APLICAÇÕES DA QUIMIOMETRIA EM DIFERENTES ÁREAS DA QUÍMICA Márcia M. C. Ferreira Laboratório de Quimiometria Teórica e Aplicada Instituto de Química UNICAMP Email: firstname.lastname@example.org URL: http://lqta.iqm.unicamp.br
Quantitative analysis and classification of AFM images of human hair S. P. GURDEN, V. F. MONTEIRO, E. LONGO J. Microsc., in press.
Top surface 70 m Central cortex Root end distal end 5 m Face surface • This work aims to build a computation algorithm capable of analysing an AFM image and calculating a set of parameters which describe as fully as possible the cuticular structure. • These descriptors are used for automatic classification of hair samples according to factors such as distance from the root-end and hair treatment. The central cortex of a hair fibre is surrounded by thin cellular sheets, known as cuticles, which overlap each other from root to tip. These cuticles fulfil a number of useful roles including protection from physical and chemical insult and a tendency to maintain the hair in a clean and disentangled state
Two types of black, Caucasian human hair samples were used: (a) untreated and (a) bleached. . Each hair fibre had a thickness of approximately 65m and a length of approximately 20cm. The AFM images were measured using a Digital Instruments NanoScope IIIa instrument under atmospheric conditions at 25C using a loading force of 3.6nN. AFM images of Caucasian hair untreated hair near the root end bleached hair near the distal end
Original hair image Background surface calculated during the planification step Hair image after planification
The quantitative analysis and classification of AFM hair images is carried out by characterizing the hair surface using descriptors which summarize the important characteristics of the cuticular structure. Step height;Tilt; Backtilt; Layer spacing;Face distance(AB); Top distance(BC); Fit error; Roughness;Fitability; Cuticle density (#cuticles per mm).. total length B backtilt step height layer spacing tilt cuticle surface A C underlying cuticle
Cuticular descriptors calculated for the example image shown before. Schematic of PLS-DA dummy variables descriptors 0 1 1 1 1 0 etc. X Y samples samples PLS The data was autoscaled before building the PLS model. From 38 samples, the model was built using 36 (2 outliers). Of the 36 samples, 31 were classified correctly, giving a success rate of 86%. descriptors R P´ R T PLS loadings PLS scores samples
The samples () are closely clustered. They are the most homogeneous in terms of cuticle structure, as the hair surface has not been greatly damaged by physical or chemical stress. They have similar descriptor values regardless of which particular image is analysed. The untreated/distal end () and bleached/root end () samples also form fairly well-defined groups (exception are the two untreated/distal end samples and the misclassified samples). • untreated/distal end; bleached/root end; • untreated/root end; bleached/distal end. The bleached/distal end samples () are very scattered, indicating that representative sampling of this group is more difficult. As PC1 describes negative contributions from cuticle density and fitability, it is logical that the samples damaged by cosmetic treatment and long-term physical stress (far from the root end) have the highest PC1 scores and so, have low cuticle density and poor fitability.
Root and distal end samples In general, the root end samples ( and ) have negative component 2 scores and the distal end samples ( and ) have positive component 2 scores. This shows that at the root end of the hair, where the cuticles are more abundant, the degree of tilt and step height of the cuticles is higher as may be expected. At the distal end, where the cuticles are less abundant, the cuticles lie flatter and have a lower degree of backtilt due to physical wear. Distal end cuticles also have a less uniform pattern, also due to the effect of prolonged physical stress which chips away at the cuticle ends, leaving an irregular cuticle edge. Effect of bleaching For the samples measured at the root end, the bleached samples () lie further to the right that the untreated samples (). This indicates that one effect of bleaching is to remove the cuticles which protect the central hair cortex, thus making the hair less resistant to breakage or splitting. The bleached samples also have lower component 2 scores, symptomatic of cuticle detachment which also leaves the hair in a weaker condition, more vulnerable to subsequent damage. For the samples measured at the distal end (untreated, ; bleached, ), the removal of cuticle layers is even more pronounced, showing that bleaching of already vulnerable distal end hair can lead to complete removal of the cuticular layer in some cases, exposing the underlying central cortex.
Quantitative Determination of Epoxidized Soybean Oil Using Near-Infrared Spectroscopy and Multivariate Calibration Thais F. Parreira Henrique J. S. Sales, and Wanderson B. de Almeida Henkel S/A Indústrias Químicas Appl. Spectrosc., 56, 1607-1614 (2002).
