Advanced statistical and numerical methods for spectroscopic characterization of protein structural evolution

Near-infrared excitation Raman spectroscopy of colored fabric contaminated with body fluids

Confirmatory identification of dyes in the physical pieces of evidence, such as hair and fabric, is critically important in forensics. This information can be used to demonstrate the link between a person of interest and a crime scene. High performance liquid chromatography is broadly used for dye analysis. However, this technique is destructive and laborious. This problem can be overcome by near-Infrared excitation Raman spectroscopy (NIeRS), non-invasive and non-destructive technique that can be used to determine chemical structure of highly fluorescent dyes. Analyzed fabric materials often possess body fluid stains, which may obscure the accuracy of NIeRS-based identification of dyes. In this study, we investigate the extent to which fabric contamination with body fluids can alter the accuracy of NIeRS. Our results showed that NIeRS coupled with partial-least squared discriminant analysis (PLS-DA) enabled on average 97.6% accurate identification of dyes on fabric contaminated with dry blood, urine and semen. We also found that NIeRS could be used to identify blood, urine and semen on such fabric with 99.4% accuracy. Furthermore, NIeRS could be used to differentiate between wet and dry blood, as well as reveal the presence of blood on washed fabric. These results indicate that NIeRS coupled with PLS-DA could be used as a robust and reliable analytical approach in forensic analysis of fabric.

Characterization of membrane-interaction mechanisms of proteins using vacuum-ultraviolet circular dichroism spectroscopy

Protein-membrane interactions play an important role in various biological phenomena, such as material transport, demyelinating diseases, and antimicrobial activity. We combined vacuum-ultraviolet circular dichroism (VUVCD) spectroscopy with theoretical (e.g., molecular dynamics and neural networks) and polarization experimental (e.g., linear dichroism and fluorescence anisotropy) methods to characterize the membrane interaction mechanisms of three soluble proteins (or peptides). α1 -Acid glycoprotein has the drug-binding ability, but the combination of VUVCD and neural-network method revealed that the membrane interaction causes the extension of helix in the N-terminal region, which reduces the binding ability. Myelin basic protein (MBP) is an essential component of the myelin sheath with a multi-layered structure. Molecular dynamics simulations using a VUVCD-guided system showed that MBP forms two amphiphilic and three non-amphiphilic helices as membrane interaction sites. These multivalent interactions may allow MBP to interact with two opposing membrane leaflets, contributing to the formation of a multi-layered myelin structure. The antimicrobial peptide magainin 2 interacts with the bacterial membrane, causing damage to its structure. VUVCD analysis revealed that the M2 peptides assemble in the membrane and turn into oligomers with a β-strand structure. Linear dichroism and fluorescence anisotropy suggested that the oligomers are inserted into the hydrophobic core of the membrane, disrupting the bacterial membrane. Overall, our findings demonstrate that VUVCD and its combination with theoretical and polarization experimental methods pave the way for unraveling the molecular mechanisms of biological phenomena related to protein-membrane interactions.

A Universal and Accurate Method for Easily Identifying Components in Raman Spectroscopy Based on Deep Learning

Raman spectroscopy has been widely used to provide the structural fingerprint for molecular identification. Due to interference from coexisting components, noise, baseline, and systematic differences between spectrometers, component identification with Raman spectra is challenging, especially for mixtures. In this study, a method entitled DeepRaman has been proposed to solve those problems by combining the comparison ability of a pseudo-Siamese neural network (pSNN) and the input-shape flexibility of spatial pyramid pooling (SPP). DeepRaman was trained, validated, and tested with 41,564 augmented Raman spectra from two databases (pharmaceutical material and S.T. Japan). It can achieve 96.29% accuracy, 98.40% true positive rate (TPR), and 94.36% true negative rate (TNR) on the test set. Another six data sets measured on different instruments were used to evaluate the performance of the proposed method from different aspects. DeepRaman can provide accurate identification results and significantly outperform the hit quality index (HQI) method and other deep learning models. In addition, it performs well in cases of different spectral complexity and low-content components. Once the model is established, it can be used directly on different data sets without retraining or transfer learning. Furthermore, it also obtains promising results for the analysis of surface-enhanced Raman spectroscopy (SERS) data sets and Raman imaging data sets. In summary, it is an accurate, universal, and ready-to-use method for component identification in various application scenarios.

Heat-Induced Conformational Transition Mechanism of Heat Shock Factor 1 Investigated by Tryptophan Probe

A transcriptional regulatory system called heat shock response (HSR) has been developed in eukaryotic cells to maintain proteome homeostasis under various stresses. Heat shock factor-1 (Hsf1) plays a central role in HSR, mainly by upregulating molecular chaperones as a transcription factor. Hsf1 forms a complex with chaperones and exists as a monomer in the resting state under normal conditions. However, upon heat shock, Hsf1 is activated by oligomerization. Thus, oligomerization of Hsf1 is considered an important step in HSR. However, the lack of information about Hsf1 monomer structure in the resting state, as well as the structural change via oligomerization at heat response, impeded the understanding of the thermosensing mechanism through oligomerization. In this study, we applied solution biophysical methods, including fluorescence spectroscopy, nuclear magnetic resonance, and circular dichroism spectroscopy, to investigate the heat-induced conformational transition mechanism of Hsf1 leading to oligomerization. Our study showed that Hsf1 forms an inactive closed conformation mediated by intramolecular contact between leucine zippers (LZs), in which the intermolecular contact between the LZs for oligomerization is prevented. As the temperature increases, Hsf1 changes to an open conformation, where the intramolecular LZ interaction is dissolved so that the LZs can form intermolecular contacts to form oligomers in the active form. Furthermore, since the interaction sites with molecular chaperones and nuclear transporters are also expected to be exposed in the open conformation, the conformational change to the open state can lead to understanding the regulation of Hsf1-mediated stress response through interaction with multiple cellular components.

