Two-dimensional correlation spectroscopy in protein science, a summary for past 20years

Composition and structure analysis of different depths in the stratum corneum using confocal Raman microscopy combined with two-dimensional correlation spectroscopy

The chemical composition and structure of the stratum corneum (SC) play a crucial role in the skin barrier function. Therefore, accurately determining the SC thickness and studying the changes in lipid and keratin structure and distribution within it are key aspects of skin barrier research. Currently, there are limited analytical tools and data analysis methods available for real-time and online studies of SC composition and structural changes. In this study, we focus on depth as a perturbation and employ confocal Raman microscopy combined with moving-window two-dimensional correlation spectroscopy (MW2D) technique to investigate the SC thickness. Additionally, we employ confocal Raman microscopy combined with perturbation-correlation moving-window two-dimensional correlation spectroscopy (PCMW2D) to precisely characterize the stratification of the SC. Furthermore, the two-dimensional correlation spectroscopy (2DCOS) method is utilized to examine the content of various conformations in the keratin secondary structure within the SC, as well as the subtle interrelationships between lipid and keratin structures.

New progress in the deep understanding of the biocake layer property: Combined effect of neglected protein secondary structure, morphology, and mechanism

The application of magnetic fields (MFs) and magnetic particles (MPs) in water treatment has attracted widespread attention due to their stability, strong biological compatibility, and less chemical consumption. This study introduced MPs and MFs to GDM and probed their effects on filtration performance. Predeposited large MPs (P-large) and batch-added little MPs (B-little) intervened biocake layer development, forming more open and porous structures, they also reduced biomass secretion, resulting in flux increases of 13 % in P-large and 40 % in B-little than P-little, respectively. Besides, MFs controlled MPs distribution on the biocake layer, resulting in forming of more rough and open structures. A relatively lower magnetic field of 20 mT facilitated biomass secretion, while a higher magnetic field of 50 mT decreased biomass. Furthermore, applying magnetic fields decreased the ratios of α-helix and β-sheet, and increased random coil percentage. Thus, applying magnetic field mediation would contribute to the flux improvements in I-20 and I-50 by 29 % and 32 % relative to I-0. Economic analysis suggested introducing MPs and MFs to GDM was economically feasible, synergy of MPs and MFs had more economic advantages on the community scale and MPs-assisted GDM had significant economic advantages on both community and household scales. Future works should focus on developing new technologies for the recycling of MPs and membranes. This study provided new insight into the protein secondary structures associated with GDM performance and would encourage new sustainable MFs and MPs-assisted GDM technological developments.

Rapid Vector-Based Peak Fitting and Resolution Enhancement for Correlation Analyses of Raman Hyperspectra

Spectroscopic peak parameters are important since they provide information about the analyte under study. Besides obtaining these parameters, peak fitting also resolves overlapped peaks. Thus, the obtained parameters should permit the construction of a higher-resolution version of the original spectrum. However, peak fitting is not an easy task due to computational reasons and because the true nature of the analyte is often unknown. These difficulties are major impediments when large hyperspectral data sets need to be processed rapidly, such as for manufacturing process control. We have developed a novel and relatively fast two-part algorithm to perform peak fitting and resolution enhancement on such data sets. In the first part of the algorithm, estimates of the total number of bands and their parameters were obtained from a representative spectrum in the data set, using a combination of techniques. Starting with these parameter estimates, all the spectra were then iteratively and rapidly fitted with Gaussian bands, exploiting intrinsic features of the Gaussian distribution with vector operations. The best fits for each spectrum were retained. By reducing the obtained bandwidths and commensurately increasing their amplitudes, high-resolution spectra were constructed that greatly improved correlation-based analyses. We tested the performance of the algorithm on synthetic spectra to confirm that this method could recover the ground truth correlations between highly overlapped peaks. To assess effective peak resolution, the method was applied to low-resolution spectra of glucose and compared to results from high-resolution spectra. We then processed a larger spectral data set from mammalian cells, fixed with methanol or air drying, to demonstrate the resolution enhancement of the algorithm on complex spectra and the effects of resolution-enhanced spectra on two-dimensional correlation spectroscopy and principal component analyses. The results indicated that the algorithm would allow users to obtain high-resolution spectra relatively fast and permit the recovery of important aspects of the data's intrinsic correlation structure.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.

