[Two new benzoyl-sesquiterpenes from Mesona chinensis].

Mesona chinensis is a common medicinal and edible plant in the Lingnan region of China, which has extensive pharmacological activity. However, the study of its chemical constituents is not sufficient. In this study, a variety of modern chromatographic separation techniques were used to isolate two compounds from 95% ethanol extract of the grass parts of M. chinensis. Their absolute configurations were determined by ultraviolet spectroscopy(UV), infrared spectroscopy(IR), high resolution mass spectrometry(HR-ESI-MS), 1D and 2D nuclear magnetic resonance(1D NMR and 2D NMR), and single-crystal X-ray diffraction(SC-XRD). Specifically, they were two new benzoyl-sesquiterpenes and named mesonanol A and mesonanol B, respectively. The results of the pharmacological activity evaluation showed that neither of the two new compounds showed obvious antiviral and anti-inflammatory activities.

Quantifying Bound Proteins on Pegylated Gold Nanoparticles Using Infrared Spectroscopy.

Protein-nanoparticle (NP) complexes are nanomaterials that have numerous potential uses ranging from biosensing to biomedical applications such as drug delivery and nanomedicine. Despite their extensive use quantifying the number of bound proteins per NP remains a challenging characterization step that is crucial for further developments of the conjugate, particularly for metal NPs that often interfere with standard protein quantification techniques. In this work, we present a method for quantifying the number of proteins bound to pegylated thiol-capped gold nanoparticles (AuNPs) using an infrared (IR) spectrometer, a readily available instrument. This method takes advantage of the strong IR bands present in proteins and the capping ligands to quantify protein-NP ratios and circumvents the need to degrade the NPs prior to analysis. We show that this method is generalizable where calibration curves made using inexpensive and commercially available proteins such as bovine serum albumin (BSA) can be used to quantify protein-NP ratios for proteins of different sizes and structures.

Rapid assessment of the blood-feeding histories of wild-caught malaria mosquitoes using mid-infrared spectroscopy and machine learning.

BACKGROUND: The degree to which Anopheles mosquitoes prefer biting humans over other vertebrate hosts, i.e. the human blood index (HBI), is a crucial parameter for assessing malaria transmission risk. However, existing techniques for identifying mosquito blood meals are demanding in terms of time and effort, involve costly reagents, and are prone to inaccuracies due to factors such as cross-reactivity with other antigens or partially digested blood meals in the mosquito gut. This study demonstrates the first field application of mid-infrared spectroscopy and machine learning (MIRS-ML), to rapidly assess the blood-feeding histories of malaria vectors, with direct comparison to PCR assays. METHODS AND RESULTS: Female Anopheles funestus mosquitoes (N = 1854) were collected from rural Tanzania and desiccated then scanned with an attenuated total reflectance Fourier-transform Infrared (ATR-FTIR) spectrometer. Blood meals were confirmed by PCR, establishing the 'ground truth' for machine learning algorithms. Logistic regression and multi-layer perceptron classifiers were employed to identify blood meal sources, achieving accuracies of 88%-90%, respectively, as well as HBI estimates aligning well with the PCR-based standard HBI. CONCLUSIONS: This research provides evidence of MIRS-ML effectiveness in classifying blood meals in wild Anopheles funestus, as a potential complementary surveillance tool in settings where conventional molecular techniques are impractical. The cost-effectiveness, simplicity, and scalability of MIRS-ML, along with its generalizability, outweigh minor gaps in HBI estimation. Since this approach has already been demonstrated for measuring other entomological and parasitological indicators of malaria, the validation in this study broadens its range of use cases, positioning it as an integrated system for estimating pathogen transmission risk and evaluating the impact of interventions.

miRNA and DNA analysis by negative ion electron transfer dissociation and infrared multiple-photon dissociation mass spectrometry.