Spontaneous Refolding of Amyloid Fibrils from One Polymorph to Another Caused by Changes in Environmental Hydrophobicity

Here, we report a new phenomenon in which lysozyme fibrils formed in a solution of acetic acid spontaneously refold to a different polymorph through a disassembled intermediate upon the removal of acetic acid. The structural changes were revealed and characterized by deep-UV resonance Raman spectroscopy, nonresonance Raman spectroscopy, intrinsic tryptophan fluorescence spectroscopy, and atomic force microscopy. A PPII-like structure with highly solvent-exposed tryptophan residues predominates the intermediate aggregates before refolding to polymorph II fibrils. Furthermore, the disulfide (SS) bonds undergo significant rearrangements upon the removal of acetic acid from the lysozyme fibril environment. The main SS bond conformation changes from gauche-gauche-trans in polymorph I to gauche-gauche-gauche in polymorph II. Changing the hydrophobicity of the fibril environment was concluded to be the decisive factor causing the spontaneous refolding of lysozyme fibrils from one polymorph to another upon the removal of acetic acid. Potential biological implications of the discovered phenomenon are discussed.

Laser-based selective BTEX sensing using deep neural networks

A mid-infrared absorption-based laser sensor is developed for selective and simultaneous benzene, toluene, ethylbenzene, and xylenes (BTEX) measurements under ambient conditions. The sensor is based on a distributed feedback inter-band cascade laser emitting near 3.3 µm. Wavelength tuning and deep neural networks were employed to differentiate the broadband absorbance of BTEX species. The sensor was validated with gas mixtures and real-time measurements were demonstrated at a temporal resolution of 1 s. Minimum detection limits for BTEX in air are 8, 20, 5, and 46 ppm, respectively. This sensor can be utilized to monitor BTEX emissions in the petrochemical, rubber, and paint industries to avoid hazardous health effects.

Mechanistic insight into differential interactions of iron oxide nanoparticles with native, glycated albumin and their effect on erythrocytes parameters

Nanoparticles and protein bioconjugates have been studied for multiple biomedical applications. We sought to investigate the interaction and structural modifications of bovine serum albumin (BSA) with iron oxide nanoparticles (IONPs). The IONPs were green synthesized using E. crassipes aqueous leaf extract following characterization using transmission electron microscopy, energy dispersive X-ray analysis and X-Ray Diffraction. Two different concentrations of native/glycated albumin (0.5 and 1.5 mg/ml) with IONPs were allowed to interact for 1 h at 37 °C. Glycation markers, protein modification markers, cellular antioxidant, and hemolysis studies showed structural modifications and conformational changes in albumin due to the presence of IONPs. UV-Visible absorbance resulted in hyperchromic and bathochromic effects of IONPs-BSA conjugates. Fluorescence measurements of tyrosine, tryptophan, advanced glycated end products, and ANS binding assay were promising and quenching effects proved IONPs-BSA conjugate formation. In FTIR of BSA-IONPs, transmittance was increased in amide A and B bands while decreased in amide I and II bands. In summary, native PAGE, HPLC, and FTIR analysis displayed a differential behaviour of IONPs with native and glycated BSA. These results provided an understanding of the interaction and structural modifications of glycated and native BSA which may provide fundamental repercussions in future studies.

Component spectra extraction and quantitative analysis for preservative mixtures by combining terahertz spectroscopy and machine learning

Preservatives are universally used in synergistic combination to enhance antimicrobial effect. Identify compositions and quantify components of preservatives are crucial steps in quality monitoring to guarantee merchandise safety. In the work, three most common preservatives, sorbic acid, potassium sorbate and sodium benzoate, are deliberately mixed in pairs with different mass ratios, which aresupposedto be the "unknown" multicomponent systems and measured by terahertz (THz) time-domain spectroscopy. Subsequently, three major challenges have been accomplished by machine learning methods in this work. The singular value decomposition (SVD) effectively obtains the number of components in mixed preservatives. Then, the component spectra are successfully extracted by non-negative matrix factorization (NMF) and self-modeling mixture analysis (SMMA), which match well with the measured THz spectra of pure reagents. Moreover, the support vector machine for regression (SVR) designed an underlying model to the target components and simultaneously identify contents of each individual component in validation mixtures with decision coefficient R² = 0.989. By taking advantages of the fingerprint-based THz technique and machine learning methods, our approach has been demonstrated the great potential to be served as a useful strategy for detecting preservative mixtures in practical applications.