Probing the interactions between amyloidogenic proteins and bio-membranes

Protein misfolding diseases (PMDs) in humans are characterized by the deposition of protein aggregates in tissues, including Alzheimer's disease, Parkinson's disease, type 2 diabetes, and amyotrophic lateral sclerosis. Misfolding and aggregation of amyloidogenic proteins play a central role in the onset and progression of PMDs, and these processes are regulated by multiple factors, especially the interaction between proteins and bio-membranes. Bio-membranes induce conformational changes in amyloidogenic proteins and affect their aggregation; on the other hand, the aggregates of amyloidogenic proteins may cause membrane damage or dysfunction leading to cytotoxicity. In this review, we summarize the factors that affect the binding of amyloidogenic proteins and membranes, the effects of bio-membranes on the aggregation of amyloidogenic proteins, mechanisms of membrane disruption by amyloidogenic aggregates, technical approaches for detecting these interactions, and finally therapeutic strategies targeting membrane damage caused by amyloidogenic proteins.

Toward Reagent-Free Discrimination of Alzheimer's Disease Using Blood Plasma Spectral Digital Biomarkers and Machine Learning

BACKGROUND

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease. The detection of early-stage AD is particularly desirable because it would allow early intervention. However, a minimally invasive, low-cost, and accurate discrimination or diagnostic method for AD is especially difficult in the earliest stage of AD.

OBJECTIVE

The aim of this research is to discover blood plasma spectral digital biomarkers of AD, develop a novel intelligent method for the discrimination of AD and accelerate the translation of Fourier transform infrared (FTIR) spectral-based disease discrimination methods from the laboratory to clinical practice.

METHODS

Since vibration spectroscopy can provide the structure and chemical composition information of biological samples at the molecular level, we investigated the potential of FTIR spectral biomarkers of blood plasma to differentiate between AD patients and healthy controls. Combined with machine learning technology, we designed a hierarchical discrimination system that provides reagent-free and accurate AD discrimination based on blood plasma spectral digital biomarkers of AD.

RESULTS

Accurate segregation between AD patients and healthy controls was achieved with 89.3% sensitivity and 85.7% specificity for early-stage AD patients, 92.8% sensitivity and 87.5% specificity for middle-stage AD patients, and 100% sensitivity and 100% specificity for late-stage AD patients.

CONCLUSIONS

Our results show that blood plasma spectral digital biomarkers hold great promise as discrimination markers of AD, indicating the potential for the development of an inexpensive, reagent-free, and less laborious clinical test. As a result, our research outcome will accelerate the clinical application of spectral digital biomarkers and machine learning.

Critical Evaluation of Spectral Resolution Enhancement Methods for Raman Hyperspectra

Overlapping peaks in Raman spectra complicate the presentation, interpretation, and analyses of complex samples. This is particularly problematic for methods dependent on sparsity such as multivariate curve resolution and other spectral demixing as well as for two-dimensional correlation spectroscopy (2D-COS), multisource correlation analysis, and principal component analysis. Though software-based resolution enhancement methods can be used to counter such problems, their performances often differ, thereby rendering some more suitable than others for specific tasks. Furthermore, there is a need for automated methods to apply to large numbers of varied hyperspectral data sets containing multiple overlapping peaks, and thus methods ideally suitable for diverse tasks. To investigate these issues, we implemented three novel resolution enhancement methods based on pseudospectra, over-deconvolution, and peak fitting to evaluate them along with three extant methods: node narrowing, blind deconvolution, and the general-purpose peak fitting program Fityk. We first applied the methods to varied synthetic spectra, each consisting of nine overlapping Voigt profile peaks. Improved spectral resolution was evaluated based on several criteria including the separation of overlapping peaks and the preservation of true peak intensities in resolution-enhanced spectra. We then investigated the efficacy of these methods to improve the resolution of measured Raman spectra. High resolution spectra of glucose acquired with a narrow spectrometer slit were compared to ones using a wide slit that degraded the spectral resolution. We also determined the effects of the different resolution enhancement methods on 2D-COS and on chemical contrast image generation from mammalian cell spectra. We conclude with a discussion of the particular benefits, drawbacks, and potential of these methods. Our efforts provided insight into the need for effective resolution enhancement approaches, the feasibility of these methods for automation, the nature of the problems currently limiting their use, and in particular those aspects that need improvement.