BACKGROUND: The use of simple and hybrid fragmentation techniques for the identification of molecules in tandem mass spectrometry provides different and complementary information on the structure of molecules. Nevertheless, these techniques have not been as widely explored for oligonucleotides as for peptides or proteins. The analysis of microRNAs (miRNAs) warrants special attention, given their regulatory role and their relationship with several diseases. The application of different fragmentation techniques will be very interesting for their identification. RESULTS: Four synthetic miRNAs and a DNA sequence were fragmented in an ESI-FT-ICR mass spectrometer using both simple and hybrid fragmentation techniques: CID, nETD followed by CID, IRMPD, and, for the first time, nETD in combination with IRMPD. The main fragmentation channel was base loss. The use of nETD-IRMPD resulted in d/z, a/w, and c/y ions at higher intensities. Moreover, nETD-IRMPD provided high sequence coverage and low internal fragmentation. Native MS analysis revealed that only miR159 and the DNA sequence formed stable dimers under physiological ionic strength. The use of organic co-solvents or additives resulted in a lower sequence coverage due to lesser overall ionization efficiency. NOVELTY: This work demonstrates that the combination of nETD and IRMPD for miRNA fragmentation constitutes a suitable alternative to common fragmentation methods. This strategy resulted in efficient fragmentation of [miRNA]5- using low irradiation times and fewer internal fragments while ensuring a high sequence coverage. Moreover, given that such low charge states predominate upon spraying in physiological-like conditions, native MS can be applied for obtaining structural information at the same time.

Inverted Transflection Spectroscopy of Live Cells Using Metallic Grating on Elevated Nanopillars.

Water absorption of mid-infrared (MIR) radiation severely limits the options for vibrational spectroscopy of the analytes-including live biological cells-that must be probed in aqueous environments. While internal reflection elements, such as attenuated total reflection prisms and metasurfaces, partially overcome this limitation, such devices have their own limitations: ATR prisms are difficult to integrate with multiwell cell culture workflows, while metasurfaces suffer from a limited spectral range and small penetration depth into analytes. In this work, we introduce an alternative live cell biosensing platform based on metallic nanogratings fabricated on top of elevated dielectric pillars. For the MIR wavelengths that are significantly longer than the grating period, reflection-based spectroscopy enables broadband sensing of the analytes inside the trenches separating the dielectric pillars. Because the depth of the analyte twice-traversed by the MIR light excludes the highly absorbing thick water layer above the grating, we refer to the technique as inverted transflection spectroscopy (ITS). The analytic power of ITS is established by measuring a wide range of protein concentrations in solution, with the limit of detection in the single-digit mg mL-1. The ability of ITS to interrogate live cells that naturally wrap themselves around the grating is used to characterize their adhesion kinetic.

Full Characterisation of Heroin Samples Using Infrared Spectroscopy and Multivariate Calibration.

The analysis of heroin samples, before use in the protected environment of user centra, could be a supplementary service in the context of harm reduction. Infrared spectroscopy hyphenated with multivariate calibration could be a valuable asset in this context, and therefore 125 heroin samples were collected directly from users and analysed with classical chromatographic techniques. Further, Mid-Infrared spectra were collected for all samples, to be used in Partial Least Squares (PLS) modelling, in order to obtain qualitative and quantitative models based on real live samples. The approach showed that it was possible to identify and quantify heroin in the samples based on the collected spectral data and PLS modelling. These models were able to identify heroin correctly for 96% of the samples of the external test set with precision, specificity and sensitivity values of 100.0, 75.0 and 95.5%, respectively. For regression, a root mean squared error of prediction (RMSEP) of 0.04 was obtained, pointing at good predictive properties. Furthermore, during mass spectrometric screening, 10 different adulterants and impurities were encountered. Using the spectral data to model the presence of each of these resulted in performant models for seven of them. All models showed promising correct-classification rates (between 92 and 96%) and good values for sensitivity, specificity and precision. For codeine and morphine, the models were not satisfactory, probably due to the low concentration of these impurities as a consequence of acetylation. For methacetin, the approach failed.

Increased accuracy of vibrational circular dichroism calculations for isotopically labeled helical peptides.

