

The effect of protonation on amino acid monomers and protein phosphorylation was studied by means of a combination of Raman scattering and Raman optical activity (ROA). In the past, identifying spectral variations in phosphorylated proteins arising from either the phosphate stretch or amide vibrational modes has proven to be challenging mainly due to the loss of amide and P═O band intensity in the presence of phosphate. By contrast, we have developed a novel strategy based on the careful monitoring of the sample pH and thereby modified the protonation state, such that these difficulties can be overcome and phosphate-derived vibrations are readily visualized with both Raman and ROA. Variations in pH-dependent spectral sets of phosphorylated amino acid monomers serine and threonine demonstrated that the protonation state could be determined by the intensity of the monobasic (-OPO(3)H(-)) phosphate stretch band occurring at ~1080 cm(-1) versus the dibasic (-OPO(3)(2-)) band measured at ~980 cm(-1) in both Raman and ROA. Furthermore, by adjustment of the pH of aqueous samples of the phosphoprotein α-casein and comparing this result with dephosphorylated α-casein, spectral variations in phosphate stretch bands and amide bands could be easily determined. Consequently, structural variations due to both protonation and dephosphorylation could be distinguished, demonstrating the potential of Raman and ROA for future investigations of phosphoprotein structure and interactions.