Evaluation of protein secondary structure from FTIR spectra improved after partial deuteration

Protein Microarrays ◽

Ftir Spectra ◽

Partial Least Square ◽

Least Square ◽

Disordered Structures ◽

Α Helix ◽

Β Sheet

AbstractFTIR spectroscopy has become a major tool to determine protein secondary structure. One of the identified obstacle for reaching better predictions is the strong overlap of bands assigned to different secondary structures. Yet, while for instance disordered structures and α-helical structures absorb almost at the same wavenumber, the absorbance bands are differentially shifted upon deuteration, in part because exchange is much faster for disordered structures. We recorded the FTIR spectra of 85 proteins at different stages of hydrogen/deuterium exchange process using protein microarrays and infrared imaging for high throughput measurements. Several methods were used to relate spectral shape to secondary structure content. While in absolute terms, β-sheet is always better predicted than α-helix content, results consistently indicate an improvement of secondary structure predictions essentially for the α-helix and the category called “Others” (grouping random, turns, bends, etc.) after 15 min of exchange. On the contrary, the β-sheet fraction is better predicted in non-deuterated conditions. Using partial least square regression, the error of prediction for the α-helix content is reduced after 15-min deuteration. Further deuteration degrades the prediction. Error on the prediction for the “Others” structures also decreases after 15-min deuteration. Cross-validation or a single 25-protein test set result in the same overall conclusions.

Use of synchrotron-based FTIR microspectroscopy to determine protein secondary structures of raw and heat-treated brown and golden flaxseeds: A novel approach

Canadian Journal of Animal Science ◽

10.4141/a05-004 ◽

2005 ◽

Vol 85 (4) ◽

pp. 437-448 ◽

Cited By ~ 21

Author(s):

P. Yu ◽

J. J. McKinnon ◽

H. W. Soita ◽

C. R. Christensen ◽

D. A. Christensen

Keyword(s):

Secondary Structure ◽

Matrix Protein ◽

Secondary Structures ◽

Heat Processing ◽

Lower Percentage ◽

Protein Secondary Structures ◽

Novel Approach ◽

Α Helix ◽

Β Sheet

The objectives of the study were to use synchrotron Fourier transform infrared microspectroscopy (S-FTIR) as a novel approach to: (1) reveal ultra-structural chemical features of protein secondary structures of flaxseed tissues affected by variety (golden and brown) and heat processing (raw and roasted), and (2) quantify protein secondary structures using Gaussian and Lorentzian methods of multi-component peak modeling. By using multi-component peak modeling at protein amide I region of 1700–1620 cm-1, the results showed that the golden flaxseed contained relatively higher percentage of α-helix (47.1 vs. 36.9%), lower percentage of β-sheet (37.2 vs. 46.3%) and higher (P < 0.05) ratio of α-helix to β-sheet than the brown flaxseed (1.3 vs. 0.8). The roasting reduced (P < 0.05) percentage of α-helix (from 47.1 to 36.1%), increased percentage of β-sheet (from 37.2 to 49.8%) and reduced α-helix to β-sheet ratio (1.3 to 0.7) of the golden flaxseed tissues. However, the roasting did not affect percentage and ratio of α-helix and β-sheet in the brown flaxseed tissue. No significant differences were found in quantification of protein secondary structures between Gaussian and Lorentzian methods. These results demonstrate the potential of highly spatially resolved S-FTIR to localize relatively pure protein in the tissue and reveal protein secondary structures at a cellular level. The results indicated relative differences in protein secondary structures between flaxseed varieties and differences in sensitivities of protein secondary structure to the heat processing. Further study is needed to understand the relationship between protein secondary structure and protein digestion and utilization of flaxseed and to investigate whether the changes in the relative amounts of protein secondary structures are primarily responsible for differences in protein availability. Key words: Synchrotron, FTIR microspectrosopy, flaxseeds, intrinsic structural matrix, protein secondary structures, protein nutritive value

Ifosfamide toxicity to the retina and the possible roles of lecithin and quercetin in albino rats

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.12483 ◽

2018 ◽

Vol 7 (2) ◽

pp. 800

Author(s):

Rehab Ahmed ◽

Eman Aly ◽

Sherif Mahmoud ◽

Sahar Awad ◽

Gehan Kamal

Keyword(s):

