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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.

In this work, we present a new four-site potential for methanol, MeOH-4P, fitted to reproduce the dielectric constant e, the surface tension γs, and the liquid density ρ of the pure liquid at T = 298.15 K and p = 1 bar. The partial charges on each site were taken from the OPLS/2016 model with the only difference of putting the negative charge on the fourth site (M) instead of on the O atom, as done in four-site water models. The original Lennard-Jones (LJ) parameters of OPLS/2016 for the methyl moiety (Me) were modified for the fitting of ρ and γs, whereas the parameters of the TIP4P-FB water model were used for the O atom without change. Taking into account the energetic cost of the enhanced dipole relative to the isolated molecule, the results from simulations with this model showed good agreement with experiments for ρ, αp, κT, Cp, and ΔHv–l. Also, the temperature dependence of γs and e is satisfactory in the interval between 260 and 360 K, and the critical point description is similar to that of OPLS/...

Traditional Chinese medicines (TCM)/herbal medicines (HM) are too complicated to comprehensively investigate their quality consistency effectively with a single detection technique. Hence, finding an effective, rapid, and comprehensive quality control (QC) method is of great importance for guaranteeing the safety and efficacy of TCM/HM in clinical applications. In our current research, a novel strategy of multi-wavelength fusion HPLC fingerprints and ultraviolet (UV) spectroscopic fingerprinting was proposed and successfully applied to monitor the quality consistency of compound liquorice tablets (CLT). The quality grades of 35 CLT samples from two manufacturers were successfully discriminated and evaluated by the averaged linear quantified fingerprint method (ALQFM) from a qualitative and quantitative perspective. The results showed that the UV spectroscopic fingerprints agreed well with the multi-wavelength fusion HPLC fingerprints. In addition, ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-Q-TOF-MS) was applied to investigate the chemical constituents in CLT samples, providing an important chemical structural foundation for further QC and bioactivity studies. Additionally, a simple flow injection analysis (FIA) was developed to investigate the antioxidant capacity in CLT, which was based on the scavenging of 2,2-diphenyl-1-picrylhydrazyl radicals by antioxidants. Furthermore, the fingerprint–efficacy relationship between high-performance liquid chromatography (HPLC) fingerprints and the antioxidant activities of CLT samples was established utilizing orthogonal projection to latent structures (OPLS). In conclusion, this study indicated that integrating UHPLC-ESI-Q-TOF-MS/MS, UV spectroscopic fingerprints, and multi-wavelength fusion HPLC fingerprints coupled with the antioxidant activities reported could give important clues for further pharmacological and clinical studies of CLT. Meanwhile, it provides a practical strategy for the rapid screening and identifying of TCM/HM quality consistency.

Tri-isoamyl phosphate (TiAP) has been proposed to be an alternative for tri-butyl phosphate (TBP) in the Plutonium Uranium Extraction (PUREX) process. Recently, we have successfully calibrated and tested all-atom optimized potentials for liquid simulations using Mulliken partial charges for pure TiAP, TBP, and dodecane by performing molecular dynamics (MD) simulation. It is of immense importance to extend this potential for the various molecular properties of TiAP and TiAP/n-dodecane binary mixtures using MD simulation. Earlier, efforts were devoted to find out a suitable force field which can explain both structural and dynamical properties by empirical parameterization. Therefore, the present MD study reports the structural, dynamical, and thermodynamical properties with different mole fractions of TiAP-dodecane mixtures at the entire range of mole fraction of 0-1 employing our calibrated Mulliken embedded optimized potentials for liquid simulation (OPLS) force field. The calculated electric dipole moment of TiAP was seen to be almost unaffected by the TiAP concentration in the dodecane diluent. The calculated liquid densities of the TiAP-dodecane mixture are in good agreement with the experimental data. The mixture densities at different temperatures are also studied which was found to be reduced with temperature as expected. The plot of diffusivities for TiAP and dodecane against mole fraction in the binary mixture intersects at a composition in the range of 25%-30% of TiAP in dodecane, which is very much closer to the TBP/n-dodecane composition used in the PUREX process. The excess volume of mixing was found to be positive for the entire range of mole fraction and the excess enthalpy of mixing was shown to be endothermic for the TBP/n-dodecane mixture as well as TiAP/n-dodecane mixture as reported experimentally. The spatial pair correlation functions are evaluated between TiAP-TiAP and TiAP-dodecane molecules. Further, shear viscosity has been computed by performing the non-equilibrium molecular dynamics employing the periodic perturbation method. The calculated shear viscosity of the binary mixture is found to be in excellent agreement with the experimental values. The use of the newly calibrated OPLS force field embedding Mulliken charges is shown to be equally reliable in predicting the structural and dynamical properties for the mixture without incorporating any arbitrary scaling in the force field or Lennard-Jones parameters. Further, the present MD simulation results demonstrate that the Stokes-Einstein relation breaks down at the molecular level. The present methodology might be adopted to evaluate the liquid state properties of an aqueous-organic biphasic system, which is of great significance in the interfacial science and technology.

