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Date palm (Phoenix dactylifera L.) is among the most ancient cultivated crops, of special value owing to its fruits high nutritive and economic benefits. Asides, date palm pollen is a high energy material that has been used traditionally used for fertility enhancement. In this study, effects of date palm pollen crude extract and its fractions viz., petroleum ether, methylene chloride, ethyl acetate and n-butanol on the female reproductive system were evaluated for the first time in relation to its metabolite fingerprint. Fertility activity was evaluated in immature female rats by assessing their FSH-, LH- and estrogen- activities. To pinpoint active hormonal agents in crude pollen extract and fractions, UPLC- MS analysis was employed for metabolites profiling, and in correlation to extract/fraction bioassays using multivariate OPLS analysis. Results revealed that both polar n-butanol and non-polar petroleum ether fractions exhibited the strongest activities; with a significant increase in FSH (25.7 mIU/ml in n-butanol group), estradiol (414.7 pg/ml in petroleum ether group) and progesterone levels (122.4 pg/ml in n-butanol group). Correlation between UPLC-MS and fraction bioassays was attempted using multivariate OPLS analysis to reveal for bioactive hits in these fractions. This study provides the first report on the fertility effect of date palm pollen in female rats and in relation to its metabolite fingerprint.

The identification of insulin resistance and hyperinsulinemia in polycystic ovary syndrome (PCOS) is not a minor issue. The homeostasis model assessment of insulin resistance index (HOMA) is the most used index of IR (Insulin Resistance), validated in overweight and obese patients but not in normal-weight PCOS subjects, who can still present with increased insulin secretion by an oral glucose tolerance test (OGTT). The evaluation of insulin secretion and resistance represents a still unresolved problem. The aim of this study is to identify a possible yet noninvasive method to properly evaluate the insulin metabolism in young non-diabetic subjects. Girls aged 14–22 years, afferent to the center of Gynecological Diseases in Childhood and Adolescence of Cagliari (Italy), were screened for PCOS. A total of 42 subjects comprised the study group. Hormonal assays, OGTT, transabdominal (TA) or transvaginal (TV) US, and urine collection for 1H-NMR analysis were assayed in the early follicular phase. A 1H-NMR coupled multivariate statistical analysis was performed. The OPLS model indicated that the NMR profile of urine had a good fit and prediction ability for the AUC OGTT with R2 = 0.813. Metabolomics can be a promising tool to the potential identification of biomarkers of an exaggerated insulin response to OGTT and can encourage substantial progress for a more accurate and early diagnosis in PCOS.

Untargeted LC–MS flavoromic profiling was utilized to identify compounds that suppress bitterness perception of coffee brew. The chemical profiles of fourteen brew samples and corresponding perceived bitterness intensities determined by descriptive sensory analysis were modeled by orthogonal partial least squares (OPLS) with good fit (R2Y > 0.9) and predictive ability (Q2 > 0.9). Ten chemical markers that were highly predictive and negatively correlated to bitter intensity were subsequently purified by multi-dimensional preparative LC–MS to conduct sensory recombination testing and/or confirm compound identifications by NMR. Three of the ten compounds evaluated, namely 4-caffeoylquinic acid, 5-caffeoylquinic acid, and 2-O-β- d -glucopyranosyl-atractyligenin were identified as bitter modulators in coffee, and significantly decreased the perceived bitterness intensity of the brew.

Salak (Salacca zalacca) is well-known as snake fruit and it is immensely studied for its antioxidative and antidiabetic active metabolites throughout the southeast Asian countries. However, there are many remaining unidentified metabolites due to very low abundance and natural variation, which need to be further explored. Nowadays mass spectrometry (MS/MS) facilitates the tentative identification of unknown compounds in the crude herbal extracts. This study described the metabolite profiling of hydroalcoholic extracts of S. zalacca analysed by LC-QTOF-MS/MS. The 60% ethanolic extract exhibited the highest α-glucosidase inhibition and ferric reducing antioxidant power activities with IC50 of 15.94 µg/mL and 78.13 μg AAE/g, respectively. Multivariate data analysis (MVDA) by an orthogonal partial least-squares (OPLS) algorithm was conducted to correlate the α-glucosidase inhibition activity with the LC- QTOF-MS data. A total of 4 compounds were reported for first time in this fruit and identified based on the molecular mass and fragment ions. LC-QTOF-MS analysis indicated the presence of carexane I, 5-phenoxytetrazol-1-yl)-2,3,5,6-hexahydrofurofuran-3-ethylurea, 3-acetylphenoxy)-N-[(2)-1-amino-4-methyl-1-oxopentan-2-yl]-4,5-dihydroxycyclohexene-1-carboxamide and Ethyl 4-[5-methyl-2-oxo-1′,2′,5′,6′,7′,7′a-hexahydro-1H-spiro[indole-3,3′-pyrrolizine]-2′-ylamido] benzoate. Molecular docking of those compounds with the α–glucosidase enzyme was performed to confirm their antidiabetic potential. These bioactive compounds could be suggested as α-glucosidase inhibitors and functional food additives.

// Xiangyi He 1 , Jie Zhong 1 , Shuwei Wang 2 , Yufen Zhou 3 , Lei Wang 1 , Yongping Zhang 1 , Yaozong Yuan 1 1 Department of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China 2 Department of Anesthesia, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China 3 Department of Gastroenterology, Ruijin Hospital North, Shanghai Jiaotong University School of Medicine, Shanghai, China Correspondence to: Yaozong Yuan, email: yyz28@medmail.com.cn Keywords: pancreatic cancer, metabolomics, serum, new-onset diabetes Received: May 31, 2016 Accepted: February 20, 2017 Published: March 16, 2017 ABSTRACT To establish a screening strategy for pancreatic cancer (PC) based on new-onset diabetic mellitus (NO-DM), serum metabolomics analysis and a search for the metabolic pathways associated with PC related DM were performed. Serum samples from patients with NO-DM (n = 30) and patients with pancreatic cancer and NO-DM were examined by liquid chromatography-mass spectrometry. Data were analyzed using principal components analysis (PCA) and orthogonal projection to latent structures (OPLS) of the most significant metabolites. The diagnostic model was constructed using logistic regression analysis. Metabolic pathways were analyzed using the web-based tool MetPA. PC patients with NO-DM were older and had a lower BMI and shorter duration of DM than those with NO-DM. The metabolomic profiles of patients with PC and NO-DM were significantly different from those of patients with NO-DM in the PCA and OPLS models. Sixty two differential metabolites were identified by the OPLS model. The logistic regression model using a panel of two metabolites including N_Succinyl_L_diaminopimelic_acid and PE (18:2) had high sensitivity (93.3%) and specificity (93.1%) for PC. The top three metabolic pathways associated with PC related DM were valine, leucine and isoleucine biosynthesis and degradation, primary bile acid biosynthesis, and sphingolipid metabolism. In conclusion, screening for PC based on NO-DM using serum metabolomics in combination with clinic characteristics and CA19-9 is a potential useful strategy. Several metabolic pathways differed between PC related DM and type 2 DM.

