Application of multivariate curve resolution-alternating least squares (MCR-ALS) for secondary structure resolving of proteins

Progress in infrared spectroscopy as an efficient tool for predicting protein secondary structure

Infrared (IR) spectroscopy is a highly sensitive technique that provides complete information on chemical compositions. The IR spectra of proteins or peptides give rise to nine characteristic IR absorption bands. The amide I bands are the most prominent and sensitive vibrational bands and widely used to predict protein secondary structures. The interference of H₂O absorbance is the greatest challenge for IR protein secondary structure prediction. Much effort has been made to reduce/eliminate the interference of H₂O, simplify operation steps, and increase prediction accuracy. Progress in sampling and equipment has rendered the Fourier transform infrared (FTIR) technique suitable for determining the protein secondary structure in broader concentration ranges, greatly simplifying the operating steps. This review highlights the recent progress in sample preparation, data analysis, and equipment development of FTIR in A/T mode, with a focus on recent applications of FTIR spectroscopy in the prediction of protein secondary structure. This review also provides a brief introduction of the progress in ATR-FTIR for predicting protein secondary structure and discusses some combined IR methods, such as AFM-based IR spectroscopy, that are used to analyze protein structural dynamics and protein aggregation.

An innovative spectroscopic approach for qualitative and quantitative evaluation of Mb-CO from myoglobin carbonylation reaction through chemometrics methods

In this work, an innovative approach using K-means and multivariate curve resolution-purity based algorithm (MCR-Purity) for the evaluation and quantification of carboxymyoglobin (Mb-CO) formation from Deoxy-Myoglobin (Deoxy-Mb) was presented. Through a multilevel multifactor experimental design, samples with different concentrations of Mb-CO were created. The UV-Vis spectra of these samples were submitted to K-means analysis, finding 3 clusters. The mean spectra of the clusters were extracted and it was possible to detect 2 totally differentiable groups through peaks 423 and 434 nm, which are wavelengths related to the Mb-CO and Deoxy-Mb components, respectively. The spectral data were subjected to MCR-Purity analysis. The MCR-Purity result successfully described the analyzed reaction, explaining more than 99.9% of the variance (R²) with a LOF of 1.43%. Then, a predictive model of MbCO was created through the linear relationship between MCR-Purity contributions and known concentrations of MbCO. The performance parameters of the created predictive model were R²CV = 0.98, RMSECV = 0.58 and RPDcv = 7.8 for the training set, and R²P = 0.98, RMSEP = 0.7 and RPDp = 6.8 for the test set. Thus, the predictive model presented an excellent performance considering that the Mb-CO variation is comprised between 0 and 21 µM. Therefore, these results demonstrate that the application of the proposed strategy to the analysis of spectral data presenting overlapping bands is feasible and robust.

Protein Structural Denaturation Evaluated by MCR-ALS of Protein Microarray FTIR Spectra

The loss of native structure is common in proteins. Among others, aggregation is one structural modification of particular importance as it is a major concern for the efficiency and safety of biotherapeutic proteins. Yet, recognizing the structural features associated with intermolecular bridging in a high-throughput manner remains a challenge. We combined here the use of protein microarrays spotted at a density of ca 2500 samples per cm² and Fourier transform infrared (FTIR) imaging to analyze structural modifications in a set of 85 proteins characterized by widely different secondary structure contents, submitted or not to mild denaturing conditions. Multivariate curve resolution alternating least squares (MCR-ALS) was used to model a new spectral component appearing in the protein set subject to denaturing conditions. In the native protein set, 6 components were found to be sufficient to obtain good modeling of the spectra. Furthermore, their shape allowed them to be assigned to α-helix, β-sheet, and other structures. Their content in each protein was correlated with the known secondary structure, confirming these assignments. In the denatured proteins, a new component was necessary and modeled by MCR-ALS. This new component could be assigned to the intermolecular β-sheet, bridging protein molecules. MCR-ALS, therefore, unveiled a potential spectroscopic marker of protein aggregation and allowed a semiquantitative evaluation of its content. Insight into other structural rearrangements was also obtained.