Background Early diagnosis of colorectal cancer (CRC) simplifies treatment and improves treatment outcomes. We previously described a diagnostic metabolomic biomarker derived from semi-quantitative gas chromatography-mass spectrometry. Our objective was to determine whether a quantitative assay of additional metabolomic features, including parts of the lipidome could enhance diagnostic power; and whether there was an advantage to deriving a combined diagnostic signature with a broader metabolomic representation. Methods The well-characterized Biocrates P150 kit was used to quantify 163 metabolites in patients with CRC (N = 62), adenoma (N = 31), and age- and gender-matched disease-free controls (N = 81). Metabolites included in the analysis included phosphatidylcholines, sphingomyelins, acylcarnitines, and amino acids. Using a training set of 32 CRC and 21 disease-free controls, a multivariate metabolomic orthogonal partial least squares (OPLS) classifier was developed. An independent set of 28 CRC and 20 matched healthy controls was used for validation. Features characterizing 31 colorectal adenomas from their healthy matched controls were also explored, and a multivariate OPLS classifier for colorectal adenoma could be proposed. Results The metabolomic profile that distinguished CRC from controls consisted of 48 metabolites (R2Y = 0.83, Q2Y = 0.75, CV-ANOVA p-value

In this study, a novel orthonormalized partial least squares (OPLS) spatial filter is proposed for the extraction of the steady-state visual evoked potential (SSVEP) components buried in the electroencephalogram (EEG) data. The proposed method avoids over-fitting of the EEG data to the ideal SSVEP reference signals by reducing the over-emphasis of the target (pure sine-cosine) space. The paper presents the comparison of the detection accuracy of the proposed method with other existing spatial filters and discusses the shortcomings of these algorithms. The OPLS was tested across ten healthy subjects and its classification performance was examined. Further, statistical tests were performed to show the significant improvements in obtained detection accuracies. The result shows that the OPLS provides a significant improvement in detection accuracy across subjects compared to spatial filters under comparison. Hence, OPLS would act as a reliable and efficient spatial filter for separation of SSVEP components in brain-computer interface (BCI) applications.

Metabolomics has become an important tool in the search for bioactive compounds from natural sources, with the recent inclusion of marine organisms. Of the several steps performed in metabolomics studies, the extraction process is a crucial step&mdash, one which has been overlooked for a long time. In the presented study, a pressurized liquid extraction system was used to investigate the effect of extraction parameters such as pressure, temperature, number of cycles, and solvent polarity on the chemical diversity of the extract obtained from the marine sponge, Xestospongia. For this, a full factorial design (24) was performed using a chemical diversity index, which was found to be a suitable tool to determine the efficiency of the extraction process, as the response variable. This index was calculated using a logarithmic transformation of 1H NMR signals. Three factors (number of cycles, temperature, and solvent polarity) and two interactions were found to affect the chemical diversity of the obtained extracts significantly. Two individual factors (temperature and solvent polarity) were selected for further study on their influence on sponge metabolites using orthogonal partial least square (OPLS) modeling. Based on the results, the groups of compounds that were most influenced by these parameters were determined, and it was concluded that ethanol as the extraction solvent together with low temperatures were the conditions that provided a higher chemical diversity in the extract.