Formation of β-Strand Oligomers of Antimicrobial Peptide Magainin 2 Contributes to Disruption of Phospholipid Membrane

The antimicrobial peptide magainin 2 (M2) interacts with and induces structural damage in bacterial cell membranes. Although extensive biophysical studies have revealed the interaction mechanism between M2 and membranes, the mechanism of membrane-mediated oligomerization of M2 is controversial. Here, we measured the synchrotron-radiation circular dichroism and linear dichroism (LD) spectra of M2 in dipalmitoyl-phosphatidylglycerol lipid membranes in lipid-to-peptide (L/P) molar ratios from 0–26 to characterize the conformation and orientation of M2 on the membrane. The results showed that M2 changed from random coil to α-helix structures via an intermediate state with increasing L/P ratio. Singular value decomposition analysis supported the presence of the intermediate state, and global fitting analysis revealed that M2 monomers with an α-helix structure assembled and transformed into M2 oligomers with a β-strand-rich structure in the intermediate state. In addition, LD spectra showed the presence of β-strand structures in the intermediate state, disclosing their orientations on the membrane surface. Furthermore, fluorescence spectroscopy showed that the formation of β-strand oligomers destabilized the membrane structure and induced the leakage of calcein molecules entrapped in the membrane. These results suggest that the formation of β-strand oligomers of M2 plays a crucial role in the disruption of the cell membrane.

Raman Spectroscopy and Machine Learning for Agricultural Applications: Chemometric Assessment of Spectroscopic Signatures of Plants as the Essential Step Toward Digital Farming

A growing body of evidence suggests that Raman spectroscopy (RS) can be used for diagnostics of plant biotic and abiotic stresses. RS can be also utilized for identification of plant species and their varieties, as well as assessment of the nutritional content and commercial values of seeds. The power of RS in such cases to a large extent depends on chemometric analyses of spectra. In this work, we critically discuss three major approaches that can be used for advanced analyses of spectroscopic data: summary statistics, statistical testing and chemometric classification. On the example of Raman spectra collected from roses, we demonstrate the outcomes and the potential of all three types of spectral analyses. We anticipate that our findings will help to design the most optimal spectral processing and preprocessing that is required to achieved the desired results. We also expect that reported collection of results will be useful to all researchers who work on spectroscopic analyses of plant specimens.

Improved folding of recombinant protein via co-expression of exogenous chaperones

Expression of heterologous genes in Escherichia coli is a routine technology for recombinant protein production, but the predictable recovery of properly folded and uniformly bioactive material remains a challenge. Misfolded proteins typically accumulate as insoluble inclusion bodies, and a variety of strategies have been employed in efforts to increase the yield of soluble product. One technique is the overexpression of E. coli protein chaperones during recombinant protein induction, in an effort to increase the folding capacity of the bacterial host. We have developed an alternative approach, by supplementing the host protein folding machinery with chaperones from other species. Extremophiles have evolved under conditions (extremes of temperature, salinity, pressure, and/or pH) that make them attractive candidates for possessing chaperones with novel folding activities. The green fluorescent protein (GFP) of Aequorea victoria, which is predominantly insoluble under typical recombinant expression culture conditions, was employed as an in vivo indicator of protein folding activity for chaperone homologs from a variety of extremophiles. For a subset of the chaperones tested, co-expression with GFP promoted an increase in both fluorescence signal intensity as well as the amount of GFP recovered in the soluble protein fraction. Several archaeal chaperones were also found to be able to refold soluble Lyt_Orn C40 peptidase from inclusion bodies in vitro. In particular, Pf Cpn(MA), a mutant chaperonin which exhibited significant refolding activity, is also shown to deconstruct the morphology and structure of inclusion bodies (Kurouski et al., 2012). Hence, the simple and rapid GFP assay provides a tool to screen for extremophilic chaperones that exhibit folding activity under E. coli growth conditions, and suggests that increasing the repertoire of heterologous chaperones might provide a partial but general solution to the problem of recombinant protein insolubility.

Raman spectroscopy-based diagnostics of water deficit and salinity stresses in two accessions of peanut

Water deficit and salinity are two major abiotic stresses that have tremendous effect on crop yield worldwide. Timely identification of these stresses can help limit associated yield loss. Confirmatory detection and identification of water deficit stress can also enable proper irrigation management. Traditionally, unmanned aerial vehicle (UAV)-based imaging and satellite-based imaging, together with visual field observation, are used for diagnostics of such stresses. However, these approaches can only detect salinity and water deficit stress at the symptomatic stage. Raman spectroscopy (RS) is a noninvasive and nondestructive technique that can identify and detect plant biotic and abiotic stress. In this study, we investigated accuracy of Raman-based diagnostics of water deficit and salinity stresses on two greenhouse-grown peanut accessions: tolerant and susceptible to water deficit. Plants were grown for 76 days prior to application of the water deficit and salinity stresses. Water deficit treatments received no irrigation for 5 days, and salinity treatments received 1.0 L of 240-mM salt water per day for the duration of 5-day sampling. Every day after the stress was imposed, plant leaves were collected and immediately analyzed by a hand-held Raman spectrometer. RS and chemometrics could identify control and stressed (either water deficit or salinity) susceptible plants with 95% and 80% accuracy just 1 day after treatment. Water deficit and salinity stressed plants could be differentiated from each other with 87% and 86% accuracy, respectively. In the tolerant accessions at the same timepoint, the identification accuracies were 66%, 65%, 67%, and 69% for control, combined stresses, water deficit, and salinity stresses, respectively. The high selectivity and specificity for presymptomatic identification of abiotic stresses in the susceptible line provide evidence for the potential of Raman-based surveillance in commercial-scale agriculture and digital farming.

Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.