Vibrational circular dichroism (VCD) spectra have been computed with qualitatively correct sign patterns for α-helical peptides using various methods, ranging from empirical models to ab initio quantum mechanical computations. However, some details, such as deuteration effects and isotope substitution shifts and sign patterns for the resultant amide I' band shape, have remained a predictive challenge. Fully optimized computations for a 25-residue Ala-rich peptide, including implicit solvent corrections and explicit side chains that experimentally stabilize these model helical peptides in water, have been carried out using density functional theory (DFT). These fully minimized structures show minor changes in the (ϕ,ψ) torsions at the termini and yield an extra negative band to the low energy side of the characteristic amide I' couplet VCD, in agreement with experiments. Additionally, these calculations give the right sign and relative intensity patterns, as compared to experimental results, for several 13C=O substituted variants. The differences from previously reported computations that used ideal helical structures and vacuum conditions imply that inclusion of distorted termini and solvent effects can have an impact on the final detailed spectral patterns. Inclusion of side chains in these calculations had very little effect on the computed amide I' IR and VCD. Tests of constrained geometries, varying dielectric, and different functionals indicate that each can affect the band shapes, particularly for the 12C=O components, but these aspects do not fully explain the difference from previous spectral simulations. Inclusion of long-range amide coupling, as obtained from DFT computation of the full structure, or transfer of parameters from a somewhat longer peptide model, rather than shorter model, seems to be more important for the final detailed band shape under isotopic substitution. However, these corrections can also induce other changes, suggesting that previously reported, limited calculations may have been qualitatively useful due to a balance of errors. This may also explain the success of simple empirical IR models.

Fabrication and characterization of curcumin-loaded composite nanoparticles based on high-hydrostatic-pressure-treated zein and pectin: Interaction mechanism, stability, and bioaccessibility.

We successfully designed curcumin (Cur)-loaded composite nanoparticles consisting of high-hydrostatic-pressure-treated (HHP-treated) zein and pectin with a pressure of 150 MPa (zein-150 MPa-P-Cur), showing nano-spherical structure with high zeta-potential (-36.72 ± 1.14 mV) and encapsulation efficiency (95.64 ± 1.23 %). We investigated the interaction mechanism of the components in zein-150 MPa-P-Cur using fluorescence spectroscopy, molecular dynamics simulation, Fourier-transform infrared spectrometry and scanning electron microscopy techniques. Compared with zein-P-Cur, the binding sites and binding energy (-53.68 kcal/mol vs. - 44.22 kcal/mol) of HHP-treated zein and Cur were increased. Meanwhile, the interaction force among HHP-treated zein, pectin, and Cur was significantly enhanced, which formed a tighter and more stable particle structure to further improve package performance. Additionally, Cur showed the best chemical stability in zein-150 MPa-P-Cur. And the bioavailability of Cur was increased to 65.53 ± 1.70 %. Collectively, composite nanoparticles based on HHP-treated zein and pectin could be used as a promising Cur delivery system.

[Exploration of detection methods for free silica with different crystal forms in dust].

Objective: To investigate the differences and applicability of free silica detection methods of different crystal forms in dust, and to provide a basis for the selection of various methods. Methods: From December 2021 to June 2022, dust samples from 20 enterprises in different industries in 18 cities in Henan Province were randomly selected as the investigation objects. X-ray diffraction (XRD) method was used to analyze the samples and classify the samples. Based on GBZ/T 192.4-2007 "Determination of Dust in the Air of Workplace-Part 4: Content of Free Silica in Dust", pyrophosphate method and infrared spectrophotometry were used for quantitative determination. The measured results were analyzed by paired sample t test to evaluate the advantages and disadvantages of the two methods and their applicable scope. Results: The XRD results of 20 dust samples could be divided into α, ß, γ crystal types and the mixed type of α and γ. There was no significant difference between pyrophosphate method and infrared spectrophotometry (P=0.180). The pyrophosphate method results of ß, γ and α, γ mixed crystalline free silica were significantly higher than those of infrared spectrophotometry, and the difference was statistically significant (P<0.001) . Conclusion: Pyrophosphate method and infrared spectrophotometry are suitable for α-type free silica, while pyrophosphate method is suitable for ß, γ and α, γ mixed crystalline free silica.