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.

Probing Protein Secondary Structure Influence on Active Centers with Hetero Two-Dimensional Correlation (Resonance) Raman Spectroscopy: A Demonstration on Cytochrome C

The functionality of active centers in proteins is governed by the secondary and higher structure of proteins which often lead to structures in the active center that are different from the structures found in protein-free models of the active center. To elucidate this structure-function relationship, it is therefore necessary to investigate both the protein structure and the local structure of the active center. In this work, we investigate the application of hetero (resonance) Raman two-dimensional correlation spectroscopy (2D-COS) to this problem. By employing a combination of near-infrared-Fourier transform-Raman- and vis-resonance Raman spectroscopy, we could show that this combination of techniques is able to directly probe the structure-function relationship of proteins. We were able to correlate the transition of the heme center in cytochrome c from low to high spin with changes in the secondary structure with the above mentioned two spectroscopic in situ techniques and without sample preparation. Thereby, we were able to reveal that the combination of a spectroscopic method to selectively observe the active center with a technique that monitors the whole system offers a promising toolkit to investigate the structure-function relationship of proteins with photoactive centers in general.

Augmented Two-Dimensional Correlation Spectroscopy for the Joint Analysis of Correlated Changes in Spectroscopic and Disparate Sources

Here, we present an augmented form of two-dimensional correlation spectroscopy, that integrates in a single format data from spectroscopic and multiple non-spectroscopic sources for analysis. The integration is affected by augmenting every spectrum in a hyperspectral data set with relevant non-spectroscopic data to permit two-dimensional correlation analysis^(2D-COS) of the ensemble of augmented spectra. A k-means clustering is then applied to the results of the perturbation domain decomposition to determine which Raman peaks cluster with any of the non-spectroscopic data. We introduce and explain the method with the aid of synthetic spectra and synthetic non-spectroscopic data. We then demonstrate this approach with data using Raman spectra from human embryonic stem cell aggregates undergoing directed differentiation toward pancreatic endocrine cells and parallel bioassays of hormone mRNA expression and C-peptide levels in spent medium. These pancreatic endocrine cells generally contain insulin or glucagon. Insulin has disulfide bonds that produce Raman scattering near 513 cm^-1, but no tryptophan. For insulin-positive cells, we found that the application of multisource correlation analysis revealed a high correlation between insulin mRNA and Raman scattering in the disulfide region. In contrast, glucagon has no disulfide bonds but does contain tryptophan. For glucagon-positive cells, we also observed a high correlation between glucagon mRNA and tryptophan Raman scattering (∼757 cm^-1). We conclude with a discussion of methods to enhance spectral resolution and its effects on the performance of multisource correlation analysis.