Herein, we propose a H-1 NMR-based metabolomics method to reveal cytotoxic metabolites from Mahonia aquifolium stem-bark. Primary and secondary metabolites in the Mahonia aquifolium extracts were identified by thorough analysis of H-1 and 2D NMR spectra, without prior isolation. An OPLS multivariate analysis method was used to correlate the chemical composition of the plant extracts with the results of cytotoxic activity against Human cervical adenocarcinoma cell line. Protoberberine alkaloids berberine and palmatine, along with bisbenzylisoquinoline alkaloid berbamine were identified as the most influential in the OPLS model, with the highest cytotoxic activity.

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.

Recently a new parameter to infer variable importance in orthogonal projections to latent structures (OPLS) was presented. Called OPLS-VIP (variable influence on projection), this paramete ...

The present AChE inhibitors have been successful in the treatment of Alzheimer׳s Diseases however suffers serious side effects. Therefore in this view, the present study was sought to identify compounds with appreciable pharmacological profile targeting AChE. Analogue of Rivastigmine and Fluoxetine hybrid synthesized by Toda et al, 2003 (dataset1), and Coumarin−Tacrine hybrids synthesized by Qi Sun et al (dataset2) formed the test compounds for the present pharmacological evaluation. p-cholorophenyl substituted Rivastigmine and Fluoxetine hybrid compound (26d) from dataset 1 and −OCH3 substitute Coumarin−Tacrine hybrids (1h) from dataset 2 demonstrated superior pharmacological profile. 26 d showed superior pharmacological profile comparison to the entire compounds in either dataset owing to its better electrostatic interactions and hydrogen bonding patterns. In order to identify still better compound with pharmacological profile than 26 d and 1h, virtual screening was performed. The best docked compound (PubCId: PubCid: 68874404) showed better affinity than its parent 26 d, however showed poor ADME profile and AMES toxicity. CHEMBL2391475 (PubCid: 71699632) similar to 1h had reduced affinity in comparison to its parent compound 1h. From, our extensive analysis involving binding affinity analysis, ADMET properties predictions and pharmacophoric mappings, we report p-cholorophenyl substituted rivastigmine and fluoxetine hybrid (26d) to be a potential candidate for AcHE inhibition which in addition can overcome narrow therapeutic window of present AChE inhibitors in clinical treatment of Alzheimer׳s disease. Abbreviations AD - Alzheimer׳s Disease, AChE - Acetyl Choline Estarase, OPLS - Optimized Potentials for Liquid Simulations, PDB - Protein Data Bank.

The aim of this study was to identify antibacterial compounds from Solanum torvum fruit extract by HPLC-based metabolomics method. The antibacterial activity of methanolic extracts, hexane, chloroform, ethyl actetate, and water fractions was assessed by well-diffusion agar method. Only methanol extracts and chloroform fraction showed medium inhibition activity against Staphylococcus aureus. The HPLC chemical profiles of the fractions were then correlated to their antibacterial activity profile by means of Orthogonal Projection to Latent Sructure (OPLS) analysis. Based on Y-related coefficient value, peak of HPLC chromatogram found to be significantly corelated to the antibacterial activity was collected by semi-preparative HPLC and its chemical profile was analyzed by LC-MS. Torvoside A, torvoside G, and torvoside H were found to be dominant in this concentrated fractions; thus, these are the possible compounds responsible for antibacterial activity of the fraction. To confirm this, chemical validation by...

Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the ‘PRISMA’ optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.

The green alga Caulerpa cylindracea is a non-autochthonous and invasive species that is severely affecting the native communities in the Mediterranean Sea. Recent researches show that the native edible fish Diplodus sargus actively feeds on this alga and cellular and physiological alterations have been related to the novel alimentary habits. The complex effects of such a trophic exposure to the invasive pest are still poorly understood. Here we report on the metabolic profiles of plasma from D. sargus individuals exposed to C. cylindracea along the southern Italian coast, using 1H NMR spectroscopy and multivariate analysis (Principal Component Analysis, PCA, Orthogonal Partial Least Square, PLS, and Orthogonal Partial Least Square Discriminant Analysis, OPLS-DA). Fish were sampled in two seasonal periods from three different locations, each characterized by a different degree of algal abundance. The levels of the algal bisindole alkaloid caulerpin, which is accumulated in the fish tissues, was used as an indicator of the trophic exposure to the seaweed and related to the plasma metabolic profiles. The profiles appeared clearly influenced by the sampling period beside the content of caulerpin, while the analyses also supported a moderate alteration of lipid and choline metabolism related to the Caulerpa-based diet.