ATR-FTIR spectroscopy and spectroscopic imaging for the analysis of biopharmaceuticals

Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a label-free, non-destructive technique that can be applied to a vast range of biological applications, from imaging cancer tissues and live cells, to determining protein content and protein secondary structure composition. This review summarises the recent advances in applications of ATR-FTIR spectroscopy to biopharmaceuticals, the application of this technique to biosimilars, and the current uses of FTIR spectroscopy in biopharmaceutical production. We discuss the use of ATR-FTIR spectroscopic imaging to investigate biopharmaceuticals, and finally, give an outlook on the possible future developments and applications of ATR-FTIR spectroscopy and spectroscopic imaging to this field. Throughout the review comparisons will be made between FTIR spectroscopy and alternative analytical techniques, and areas will be identified where FTIR spectroscopy could perhaps offer a better alternative in future studies. This review focuses on the most recent advances in the field of using ATR-FTIR spectroscopy and spectroscopic imaging to characterise and evaluate biopharmaceuticals, both in industrial and academic research based environments.

Probing protein-induced membrane fouling with in-situ attenuated total reflectance fourier transform infrared spectroscopy and multivariate curve resolution-alternating least squares

Proteins are one of the major contributors to membrane fouling. The interaction between proteins and the polymer membrane at the molecular level is essential for the alleviation/prevention of membrane fouling, but remains unclear. In this work, time-dependent in-situ attenuated total reflectance Fourier transform infrared spectroscopy is applied to investigate the interaction process between two model proteins, bovine serum albumin and lysozyme, and the poly(vinylidene fluoride) (PVDF) membrane. Multivariate curve resolution-alternating least squares is integrated with two-dimensional correlation spectroscopy analysis to resolve the membrane-induced conformational changes of proteins. The multivariate curve resolution-alternating least squares analysis reveals a two-step process in the protein-membrane interaction and provides the kinetics of the conformational transition, which aids the segmentation of the spectral dataset. By applying two-dimensional correlation spectroscopy analysis to different groups of the time-dependent spectra, the sequential order of the secondary structural changes of proteins is determined. The proteins initially undergo unfolding transition to a more open, less structured state, which appears to be triggered by the hydrophobic membrane surface. Afterwards, the proteins become aggregated with the high anti-parallel β-sheet content, aggravating the membrane fouling. The conformational transition process of proteins was also confirmed by the atomic force microscopic images and quartz crystal microbalance measurement. Overall, this work provides an in-depth understanding of the interaction between proteins and the membrane surface, which is helpful for the development of membrane anti-fouling strategies.

Raman Spectroscopy vs Quantitative Polymerase Chain Reaction In Early Stage Huanglongbing Diagnostics

Raman spectroscopy (RS) is an emerging analytical technique that can be used to develop and deploy precision agriculture. RS allows for confirmatory diagnostic of biotic and abiotic stresses on plants. Specifically, RS can be used for Huanglongbing (HLB) diagnostics on both orange and grapefruit trees, as well as detection and identification of various fungal and viral diseases. The questions that remain to be answered is how early can RS detect and identify the disease and whether RS is more sensitive than qPCR, the "golden standard" in pathogen diagnostics? Using RS and HLB as case study, we monitored healthy (qPCR-negative) in-field grown citrus trees and compared their spectra to the spectra collected from healthy orange and grapefruit trees grown in a greenhouse with restricted insect access and confirmed as HLB free by qPCR. Our result indicated that RS was capable of early prediction of HLB and that nearly all in-field qPCR-negative plants were infected by the disease. Using advanced multivariate statistical analysis, we also showed that qPCR-negative plants exhibited HLB-specific spectral characteristics that can be distinguished from unrelated nutrition deficit characteristics. These results demonstrate that RS is capable of much more sensitive diagnostics of HLB compared to qPCR.

Infrared Difference Spectroscopy of Proteins: From Bands to Bonds

Infrared difference spectroscopy probes vibrational changes of proteins upon their perturbation. Compared with other spectroscopic methods, it stands out by its sensitivity to the protonation state, H-bonding, and the conformation of different groups in proteins, including the peptide backbone, amino acid side chains, internal water molecules, or cofactors. In particular, the detection of protonation and H-bonding changes in a time-resolved manner, not easily obtained by other techniques, is one of the most successful applications of IR difference spectroscopy. The present review deals with the use of perturbations designed to specifically change the protein between two (or more) functionally relevant states, a strategy often referred to as reaction-induced IR difference spectroscopy. In the first half of this contribution, I review the technique of reaction-induced IR difference spectroscopy of proteins, with special emphasis given to the preparation of suitable samples and their characterization, strategies for the perturbation of proteins, and methodologies for time-resolved measurements (from nanoseconds to minutes). The second half of this contribution focuses on the spectral interpretation. It starts by reviewing how changes in H-bonding, medium polarity, and vibrational coupling affect vibrational frequencies, intensities, and bandwidths. It is followed by band assignments, a crucial aspect mostly performed with the help of isotopic labeling and site-directed mutagenesis, and complemented by integration and interpretation of the results in the context of the studied protein, an aspect increasingly supported by spectral calculations. Selected examples from the literature, predominately but not exclusively from retinal proteins, are used to illustrate the topics covered in this review.