Label-free mapping of cetuximab in multi-layered tumor oral mucosa models by atomic force-microscopy-based infrared spectroscopy.

Sensitive mapping of drugs and drug delivery systems is pivotal for the understanding and improvement of treatment options. Since labeling alters the physicochemical and potentially the pharmacological properties of the molecule of interest, its label-free detection by photothermal expansion is investigated. We report on a proof-of-concept study to map the cetuximab distribution by atomic-force microscopy-based infrared spectroscopy (AFM-IR). The monoclonal antibody cetuximab was applied to a human tumor oral mucosa model, consisting of a tumor epithelium on a lamina propria equivalent. Hyperspectral imaging in the wavenumber regime between 903 cm-1 and 1312 cm-1 and a probing distance between the data points down to 10 × 10 nm are used for determining the local drug distribution. The local distinction of cetuximab from the tissue background is gained by linear combination modeling making use of reference spectra of the drug and untreated models. The results from this approach are compared to principal component analyses, yielding comparable results. Even single molecule detection appears feasible. The results indicate that cetuximab penetrates the cytosol of tumor cells but does not bind to structures in the cell membrane. In conclusion, AFM-IR mapping of cetuximab proved to sensitively determine drug concentrations at an unprecedented spatial resolution without the need for drug labeling.

Intermolecular Protein-Water Coupling Impedes the Coupling Between the Amide A and Amide I Mode in Interfacial Proteins.

The coupling between different vibrational modes in proteins is essential for chemical dynamics and biological functions and is linked to the propagation of conformational changes and pathways of allosteric communication. However, little is known about the influence of intermolecular protein-H2O coupling on the vibrational coupling between amide A (NH) and amide I (C═O) bands. Here, we investigate the NH/CO coupling strength in various peptides with different secondary structures at the lipid cell membrane/H2O interface using femtosecond time-resolved sum frequency generation vibrational spectroscopy (SFG-VS) in which a femtosecond infrared pump is used to excite the amide A band, and SFG-VS is used to probe transient spectral evolution in the amide A and amide I bands. Our results reveal that the NH/CO coupling strength strongly depends on the bandwidth of the amide I mode and the coupling of proteins with water molecules. A large extent of protein-water coupling significantly reduces the delocalization of the amide I mode along the peptide chain and impedes the NH/CO coupling strength. A large NH/CO coupling strength is found to show a strong correlation with the high energy transfer rate found in the light-harvesting proteins of green sulfur bacteria, which may understand the mechanism of energy transfer through a molecular system and assist in controlling vibrational energy transfer by engineering the molecular structures to achieve high energy transfer efficiency.

Discrimination of opium from Afghanistan and Myanmar by infrared spectroscopy coupled with machine learning methods.

Afghanistan and Myanmar are two overwhelming opium production places. In this study, rapid and efficient methods for distinguishing opium from Afghanistan and Myanmar were developed using infrared spectroscopy (IR) coupled with multiple machine learning (ML) methods for the first time. A total of 146 authentic opium samples were analyzed by mid-IR (MIR) and near-IR (NIR), within them 116 were used for model training and 30 were used for model validation. Six ML methods, including partial least squares discriminant analysis (PLS-DA), orthogonal PLS-DA (OPLS-DA), k-nearest neighbour (KNN), support vector machine (SVM), random forest (RF), and artificial neural networks (ANNs) were constructed and compared to get the best classification effect. For MIR data, the average of precision, recall and f1-score for all classification models were 1.0. For NIR data, the average of precision, recall and f1-score for different classification models ranged from 0.90 to 0.94. The comparison results of six ML models for MIR and NIR data showed that MIR was more suitable for opium geography classification. Compared with traditional chromatography and mass spectrometry profiling methods, the advantages of MIR are simple, rapid, cost-effective, and environmentally friendly. The developed IR chemical profiling methodology may find wide application in classification of opium from Afghanistan and Myanmar, and also to differentiate them from opium originating from other opium producing countries. This study presented new insights into the application of IR and ML to rapid drug profiling analysis.