Application of Chemometrics Tools to the Study of the Fe(III)-Tannic Acid Interaction

Chemometric techniques were applied to the study of the interaction of iron(III) and tannic acid (TA). Modeling the interaction of Fe(III)–TA is a challenge, as can be the modeling of the metal complexation upon natural macromolecules without a well-defined molecular structure. The chemical formula for commercial TA is often given as C₇₆H₅₂O₄₆, but in fact, it is a mixture of polygalloyl glucoses or polygalloyl quinic acid esters with the number of galloyl moieties per molecule ranging from 2 up to 12. Therefore, the data treatment cannot be based on just the stoichiometric approach. In this work, the redox behavior and the coordination capability of the TA toward Fe(III) were studied by UV-vis spectrophotometry and fluorescence spectroscopy. Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Parallel Factor Analysis (PARAFAC) were used for the data treatment, respectively. The pH range in which there is the redox stability of the system Fe(III)–TA was evaluated. The binding capability of TA toward Fe(III), the spectral features of coordination compounds, and the concentration profiles of the species in solution as a function of pH were defined. Moreover, the stability of the interaction between TA and Fe(III) was interpreted through the chemical models usually employed to depict the interaction of metal cations with humic substances and quantified using the concentration profiles estimated by MCR-ALS.

The Next Generation of IR Spectroscopy: EC-QCL-Based Mid-IR Transmission Spectroscopy of Proteins with Balanced Detection

We report a mid-IR transmission setup for the analysis of the protein amide I and amide II band in aqueous solutions that achieves a limit of detection as low as 0.0025 mg mL-1 (outperforming our previous results and other state-of-the-art mid-IR-based techniques by almost an order of magnitude). This large improvement is made possible by combining the latest-generation external cavity-quantum cascade laser (EC-QCL) operated at room temperature with an optimized double-beam optical setup that adjusts the path length (26 μm) to ensure robust sample handling. For minimizing the noise introduced by the high-intensity laser light source, a thermoelectrically cooled mercury cadmium telluride balanced detection module was employed. In this way, noise levels better by a factor of up to 20 were achieved compared with single-channel measurements. Characteristic spectral features of proteins with different secondary structures were successfully identified at concentrations as low as 0.1 mg mL-1. Furthermore, a highly linear response was demonstrated for concentrations between 0.05 and 10 mg mL-1. The total acquisition time of the setup can be adapted to fulfill the required sensitivity of the protein measurements and to ensure maximum flexibility for future applications. The presented setup combines high sensitivity, large optical path lengths, and short measurement times and thus outperforms previous research type EC-QCL setups as well as commercially available instruments. This opens a wide range of future applications including protein-ligand interaction studies as well as qualitative and quantitative analyses of proteins in complex matrices such as those found in up- and downstream bioprocess monitoring and similar challenging applications which can not be readily met by conventional FT-IR spectroscopy.

Analysis of Carcinogenic Polycyclic Aromatic Hydrocarbons in the Complex Background of the Petroleum Fluorescence Using Multivariate Curve Resolution Alternating Least Square and Total Synchronous Fluorescence Spectroscopic Technique

Multivariate curve resolution alternating least square (MCR-ALS) analysis allows the simultaneous retrieval of pure concentration and spectral profiles for each of the analysed chemical components from the composite spectrum even in the presence of unknown interferences. Total synchronous fluorescence spectroscopy (TSFS), a multidimensional fluorescence technique that describes the variation of synchronous fluorescence profile acquired as a function of increasing offset, has become a useful analytical technique. Suitably arranged TSFS data set can be easily processed using MCR-ALS and thereby a simple and sensitive analytical tool could be developed. The present work successfully used the combination of the MCR-ALS and TSFS to analyse the three carcinogenic and mutagenic polycyclic aromatic hydrocarbons (PAHs) namely Benzo[a]Pyrene, Chrysene and Pyrene in the presence of complex fluorescence background originated from petroleum product. MCR-ALS assisted TSFS can be used for the routine analyses of these carcinogenic PAHs to ensure the quality of water and other samples belonging to different part of the ecosystem.