Orthogonal partial least squares regression (OPLS) is being increasingly adopted as an alternative to partial least squares (PLS) regression due to the better generalization that can be achieved. Particularly in multivariate batch statistical process control (BSPC), the use of OPLS for estimating nominal trajectories is advantageous. In OPLS, the nominal process trajectories are expected to be captured in a single predictive principal component while uncorrelated variations are filtered out to orthogonal principal components. In theory, OPLS will yield a better estimation of the Hotelling's T2 statistic and corresponding control limits thus lowering the number of false positives and false negatives when assessing the process disturbances. Although OPLS advantages have been demonstrated in the context of regression, its use on BSPC was seldom reported. This study proposes an OPLS-based approach for BSPC of a cocrystallization process between hydrochlorothiazide and p-aminobenzoic acid monitored on-line with near infrared spectroscopy and compares the fault detection performance with the same approach based on PLS. A series of cocrystallization batches with imposed disturbances were used to test the ability to detect abnormal situations by OPLS and PLS-based BSPC methods. Results demonstrated that OPLS was generally superior in terms of sensibility and specificity in most situations. In some abnormal batches, it was found that the imposed disturbances were only detected with OPLS.

Atomistic molecular dynamics simulations were carried out to obtain the diffusion coefficients of CO2 in n-hexane, n-decane, n-hexadecane, cyclohexane, and squalane at temperatures up to 423.15 K and pressures up to 65 MPa. Three popular models were used for the representation of hydrocarbons: the united atom TraPPE (TraPPE-UA), the all-atom OPLS, and an optimized version of OPLS, namely, L-OPLS. All models qualitatively reproduce the pressure dependence of the diffusion coefficient of CO2 in hydrocarbons measured recently, and L-OPLS was found to be the most accurate. Specifically for n-alkanes, L-OPLS also reproduced the measured viscosities and densities much more accurately than the original OPLS and TraPPE-UA models, indicating that the optimization of the torsional potential is crucial for the accurate description of transport properties of long chain molecules. The three force fields predict different microscopic properties such as the mean square radius of gyration for the n-alkane molecules and pair correlation functions for the CO2–n-alkane interactions. CO2 diffusion coefficients in all hydrocarbons studied are shown to deviate significantly from the Stokes–Einstein behavior.

Objective To investigate the relationships between two single-nucleotide polymorphisms at miR-146a C > G (rs2910164) and miR-1269b G > C (rs7210937) and the risk of developing oral premalignant lesions (OPLs), oral squamous cell carcinoma (OSCC), pharyngeal SCC (PSCC), and oral and pharyngeal SCC (OPSCC). Design Genotyping of miR-146a C > G and miR-1269b G > C was performed in two case-control studies using the TaqMan assay. A total of 197 healthy control subjects, 241 OPLs patients, and 188 OPSCC patients who habitually chewed betel quid (BQ) were recruited into one case-control study. Additionally, 668 cancer-free control subjects and 658 OPSCC patients were recruited into the other case-control study. Results The G/G genotype at miR-146a C > G was associated with the decreased risk of OSCC [adjusted odds ratio (AOR) = 0.66, P = 0.040], PSCC (AOR = 0.42, P = 0.013), and OPSCC (AOR = 0.63, P = 0.020). Additionally, the C allelic type and C/C genotype at miR-1269b G > C decreased the risk of BQ-related oral leukoplakia (C vs. G: AOR = 0.68, P = 0.012; C/C vs. G/G: AOR = 0.43, P = 0.009), BQ-related OPLs (C vs. G: AOR = 0.69, P = 0.008; C/C vs. G/G: AOR = 0.44, P = 0.005), and BQ-related OPSCC (C vs. G: AOR = 0.65, P = 0.003; C/C vs. G/G: AOR = 0.47, P = 0.011). In OPSCC patients, the G/G genotype of miR-146a was correlated with well-differentiated cells (P = 0.041), and the G/C and C/C genotypes of miR-1269b were correlated with the absence of lymph node involvement (P = 0.031), especially in OSCC patients (P = 0.038 and P = 0.007, respectively). Conclusion The genetic variants of miR-146a and miR-1269b are biomarkers against the development of OPLs and OPSCC.

Intrinsically disordered proteins are essential for biological processes such as cell signalling, but are also associated to devastating diseases including Alzheimer's disease, Parkinson's disease or type II diabetes. Because of their lack of a stable three-dimensional structure, molecular dynamics simulations are often used to obtain atomistic details that cannot be observed experimentally. The applicability of molecular dynamics simulations depends on the accuracy of the force field chosen to represent the underlying free energy surface of the system. Here, we use replica exchange molecular dynamics simulations to test five modern force fields, OPLS, AMBER99SB, AMBER99SB*ILDN, AMBER99SBILDN-NMR and CHARMM22*, in their ability to model Aβ42 , an intrinsically disordered peptide associated with Alzheimer's disease, and compare our results to nuclear magnetic resonance (NMR) experimental data. We observe that all force fields except AMBER99SBILDN-NMR successfully reproduce local NMR observables, with CHARMM22* being slightly better than the other force fields.

The risk of malignant transformation in oral preneoplastic lesions (OPLs) is challenging to assess. The objective of the present study was to determine the expression of ELAV like RNA binding protein 1 (HuR) and podoplanin in OPLs, and to evaluate the use of each protein as biomarkers for the risk assessment of malignant transformations. Immunohistochemistry for HuR and podoplanin was performed on the tissues of 51 patients with OPL, including cases of low grade dysplasia (LGD) and high grade dysplasia (HGD). The association between the protein expression patterns and clinicopathological parameters, including oral cancer free survival (OCFS) time, was analyzed during the follow-up period. HuR and podoplanin expression was observed in 28 (55%) and 36 (71%) of 51 patients, respectively. Kaplan-Meier analysis showed that the expression of HuR and podoplanin was associated with the risk of progression to oral cancer (P