Apart from number and size of machines in a farm, operations scheduling can also affect timeliness costs. In this study a new equation was presented for determination of timeliness costs and it was proven that the new equation is a generalization of the well-known ASABE standard equation. Based on the new equation, a parameter namely, timeliness index was defined that indicates the distribution of an operation around the optimum time of performing the operation. By minimizing the index, the whole timeliness cost is minimized. Since the planting operation is highly sensitive to its time of accomplishment, timeliness cost was determined accordingly. In this study the optimum planting starting date ( opls ) regarding the concept of timeliness costs, was determined. If for any reason, the planting operation cannot be performed in the minimum planting period, an optimization method is required to determine opls . For this, a numerical approach and an analytical method were presented for determination of opls and a computer model was developed based on the numerical method. opls was determined for the Research Farm of the University of Tehran in Karaj–Iran and both methods showed good agreement with each other.

Motivated by the need for reliable experimental data for the assessment of theoretical predictions, this work proposes a data set of enthalpies of sublimation determined for specific crystalline structures, for the validation of molecular force fields (FF). The selected data were used to explore the ability of the OPLS-AA parametrization to investigate the properties of solid materials in molecular dynamics simulations. Furthermore, several approaches to improve this parametrization were also considered. These modifications consisted in replacing the original FF atomic point charges (APC), by values calculated using quantum chemical methods, and by the implementation of a polarizable FF. The obtained results indicated that, in general, the best agreement between theoretical and experimental data is found when the OPLS-AA force field is used with the original APC or when these are replaced by ChelpG charges, computed at the MP2/aug-cc-pVDZ level of theory, for isolated molecules in the gaseous phase. If a good description of the energetic relations between the polymorphs of a compound is required then either the use of polarizable FF or the use of charges determined taking into account the vicinity of the molecules in the crystal (combining the ChelpG and MP2/cc-pVDZ methods) is recommended. Finally, it was concluded that density functional theory methods, like B3LYP or B3PW91, are not advisable for the evaluation of APC of organic compounds for molecular dynamic simulations. Instead, the MP2 method should be considered.

The hydration behavior of α,ω-alkyl diammonium cations of varying alkyl chain length in water clusters has been investigated with molecular dynamics simulations. The OPLS force field was validated against high-level quantum-chemistry results and experimental data and subsequently used to describe the interatomic interactions in the simulations. The results were further validated with simulations performed with approximate density-functional theory (DFTB3). Surface solvation structures are found to be the most stable, accompanied by considerable ion conformational changes, especially in the case of longer dications. This work demonstrates that, depending on their structure, doubly charged cations may have a high affinity towards aqueous interfaces.

Malignant melanoma is one of the most common and aggressive tumors in the oral cavity of dog. The tumor has a poor prognosis, and methods for diagnosis and prediction of prognosis after treatment are required. Here, we examined metabolite profiling using gas chromatography-mass spectrometry (GC-MS) for development of a discriminant model for evaluation of prognosis. Metabolite profiles were evaluated in healthy and melanoma plasma samples using orthogonal projection to latent structure using discriminant analysis (OPLS-DA). Cases that were predicted to be healthy using the OPLS discriminant model had no advanced lesions after radiation therapy. These results indicate that metabolite profiling may be useful in diagnosis and prediction of prognosis of canine malignant melanoma.

ABEEM σπ polarizable force field (PFF) with fluctuating charges works well for searching stable conformations of Val-dipeptide and Val-tripeptide, showing importance of the polarization. The results demonstrate that ABEEM σπ PFF is able to search out all the 6 types of stable conformations of Val-dipeptide and 34 types of stable conformations of Val-tripeptide that are just all exactly found by the calculations of ab initio B3LYP/6-311++G(d,p) and MP2/6-311++G(d,p) methods. In contrast, the force fields with the fixed-charges (FC), such as ABEEM σπ -FC, AMBER-FC 99sb and OPLS/AA-FC force fields can only search out less or much less numbers of stable conformations for them.