The molecular insights into protein adsorption on hematite surface disclosed by in-situ ATR-FTIR/2D-COS study

Proteins are of ubiquitous interest in the Life Sciences but are of interest in the Geosciences as well because of the significant role these compounds play in the biogeochemical cycling of trace and nutrient elements. Structural changes resulting from the adsorption of proteins onto mineral surfaces may alter protein biological function and other environmental interactions. Iron oxides are major sinks of a range of environmental elements including organic compounds. In this study, the adsorption of the broadly studied model protein BSA onto the hematite mineral surface was characterized as a function of pH, ionic strength, and BSA concentration using in-situ Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy. BSA lost the α-helix and gain β-sheets in the secondary structure during adsorption on hematite. BSA adsorption was maximum at pH 5, a value close to the BSA isoelectric point (~ pH 5), and lower at pH 4 and pH 7. Increasing ionic strength decreased to total BSA adsorption. Two-dimensional correlation spectroscopy analysis of the ATR-FTIR spectra revealed that higher initial BSA concentration and the consequent higher BSA surface loading enhanced BSA adsorption by protein–protein interaction, which less ordered structures changes into more compact forms decrease, hence compacting the structural arrangement and could promoting multilayers/aggregation formation on the mineral surface. The activity of enzymes following adsorption on mineral surfaces requires further study.

Revealing the Inner Dynamics of Fulvic Acid from Different Compost-Amended Soils through Microbial and Chemical Analyses

The formation of fulvic acid (FA), an aromatic compound, is affected by the compost amendment. This study aimed to assess the extent of the humification of FA in soil amended with seven different composts. Results showed that composts improved the FA concentration in soil. Parallel factor (PARAFAC) analysis, combined with hetero-two-dimensional correlation spectroscopy (hetero-2DCOS), indicated that the inner changes in FA components determined the evolution of mineralization. The diversity in the composts used and the dominant microbes present might be responsible for the evolution of different mechanisms of FA transformation. Structural equation models (SEMs) demonstrated that the FA components were transformed directly by microbes, or indirectly via changes in the total organic carbon (TOC) and total nitrogen (TN) contents, C:N ratio, humic substance (HS) levels, and humic acid (HA): FA ratio, which regulate the microbial community structure. Our results will be useful for improving the bioavailability of compost products and realizing sustainable utilization of the soil.

Structural properties of an engineered outer membrane protein G mutant, OmpG-16SL, investigated with infrared spectroscopy

The structural and functional differences between wild type (WT) outer membrane protein G and its two mutants are investigated with Fourier transform infrared spectroscopy. Both mutants have a long extension to the primary sequence to increase the number of β-strands from 14 (wild type) to 16 in an attempt to enlarge the pore diameter. The comparison among proteins is made in terms of pH-dependent conformational changes and thermal stability. Results show that all proteins respond to pH change but at different degrees. At acidic environment, all proteins contain the same number of residues participated in β-sheet structure. However, at neutral pH, the mutants have less ordered structure compared to WT porin. Thermal stability tests show that mutants differ significantly from WT porin at neutral pH. Although the transition temperature is directly proportional with the amount of β-sheet content, the changes in the pre-transition phase that pave the way to structural breakdown are shown to involve interactions among charged residues by two-dimensional correlation spectroscopy analysis. Results of the analysis show that side chain interactions play an active role under increasing temperature. Both mutants have more unordered secondary structure but they respond to pH change in tertiary structure level. Findings of this study provided deeper insight on the active players in structural stability of the WT porin.Communicated by Ramaswamy H. Sarma [Formula: see text].

Spectroscopic study of uracil, 1-methyluracil and 1-methyl-4-thiouracil: Hydrogen bond interactions in crystals and ab-initio molecular dynamics

Hydrogen bond networks in uracil, 1-methyluracil and 1-methyl-4-thiouracil were studied by ab initio molecular dynamics as well as analysis of the orbital interactions. The power spectra calculated by ab initio molecular dynamics for atoms involved in hydrogen bonds were analyzed. We calculated spectra by using anharmonic approximation based on the autocorrelation function of the atom positions obtained from the Born-Oppenheimer simulations. Our results show the differences between hydrogen bond networks in uracil and its methylated derivatives. The studied methylated derivatives, 1-methyluracil as well as 1-methyl-4-thiouracil, form dimeric structures in the crystal phase, while uracil does not form that kind of structures. The presence of sulfur atom instead oxygen atom reflects weakness of the hydrogen bonds that build dimers.