Periodontal disease is characterized by chronic inflammation in subgingival areas, where a vast array of inflammation-associated metabolites are likely produced from tissue breakdown, increased vascular permeability, and microbial metabolism and then eventually show a steady flow into saliva. Thus, prolonged periodontal inflammation is a key feature of disease activity. Although salivary metabolomics has drawn attention for its potential use in diagnosis of periodontal disease, few authors have used that to investigate periodontal inflammation detection. In this pilot study, the authors explored the use of salivary metabolites to reflect periodontal inflammation severity with a recently proposed parameter—periodontal inflamed surface area (PISA)—used to quantify the periodontal inflammatory burden of individual patients with high accuracy. Following PISA determination, whole saliva samples were collected from 19 subjects before and after removal of supragingival plaque and calculus (debridement) with an ultrasonic scaler to assess the influence of the procedure on salivary metabolic profiles. Metabolic profiling of saliva was performed with gas chromatography coupled to time-of-flight mass spectrometry, followed by multivariate regression analysis with orthogonal projections to latent structures (OPLS) to investigate the relationship between PISA and salivary metabolic profiles. Sixty-three metabolites were identified. OPLS analysis showed that postdebridement saliva provided a more refined model for prediction of PISA than did predebridement samples, which indicated that debridement may improve detection of metabolites eluted from subgingival areas in saliva, thus more accurately reflecting the pathophysiology of periodontitis. Based on the variable importance in the projection values obtained via OPLS, 8 metabolites were identified as potential indicators of periodontal inflammation, of which the combination of cadaverine, 5-oxoproline, and histidine yielded satisfactory accuracy (area under the curve = 0.881) for diagnosis of periodontitis. The authors’ findings identified potential biomarkers that may be useful for reflecting the severity of periodontal inflammation as part of monitoring disease activity in periodontitis patients.

Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation.A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models.With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected.Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

A new method consisting of orthogonal projection to latent structure (OPLS) and modified principal component analysis (PCA) was applied to a screening of bioactive compounds from natural products. In this report, extracts of 52 Scutellaria Root (the root of Scutellaria baicalensis Georgi) samples were analyzed by high-performance liquid chromatography (HPLC), and their inhibitory activities towards prostaglandin E2 (PGE2) production in a murine macrophage-like cell line J774.1 were examined. Wogonin and oroxylin A were predicted to be strong inhibitors of PGE2 production by OPLS analysis of the data. However, 6-methoxywogonin, which has been reported to have inhibitory activity, was omitted. Modified PCA was then applied to these data as a filter to exclude compounds less relevant to the activity, and OPLS analysis was applied to the modified data. As a result, this method predicted wogonin, oroxylin A and 6-methoxywogonin to be strong inhibitors of PGE2 production without any prior knowledge. The predictions by the OPLS combined with PCA method of PGE2 production inhibitory activities of 52 samples showed good agreement with the actual data. This method is simple and effective and can be used in screening of bioactive natural compounds without any prior knowledge.

Rhizoma Paridis (RP) with significant anti-tumor and haemostatic effects, has been used as the raw material of many Traditional Chinese preparations. However, its active ingredients are still unclear. The present study aimed to discover bioactive ingredients from RP based on spectrum-relationship and chemometric methods. Firstly, the saponins extract was prepared by phytochemical methods. Furthermore, UHPLC-QTOF-MS and UHPLC-qMS were incorporated to establish an efficient and sensitive method for obtaining the chemical profiles of RP. A total of 34 saponins were characterized in RP and 13 of them were assigned as common peaks in 25 batches of samples. After evaluation of the anti-tumor and haemostatic activities of samples, spectrum-effect relationships were investigated by the grey relational analysis (GRA), orthogonal projections to latent structures (OPLS) and back propagation artificial neural network (BP-ANN). These analyses showed that polyphyllin VII (P27), polyphyllin II (P30), dioscin (P31) and polyphyllin I (P33) play a role in the haemostatic effects of RP whereas polyphyllin VII (P27), dioscin (P31), polyphyllin I (P33), progenin III (P34) were assigned as candidate ingredients accounting for the anti-tumor activity of RP. The anti-tumor and haemostatic activities of these screened ingredients were subsequently verified in vitro. Collectively, the present study established the spectrum-effect relationship mode of RP and discovered the bioactive compounds of RP, which could be also used for exploration of bioactive compounds in herbal medicines, especially for trace compounds.

Force fields based on molecular mechanics (MM) are the main computational tool to study the relationship between protein structure and function at the molecular level. To validate the quality of such force fields, high-level quantum-mechanical (QM) data are employed to test their capability to reproduce the features of all major conformational substates of a series of blocked amino acids. The phase-space overlap between MM and QM is quantified in terms of the average structural reorganization energies over all energy minima. Here, the structural reorganization energy is the MM potential-energy difference between the structure of the respective QM energy minimum and the structure of the closest MM energy minimum. Thus, it serves as a measure for the relative probability of visiting the QM minimum during an MM simulation. We evaluate variants of the AMBER, CHARMM, GROMOS and OPLS biomolecular force fields. In addition, the two blocked amino acids alanine and serine are used to demonstrate the dependence of the measured agreement on the QM method, the phase, and the conformational preferences. Blocked serine serves as an example to discuss possible improvements of the force fields, such as including polarization with Drude particles, or using tailored force fields. The results show that none of the evaluated force fields satisfactorily reproduces all energy minima. By decomposing the average structural reorganization energies in terms of individual energy terms, we can further assess the individual weaknesses of the parametrization strategies of each force field. The dominant problem for most force fields appears to be the van der Waals parameters, followed to a lesser degree by dihedral and bonded terms. Our results show that performing a simple QM energy optimization from an MM-optimized structure can be a first test of the validity of a force field for a particular target molecule.

Four different charge methods such as RESP2, AM1-BCC, CM1 and CM5 were employed to investigate the properties of acetic anhydride in a bulk phase simulation with GAFF and OPLS force field potentials. These charges were obtained from quantum mechanical calculations by fitting electrostatic potential for RESP2, AM1-BCC and Hirshfeld population for CM5 charges. The calculated density and viscosity values from molecular dynamics simulation were in good agreement with experimental values; however, GAFF with RESP2/AM1-BCC models exhibited higher error (2.1–3.07%) than OPLS with CM1/CM5, which showed the error around 0.1–0.5%.

Background We aimed to identify novel metabolite and lipid signatures connected with the metabolic syndrome in a Dutch middle-aged population. Methods 115 individuals with a metabolic syndrome score ranging from 0 to 5 [50 cases of the metabolic syndrome (score ≥ 3) and 65 controls] were enrolled from the Leiden Longevity Study, and LC/GC–MS metabolomics and lipidomics profiling were performed on fasting plasma samples. Data were analysed with principal component analysis and orthogonal projections to latent structures (OPLS) to study metabolite/lipid signatures associated with the metabolic syndrome. In addition, univariate analyses were done with linear regression, adjusted for age and sex, for the study of individual metabolites/lipids in relation to the metabolic syndrome. Results Data was available on 103 metabolites and 223 lipids. In the OPLS model with metabolic syndrome score (Y-variable), 9 metabolites were negatively correlated and 26 metabolites (mostly acylcarnitines, amino acids and keto acids) were positively correlated with the metabolic syndrome score. In addition, a total of 100 lipids (mainly triacylglycerides) were positively correlated and 10 lipids from different lipid classes were negatively correlated with the metabolic syndrome score. In the univariate analyses, the metabolic syndrome (score) was associated with multiple individual metabolites (e.g., valeryl carnitine, pyruvic acid, lactic acid, alanine) and lipids [e.g., diglyceride(34:1), diglyceride(36:2)]. Conclusion In this first study on metabolomics/lipidomics of the metabolic syndrome, we identified multiple novel metabolite and lipid signatures, from different chemical classes, that were connected to the metabolic syndrome and are of interest to cardiometabolic disease biology. Electronic supplementary material The online version of this article (10.1007/s11306-019-1484-7) contains supplementary material, which is available to authorized users.