International audience; Erythropoiesis Stimulating Agents (ESAs) were developed for therapeutic purposes to stimulate red blood cell (RBC) production. Consequently, tissue oxygenation is enhanced as athlete's endurance and ESAs misuse now benefits doping. Our hypothesis is that most of ESAs should have similar mechanisms and thus have the same effects on metabolism. Studying the metabolome variations could allow suspecting the use of any ESAs with a single method by targeting their effects. In this objective, a metabolomic study was carried out on 3 thoroughbred horses with a single administration of 4.2μg/kg of Mircera®, also called Continuous Erythropoiesis Receptor Activator (CERA). Blood and urine samples were collected from D-17to D+74and haematological parameters were followed throughout the study as plasmatic CERA concentration (ELISA). Urine and plasma metabolic fingerprints were recorded by Liquid Chromatography coupled to High Resolution Mass Spectrometry (LC-HRMS) in positive and negative mode. After preprocessing steps, normalized data were analyzed by multivariate statistics to build OPLS models. Hemoglobin concentration and hematocrit showed a significant increase after CERA administration unlike reticulocytes. CERA concentration showed a high intensity peak and then a slow decrease until becoming undetectable after D+31. Models built with multivariate statistics allow a discrimination between pre and post-administration plasma and urine samples until 74days after administration, i.e. 43days longer than ELISA method. By reducing and studying variables (ions), some potential candidate biomarkers were found.

Introduction: Metabolomics is the identification and quantification of small molecular weight metabolites in a biofluid. Specific profiles of volatile compounds and metabolites in EBC are potentially useful surrogate markers of distinct phenotypes in asthma. Aim: To detect possible differences in the metabolic profile between mild-to-moderate and severe asthmatic patients based on Gas Chromatography-Mass Spectrometry (GC-MS) metabolomics of EBC. Methods: 37 asthmatic patients at stable state for at least 6 months were studied (15 with severe asthma, non-smokers). EBC was collected with Ecoscreen device (Jaeger, Germany) and EBC metabolomics were analyzed with GC-MS. PCA and further PLS/OPLS analysis were performed to separate the two groups of asthmatic patients. T-test and VIP total were performed to detect the peaks that were statistically different in the groups. Results: PCA and PLS-DA UV Scaling could not separate mild-to-moderate from severe asthmatics whereas OPLS-DA UV Scaling demonstrated distinct separation between the two groups. In VIP total diagram 20 unidentified peaks differed significantly between the groups according to 2-tail t-test assuming unequal variances. Further analysis was performed to identify the metabolites and 2 metabolites have been identified so far using available libraries. Further identification will be performed using public available libraries and software systems. Conclusion: EBC is a difficult-to-handle biofluid for metabolomic analysis. GC-MS based metabolomics analysis of biofluids is a novel, promising method to distinguish different asthma phenotypes and could add information used in personalized therapy.

The knowledge of dielectric response properties of the environment is of paramount importance in many theoretical embedding methods and studies of solutes and of catalytic sites and processes in condensed phases. In particular, the realistic embedding of active sites into solid/liquid and liquid/liquid interfaces is a crucial point in the context of modeling energy conversion (e.g., electrochemical, photochemical, power-to-X) processes. Recently, the finding that the dielectric permeability of liquids near solid/liquid interfaces is far from being constant but deviates strongly from the bulk value within several nanometers from the interface has raised the interest in a more fundamental understanding of the response properties near interfaces. As these questions are hard to study experimentally, reliable theoretical models are required. Here we describe a careful first-principles based reparametrization of nonpolarizable molecular mechanics force fields for a class of technological relevant organic chlorinated hydrocarbon solvents which are immiscible with water. For the solvent 1,2-dichloroethane (1,2-DCE) we also present a new polarizable force field based on the Drude oscillator model. Its parametrization needs particular attention to avoid unphysical couplings between the internal torsional degree of freedom and the Drude oscillators, which could severely skew the response properties. The performance of this new set of force fields is critically assessed based on a comprehensive molecular dynamics study.