Infrared spectroscopy combined with chemometrics in transflectance mode: An alternative approach in the photodiagnosis of COVID-19 using saliva.

The Coronavirus Disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has required the search for sensitive, rapid, specific, and lower-cost diagnostic methods to meet the high demand. The gold standard method of laboratory diagnosis is real-time reverse transcription polymerase chain reaction (RT-PCR). However, this method is costly and results can take time. In the literature, several studies have already described the potential of Fourier transform infrared spectroscopy (FTIR) as a tool in the biomedical field, including the diagnosis of viral infections, while being fast and inexpensive. In view of this, the objective of this study was to develop an FTIR model for the diagnosis of COVID-19. For this analysis, all private clients who had performed a face-to-face collection at the Univates Clinical Analysis Laboratory (LAC Univates) within a period of six months were invited to participate. Data from clients who agreed to participate in the study were collected, as well as nasopharyngeal secretions and a saliva sample. For the development of models, the RT-PCR result of nasopharyngeal secretions was used as a reference method. Absorptions with high discrimination (p < 0.001) between GI (28 patients, RT-PCR test positive to SARS-CoV-2 virus) and GII (173 patients who did not have the virus detected in the test) were most relevant at 3512 cm-1, 3385 cm-1 and 1321 cm-1 after 2nd derivative data transformation. To carry out the diagnostic modeling, chemometrics via FTIR and Discriminant Analysis of Orthogonal Partial Least Squares (OPLS-DA) by salivary transflectance mode with one latent variable and one orthogonal signal correction component were used. The model generated predictions with 100 % sensitivity, specificity and accuracy. With the proposed model, in a single application of an individual's saliva in the FTIR equipment, results related to the detection of SARS-CoV-2 can be obtained in a few minutes of spectral evaluation.

Mid-Infrared Photothermal Spectroscopy for the Detection of Caffeine in Beverages.

Caffeine is the most widely consumed stimulant and is the subject of significant ongoing research and discussions due to its impact on human health. The industry's need to comply with country-specific food and beverage regulations underscores the importance of monitoring caffeine levels in commercial products. In this study, we propose an alternative technique for caffeine analysis that relies on mid-infrared laser-based photothermal spectroscopy (PTS). PTS exploits the high-power output of the quantum cascade laser (QCL) sources to enhance the sensitivity of the mid-IR measurement. The laser-induced thermal gradient in the sample scales with the analytes' absorption coefficient and concentration, thus allowing for both qualitative and quantitative assessment. We evaluated the performance of our experimental PTS spectrometer, incorporating a tunable QCL and a Mach-Zehnder interferometer, for detecting caffeine in coffee, black tea, and an energy drink. We calibrated the setup with caffeine standards (0.1-2.5 mg mL-1) and we benchmarked the setup's capabilities against gas chromatography (GC) and Fourier-transform infrared (FTIR) spectroscopy. Quantitative results aligned with GC analysis, and limits of detection matched the research-grade FTIR spectrometer, indicating an excellent performance of our custom-made instrument. This method offers an alternative to established techniques, providing a platform for fast, sensitive, and non-destructive analysis without consumables as well as with high potential for miniaturization.

Insights into the Effect of Charges on Hydrogen Bonds.