Made available in DSpace on 2019-03-14T02:03:17Z (GMT). No. of bitstreams: 1 tese_12967_Andressa Pagotto Vieira.pdf: 1336944 bytes, checksum: bcc6e85686d38135625a90b19adea0f2 (MD5) Previous issue date: 2019-02-22 O uso da espectroscopia de ressonância Magnética Nuclear (RMN) associada com ferramentas quimiométricas tem se tornado uma metodologia eficiente para estimar propriedades físico-químicas de petróleos brasileiros com sua elevada precisão, rapidez e confiabilidade. Neste trabalho, a técnica de RMN de hidrogênio (RMN de 1H) associada à calibração multivariada PLS, OPLS e seleção de variávies (iPLS, siPLS e MPA) foi utilizada para estimar o fator de caracterização, teor de nitrogênio total, parâmetro de solubilidade, teor de enxofre total, ponto de fluidez máximo e mínimo e viscosidade cinemática à 20°C, 30°C, 40°C e 50°C de 138 amostras de óleo bruto brasileiro. Os modelos foram avaliados pelo coeficiente de determinação (R2), erro quadrático médio de previsão (RMSEP) e resíduos. O teor de enxofre total e o parâmetro de solubilidade tiveram suas estimativas melhoradas por PLS e OPLS, respectivamente, com um RMSEP de 0,09 wt% (enxofre total) e 1,06 (MPa)1/2 (parâmetro de solubilidade). O MPA apresentou melhores resultados na estimativa do fator de caracterização, teor de nitrogênio total, viscosidade cinemática à 40°C e ponto de fluidez mínimo e máximo com RMSEP de 0,06, 0,048 wt%, 0,036, 14,6°C e 19,2°C respectivamente. Os modelos mínimo e máximo para o ponto de fluidez tiveram os desempenhos menos satisfatórios, talvez devido a complexidade desta propriedade. Com resultados bem promissores, o MPA tem mostrado que é uma técnica quimiométrica inovadora.

Mycobacterium tuberculosis remains a persistent pathogen, partly due to its lipid rich cell wall, of which mycolic acids (MAs) are a major component. The fluidity and conformational flexibilities of different MAs in the bacterial cell wall significantly influence its properties, function, and observed pathogenicity; thus, a proper conformational description of different MAs in different environments (e.g., in vacuum, in solution, in monolayers) can inform about their potential role in the complex setup of the bacterial cell wall. Previously, we have shown that molecular dynamics (MD) simulations of MA folding in vacuo can be used to characterize MA conformers in seven groupings relating to bending at the functional groups (W, U and Z-conformations). Providing a new OPLS-based forcefield parameterization for the critical cyclopropyl group of MAs and extensive simulations in explicit solvents (TIP4P water, hexane), we now present a more complete picture of MA folding properties together with improved simulation analysis techniques. We show that the ‘WUZ’ distance-based analysis can be used to pinpoint conformers with hairpin bends at the functional groups, with these conformers constituting only a fraction of accessible conformations. Applying principle component analysis (PCA) and refinement using free energy landscapes (FELs), we are able to discriminate a complete and unique set of conformational preferences for representative alpha-, methoxy- and keto-MAs, with overall preference for folded conformations. A control backbone-MA without any mero-chain functional groups showed significantly less folding in the mero-chain, confirming the role of functionalization in directing folding. Keto-MA showed the highest percentage of WUZ-type conformations and, in particular, a tendency to fold at its alpha-methyl trans-cyclopropane group, in agreement with results from Villeneuve et al. MAs demonstrate similar folding in vacuum and water, with a majority of folded conformations around the W-conformation, although the molecules are more flexible in vacuum than in water. Exchange between conformations, with a disperse distribution that includes unfolded conformers, is common in hexane for all MAs, although with more organization for Keto-MA. Globular, folded conformations are newly defined and may be specifically relevant in biofilms. Graphical abstractThrough advanced simulation analysis, including principle component analysis and free energy landscapes, we reveal detailed physical insights into the solvent-dependant folding behavior of mycolic acids from M. tb. Electronic supplementary material The online version of this article (10.1007/s00894-019-3943-5) contains supplementary material, which is available to authorized users.

Molecular dynamics simulations have been performed to compute the solvation free energy and the octanol/water partition coefficients for a challenging set of selected organic molecules, characterized by the simultaneous presence of functional groups coarsely spanning a large portion of the chemical space in druglike compounds and, in many cases, by a complex conformational landscape (2-propoxyethanol, acetylsalicylic acid, cyclohexanamine, dialifor, ketoprofen, nitralin, profluralin, terbacil). OPLS-AA and GAFF2 parametrizations of the organic molecules and of 1-octanol have been done via the Web-based automatic parameter generators, LigParGen [ Dodda et al. Nucl. Acids Res. 2017 , 121 , 3864 ] and PrimaDORAC [ Procacci J. Chem. Inf. Model. 2017 , 57 , 1240 ], respectively. For the water solvent, three popular three-point site models, TIP3P, SPCE, and OPC3, were tested. Solvation free energies in water and 1-octanol are evaluated using a recently developed nonequilibrium alchemical technology [ Procacci et al. J. Chem. Theory Comput. 2014 , 10 , 2813 ]. Extensive and accurate simulations, including all possible combinations of organic molecule, solvent, and solvent model, are allowed to assess the accuracy with regard to solvation free energies of the latest release of two widespread force fields, OPLS and GAFF. The collected data are relevant in the evaluation of the predictive power of these classical force fields (and of the related support software for automated parametrization) with regard to binding free energies in a drug-receptor system for industrial applications.

Novel manipulations of the well-established multivariate calibration models namely; partial least square regression (PLSR) and support vector regression (SVR) are introduced in the presented comparative study. Two preprocessing methods comprising first derivatization and orthogonal projection to latent structures (OPLS) are implemented prior to modeling with PLSR and SVR. Quantitative determination of pyridostigmine bromide (PR) in existence of its two associated substances; impurity a (IMP A) and impurity b (IMP B); was utilized as a case study for achieving comparison. A series consisting of 16 mixtures with numerous percentages of the studied compounds was applied for implementation of a 3 factor 4 level experimental design. Additionally, a series consisting of 9 mixtures was employed in an independent test to verify the predictive power of the suggested models. Significant improvement of predictive abilities of the two studied chemometric models was attained via implementation of OPLS processing method. The root mean square error of prediction RMSEP for the test set mixtures was employed as a key comparison tool. About PLSR model, RMSEP was found 0.5283 without preprocessing method, 1.1750 when first derivative data was used and 0.2890 when OPLS preprocessing method was applied. With regard to SVR model, RMSEP was found 0.2173 without preprocessing method, 0.3516 when first derivative data was used and 0.1819 when OPLS preprocessing method was applied.