Introduction Availability of large cohorts of samples with related metadata provides scientists with extensive material for studies. At the same time, recent development of modern high-throughput ‘omics’ technologies, including metabolomics, has resulted in the potential for analysis of large sample sizes. Representative subset selection becomes critical for selection of samples from bigger cohorts and their division into analytical batches. This especially holds true when relative quantification of compound levels is used. Objectives We present a multivariate strategy for representative sample selection and integration of results from multi-batch experiments in metabolomics. Methods Multivariate characterization was applied for design of experiment based sample selection and subsequent subdivision into four analytical batches which were analyzed on different days by metabolomics profiling using gas-chromatography time-of-flight mass spectrometry (GC–TOF–MS). For each batch OPLS-DA® was used and its p(corr) vectors were averaged to obtain combined metabolic profile. Jackknifed standard errors were used to calculate confidence intervals for each metabolite in the average p(corr) profile. Results A combined, representative metabolic profile describing differences between systemic lupus erythematosus (SLE) patients and controls was obtained and used for elucidation of metabolic pathways that could be disturbed in SLE. Conclusion Design of experiment based representative sample selection ensured diversity and minimized bias that could be introduced at this step. Combined metabolic profile enabled unified analysis and interpretation. Electronic supplementary material The online version of this article (doi:10.1007/s11306-017-1248-1) contains supplementary material, which is available to authorized users.

Background To delineate the metabolomic profiling and identify early diagnostic biomarkers in maternal plasma from the pregnant women who subsequently developed gestational diabetes mellitus (GDM) using liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC-Q-TOF MS). Methods Plasma samples were collected from GDM pregnant women (n = 30) and healthy controls (n = 30) at the 20th gestational week in Huzhou Central Hospital and Huzhou Maternal and Child Health Hospital. The principle component analysis (PCA), partial least-squares discriminant analysis (PLS-DA), and orthogonal PLS (OPLS) were sequentially applied to discover the differential metabolites for GDM diagnosis. Further, we analyzed the identified biomarkers in the MetPA database in order to reveal the key relevant metabolism in GDM. Results Twenty-four out of 975 aligned metabolites were distinguished among GDM plasma and healthy controls. In particular, the level of linolenic acid and arachidonic acid was significantly elevated in GDM. Conclusions The linolenic acid and arachidonic acid could be selected as new potent biomarkers for GDM diagnosis and prognosis in early pregnancy; however, they still need to be confirmed from large samples in future.

Roselle (Hibiscus sabdariffa) is a functional food with potential health benefits, consumed either as hot or cold beverage. To ensure quality control of its various products, accurate measurement of active metabolites is warranted. Herein, we propose a combination of ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) and nuclear magnetic resonance (NMR) analytical platforms for the untargeted characterization of metabolites in two roselle cultivars, Aswan and Sudan-1. The analyses revealed 33 metabolites, including sugars, flavonoids, anthocyanins, phenolic and aliphatic organic acids. Their relative contents in cultivars were assessed via principle component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS). Impact of the different extraction methods (decoction, infusion and maceration) was compared by quantitative 1H NMR spectroscopy, revealing cold maceration to be optimal for preserving anthocyanins, whereas infusion was more suited for recovering organic acids. The metabolite pattern revealed by the different extraction methods was found in good correlation for their ability to inhibit α-glucosidase enzyme.

Euodia rutaecarpa is a common traditional Chinese medicine (TCM) in clinical practice, having the ability to suppress pain and cease coughing; however, with the increasing reports showing that it is toxic, particularly hepatotoxic, the concerns raised by what cause its toxicity is growing. In the current study, an analysis method based on the spectrum effect has been employed to screen the major hepatotoxic components in Euodia rutaecarpa so that the toxic material’s basis would be elucidated. A fingerprinting method of the Euodia rutaecarpa extracts (which were petroleum ether, chloroform, ethyl acetate, n-butanol, and water) using ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometer (UHPLC-QTOF/MS) has been developed. Orthogonal partial least squares (OPLS) was used to establish the spectrum-toxicity relationship with the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in mice serum as evaluation indices for liver injury. The UHPLC-MS fingerprint was established and the OPLS analytical results suggested that coniferin, 1-methyl-2-undecyl-4(1H)-quinolone, 1-methyl-2-[(6Z,9Z,12E)-pentadeca triene]-4(1H)-quinolone, evocarpine, 1-methyl-2-[(Z)-7-tridecenyl]-4(1H)-quinolone, dihydroevocarpine, and 1-methyl-2-tetradecy-4-(1H)-quinolone probably associated with the hepatotoxicity of Euodia rutaecarpa. This paper offered considerable methods and insight for the fundamental research of the toxic material basis of similar toxic TCMs.