The activity prediction of indole inhibitors against HCV NS5B polymerase

Non-structural viral protein 5B (NS5B) is a viral protein in hepatitis C virus. Although various inhibitors against NS5B have been found, the activity prediction of similar untested inhibitors is still highly desirable. In this respect, the Tchebichef moments (TMs) calculated from the images of molecular structures were regarded as the independent variables while the inhibitory activity (pIC₅₀ ) was the dependent variable, and the predictive model was established by means of stepwise regression. The R-squared of leave-one-out cross-validation (Q² ) for the training set and the R-squared of prediction ( R p 2 ) for external independent test set were 0.919 and 0.927, respectively. The obtained model was also evaluated strictly. Compared with the multivariate curve resolution with alternating least squares (MCR-ALS) and the QSAR approaches derived from the literature, the proposed method is more accurate and reliable. This study not only provides an effective approach to predict the biological activity of RNA replication's inhibitors, but also extends the QSAR modeling technique.

Band target entropy minimization and target partial least squares for spectral recovery and quantitation

The resolution and quantitation of pure spectra of minority components in measurements of chemical mixtures without prior knowledge of the mixture is a challenging problem. In this work, a combination of band target entropy minimization (BTEM) and target partial least squares (T-PLS) was used to obtain estimates for single pure component spectra and to calibrate those estimates in a true, one-at-a-time fashion. This approach allows for minor components to be targeted and their relative amounts estimated in the presence of other varying components in spectral data. The use of T-PLS estimation is an improvement to the BTEM method because it overcomes the need to identify all of the pure components prior to estimation. Estimated amounts from this combination were found to be similar to those obtained from a standard method, multivariate curve resolution-alternating least squares (MCR-ALS), on a simple, three component mixture dataset. Studies from two experimental datasets demonstrate where the combination of BTEM and T-PLS was used to model the pure component spectra and to obtain concentration profiles of minor components, but MCR-ALS could not.

Determination of hydrogen sulfide and hydrogen peroxide in complex samples of milk and urine by spectroscopic standard addition data and chemometrics methods

Spectroscopic data of standard additions were collected and used to calculate the spectrum of the unknown interferent(s) based on the net analyte signal.

Extracting Infrared Spectra of Protein Secondary Structures Using a Library of Protein Spectra and the Ramachandran Plot

Infrared (IR) spectra from 1200 to 1800 cm(-1) of the pure α-helix and β-sheet secondary structures have been extracted using a covariant least-squares procedure which relates a library of 40 infrared (IR) solution protein spectra from the work of Dong, Carpenter, and Caughey and amino acid fractions of the proteins based on assignments by STRIDE (secondary structure identification) of Eisenhaber and Argos. The excitonic splitting of the β-sheet structures is determined for this library of solution proteins. The method is extended to find a set of spectral basis functions that analyze IR spectra of protein samples for α-helix and β-sheet content. A rigorous error analysis including covariance, the correlations between the input library spectra, was used to justify the results and avoid less meaningful results. The utility of the results on α-helix and β-sheet regions is demonstrated by detecting protein changes due to cancer in imaging Fourier transform IR (FTIR) spectra of liver tissue slices. This work ends with a method to extract IR spectra of less prominent torsional angle distributions.

Fourier transform infrared imaging and microscopy studies of Pinus radiata pulps regarding the simultaneous saccharification and fermentation process

The distribution and chemical patterns of lignocellulosic components at microscopic scale and their effect on the simultaneous saccharification and fermentation process (SSF) in the production of bioethanol from Pinus radiata pulps were analyzed by the application of diverse microscopical techniques, including scanning electronic microscopy (SEM), confocal laser scanning microscopy (CLSM) and attenuated total reflectance (ATR) - Fourier transform infrared microspectroscopy. This last technique was accompanied with multivariate methods, including principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS) to evaluate the distribution patterns and to generate pure spectra of the lignocellulosic components of fibers. The results indicate that the information obtained by the techniques is complementary (ultrastructure, confocality and chemical characterization) and that the distribution of components affects the SSF yield, identifying lignin coalescence droplets as a characteristic factor to increase the SSF yield. Therefore, multivariate analysis of the infrared spectra enabled the in situ identification of the cellulose, lignin and lignin-carbohydrates arrangements. These techniques could be used to investigate the lignocellulosic components distribution and consequently their recalcitrance in many applications where minimal sample manipulation and microscale chemical information is required.