Alzheimer's disease (AD) is characterized by the accumulation of extracellular Aβ42 and Aβ40 oligomers and plaques. In a recent computational study, we found that the presence of the residues I41 and A42 increases significantly the propensity of Aβ to form a tetrameric β-barrel structure in a bilayer mimicking a neuronal membrane. In this work, we have determined the propensity of the two Aβ proteins to form tetrameric β-barrel structures in aqueous solution using four atomistic protein fields, that is, Amber99SB-ILDN/TIP3P, OPLS/TIP3P, CHARMM36m/TIP3P-modified, and Amber99SB/DISP. Extensive replica exchange molecular dynamics simulations make it clear that a β-barrel, made of two distinct β-hairpin motifs and an asymmetric arrangement of eight antiparallel β-strands with an inner pore diameter of 0.7 nm, exists transiently for Aβ42 peptide, but this is less the case for Aβ40 peptide, due to the change of the CHC-CHC and the Cter-Cter interfaces. This study has several implications in AD.

Attenuated total reflection–Fourier transform infrared (ATR–FTIR) spectroscopy is a simple, cheap, and fast method to collect chemical compositional information from microalgae. However, (semi)quantitative evaluation of the collected data can be daunting. In this work, ATR–FTIR spectroscopy was used to monitor changes of protein, lipid, and carbohydrate content in seven green microalgae grown under nitrogen starvation. Three statistical methods—univariate linear regression analysis (ULRA), orthogonal partial least squares (OPLS), and multivariate curve resolution-alternating least squares (MCR–ALS)—were compared in their ability to model and predict the concentration of these compounds in the biomass. OPLS was found superior, since it i) included all three compounds simultaneously, ii) explained variations in the data very well, iii) had excellent prediction accuracy for proteins and lipids, and acceptable for carbohydrates, and iv) was able to discriminate samples based on cultivation stage and type of storage compounds accumulated in the cells. ULRA models worked well for the determination of proteins and lipids, but carbohydrates could only be estimated if already determined protein contents were used for scaling. Results obtained by MCR–ALS were similar to ULRA, however, this method is considerably easier to perform and interpret than the more abstract statistical/chemometric methods. FTIR-spectroscopy-based models allow high-throughput, cost-effective, and rapid estimation of biomass composition of green microalgae.

The leaves of Morus alba (LMA) are crucial traditional Chinese medicine (TCM) of clearing heat. In ancient Chinese materia medica and the current Pharmacopoeia of the People’s Republic of China, LMA are recorded to be harvested after frost for medicinal purpose. However, the reason and mechanism of this traditional usage have been still unknown so far. In this work, it was confirmed firstly that the antipyretic effect of LMA after frost was better than that of before frost significantly on feverish rats. Subsequently, the chemical profiles of LMA before and after frost were characterized by fingerprint, respectively. Then, the endemic peaks after frost and positive differential peaks were screened as the research object of spectrum-effect correlation by orthogonal signal correction partial least square discrimination (OPLS). Finally, a multivariable and continuous-index spectrum-effect correlation model coupled with OPLS was established. As a result, the antipyretic components of postfrost LMA were screened and identified as citric acid derivative and tryptophan which may be the synergistic material basis. The study can provide a scientific foundation for the enhancement of effects in the postfrost LMA. Moreover, the strategy of this research could provide a valuable reference for revealing the material basis of synergetic or antagonistic effects among other complex drug systems.

Lipid membrane permeation of drug molecules was investigated with Heterogeneous Dielectric Generalized Born (HDGB)-based models using solubility-diffusion theory and machine learning. Free energy profiles were obtained for neutral molecules by the standard HDGB and Dynamic HDGB (DHDGB) to account for the membrane deformation upon insertion of drugs. We also obtained hybrid free energy profiles where the neutralization of charged molecules was taken into account upon membrane insertion. The evaluation of the predictions was done against experimental permeability coefficients from Parallel Artificial Membrane Permeability Assays (PAMPA), and effects of partial charge sets, CGenFF, AM1-BCC, and OPLS, on the performance of the predictions were discussed. (D)HDGB-based models improved the predictions over the two-state implicit membrane models, and partial charge sets seemed to have a strong impact on the predictions. Machine learning increased the accuracy of the predictions, although it could not outperform the physics-based approach in terms of correlations.

GC–MS metabolomics was used to discriminate the phytochemicals profile of Indonesian white, red, and black rice brans, and Japanese white rice brans. This technique was used for the first time to identify compounds in rice brans having cytotoxic activity against WiDr colon cancer cells. Orthogonal Projection to the Latent Structure (OPLS) analysis showed that protocatechuic acid (PA) was a discriminating factor found in black rice brans which strongly correlated with its cytotoxicity (IC(50) 8.53 ± 0.26 µM). Real time-PCR data demonstrated that PA cytotoxicity at different concentrations (1, 5, 10, 25 and 50 µg/mL) was mediated through different pathways. Bcl-2 expression was downregulated at all tested concentrations indicating apoptosis stimulation. At 1–10 ppm concentration, PA activated both intrinsic and extrinsic apoptosis pathways since the expression of p53, Bax, caspase-8, and caspase-9 were upregulated. At a higher dose (25 and 50 µg/mL), PA possibly involved in pyroptosis-mediated pro-inflammatory cell death by upregulating the expression of caspase-1 and caspase-7. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10068-019-00725-2) contains supplementary material, which is available to authorized users.