A feasibility study to predict antimicrobial and antioxidant activity properties of propolis extracts using 700-MHz 1H-NMR spectra and multivariate regression data analysis is presented. The study was conducted with thirty-five propolis samples to develop a rapid and reliable method for the evaluation of their quality. The extracts have been evaluated by measuring phenolic and flavonoid contents; the antioxidant activity; and the antimicrobial activity. The obtained spectral data were submitted to multivariate calibration (partial least squares (PLS) and orthogonal partial least squares (OPLS)) to correlate the relative intensity and position of NMR resonance peaks with the metabolites contents and biological activities. The developed PLS and OPLS model were successfully applied to the determination of the target properties for proof of the concept. The OPLS observed vs. predicted properties plots indicate the absence of systematic errors with determination coefficients between the ranges 0.7207 to 0.9990. Up to 86.1% of explication of variation in the spectral data and 99.9% in the measured properties were attained with 88.6% of prediction capabilities in the best case (S. mutans activity) according to the cross-validation procedure. The figures of merit of the developed PLS and OPLS methods were evaluated and compared as well.

The properties of model solutions consisting of a solute --- single curcumin molecule in water, methanol and dimethyl sulfoxide solvents have been studied using molecular dynamics (MD) computer simulations in the isobaric-isothermal ensemble. The united atom OPLS force field (OPLS-UA) model for curcumin molecule proposed by us recently [J. Mol. Liq., 2016, 223, 707] in combination with the SPC/E water, and the OPLS-UA type models for methanol and dimethyl sulfoxide have been applied. We have described changes of the internal structure of the solute molecule induced by different solvent media in very detail. The pair distribution functions between particular fragments of a solute molecule with solvent particles have been analyzed. Statistical features of the hydrogen bonding between different species were explored. Finally, we have obtained a self-diffusion coefficient of curcumin molecules in three model solvents., 20 pages, 17 figures, 4 tables

This study presents computations of three energy related properties for 26 previously published multisite intermolecular potentials of methane: MM2, MM3, MM2en, MM3en, MM2mc, MM3mc, MM3envir, RMK, OPLS all-atom, MUB-2, AMBER, BOYD, Williams, Sheikh, MG, Tsuzuki, E2-Gay, E4-Gay, MP4exp-6(iii), MP4exp-6(iv), Rowley-A, Rowley-B, TraPPE-EH, Ouyang, CLC, and Chao and three united atom potentials: Saager–Fischer (SF), OPLS united atom, and HFD. The three properties analyzed are the second virial coefficients for 14 temperature points in the range of 110 to 623.15 K, the interaction energies for 12 orientations of the methane dimer as a function of distance followed by a comparison to three ab initio data sets and the cohesive energy of the aggregate of 512 methane molecules. The latter computed energies are correlated to latent heat of evaporation of 11 potentials and are proposed as surrogate approximate parameters for ΔHvap for the studied potentials. The 10 best performing potentials are selected by rms orde...

This work introduces a strategy for decomposing variable contributions within the data obtained from structured metabolomic studies. This approach was applied in the context of an in vitro human neural model to investigate biochemical changes related to neuroinflammation. Neural cells were exposed to the neuroinflammatory toxicant trimethyltin at different doses and exposure times. In the frame of an untargeted approach, cell contents were analysed using HILIC hyphenated with HRMS. Detected features were annotated at level 1 by comparison against a library of standards, and the 126 identified metabolites were analysed using a recently proposed chemometric tool dedicated to multifactorial Omics datasets, namely, ANOVA multiblock OPLS (AMOPLS). First, the total observed variability was decomposed to highlight the contribution of each effect related to the experimental factors. Both the dose of trimethyltin and the exposure time were found to have a statistically significant impact on the observed metabolic alterations. Cells that were exposed for a longer time exhibited a more mature and differentiated metabolome, whereas the dose of trimethyltin was linked to altered lipid pathways, which are known to participate in neurodegeneration. Then, these specific metabolic patterns were further characterised by analysing the individual variable contributions to each effect. AMOPLS was highlighted as a useful tool for analysing complex metabolomic data. The proposed strategy allowed the separation, quantitation and characterisation of the specific contribution of the different factors and the relative importance of every metabolite to each effect with respect to the total observed variability of the system.