Library-assisted nonlinear blind separation and annotation of pure components from a single 1H nuclear magnetic resonance mixture spectra

Due to its capability for high-throughput screening ¹H nuclear magnetic resonance (NMR) spectroscopy is commonly used for metabolite research. The key problem in ¹H NMR spectroscopy of multicomponent mixtures is overlapping of component signals and that is increasing with the number of components, their complexity and structural similarity. It makes metabolic profiling, that is carried out through matching acquired spectra with metabolites from the library, a hard problem. Here, we propose a method for nonlinear blind separation of highly correlated components spectra from a single ¹H NMR mixture spectra. The method transforms a single nonlinear mixture into multiple high-dimensional reproducible kernel Hilbert Spaces (mRKHSs). Therein, highly correlated components are separated by sparseness constrained nonnegative matrix factorization in each induced RKHS. Afterwards, metabolites are identified through comparison of separated components with the library comprised of 160 pure components. Thereby, a significant number of them are expected to be related with diabetes type 2. Conceptually similar methodology for nonlinear blind separation of correlated components from two or more mixtures is presented in the Supplementary material. Single-mixture blind source separation is exemplified on: (i) annotation of five components spectra separated from one ¹H NMR model mixture spectra; (ii) annotation of fifty five metabolites separated from one ¹H NMR mixture spectra of urine of subjects with and without diabetes type 2. Arguably, it is for the first time a method for blind separation of a large number of components from a single nonlinear mixture has been proposed. Moreover, the proposed method pinpoints urinary creatine, glutamic acid and 5-hydroxyindoleacetic acid as the most prominent metabolites in samples from subjects with diabetes type 2, when compared to healthy controls.

Interdisciplinary Round-Robin Test on Molecular Spectroscopy of the U(VI) Acetate System

A comprehensive molecular analysis of a simple aqueous complexing system-U(VI) acetate-selected to be independently investigated by various spectroscopic (vibrational, luminescence, X-ray absorption, and nuclear magnetic resonance spectroscopy) and quantum chemical methods was achieved by an international round-robin test (RRT). Twenty laboratories from six different countries with a focus on actinide or geochemical research participated and contributed to this scientific endeavor. The outcomes of this RRT were considered on two levels of complexity: first, within each technical discipline, conformities as well as discrepancies of the results and their sources were evaluated. The raw data from the different experimental approaches were found to be generally consistent. In particular, for complex setups such as accelerator-based X-ray absorption spectroscopy, the agreement between the raw data was high. By contrast, luminescence spectroscopic data turned out to be strongly related to the chosen acquisition parameters. Second, the potentials and limitations of coupling various spectroscopic and theoretical approaches for the comprehensive study of actinide molecular complexes were assessed. Previous spectroscopic data from the literature were revised and the benchmark data on the U(VI) acetate system provided an unambiguous molecular interpretation based on the correlation of spectroscopic and theoretical results. The multimethodologic approach and the conclusions drawn address not only important aspects of actinide spectroscopy but particularly general aspects of modern molecular analytical chemistry.

Rapid and noninvasive diagnostics of Huanglongbing and nutrient deficits on citrus trees with a handheld Raman spectrometer

Huanglongbing (HLB) or citrus greening is a devastating disease of citrus trees that is caused by the gram-negative Candidatus Liberibacter spp. bacteria. The bacteria are phloem limited and transmitted by the Asian citrus psyllid, Diaphorina citri, and the African citrus psyllid, Trioza erytreae, which allows for a wider dissemination of HLB. Infected trees exhibit yellowing of leaves, premature leaf and fruit drop, and ultimately the death of the entire plant. Polymerase chain reaction (PCR) and antibody-based assays (ELISA and/or immunoblot) are commonly used methods for HLB diagnostics. However, they are costly, time-consuming, and destructive to the sample and often not sensitive enough to detect the pathogen very early in the infection stage. Raman spectroscopy (RS) is a noninvasive, nondestructive, analytical technique which provides insight into the chemical structures of a specimen. In this study, by using a handheld Raman system in combination with chemometric analyses, we can readily distinguish between healthy and HLB (early and late stage)-infected citrus trees, as well as plants suffering from nutrient deficits. The detection rate of Raman-based diagnostics of healthy vs HLB infected vs nutrient deficit is ~ 98% for grapefruit and ~ 87% for orange trees, whereas the accuracy of early- vs late-stage HLB infected is 100% for grapefruits and ~94% for oranges. This analysis is portable and sample agnostic, suggesting that it could be utilized for other crops and conducted autonomously. Graphical abstract.

Deep learning-based component identification for the Raman spectra of mixtures

Raman spectroscopy is widely used as a fingerprint technique for molecular identification. However, Raman spectra contain molecular information from multiple components and interferences from noise and instrumentation. Thus, component identification using Raman spectra is still challenging, especially for mixtures. In this study, a novel approach entitled deep learning-based component identification (DeepCID) was proposed to solve this problem. Convolution neural network (CNN) models were established to predict the presence of components in mixtures. Comparative studies showed that DeepCID could learn spectral features and identify components in both simulated and real Raman spectral datasets of mixtures with higher accuracy and significantly lower false positive rates. In addition, DeepCID showed better sensitivity when compared with the logistic regression (LR) with L1-regularization, k-nearest neighbor (kNN), random forest (RF) and back propagation artificial neural network (BP-ANN) models for ternary mixture spectral datasets. In conclusion, DeepCID is a promising method for solving the component identification problem in the Raman spectra of mixtures.