Synchrotron X-ray diffraction experiments and molecular dynamics simulations have been performed on simple aliphatic aldehydes, from propanal to nonanal. The performance of the OPLS all-atom interaction potential model for aldehydes has been assessed via direct comparison of simulated and experimental total scattering structure factors. In general, MD results reproduce the experimental data at least semi-quantitatively. However, a slight mismatch can be observed between the two datasets in terms of the position of the main diffraction maxima. Partial radial distribution functions have also been calculated from the simulation results. Clear differences could be detected between the various O-H partial radial distribution functions, depending on whether the H atom is attached to the carbon atom that is doubly bonded to the oxygen atom of the aldehyde group or not., This is the pre-peer reviewed version of the article, which has been published in final form at Physica Status Solidi B

Introduction The increase in multidrug resistance and lack of efficacy in malaria therapy has propelled the urgent discovery of new antiplasmodial drugs, reviving the screening of secondary metabolites from traditional medicine. In plant metabolomics, NMR-based strategies are considered a golden method providing both a holistic view of the chemical profiles and a correlation between the metabolome and bioactivity, becoming a corner stone of drug development from natural products. Objective Create a multivariate model to identify antiplasmodial metabolites from 1H NMR data of two African medicinal plants, Keetia leucantha and K. venosa. Methods The extracts of twigs and leaves of Keetia species were measured by 1H NMR and the spectra were submitted to orthogonal partial least squares (OPLS) for antiplasmodial correlation. Results Unsupervised 1H NMR analysis showed that the effect of tissues was higher than species and that triterpenoids signals were more associated to Keetia twigs than leaves. OPLS–DA based on Keetia species correlated triterpene signals to K. leucantha, exhibiting a higher concentration of triterpenoids and phenylpropanoid-conjugated triterpenes than K. venosa. In vitro antiplasmodial correlation by OPLS, validated for all Keetia samples, revealed that phenylpropanoid-conjugated triterpenes were highly correlated to the bioactivity, while the acyclic squalene was found as the major metabolite in low bioactivity samples. Conclusion NMR-based metabolomics combined with supervised multivariate data analysis is a powerful strategy for the identification of bioactive metabolites in plant extracts. Moreover, combination of statistical total correlation spectroscopy with 2D NMR allowed a detailed analysis of different triterpenes, overcoming the challenge posed by their structure similarity and coalescence in the aliphatic region. Electronic supplementary material The online version of this article (10.1007/s11306-019-1487-4) contains supplementary material, which is available to authorized users.

The Hirshfeld charges are linearly increased to reproduce the experimental dielectric constant of 10 polar solvents having values between 13 (pyridine) and 182 ( N-methylformamide). The OPLS/AA force field is used to obtain the new parameters. The surface tension and liquid density are also target properties to determine the new nonbonding parameters. The charge scaling factor is between 1.2 and 1.3. In addition, properties that were not used in the parametrization procedure, such as the heat of vaporization, self-diffusion coefficient, shear viscosity, isothermal compressibility, and volumetric expansion coefficient are obtained. Binary mixtures of amide/water and amide/amide are also studied. The original parameters of OPLS/AA, CGenFF, and GAFF force fields are evaluated. The TIP4P/ε force field is used to simulate water. The results from this work with the new parameters, for both pure components and binary mixtures, are in better agreement with experimental data than those obtained with the original values for most of the calculated properties. The maximum density of N-methylformamide in aqueous solutions is correctly predicted only with the new parameters. The high value of the dielectric constant of acetamide, formamide, and N-methylformamide is discussed in terms of the chain formation from the hydrogen bond interactions.

Potential model descriptions for alkylsulfonic acids, methanesulfonic, ethanesulfonic, and propanesulfonic acids, are developed based on CHARMM and OPLS parameters and protocols. Thermodynamic, structural, and transport properties of these alkylsulfonic acids, including density, heat of vaporization, radial and spatial distribution functions, hydrogen bond structure, shear viscosity, and translational diffusion coefficients, are examined via molecular dynamics simulations using these potential models. The results are compared with the predictions of ab initio molecular dynamics simulations as well as with available experimental information. A good overall agreement indicates that the force-field descriptions developed here provide a reliable framework to study liquid systems containing alkylsulfonic acids.

International audience; Oral squamous cell carcinoma (OSCC) is a major cause of cancer-associated morbidity and mortality and may develop from oral premalignant lesions (OPL). An improved molecular classification of OPL may help refining prevention strategies. We identified two main OPL gene-expression subtypes, named immunological and classical, in 86 OPL (discovery dataset). A gene expression-based score was then developed to classify OPL samples from three independent datasets, including 17 (GSE30784),13 (GSE10174) and 15 (GSE85195) OPLs, into either one of the two gene-expression subtypes. Using the single sample gene set enrichment analysis, enrichment scores for immune-related pathways were different between the two OPL subtypes. In OPL from the discovery set, loss of heterozygosities (LOH) at 3p14, 17p13, TP53, 9p21 and 8p22 and miRNA gene expression profiles were analyzed. Deconvolution of the immune infiltrate was performed using the Microenvironment Cell Populations-counter tool. A multivariate analysis revealed that decreased miRNA-142-5p expression (P = 0.0484) and lower T-cell, monocytic and myeloid dendritic cells (MDC) immune infiltration (T-cells, P = 0.0196; CD8 T cells, P = 0.0129; MDC, P = 0.0481; and monocytes, P = 0.0212) were associated with oral cancer development in the immunological subtype only. In contrast, LOH at 3p14 (P = 0.0241), 17p13 (P = 0.0348) and TP53 (P = 0.004) were associated with oral cancer development in the classical subtype only. In conclusion, we identified 2 subtypes of OPLs, namely immune and classical, which may benefit from different and specific personalized prevention interventions. ARTICLE HISTORY

Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a versatile multivariate projection method for analysis of correlation, discrimination and effect changes. However, currently OPLS is not fully using its multivariate potential since orthogonal systematic variation is not considered in model interpretation, resulting in univariate interpretation of variable significance. We present a strategy for improved interpretation of OPLS models based upon a post-hoc linear regression analysis that can be used with or without the orthogonal OPLS score(s) as a covariate to make the interpretation multivariate or univariate respectively. By selecting the observed response y or estimated response yhat as a one of the factors in the linear regression the results are related to either of the OPLS loadings w or p. Furthermore, converting the OPLS loading values to statistical t-values creates a direct link to statistical significance. Finally, by applying three different Boolean loadings W, P and W∧P variable significance can be summarized based on three criteria. W and P reveal if the values in w or p respectively are outside the statistical limits with W∧P being the logical conjunction of W and P (significant if outside limits in both W and P). Two examples are used to verify the proposed strategy. First, a synthetic example, simulating a mix of mass spectra, and second a clinical metabolomics study of a dietary intervention. In the simulated example we show that multivariate interpretation gives higher accuracy for estimation of true differences, mainly due to higher true positive rate. Furthermore, we highlight how application of W∧P for summarizing variable significance leads to higher accuracy. For the metabolomics example, we show that a more detailed interpretation, i.e. larger number of significant metabolites of relevance, is obtained using the multivariate interpretation. In summary, the suggested strategy provides means for facilitated interpretation of OPLS models, beyond univariate statistics, and offers a multivariate tool for discovery of biomarker patterns, i.e. latent biomarkers.