Oxidative Stress ◽

Side Effects ◽

Secondary Structure ◽

Combined Treatment ◽

Tail Length ◽

Albino Rats ◽

Drug Induced ◽

Α Helix ◽

Β Sheet

Background: During cancer chemotherapy, drug-induced oxidative stress can limit therapeutic efficiency and cause a number of side effects. Objectives: Our study aimed to characterize the side effects of an alkylating agent chemotherapy ifosfamide to the retina and if the supplementation of lecithin and or quercetin can diminish its oxidative stress by means of comet assay and FTIR.Methods: Seventy female albino rats divided as control, rats given orally quercetin or lecithin, rats injected with ifosfamide, rats given quercetin or lecithin and in combination of them with ifosfamide injection.Results: Lecithin and quercetin groups indicate a normal comet parameters and distribution of protein secondary structure components content of β-turn, α-helix and β-sheet. After Ifosfamide injection, all comet parameters and β-Turns content were significant increase (p˂0.05) with the same context significant decrease (p˂0.05) of α-helix was observed. Lecithin or quercetin reduces the effect of ifosfamide injection in tail length and percentage tailed DNA. Combined treatment gives more protection against DNA damage. Lecithin role is cleared in returning the normal distribution of β-turn, α-helix, β-sheet and lack of protective effect of quercetin regarding the protein secondary structure of retina was observed.Conclusion: We suggest using lecithin and quercetin in combined treatment to reduce the oxidative stress due to ifosfamide.

The Use of Chemometrics for Classification of Sidaguri (Sida rhombifolia) Based on FTIR Spectra and Antiradical Activities

Indonesian Journal of Chemistry ◽

10.22146/ijc.64360 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1568

Author(s):

Abdul Rohman ◽

Asefin Nurul Ikhtiarini ◽

Widiastuti Setyaningsih ◽

Mohamad Rafi ◽

Nanik Siti Aminah ◽

...

Keyword(s):

Multivariate Calibration ◽

Antioxidant Activities ◽

Radical Scavenging ◽

Principal Component ◽

Ftir Spectra ◽

Partial Least Square ◽

Least Square ◽

Ultrasound Assisted Extraction ◽

Sida Rhombifolia

Sidaguri (Sida rhombifolia) is one of the herbal components used in traditional medicine. The application of chemometrics in the standardization of herbal medicine is common. The objective of this study was to classify Sidaguri from different regions based on FTIR spectra with chemometrics of principal component analysis (PCA) and to correlate the antioxidant activities with FTIR spectra using the multivariate calibration of partial least square regression (PLSR). The extraction of Sidaguri powder was performed using ultrasound-assisted extraction (UAE) at optimum conditions. The obtained extracts were subjected to antiradical scavenging activities using DPPH (2,2’-diphenyl-1-picrylhydrazyl) and ABTS (2,2′-azinobis-3-ethylbenzothiazoline-6-sulfonic acid) radicals. The PCA result shows that Sidaguri from different regions could be separated using 14 wavenumbers of FTIR spectra based on the PCA's loading plot. PLSR regression using the second derivative FTIR spectra at wavenumbers of 3662–659 cm–1 could predict radical scavenging activities (RSA) of Sidaguri with R2 values of 0.9636 and 0.9024 for calibration and validation models, with RMSEC and RMSEP values of 1.45% and 2.65%, respectively. It can be concluded that FTIR spectra treated by PCA were reliable for classifying Sidaguri from different regions. At the same time, PLSR was accurate and precise enough to predict the RSA of Sidaguri.

The employment of FTIR spectroscopy and chemometrics for authentication of essential oil of Curcuma mangga from candle nut oil

Food Research ◽

10.26656/fr.2017.4(2).313 ◽

2019 ◽

Vol 4 (2) ◽

pp. 515-521 ◽

Cited By ~ 1

Author(s):

M. Khudzaifi ◽

S.S. Retno ◽

Abdul Rohman

Keyword(s):

Discriminant Analysis ◽

Essential Oil ◽

Ftir Spectroscopy ◽

Prediction Models ◽

Multivariate Calibration ◽

Ftir Spectra ◽

Partial Least Square ◽

Least Square ◽

High Price

The adulteration of high price oil such as essential oil of Curcuma mangga Val. (EOCM) with lower price oil is common to get economical profit. This study was to investigate the authentication of EOCM toward candlenut oil (CNO) using FTIR spectroscopy combined with multivariate calibration and discriminant analysis. The selection of CNO as adulterant oil model was due to its close similarity to EOCM in terms of FTIR spectra. Besides, EOCM has similar color with CNO, therefore, CNO is potential adulterant toward EOCM. Two multivariate calibrations of partial least square regression (PLSR) and principle component regression (PCR) along with FTIR spectra (normal versus derivatization) were optimized to get prediction models for quantification. The results showed that the combination of PLSR and normal FTIR spectra at optimized wavenumbers of 1614-1068 cm-1 was capable of predicting the levels of EOCM adulterated with CNO. Discriminant analysis was also success to differentiate the classification of EOCM and EOCM adulterated with CNO with accuracy levels of 100%. Using FTIR spectroscopy for oil authentication is rapid, simple without any chemicals, solvents, and sample preparation so that this technique is considered as a green analytical method.

Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

Blood Glucose Level Regression for Smartphone PPG Signals Using Machine Learning

Applied Sciences ◽

10.3390/app11020618 ◽

2021 ◽

Vol 11 (2) ◽

pp. 618

Author(s):

Tanvir Tazul Islam ◽

Md Sajid Ahmed ◽

Md Hassanuzzaman ◽

Syed Athar Bin Amir ◽

Tanzilur Rahman

Keyword(s):

Blood Glucose ◽

Blood Glucose Level ◽

Glucose Level ◽

Quantitative Estimation ◽

Partial Least Square ◽

Least Square ◽

Support Vector ◽

Frequency Noise ◽

Optical Noise

Diabetes is a chronic illness that affects millions of people worldwide and requires regular monitoring of a patient’s blood glucose level. Currently, blood glucose is monitored by a minimally invasive process where a small droplet of blood is extracted and passed to a glucometer—however, this process is uncomfortable for the patient. In this paper, a smartphone video-based noninvasive technique is proposed for the quantitative estimation of glucose levels in the blood. The videos are collected steadily from the tip of the subject’s finger using smartphone cameras and subsequently converted into a Photoplethysmography (PPG) signal. A Gaussian filter is applied on top of the Asymmetric Least Square (ALS) method to remove high-frequency noise, optical noise, and motion interference from the raw PPG signal. These preprocessed signals are then used for extracting signal features such as systolic and diastolic peaks, the time differences between consecutive peaks (DelT), first derivative, and second derivative peaks. Finally, the features are fed into Principal Component Regression (PCR), Partial Least Square Regression (PLS), Support Vector Regression (SVR) and Random Forest Regression (RFR) models for the prediction of glucose level. Out of the four statistical learning techniques used, the PLS model, when applied to an unbiased dataset, has the lowest standard error of prediction (SEP) at 17.02 mg/dL.

A Novel Validated Injectable Colistimethate Sodium Analysis Combining Advanced Chemometrics and Design of Experiments

Molecules ◽

10.3390/molecules26061546 ◽

2021 ◽

Vol 26 (6) ◽

pp. 1546

Author(s):

Ioanna Dagla ◽

Anthony Tsarbopoulos ◽

Evagelos Gikas

Keyword(s):

Design Of Experiments ◽

High Performance ◽

Similarity Index ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Distance Measures ◽

Linear Regression Models ◽

Colistimethate Sodium

Colistimethate sodium (CMS) is widely administrated for the treatment of life-threatening infections caused by multidrug-resistant Gram-negative bacteria. Until now, the quality control of CMS formulations has been based on microbiological assays. Herein, an ultra-high-performance liquid chromatography coupled to ultraviolet detector methodology was developed for the quantitation of CMS in injectable formulations. The design of experiments was performed for the optimization of the chromatographic parameters. The chromatographic separation was achieved using a Waters Acquity BEH C8 column employing gradient elution with a mobile phase consisting of (A) 0.001 M aq. ammonium formate and (B) methanol/acetonitrile 79/21 (v/v). CMS compounds were detected at 214 nm. In all, 23 univariate linear-regression models were constructed to measure CMS compounds separately, and one partial least-square regression (PLSr) model constructed to assess the total CMS amount in formulations. The method was validated over the range 100–220 μg mL−1. The developed methodology was employed to analyze several batches of CMS injectable formulations that were also compared against a reference batch employing a Principal Component Analysis, similarity and distance measures, heatmaps and the structural similarity index. The methodology was based on freely available software in order to be readily available for the pharmaceutical industry.

Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽

10.3390/foods10040885 ◽

2021 ◽

Vol 10 (4) ◽

pp. 885

Author(s):

Sergio Ghidini ◽

Luca Maria Chiesa ◽

Sara Panseri ◽

Maria Olga Varrà ◽

Adriana Ianieri ◽

...