G protein-coupled receptor 3 (GPR3) is an orphan GPCR; GPR3 has been reported to play a key role in Alzheimer’s disease through modulation of amyloid-beta production. The understanding of molecular mechanism involved has been limited due to unavailability of crystal structure of GPR3 and lack of different specific agonists. In this paper, we report the modeled 3D structure of GPR3 using threading and ab initio techniques with an objective to understand the mechanism underlying its interaction with agonists. The predicted model was optimized through 50 ns molecular dynamics simulation. Molecular dynamics (MD) simulation for 50 ns was performed on the 3D model of GPR3 embedded in 1-hexadecanoyl-2-(9Z-octadecenoyl)-sn-glycero-3-phosphocholine (POPC) lipid bilayer with aqueous system using OPLS (optimized potentials for liquid simulations) force field. The MD trajectories were analyzed to optimize the helical bundle conformations, active site, and 7TM domain variability during production phase of simulation. Binding pocket gave an insight into the size and chemical nature of compounds which could be potential agonists. The optimized structure would be significant in screening potential ligands through virtual screening.

We have calculated the excess free energy of mixing of 1053 binary mixtures with the OPLS-AA force field using two different methods: thermodynamic integration (TI) of molecular dynamics simulations and the Pair Configuration to Molecular Activity Coefficient (PAC-MAC) method. PAC-MAC is a force field based quasi-chemical method for predicting miscibility properties of various binary mixtures. The TI calculations yield a root mean squared error (RMSE) compared to experimental data of 0.132 kBT (0.37 kJ/mol). PAC-MAC shows a RMSE of 0.151 kBT with a calculation speed being potentially 1.0 × 104 times greater than TI. OPLS-AA force field parameters are optimized using PAC-MAC based on vapor–liquid equilibrium data, instead of enthalpies of vaporization or densities. The RMSE of PAC-MAC is reduced to 0.099 kBT by optimizing 50 force field parameters. The resulting OPLS-PM force field has a comparable accuracy as the OPLS-AA force field in the calculation of mixing free energies using TI.

Inflammation is a hallmark of some of today's most life-threatening diseases such as arteriosclerosis, cancer, diabetes and Alzheimer's disease. Herbal medicines (HMs) are re-emerging resources in the fight against these conditions and for many of them, anti-inflammatory activity has been demonstrated. However, several aspects of HMs such as their multi-component character, natural variability and pharmacodynamic interactions (e.g. synergism) hamper identification of their bioactive constituents and thus the development of appropriate quality control (QC) workflows. In this study, we investigated the potential use of Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) spectroscopy as a tool to rapidly and non-destructively assess different anti-inflammatory properties of ethanolic extracts from various species of the Genus Lonicera (Caprifoliaceae). Reference measurements for multivariate calibration comprised in vitro bioactivity of crude extracts towards four key players of inflammation: Nitric oxide (NO), interleukin 8 (IL-8), peroxisome proliferator-activated receptor β/δ (PPAR β/δ), and nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-κB). Multivariate analysis of variance (MANOVA) revealed a statistically significant, quantitative pattern-activity relationship between the extracts' ATR-FTIR spectra and their ability to modulate these targets in the corresponding cell models. Ensemble orthogonal partial least squares (OPLS) discriminant models were established for the identification of extracts exhibiting high and low activity with respect to their potential to suppress NO and IL-8 production. Predictions made on an independent test set revealed good generalizability of the models with overall sensitivity and specificity of 80% and 100%, respectively. Partial least squares (PLS) regression models were successfully established to predict the extracts' ability to suppress NO production and NF-κB activity with root mean squared errors of cross-validation (RMSECV) of 8.7% and 0.05-fold activity, respectively.