Engaging with Raman Spectroscopy to Investigate Antibody Aggregation

In the last decade, a number of studies have successfully demonstrated Raman spectroscopy as an emerging analytical technique for monitoring antibody aggregation, especially in the context of drug development and formulation. Raman spectroscopy is a robust method for investigating protein conformational changes, even in highly concentrated antibody solutions. It is non-destructive, reproducible and can probe samples in an aqueous environment. In this review, we focus on the application and challenges associated with using Raman spectroscopy as a tool to study antibody aggregates.

Detection and Identification of Plant Pathogens on Maize Kernels with a Hand-Held Raman Spectrometer

Rapid detection and identification of crop pathogens is essential for improving crop yield. Typical pathogen assaying methods, such as polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA), are time-consuming and destructive to the sample. Raman spectroscopy (RS) is a noninvasive nondestructive analytical technique that provides insight on the chemical structure of the specimen. In this study, we demonstrate that using a hand-held Raman spectrometer, in combination with chemometric analyses, we can distinguish between healthy and diseased maize ( Zea mays) kernels, as well as between different diseases with 100% accuracy. Our analysis is portable and sample-agnostic, suggesting that it could be retooled for other crops and conducted autonomously.

Interaction between 8-methoxypsoralen and trypsin: Monitoring by spectroscopic, chemometrics and molecular docking approaches

8-Methoxypsoralen (8-MOP) is a naturally occurring furanocoumarin with various biological activities. However, there is little information on the binding mechanism of 8-MOP with trypsin. Here, the interaction between 8-MOP and trypsin in vitro was determined by multi-spectroscopic methods combined with the multivariate curve resolution-alternating least squares (MCR-ALS) chemometrics approach. An expanded UV-vis spectral data matrix was analysed by MCR-ALS, the concentration profiles and pure spectra for the three reaction species (trypsin, 8-MOP and 8-MOP-trypsin) were obtained to monitor the interaction between 8-MOP and trypsin. The fluorescence data suggested that a static type of quenching mechanism occurred in the binding of 8-MOP to trypsin. Hydrophobic interaction dominated the formation of the 8-MOP-trypsin complex on account of the positive enthalpy and entropy changes, and trypsin had one high affinity binding site for 8-MOP with a binding constant of 3.81×10⁴Lmol^-1 at 298K. Analysis of three dimensional fluorescence, UV-vis absorption and circular dichroism spectra indicated that the addition of 8-MOP induced the rearrangement of the polypeptides carbonyl hydrogen-bonding network and the conformational changes in trypsin. The molecular docking predicted that 8-MOP interacted with the catalytic residues His57, Asp102 and Ser195 in trypsin. The binding patterns and trypsin conformational changes may result in the inhibition of trypsin activity. This study has provided insights into the binding mechanism of 8-MOP with trypsin.

Simultaneous Analysis of Secondary Structure and Light Scattering from Circular Dichroism Titrations: Application to Vectofusin-1

Circular Dichroism data are often decomposed into their constituent spectra to quantify the secondary structure of peptides or proteins but the estimation of the secondary structure content fails when light scattering leads to spectral distortion. If peptide-induced liposome self-association occurs, subtracting control curves cannot correct for this. We show that if the cause of the light scattering is independent from the peptide structural changes, the CD spectra can be corrected using principal component analysis (PCA). The light scattering itself is analysed and found to be in good agreement with backscattering experiments. This method therefore allows to simultaneously follow structural changes related to peptide-liposome binding as well as peptide induced liposome self-association. We apply this method to study the structural changes and liposome binding of vectofusin-1, a transduction enhancing peptide used in lentivirus based gene therapy. Vectofusin-1 binds to POPC/POPS liposomes, causing a reversal of the negative liposome charge at high peptide concentrations. When the peptide charges exactly neutralise the lipid charges on both leaflets reversible liposome self-association occurs. These results are in good agreement with biological observations and provide further insight into the conditions required for efficent transduction enhancement.

A New Method to Determine the Transmembrane Conformation of Substrates in Intramembrane Proteolysis by Deep-UV Resonance Raman Spectroscopy

We present a new method based on deep-UV resonance Raman spectroscopy to determine the backbone conformation of intramembrane protease substrates. The classical amide vibrational modes reporting on the conformation of just the transmembrane region of the substrate can be resolved from solvent exchangeable regions outside the detergent micelle by partial deuteration of the solvent. In the presence of isotopically triple-labeled intramembrane protease, these amide modes can be accurately measured to monitor the transmembrane conformation of the substrate during intramembrane proteolysis.

Exploring the structure and formation mechanism of amyloid fibrils by Raman spectroscopy: a review

Amyloid fibrils are β-sheet rich protein aggregates that are strongly associated with various neurodegenerative diseases. Raman spectroscopy has been broadly utilized to investigate protein aggregation and amyloid fibril formation and has been shown to be capable of revealing changes in secondary and tertiary structures at all stages of fibrillation. When coupled with atomic force (AFM) and scanning electron (SEM) microscopies, Raman spectroscopy becomes a powerful spectroscopic approach that can investigate the structural organization of amyloid fibril polymorphs. In this review, we discuss the applications of Raman spectroscopy, a unique, label-free and non-destructive technique for the structural characterization of amyloidogenic proteins, prefibrilar oligomers, and mature fibrils.