Previous computational and experimental studies showed that charges located at the surroundings of hydrogen bonds can exert two opposite effects on them: rupture or strengthening of the hydrogen bond. This work aims to generalize the effect of charges in different hydrogen-bonded systems and to propose a coherent explanation of this effect. For these purposes, 19 systems with intra- and intermolecular hydrogen bonds were studied computationally with DFT. The FT-IR spectra of the systems were simulated, and two energy components of the hydrogen bond were studied separately to determine their variation upon the presence of a charge: charge transfer and molecular overlap. It was determined that either the breaking or strengthening of the hydrogen bond can be favored one over the other, for instance, depending on the heteroatom involved in the hydrogen bond. In addition, it is showed that the strengthening of the hydrogen bond by the presence of a charge is directly related to the decrease in charge transfer between the monomers, which is explained by an increase in molecular overlapping, suggesting a more covalent character of the interaction. The understanding of how hydrogen bonds are affected by charges is important, as it is a key towards a strategy to manipulate hydrogen bonds at convenience.

Application of mid-infrared spectroscopy to the prediction and specification of pesticide sorption: A promising and cost-effective tool.

The cocktail of pesticides sprayed to protect crops generates a miscellaneous and generalized contamination of water bodies. Sorption, especially on soils, regulates the spreading and persistence of these contaminants. Fine resolution sorption data and knowledge of its drivers are needed to manage this contamination. The aim of this study is to investigate the potential of Mid-Infrared spectroscopy (MIR) to predict and specify the adsorption and desorption of a diversity of pesticides. We constituted a set of 37 soils from French mainland and West Indies covering large ranges of texture, organic carbon, minerals and pH. We measured the adsorption and desorption coefficients of glyphosate, 2,4-dichlorophenoxyacetic acid (2,4-D) and difenoconazole and acquired MIR Lab spectra for these soils. We developed Partial Least Square Regression (PLSR) models for the prediction of the sorption coefficients from the MIR spectra. We further identified the most influencing spectral bands and related these to putative organic and mineral functional groups. The prediction performance of the PLSR models was generally high for the adsorption coefficients Kdads (0.4 < R2 < 0.9 & RPIQ >1.8). It was contrasted for the desorption coefficients and related to the magnitude of the desorption hysteresis. The most significant spectral bands in the PLSR differ according to the pesticides indicating contrasted interactions with mineral and organic functional groups. Glyphosate interacts primarily with polar mineral groups (OH) and difenoconazole with hydrophobic organic groups (CH2, CC, COO-, C-O, C-O-C). 2,4-D has both positive and negative interactions with these groups. Finally, this work suggests that MIR combined with PLSR is a promising and cost-effective tool. It allows both the prediction of adsorption and desorption parameters and the specification of these mechanisms for a diversity of pesticides including polar active ingredients.

Using 2D-IR Spectroscopy to Measure the Structure, Dynamics, and Intermolecular Interactions of Proteins in H2O.

ConspectusInfrared (IR) spectroscopy probes molecular structure at the level of the chemical bond or functional group. In the case of proteins, the most informative band in the IR spectrum is the amide I band, which arises predominantly from the C═O stretching vibration of the peptide link. The folding of proteins into secondary and tertiary structures leads to vibrational coupling between peptide units, generating specific amide I spectral signatures that provide a fingerprint of the macromolecular conformation. Ultrafast two-dimensional IR (2D-IR) spectroscopy allows the amide I band of a protein to be spread over a second frequency dimension in a way that mirrors 2D-NMR methods. This means that amide I 2D-IR spectroscopy produces a spectral map that is exquisitely sensitive to protein structure and dynamics and so provides detailed insights that cannot be matched by IR absorption spectroscopy. As a result, 2D-IR spectroscopy has emerged as a powerful tool for probing protein structure and dynamics over a broad range of time and length scales in the solution phase at room temperature. However, the protein amide I band coincides with an IR absorption from the bending vibration of water (δHOH), the natural biological solvent. To circumvent this problem, protein IR studies are routinely performed in D2O solutions because H/D substitution shifts the solvent bending mode (δDOD) to a lower frequency, revealing the amide I band. While effective, this method raises fundamental questions regarding the impact of the change in solvent mass on the structural or solvation dynamics of the protein and the removal of the energetic resonance between solvent and solute.In this Account, a series of studies applying 2D-IR to study the spectroscopy and dynamics of proteins in H2O-rich solvents is reviewed. A comparison of IR absorption spectroscopy and 2D-IR spectroscopy of protein-containing fluids is used to demonstrate the basis of the approach before a series of applications is presented. These range from measurements of fundamental protein biophysics to recent applications of machine learning to gain insight into protein-drug binding in complex mixtures. An outlook is presented, considering the potential for 2D-IR measurements to contribute to our understanding of protein behavior under near-physiological conditions, along with an evaluation of the obstacles that still need to be overcome.