Salak fruit (Salacca zalacca), commonly known as snake fruit, is used indigenously as food and for medicinal applications in Southeast Asia. This study was conducted to evaluate the &alpha, glucosidase inhibitory activity of salak fruit extracts in correlation to its Fourier transform infrared spectroscopy (FT-IR) fingerprint, utilizing orthogonal partial least square. This calibration model was applied to develop a rapid analytical method tool for quality control of this fruit. A total of 36 extracts prepared with different solvent ratios of ethanol&ndash, water (100, 80, 60, 40.20, 0% v/v) and their &alpha, glucosidase inhibitory activities determined. The FT-IR spectra of ethanol&ndash, water extracts measured in the region of 400 and 4000 cm&minus, 1 at a resolution of 4 cm&minus, 1. Multivariate analysis with a combination of orthogonal partial least-squares (OPLS) algorithm was used to correlate the bioactivity of the samples with the FT-IR spectral data. The OPLS biplot model identified several functional groups (C&ndash, H, C=O, C&ndash, N, N&ndash, H, C&ndash, O, and C=C) which actively induced &alpha, glucosidase inhibitory activity.

We show that the free energy of inserting hydrophobic peptides into lipid bilayer membranes from surface-aligned to transmembrane inserted states can be reliably calculated using atomistic models. We use two entirely different computational methods: high temperature spontaneous peptide insertion calculations as well as umbrella sampling potential-of-mean-force (PMF) calculations, both yielding the same energetic profiles. The insertion free energies were calculated using two different protein and lipid force fields (OPLS protein/united-atom lipids and CHARMM36 protein/all-atom lipids) and found to be independent of the simulation parameters. In addition, the free energy of insertion is found to be independent of temperature for both force fields. However, we find major difference in the partitioning kinetics between OPLS and CHARMM36, likely due to the difference in roughness of the underlying free energy surfaces. Our results demonstrate not only a reliable method to calculate insertion free energies for peptides, but also represent a rare case where equilibrium simulations and PMF calculations can be directly compared.

Hybrid cyclic α/β-peptides, in which one or more β-amino acids are incorporated into the backbone, are gaining increasing interest as potential therapeutics, thanks to their ability to achieve enhanced binding affinities for a biological target through pre-organization in solution. The in silico prediction of their three dimensional structure through strategies such as MD simulations would substantially advance the rational design process. However, whether the molecular mechanics force fields are accurate in sampling highly constrained cyclopeptides containing β-amino acids remains to be verified. Here, we present a systematic assessment of the ability of 8 widely used force fields to reproduce 79 NMR observables (including chemical shifts and 3J scalar couplings) on five cyclic α/β-peptides that contain the integrin recognition motif isoDGR. Most of the investigated force fields, which include force fields from AMBER, OPLS, CHARMM and GROMOS families, display very good agreement with experimental 3J(HN,Hα), suggesting that MD simulations could be an appropriate tool in the rational design of therapeutic cyclic α-peptides. However, for NMR observables directly related to β-amino acids, we observed a poor agreement with experiments and a remarkable dependence of our evaluation on the choice of Karplus parameters. The force field weaknesses herein unveiled might constitute a source of inspiration for further force field optimization.

A series of N-(aryl/heteroaryl)-4-(1H-pyrrol-1-yl)benzenesulfonamides were synthesized from 4-amino-N-(aryl/heteroaryl)benzenesulfonamides and 2,5-dimethoxytetrahydrofuran. All the synthesized compounds were evaluated for their anticancer activity on HeLa, HCT-116, and MCF-7 human tumor cell lines. Compound 28, bearing 8-quinolinyl moiety, exhibited the most potent anticancer activity against the HCT-116, MCF-7, and HeLa cell lines, with IC50 values of 3, 5, and 7 µM, respectively. The apoptotic potential of the most active compound (28) was analyzed through various assays: phosphatidylserine translocation, cell cycle distribution, and caspase activation. Compound 28 promoted cell cycle arrest in G2/M phase in cancer cells, induced caspase activity, and increased the population of apoptotic cells. Relationships between structure and biological activity were determined by the QSAR (quantitative structure activity relationships) method. Analysis of quantitative structure activity relationships allowed us to generate OPLS (Orthogonal Projections to Latent Structure) models with verified predictive ability that point out key molecular descriptors influencing benzenosulfonamide’s activity.

Hemicelluloses are a relatively unused renewable resource. One reason is their broad structure variety that makes it hard to understand structure-property relations. In this study arbinoxylan, extracted from barley husk, was chemically modified into hydroxypropyl methyl-, hydroxypropyl- and methyl arabinoxylan. The relationship between structure and phase behavior was investigated by using multivariate analysis. The arabinoxylan ethers were characterized using mid-infrared FTIR spectroscopy and from principal components analysis, PCA, structural or physical variations between samples were visualized. With orthogonal projections to latent structures, OPLS, vibrations specific for arabinoxylan hydroxypropyl and methyl substitutions was assigned. Among the observed differences between chemical derivatives was an intensity change in the water vibration. The differences in hydration were related to clouding phase behavior of the arabinoxylan ethers. This study shows that multivariate analysis methods are useful for finding unexpected and/or hidden features in the polysaccharide structure.

A series of N-(aryl/heteroaryl)-4-(1H-pyrrol-1-yl)benzenesulfonamides were synthesized from 4-amino-N-(aryl/heteroaryl)benzenesulfonamides and 2,5-dimethoxytetrahydrofuran. All the synthesized compounds were evaluated for their anticancer activity on HeLa, HCT-116, and MCF-7 human tumor cell lines. Compound 28, bearing 8-quinolinyl moiety, exhibited the most potent anticancer activity against the HCT-116, MCF-7, and HeLa cell lines, with IC50 values of 3, 5, and 7 µM, respectively. The apoptotic potential of the most active compound (28) was analyzed through various assays: phosphatidylserine translocation, cell cycle distribution, and caspase activation. Compound 28 promoted cell cycle arrest in G2/M phase in cancer cells, induced caspase activity, and increased the population of apoptotic cells. Relationships between structure and biological activity were determined by the QSAR (quantitative structure activity relationships) method. Analysis of quantitative structure activity relationships allowed us to generate OPLS (Orthogonal Projections to Latent Structure) models with verified predictive ability that point out key molecular descriptors influencing benzenosulfonamide’s activity.