Keyword(s):

Root Mean Square ◽

Near Infrared ◽

Nir Spectroscopy ◽

Partial Least Square ◽

Least Square ◽

Coefficient Of Determination ◽

Mean Square ◽

Tuna Fish ◽

Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study

BMC Neuroscience ◽

10.1186/s12868-021-00608-5 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Mika Jönsson ◽

Björn Gerdle ◽

Bijar Ghafouri ◽

Emmanuel Bäckryd

Keyword(s):

Cerebrospinal Fluid ◽

Neuropathic Pain ◽

Pain Intensity ◽

Principal Component ◽

Partial Least Square ◽

Least Square ◽

Therapeutic Interventions ◽

Healthy Controls ◽

Inflammatory Profile

Abstract Background Neuropathic pain (NeuP) is a complex, debilitating condition of the somatosensory system, where dysregulation between pro- and anti-inflammatory cytokines and chemokines are believed to play a pivotal role. As of date, there is no ubiquitously accepted diagnostic test for NeuP and current therapeutic interventions are lacking in efficacy. The aim of this study was to investigate the ability of three biofluids - saliva, plasma, and cerebrospinal fluid (CSF), to discriminate an inflammatory profile at a central, systemic, and peripheral level in NeuP patients compared to healthy controls. Methods The concentrations of 71 cytokines, chemokines and growth factors in saliva, plasma, and CSF samples from 13 patients with peripheral NeuP and 13 healthy controls were analyzed using a multiplex-immunoassay based on an electrochemiluminescent detection method. The NeuP patients were recruited from a clinical trial of intrathecal bolus injection of ziconotide (ClinicalTrials.gov identifier NCT01373983). Multivariate data analysis (principal component analysis and orthogonal partial least square regression) was used to identify proteins significant for group discrimination and protein correlation to pain intensity. Proteins with variable influence of projection (VIP) value higher than 1 (combined with the jack-knifed confidence intervals in the coefficients plot not including zero) were considered significant. Results We found 17 cytokines/chemokines that were significantly up- or down-regulated in NeuP patients compared to healthy controls. Of these 17 proteins, 8 were from saliva, 7 from plasma, and 2 from CSF samples. The correlation analysis showed that the most important proteins that correlated to pain intensity were found in plasma (VIP > 1). Conclusions Investigation of the inflammatory profile of NeuP showed that most of the significant proteins for group separation were found in the less invasive biofluids of saliva and plasma. Within the NeuP patient group it was also seen that proteins in plasma had the highest correlation to pain intensity. These preliminary results indicate a potential for further biomarker research in the more easily accessible biofluids of saliva and plasma for chronic peripheral neuropathic pain where a combination of YKL-40 and MIP-1α in saliva might be of special interest for future studies that also include other non-neuropathic pain states.

Structural and Technological Approach to Reveal the Role of the Lipid Phase in the Formation of Soy Emulsion Gels with Chia Oil

Gels ◽

10.3390/gels7020048 ◽

2021 ◽

Vol 7 (2) ◽

pp. 48

Author(s):

Ana M. Herrero ◽

Claudia Ruiz-Capillas

Keyword(s):

Raman Spectroscopy ◽

Structural Properties ◽

Structural Changes ◽

Lipid Phase ◽

Α Helix ◽

Β Sheet ◽

Shed Light ◽

Chia Oil

Considerable attention has been paid to emulsion gels (EGs) in recent years due to their interesting applications in food. The aim of this work is to shed light on the role played by chia oil in the technological and structural properties of EGs made from soy protein isolates (SPI) and alginate. Two systems were studied: oil-free SPI gels (SPI/G) and the corresponding SPI EGs (SPI/EG) that contain chia oil. The proximate composition, technological properties (syneresis, pH, color and texture) and structural properties using Raman spectroscopy were determined for SPI/G and SPI/EG. No noticeable (p > 0.05) syneresis was observed in either sample. The pH values were similar (p > 0.05) for SPI/G and SPI/EG, but their texture and color differed significantly depending on the presence of chia oil. SPI/EG featured significantly lower redness and more lightness and yellowness and exhibited greater puncture and gel strengths than SPI/G. Raman spectroscopy revealed significant changes in the protein secondary structure, i.e., higher (p < 0.05) α-helix and lower (p < 0.05) β-sheet, turn and unordered structures, after the incorporation of chia oil to form the corresponding SPI/EG. Apparently, there is a correlation between these structural changes and the textural modifications observed.