Deep UV Resonance Raman Spectroscopy for Characterizing Amyloid Aggregation

Deep UV resonance Raman spectroscopy is a powerful technique for probing the structure and formation mechanism of protein fibrils, which are traditionally difficult to study with other techniques owing to their low solubility and noncrystalline arrangement. Utilizing a tunable deep UV Raman system allows for selective enhancement of different chromophores in protein fibrils, which provides detailed information on different aspects of the fibrils' structure and formation. Additional information can be extracted with the use of advanced data treatment such as chemometrics and 2D correlation spectroscopy. In this chapter we give an overview of several techniques for utilizing deep UV resonance Raman spectroscopy to study the structure and mechanism of formation of protein fibrils. Clever use of hydrogen-deuterium exchange can elucidate the structure of the fibril core. Selective enhancement of aromatic amino acid side chains provides information about the local environment and protein tertiary structure. The mechanism of protein fibril formation can be investigated with kinetic experiments and advanced chemometrics.

Rapid Filament Supramolecular Chirality Reversal of HET-s (218-289) Prion Fibrils Driven by pH Elevation

Amyloid fibril polymorphism is not well understood despite its potential importance for biological activity and associated toxicity. Controlling the polymorphism of mature fibrils including their morphology and supramolecular chirality by postfibrillation changes in the local environment is the subject of this study. Specifically, the effect of pH on the stability and dynamics of HET-s (218-289) prion fibrils has been determined through the use of vibrational circular dichroism (VCD), deep UV resonance Raman, and fluorescence spectroscopies. It was found that a change in solution pH causes deprotonation of Asp and Glu amino acid residues on the surface of HET-s (218-289) prion fibrils and triggers rapid transformation of one supramolecular chiral polymorph into another. This process involves changes in higher order arrangements like lateral filament and fibril association and their supramolecular chirality, while the fibril cross-β core remains intact. This work suggests a hypothetical mechanism for HET-s (218-289) prion fibril refolding and proposes that the interconversion between fibril polymorphs driven by the solution environment change is a general property of amyloid fibrils.

Identifying the lineages of individual cells in cocultures by multivariate analysis of Raman spectra

The cellular and matrix cues that induce stem cell differentiation into distinct cell lineages must be identified to permit the ex vivo expansion of desired cell populations for clinical applications. Combinatorial biomaterials enable screening multiple different microenvironments while using small numbers of rare stem cells. New methods to identify the phenotypes of individual cells in cocultures with location specificity would increase the efficiency and throughput of these screening platforms. Here, we demonstrate that partial least-squares discriminant analysis (PLS-DA) models of calibration Raman spectra from cells in pure cultures can be used to identify the lineages of individual cells in more complex culture environments. The calibration Raman spectra were collected from individual cells of four different lineages, and a PLS-DA model that captured the Raman spectral profiles characteristic of each cell line was created. The application of these models to Raman spectra from test sets of cells indicated individual, fixed and living cells in separate monocultures, as well as those in more complex culture environments, such as cocultures, could be identified with low error. Cells from populations with very similar biochemistries could also be identified with high accuracy. We show that these identifications are based on reproducible cell-related spectral features, and not spectral contributions from the culture environment. This work demonstrates that PLS-DA of Raman spectra acquired from pure monocultures provides an objective, noninvasive, and label-free approach for accurately identifying the lineages of individual, living cells in more complex coculture environments.

Is supramolecular filament chirality the underlying cause of major morphology differences in amyloid fibrils?

The unique enhanced sensitivity of vibrational circular dichroism (VCD) to the formation and development of amyloid fibrils in solution is extended to four additional fibril-forming proteins or peptides where it is shown that the sign of the fibril VCD pattern correlates with the sense of supramolecular filament chirality and, without exception, to the dominant fibril morphology as observed in AFM or SEM images. Previously for insulin, it has been demonstrated that the sign of the VCD band pattern from filament chirality can be controlled by adjusting the pH of the incubating solution, above pH 2 for “normal” left-hand-helical filaments and below pH 2 for “reversed” right-hand-helical filaments. From AFM or SEM images, left-helical filaments form multifilament braids of left-twisted fibrils while the right-helical filaments form parallel filament rows of fibrils with a flat tape-like morphology, the two major classes of fibril morphology that from deep UV resonance Raman scattering exhibit the same cross-β-core secondary structure. Here we investigate whether fibril supramolecular chirality is the underlying cause of the major morphology differences in all amyloid fibrils by showing that the morphology (twisted versus flat) of fibrils of lysozyme, apo-α-lactalbumin, HET-s (218–289) prion, and a short polypeptide fragment of transthyretin, TTR (105–115), directly correlates to their supramolecular chirality as revealed by VCD. The result is strong evidence that the chiral supramolecular organization of filaments is the principal underlying cause of the morphological heterogeneity of amyloid fibrils. Because fibril morphology is linked to cell toxicity, the chirality of amyloid aggregates should be explored in the widely used in vitro models of amyloid-associated diseases.