It is believed that traditional Korean medicines can be managed more scientifically through the development of logical criteria to verify their region of cultivation, and that this could contribute to the advancement of the traditional herbal medicine industry. This study attempted to determine such criteria for Sansuyu. The volatile compounds were obtained from 20 samples of domestic Corni fructus (Sansuyu) and 45 samples of Chinese Sansuyu by steam distillation. The metabolites were identified in the NIST Mass Spectral Library via the obtained gas chromatography/ mass spectrometer (GC/MS) data of 53 training samples. Data binning at 0.2 min intervals was performed to normalize the number of variables used in the statistical analysis. Multivariate statistical analyses, such as principle component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using the SIMCA-P software package. Significant variables with a variable importance in the projection (VIP) score higher than 1.0 were obtained from OPLS-DA, and variables that resulted in a p-value of less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Finally, among the 11 variables extracted, 1-ethylbutyl-hydroperoxide (9.089 min), nonadecane (20.170 min), butylated hydroxytoluene (25.319 min), 5β,7βH,10α-eudesm-11-en-1α-ol (25.921 min), 7,9-bis(2-methyl-2-propanyl)-1-oxaspiro(4.5)deca-6,9- diene-2,8-dione (34.257 min), and 2-decyldodecyl-benzene (54.717 min) were selected as markers to indicate the origin of Sansuyu. The statistical model developed was suitable for the determination of the geographical origin of Sansuyu. The cultivation areas of four Korean and eight Chinese Sansuyu samples were predicted via the established OPLS- DA model, and it was confirmed that 11 of the 12 samples were accurately classified. 요약 : 생약의 원산지를 판별하는 논리적인 일련의 기준을 개발한다면, 현재 유통되는 한약을 좀 더 과 학적으로 관리 할 수 있을 것이다. 이러한 노력은 전통적인 한약 산업 발전에 기여할 것이라고 사료된다. 산수유의 원산지 판별법을 개발하기 위해, 본 연구에서는 우선 국산 산수유와 중국산 산수유를 각각 수 증기 증류하고 이 때 얻은 휘발성분을 GC/MS를 이용하여 분석하였다. NIST mass spectral library의 데이

Fatigue occurs in all chronic inflammatory diseases, in cancer, and in some neurological conditions. Patients often regard fatigue as one of their most debilitating problems, but currently there is no established treatment and the mechanisms that lead to and regulate fatigue are incompletely understood. Our objective was to more completely understand the physiology of this phenomenon. Twenty-four patients with rheumatoid arthritis (RA) naïve to treatment with biological drugs were enrolled for the study. Fatigue was measured with a fatigue visual analogue scale (fVAS). Ethylenediaminetetraacetic acid (EDTA) plasma samples were subjected to gas chromatography-time-of-flight mass spectrometry (GC/MS-TOF)-based metabolite profiling. Obtained metabolite data were evaluated by multivariate data analysis with orthogonal projections to latent structures (OPLS) method to pinpoint metabolic changes related to fatigue severity. A significant multivariate OPLS model was obtained between the fVAS scores and the measured metabolic levels. Increasing fatigue scores were associated with a metabolic pattern characterized by down-regulation of metabolites from the urea cycle, fatty acids, tocopherols, aromatic amino acids, and hypoxanthine. Uric acid levels were increased. Apart from fatigue, we found no other disease-related variables that might be responsible for these changes. Our MS-based metabolomic approach demonstrated strong associations between fatigue and several biochemical patterns related to oxidative stress.

Myrtus communis L. is an aromatic evergreen shrub and its essential oil possesses known powerful antimicrobial activity. However, the contribution of each component of the plant essential oil in observed antimicrobial ability is unclear. In this study, chemical components of the essential oil samples of the plant were identified qualitatively and quantitatively using GC/FID/Mass spectrometry system, antimicrobial activity of these samples against three microbial strains were evaluated and, these two set of data were correlated using chemometrics methods. Three chemometric methods including principal component regression (PCR), partial least squares (PLS) and orthogonal projections to latent structures (OPLS) were applied for the study. These methods showed similar results, but, OPLS was selected as preferred method due to its predictive and interpretational ability, facility, repeatability and low time-consuming. The results showed that α-pinene, 1,8 cineole, β-pinene and limonene are the highest contributors in antimicrobial properties of M. communis essential oil. Other researches have reported high antimicrobial activities for the plant essential oils rich in these compounds confirming our findings.

In this data article we provide topologies and force field parameters files for molecular dynamics simulations of lipids in the OPLS-aa force field using the GROMACS package. This is the first systematic parameterization of lipid molecules in this force field. Topologies are provided for four phosphatidylcholines: saturated DPPC, mono-cis unsaturated POPC and DOPC, and mono-trans unsaturated PEPC. Parameterization of the phosphatidylcholines was achieved in two steps: first, we supplemented the OPLS force field parameters for DPPC with new parameters for torsion angles and van der Waals parameters for the carbon and hydrogen atoms in the acyl chains, as well as new partial atomic charges and parameters for torsion angles in the phosphatidylcholine and glycerol moieties [1]. Next, we derived parameters for the cis and trans double bonds and the neighboring them single bonds [2]. Additionally, we provide GROMACS input files with parameters describing simulation conditions (md.mdp), which are strongly recommended to be used with these lipids models. The data are associated with the research article “Cis and trans unsaturated phosphatidylcholine bilayers: a molecular dynamics simulation study” [2] and provided as supporting materials.

Interactions between transmembrane (TM) peptides are important in biophysical chemistry, but there are few studies assessing atomistic simulation parameters concerning the energetics of interactions of TM helical peptides. Our potential of mean force analysis using OPLS-AA protein/Berger lipid force fields (FFs) shows that the dimerisation energy of (AALALAA)3 helical peptides in the dioleoylphosphatidylcholine bilayer is −4.4 kJ/mol, which was much smaller than the reported experimental value (−12.7 kJ/mol), thus calling for improvement of parameters of the combined FFs. As each of the FFs has been independently developed, we then tested the effects of downscaling the Lennard-Jones (LJ) energy terms between the OPLS-AA atoms and Berger lipid atoms, preserving the parameters within each FF. A 0.9-fold rescaling of the LJ energies was found to render the dimerisation energy close to the experimental value. Solvation of backbone atoms as well as side chain atoms in lipids is crucial for the TM helix interaction. In similar analyses, GROMOS 53A6 FF exhibited as weak dimerisation propensity (~−5.2 kJ/mol) as OPLS-AA/Berger, but CHARMM36 showed relatively accurate propensity (~−9.9 kJ/mol). Challenges and strategies in rendering the TM interaction energy realistic within the framework of current FFs are discussed.

The characterization of differences among polar lipid classes in tumors and surrounding normal tissues of 20 kidney cancer patients is performed by hydrophilic interaction liquid chromatography (HILIC) coupled to electrospray ionization mass spectrometry (ESI-MS). The detailed analysis of identified lipid classes using relative abundances of characteristic ions in negative- and positive-ion modes is used for the determination of more than 120 individual lipid species containing attached fatty acyls of different chain length and double bond number. Lipid species are described using relative abundances, providing a better visualization of lipidomic differences between tumor and normal tissues. The multivariate data analysis methods using unsupervised principal component analysis (PCA) and supervised orthogonal partial least square (OPLS) are used for the characterization of statistically significant differences in identified lipid species. Ten most significant up- and down-regulated lipids in OPLS score plots are also displayed by box plots. A notable increase of relative abundances of lipids containing four and more double bonds is detected in tumor compared to normal tissues.