Unlock Protein Structures with Microfluidic Modulation Spectroscopy in Higher-Order Structure Analysis

Biomolecules such as nucleic acids and proteins fold into complex three-dimensional (3D) structures in physiological settings. Correct molecular structure affects function and activity, which is critical for producing therapeutic drugs. Small structural alterations, including binding pocket re-orientation, may lead to a loss of binding recognition.

X-ray crystallography, cryo-electron microscopy (Cryo-EM), and nuclear magnetic resonance (NMR) are the standard approaches for obtaining a high-resolution protein structure. Although they offer the most optimal resolution of 3D structures, such approaches have labor-intensive workflows and complex data analysis, constraining their usage as daily analytical tools.

All protein structure levels may be considered when a protein structure is characterized. The protein’s secondary structure (i.e., α-helix and β-sheet) has a large quantity of data often ignored by scientists. The secondary structure is tightly linked to aggregate formation: the likelihood of forming β-sheets in a primary sequence dictates the chances of permanent protein aggregation.¹

Determining the concentrations of different secondary structural motifs is thus highly valuable. Like its predecessors AQS³pro and Apollo, the Aurora protein analyzer uses microfluidic modulation spectroscopy (MMS) and is powered by autonomous infrared (IR) spectroscopy technology for protein secondary structure analysis.

Using a quantum cascade laser and microfluidic flow cell, MMS supplies data superior in quality and offers a significant upgrade in sensitivity, dynamic range, and precision for protein analysis compared to current far-UV CD and FTIR approaches, as previously shown.^2,3

MMS was used for this research to calculate the higher-order structure (HOS) of eight common proteins that display a spectrum of secondary structural properties.

Excellent MMS data demonstrated individual properties of each of the proteins, and HOS outcomes were seen in parallel with those calculated via more traditional FTIR, X-ray crystallography, and AlphaFold approaches.^4,5

The results suggested an extremely elevated reproducibility of assessments for the proteins investigated. In the delta analytical software, the proteins’ spectral information was used as model proteins for samples in each class.

Methods

For the research, four α-helix-rich proteins (hemoglobin, bovine serum albumin, lysozyme, and cytochrome C) and four β-sheet-rich proteins (immunoglobulin [IgG], carbonic anhydrase, chymotrypsinogen A, and chymotrypsin A) were assessed. MilliporeSigma provided the proteins in lyophilized powder form, which dissolved in suitable buffers or water (as demonstrated in Table 1).

Proteins were originally formulated at 10 mg/mL concentration and later diluted in a concentration series of 5, 2, 1, 0.5, and 0.1 mg/mL in each of their buffers. Triplicates of sample solution, alongside a referencing buffer, were administered into the RedShiftBio AQS³pro at a flow rate of around 1 µL/s with a backing pressure of 5 psi.

The sample solution and accompanying referencing buffer were adjusted to 1 Hz for background subtraction, and the differential absorbance between the sample solution and the buffer was assessed in the region of the amide I band (1588–1711 cm^-1).

Spectral data and HOS data were processed and quantified using the integrated RedShiftBio delta analytical software.

Table 1. A list of the proteins analyzed in this study with their buffer information. Source: RedShiftBio 

Results

α-Helix-Rich Proteins

The amide I band is the most intense absorption region in the IR spectrum among α-helix structures, dominated by the C=O groups’ stretching vibration in the protein backbone in the 1600–1700 cm^-1 region.

The precise location of the C=O absorption peak is calculated via the protein structure. Polypeptide chains fold into secondary structures because of hydrogen bonding between backbone atoms (oxygen on C=O and hydrogen in N-H). The peptide backbones thus have differing torsion angles and form varying hydrogen bond lengths in different secondary structures. This leads to the C=O group absorbing at varying wavenumbers. For instance, α-Helices absorb at 1656 cm^-1.⁶

Figure 1. Crystal structures of α-helix-rich proteins: hemoglobin (PDB: 2QSS), BSA (PDB: 3V03), lysozyme (PDB: 1DPX), and cytochrome C (PDB: 1HRC). Image Credit: RedShiftBio

These particular structural properties, frequently attained from high-resolution crystal structure information, were precisely calculated via MMS. Figure 2 showcases the second derivative of the MMS spectra attained from the four α-helix-rich proteins with principal peak positions specified.

Figure 2. Second derivative spectra of α-helix-rich proteins: hemoglobin, BSA, lysozyme, and cytochrome C. Inset in the hemoglobin spectra shows the quantitation linearity of the concentrations measured, from 0.1 to 10 mg/mL. Image Credit: RedShiftBio

A significant benefit of MMS is its integrated delta analytical software, which enables quick and easy data processing after acquisition.