Study of the structural characterization, physicochemical properties and antioxidant activities of phosphorylated long-chain inulin with different degrees of substitution.

In this study, phosphorylated derivatives of long-chain inulin with different substitution degrees were prepared. The synthesized samples were named PFXL-1, PFXL-2, PFXL-3, and PFXL-4 according to their degree of substitution (from low to high). The structures of FXL and PFXL were characterized by infrared spectroscopy and nuclear magnetic resonance spectroscopy, and the results indicated the successful introduction of phosphate groups. FXL and PFXL were composed of two types of sugar, fructose and glucose, with a molar ratio of 0.977:0.023. The SEM results showed that phosphorylation changed the morphology of FXL from an irregular mass to small spherical aggregates. The XRD pattern showed that the crystallinity was reduced by the introduction of phosphate groups. The Mw of FXL was 2649 g/mol, and the Mw of PFXL-4 increased the most (2965 g/mol). Additionally, PFXL was more stable and uniform, and the absolute value of the PFXL potential reached 7.83 mV. Phosphorylation decreased the weight loss rate of FXL and improved the viscoelastic properties and antioxidant activity of FXL. This study presents a method for the modification of FXL, demonstrating that phosphorylation can enhance its physicochemical properties and physiological activity and suggesting its potential as a functional food and quality modifier.

Identification of Binding Sites in Copper(II)-Peptide Complexes Using Infrared Spectroscopy.

Complex formation of the copper(II) ion (CuII) with histidine (H) and H-containing peptides plays a crucial role in various metallo-enzymatic reactions. To elucidate the nature of coordinate bonding in CuII complexes, Fourier-transform infrared spectroscopy and 2D IR spectroscopy were employed to investigate the coordination geometries of CuII with diglycine, l-histidylglycine (HG), glycyl-l-histidine (GH), and glycylglycyl-l-histidine. The coordination of CuII to different peptide groups, including the peptide N- and C-termini, the amide group, and the imidazole of the H side chain, exhibits distinct spectral features. The derived molecular structure of the CuII-HG complex based on these spectral features significantly differs from that of CuII-GH, suggesting a preference of the N-terminus and the steric hindrance of the H side chain in CuII chelation.

A Robust TabNet-Based Multi-Classification Algorithm for Infrared Spectral Data of Chinese Herbal Medicine with High-Dimensional Small Samples.

Robust classification algorithms for high-dimensional, small-sample datasets are valuable in practical applications. Faced with the infrared spectroscopic dataset with 568 samples and 3448 wavelengths (features) to identify the origins of Chinese medicinal materials, this paper proposed a novel embedded multiclassification algorithm, ITabNet, derived from the framework of TabNet. Firstly, a refined data pre-processing (DP) mechanism was designed to efficiently find the best adaptive one among 50 DP methods with the help of Support Vector Machine (SVM). Following this, an innovative focal loss function was designed and joined with a cross-validation experiment strategy to mitigate the impact of sample imbalance on algorithm. Detailed investigations on ITabNet were conducted, including comparisons of ITabNet with SVM for the conditions of DP and Non-DP, GPU and CPU computer settings, as well as ITabNet against XGBT (Extreme Gradient Boosting). The numerical results demonstrate that ITabNet can significantly improve the effectiveness of prediction. The best accuracy score is 1.0000, and the best Area Under the Curve (AUC) score is 1.0000. Suggestions on how to use models effectively were given. Furthermore, ITabNet shows the potential to apply the analysis of medicinal efficacy and chemical composition of medicinal materials. The paper also provides ideas for multi-classification modeling data with small sample size and high-dimensional feature.