Recent applications of ATR FTIR spectroscopy and imaging to proteins

Attenuated Total Reflection (ATR) Fourier Transform Infrared (FTIR) spectroscopy is a label-free, non-destructive analytical technique that can be used extensively to study a wide variety of different molecules in a range of different conditions. The aim of this review is to discuss and highlight the recent advances in the applications of ATR FTIR spectroscopic imaging to proteins. It briefly covers the basic principles of ATR FTIR spectroscopy and ATR FTIR spectroscopic imaging as well as their advantages to the study of proteins compared to other techniques and other forms of FTIR spectroscopy. It will then go on to examine the advances that have been made within the field over the last several years, particularly the use of ATR FTIR spectroscopy for the understanding and development of protein interaction with surfaces. Additionally, the growing potential of Surface Enhanced Infrared Spectroscopy (SEIRAS) within this area of applications will be discussed. The review includes the applications of ATR FTIR imaging to protein crystallisation and for high-throughput studies, highlighting the future potential of the technology within the field of protein structural studies and beyond.

Sensitive metal ions (II) determination with resonance Raman method

In this paper, a new proposal for the quantitative evaluation of divalent metal ions (M(2+)) is developed by the use of the competitive resonance Raman (RR)-based method. Upon excitation with light of the appropriate wavelength (532 nm), a strong electric field is generated that couples with the resonance of the complex (zincon-M(2+)), increasing the character signals of these complexes, resulting in sensitive detection. Herein, the RR probe, zincon-M(2+) complex that the RR intensity gets lower with the decreasing of the M(2+) concentration, which leads to the transformation of the Raman information. As a result, by using the proposed RR-based method, we could find the liner calibration curves of Cu(2+) and Ni(2+), which show the potential in quantitative evaluation of an unknown sample. In addition, the abundant fingerprint information shows that RR leads to the successful analysis of a blended solution, which contains two ions: Cu(2+) and Ni(2+).

Deep-ultraviolet (UV) resonance raman spectroscopy as a tool for quality control of formulated therapeutic proteins

A deep-ultraviolet (UV) Raman spectrometer with excitation source tunable from 193 to 210 nm has been built and characterized. The dispersion of the spectrometer over the entire range was measured and described theoretically. The relative sensitivity of the spectrometer was estimated using the integrated intensity ratio of two Raman bands of cyclohexane. Resonance Raman spectra of three formulated insulin products were measured and compared. A band-targeted entropy minimization algorithm was applied to the collected spectra for mixture analysis of insulin products. We conclude that it is feasible to develop robust qualitative methods for quality control of protein-based formulated drug using DUVRR spectroscopy.

Disulfide bridges remain intact while native insulin converts into amyloid fibrils

Amyloid fibrils are β-sheet-rich protein aggregates commonly found in the organs and tissues of patients with various amyloid-associated diseases. Understanding the structural organization of amyloid fibrils can be beneficial for the search of drugs to successfully treat diseases associated with protein misfolding. The structure of insulin fibrils was characterized by deep ultraviolet resonance Raman (DUVRR) and Nuclear Magnetic Resonance (NMR) spectroscopy combined with hydrogen-deuterium exchange. The compositions of the fibril core and unordered parts were determined at single amino acid residue resolution. All three disulfide bonds of native insulin remained intact during the aggregation process, withstanding scrambling. Three out of four tyrosine residues were packed into the fibril core, and another aromatic amino acid, phenylalanine, was located in the unordered parts of insulin fibrils. In addition, using all-atom MD simulations, the disulfide bonds were confirmed to remain intact in the insulin dimer, which mimics the fibrillar form of insulin.

Fibrillation mechanism of a model intrinsically disordered protein revealed by 2D correlation deep UV resonance Raman spectroscopy

Understanding of numerous biological functions of intrinsically disordered proteins (IDPs) is of significant interest to modern life science research. A large variety of serious debilitating diseases are associated with the malfunction of IDPs including neurodegenerative disorders and systemic amyloidosis. Here we report on the molecular mechanism of amyloid fibrillation of a model IDP (YE8) using 2D correlation deep UV resonance Raman spectroscopy. YE8 is a genetically engineered polypeptide, which is completely unordered at neutral pH yet exhibits all properties of a fibrillogenic protein at low pH. The very first step of the fibrillation process involves structural rearrangements of YE8 at the global structure level without the detectable appearance of secondary structural elements. The formation of β-sheet species follows the global structural changes and proceeds via the simultaneous formation of turns and β-strands. The kinetic mechanism revealed is an important new contribution to understanding of the general fibrillation mechanism proposed for IDP.

Elucidating Peptide and Protein Structure and Dynamics: UV Resonance Raman Spectroscopy

UV resonance Raman spectroscopy (UVRR) is a powerful method that has the requisite selectivity and sensitivity to incisively monitor biomolecular structure and dynamics in solution. In this perspective, we highlight applications of UVRR for studying peptide and protein structure and the dynamics of protein and peptide folding. UVRR spectral monitors of protein secondary structure, such as the Amide III(3) band and the C(α)-H band frequencies and intensities can be used to determine Ramachandran Ψ angle distributions for peptide bonds. These incisive, quantitative glimpses into conformation can be combined with kinetic T-jump methodologies to monitor the dynamics of biomolecular conformational transitions. The resulting UVRR structural insight is impressive in that it allows differentiation of, for example, different α-helix-like states that enable differentiating π- and 3(10)- states from pure α-helices. These approaches can be used to determine the Gibbs free energy landscape of individual peptide bonds along the most important protein (un)folding coordinate. Future work will find spectral monitors that probe peptide bond activation barriers that control protein (un)folding mechanisms. In addition, UVRR studies of sidechain vibrations will probe the role of side chains in determining protein secondary, tertiary and quaternary structures.