HOS analysis is a part of the data processing workflow and offers a direct reading of the respective concentrations of the different secondary structural motifs. The HOS, which includes α-helix, β-sheet, coil (unord), and turn structures, was quantified through Gaussian curve fitting using inverted and baselined plots of the second derivative spectra.⁷

Figure 3 compares these quantitative outcomes with those attained via FTIR, X-ray crystallography, and AlphaFold.^4,5,8,9 MMS and FTIR dictate protein structures in solution, while X-ray approaches dictate these structures when in a solid state.

With lysozyme and cytochrome C, MMS and FTIR found a higher percentage of β-sheets than X-ray and AlphaFold, signaling a higher concentration of β-sheet structures in proteins in solution than in a crystal state. Buffer type and pH can also impact a protein’s secondary structure, as previously explored via MMS [AN-850-0124, AN-850- 0125].

The HOS outcomes ultimately supported each other and demonstrated similar patterns across all four platforms.

Figure 3. Higher order structure bar graphs showing the relative abundance of the secondary structural motifs for each protein, compared across four different structural analysis platforms: MMS, FTIR, X-ray crystallography, and AlphaFold. MMS data shown used the values from the 10 mg/mL samples. Image Credit: RedShiftBio

β-Sheet-Rich Proteins

Like α-helix structures, β-sheets form via hydrogen bonding interactions between the protein backbone. However, they vary according to the types of hydrogen bonding that make way for varying oscillation frequencies of their backbone C=O bonds. From this, it is possible to differentiate α-helix and β-sheet structures based on the shape and position of the related absorption bands.

α-helix structures are typically extremely strong with a narrow absorption band. β-sheet structures can manifest differently in two types and usually display broader absorption bands.

The intramolecular β-sheet is the native structure existing in proteins and absorbs at 1632–1642 cm^-1. When proteins aggregate due to unfolding, native β-sheets can form intermolecular bonds, creating tightly bound β-sheets, like β-amyloids, that absorb at 1618–1624 cm^-1 and 1695–1700 cm^-1.⁶

Figure 4. Crystal structures of β-sheet-rich proteins: IgG (PDB: 5DK3), carbonic anhydrase (PDB: 1V9E), chymotrypsinogen A (PDB: 2CGA), and chymotrypsin A (PDB: 4CHA). Image Credit: RedShiftBio

Figure 4 demonstrates the crystal structures of the β-sheet-rich proteins examined in this study. All structures mainly comprise β-sheet structures and include some α-helix and coil/turn structures. MMS was utilized to distinguish and assess these secondary structures.

Figure 5 highlights the second derivative spectra of these four proteins, and their primary peaks are shown in each plot. Sharp peaks in each spectrum existed between 1635 and 1639 cm^-1. This indicates that intramolecular β-sheets were the primary secondary structure in each protein.

Figure 5. Second derivative spectra of β-sheet-rich proteins: IgG, carbonic anhydrase, chymotrypsinogen A, and chymotrypsin A. Image Credit: RedShiftBio

Chymotrypsinogen A and chymotrypsin A both demonstrated a smaller peak of 1650 cm^-1 assigned to the coil structure existing in these proteins.⁶ Despite their structural similarity—chymotrypsinogen is the inactive precursor of chymotrypsin—there were significantly noticeable spectral differences between the two proteins, which will be a focus of future research.

Figure 6. Higher order structure bar graphs showing the relative abundance of the secondary structural motifs for each protein, compared across four different structural analysis platforms: MMS, FTIR, X-ray crystallography, and AlphaFold. MMS data shown used the values from the 10 mg/mL samples. Note: there is no FTIR data available for chymotrypsinogen A. Image Credit: RedShiftBio

Figure 6 highlights the HOS of β-sheet-rich proteins and compares it with FTIR, X-ray crystallography, and AlphaFold, showing good agreement across all platforms.

Minor differences were visible when contrasting each secondary structural motif. β-sheet concentration in IgG (polyclonal) was predicted to be 64 % via MMS and FTIR and 55 % via X-ray and AlphaFold. Related shifts in the abundance of the β-sheets have been found in other proteins and seemingly stem from variations between solution-based (MMS and FTIR) and solid-state (X-ray) analyses.

Previous research found that protein conformations with more hydrophobic amino acids demonstrate more similarity between crystal and solution-based forms than amino acids, which are more hydrophilic.¹⁰

Data Reproducibility

MMS used a microfluidic cell that adjusted the sample in solution and the referencing buffer every second to engender instantaneous buffer subtraction. The data utilized for spectra and HOS quantifications is, therefore, easily reproducible, as shown in Figure 7, for all concentrations of investigated proteins.

Reproducibility was quantified using the area of overlap between each replicated spectrum and the averaged spectrum.² In this research, all samples at 1 mg/mL concentration regularly displayed a minimum of 98 % reproducibility.

The detection limit for MMS is 0.1 mg/m, indicating a higher reproducibility variance at this specific concentration. Higher sample concentration usually leads to a higher signal-to-noise ratio and, consequently, higher reproducibility. 10 mg/mL samples attained a minimum 99.8 % reproducibility.

This especially high reproducibility showcases the powerful nature of MMS assessments of biomolecule structural changes.

Figure 7. Reproducibility of measurements at different concentrations for each protein. Image Credit: RedShiftBio

Conclusions

A spectrum of common proteins exhibiting various secondary structural properties was assessed via MMS to calculate the HOS, which was subsequently contrasted with different structural characterization and prediction instruments (X-ray, FTIR, AlphaFold, and crystallography).

The established HOS calculated via MMS supports other techniques. As previously shown, small changes were deemed to be caused by variations between solution-based methods (MMS and FTIR) and solid-state approaches (X-ray crystallography).¹⁰ It is also known that buffer conditions, pH, and protein concentration play critical roles in these variations.

The high reproducibility of the measurements (> 99.8 % for 10 mg/mL samples) validated the spectral data’s quality.

The research also grew the library of model proteins within the delta analytical software. This addition will elevate the relevance and precision of the processed information by enabling the usage of suitable model proteins to assess samples whose structures are unknown.

References and Further Reading

1.  Uversky, V. N. et al. (2006) Protein Misfolding, Aggregation, and Conformational Diseases. In Protein Reviews; Springer, 4.
2.  Kendrick, B. S., et al. (2020) Determining Spectroscopic Quantitation Limits for Misfolded Structures. J Pharm Sci. 109(1), pp.933–936. doi. org/10.1016/j.xphs.2019.09.004.
3.  Liu, L. L., et al. (2020) Automated, High-Throughput Infrared Spectroscopy for Secondary Structure Analysis of Protein Biopharmaceuticals. J Pharm Sci, 109(10), pp.3223–3230. doi.org/10.1016/j.xphs.2020.07.030.
4.  Jumper, J., et al. (2021) Highly Accurate Protein Structure Prediction with AlphaFold. Nature, 596(7873), pp.583–589. doi.org/10.1038/s41586-021-03819-2.
5.  Varadi, M., et al. (2022) AlphaFold Protein Structure Database: Massively Expanding the Structural Coverage of Protein-Sequence Space with High-Accuracy Models. Nucleic Acids Res, 50(D1), pp.D439–D444. doi.org/10.1093/nar/ gkab1061.
6.  Dong, A.; Huang, P., et al. (1990) Protein Secondary Structures in Water from Second-Derivative Amide I Infrared Spectra. 29.
7.  Byler, D. M., et al. (1986) Examination of the Secondary Structure of Proteins by Deconvolved FTIR Spectra. Biopolymers, 25(3), pp.469–487.
8.  Yang, H., et al. (2015) Obtaining Information about Protein Secondary Structures in Aqueous Solution Using Fourier Transform IR Spectroscopy. Nat Protoc, 10(3), pp.382–396. doi.org/10.1038/nprot.2015.024.
9.  Kong, J., et al. (2007) Fourier Transform Infrared Spectroscopic Analysis of Protein Secondary Structures. Acta Biochim Biophys Sin (Shanghai), 39(8), pp.549–559. doi.org/10.1111/j.1745- 7270.2007.00320.x.
10.  Sikic, K., et al. Systematic Comparison of Crystal and NMR Protein Structures Deposited in the Protein Data Bank. Open Biochem J, 4, pp.83–95

About RedShiftBio

RedShiftBio is redefining the possibilities for analyzing protein structure and concentration.

RedShiftBio has developed a proprietary life sciences platform combining our Microfluidic Modulation Spectroscopy (MMS) and expertise in high-powered quantum cascade lasers that provide ultra-sensitive and ultra-precise measurements of molecular structure. These structural changes affect critical quality attributes governing the safety, efficacy, and stability of biomolecules and their raw materials. This combination of technologies is available to researchers in our fully-automated Aurora and Apollo systems and is backed by a global network of sales, applications, service, and support teams to address all market needs.

Alongside our commitment to further innovation in the formulations and development space, RedShiftBio also supports biopharmaceutical manufacturing with HaLCon, our bioprocess analytics platform, purpose-built to measure protein titer at time of need.

Led by an experienced management team with a proven track record of success in both large instrumentation companies and commercializing disruptive technologies, RedShiftBio is here to support your research, development, and manufacturing goals. Our instruments can be found in the majority of the leading biopharmaceutical companies and CDMOs in the world. We also run product demonstrations and process samples in the StructIR Lab, located in our Boxborough, MA headquarters, as well as at partner sites including the Wood Centre in Oxford, UK, Spectralys/UCB in Brussels, Belgium, and at Sciex laboratories in Redwood Shores, CA.

RedShiftBio is backed by Waters Corporation, Illumina Ventures, Technology Venture Partners, and one undisclosed leading life science company.

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