Soybean oil is a triglyceride which typically contains 14% stearic, 23% oleic, 55% linoleic and 8% linolenic acid. Three of them are unsaturated acids: oleic (18:1), linoleic (18:2) and linolenic (18:3). Chemical modification on commercial available soybean oil such as epoxidation can enhance its properties (reactivity) for industrial applications.
The epoxidized soybean oil (ESO) is extensively used in plastic industry as plasticizer (at levels ranging from 0.1 to 27%), to increase flexibility and as stabilizer to minimize decomposition in polyvinyl chloride (PVC) products. PVC degradation caused by sunlight eliminating HCl Reaction of oxirane ring with HCl inhibiting the degradation process.
To follow the soybean oil epoxidation process, it is necessary to quantify some analytes related to the product’s quality: •The epoxide index (E.I.) is directly related to the stabilizer feature of the product the higher the epoxide content, the more efficient the additive as thermal stabilizer. • The iodine content (I.I.) is an indicator to the amount of unsaturations present in the epoxidized soybean oil. • The percentage of water (% of Water), results from washing of the final product. Its concentration must be minimal since water can cause degradation of the epoxide group. Iodine index determination halogenation of double bonds. Degradation of epoxide groups by water
NIR absorbance spectra were recorded from 9300 cm-1 to 4500 cm-1 with a 2 cm-1 increment, using a BOMEM – MB160 – FTIR spectrophotometer. ORIGINAL DATA SET (2400 VARIABLES) Box car average REDUCED DATA SET (160 VARIABLES) Generic recorded spectrum SELECTION A (Spectral difference) SELECTION B CORRELOGRAM SELECTION C (Loadings & Regression vector) EXTERNAL VALIDATION (using the best model)
7050-7290, 5190-5310 cm-1 % WATER 7110-7230, 5130-5310 cm-1 6930-7350, 4500-5910 cm-1
PLS Models a Variable selection by the correlogram applied to Selection B for cutoff = 0.80. b External prediction using PLS model with 14 variables selected in Selection A. c SEP (standard error of prediction). FIGURA
Experimental, estimated, and residual values for water % for external set. PLS model from Selection A (3 LVs) aRPD = std(exp.)/std(residuals). bRER = range(exp.)/std(residuals).
CONCLUSIONS The use of NIRS combined with multivariate regression is a feasible alternative to the widely established techniques, especially in industrial processes. Using simple and intuitive variable selection methods, such as loadings/regression vector analysis and the correlogram, the number of variables can be significantly reduced without impairing the model quality. The statistical parameters used, RPD and RER, indicated that NIRS determination was accurate for E.I. (RDP=26.0 and RER=80.7) and fairly good for water % and I.I. From the results obtained, it can be concluded that the proposed methodology is appropriate for monitoring the epoxidation of soybean oil and to evaluate the additive’s quality in the industrial process, where time, effort and money are crucial.
Chemometric and Molecular Graphics and Modeling Study on Bacterial b-Lactam Efflux Mechanism by Multidrug Resistance AcrB Pump Rudolf Kiralj
ABSTRACT • The primary purposes of this work • To establish relationships between activity expressed as log of minimal inhibitor concentration (pMIC) elevated by three strains of Salmonella typhimurium (HN891, SH7616, SH5014), and calculated descriptors for 16 penicillins and cephalosporins at neutral pH. • To visualize pump – drug molecular recognition mechanism, using crystal structure of AcrB transporter from Escherichia coli. • These results can aid in explaining bacterial drug efflux mechanism, and design of novel b-lactams which would not be excreted from bacterial cells.
INTRODUCTION Antibiotics are characterized by their chemical composition and mode of action. Penicillins and cephalosporins have the cell wall as target for their action. b-lactam antibiotics are the most used antibacterial inhibitors of the Penicillin- Binding-Proteins (PBPs), which are responsible for the construction and maintenance of bacterial cell wall. There are different mechanisms by which bacteria exhibit resistance to antibiotics: 1- Bacteria produce b-lactamases which hydrolyze the b -lactam antibiotic ring before their binding to PBPs. 2- Bacteria change their permeability to the drug (passive membrane transport). 3- Bacteria develop a structurally altered PBP that is still able to perform its metabolic function, but less affected by the drug. 4- Bacteria change their express transport system that actively pump the drug to the outer cellular environment.
The major mechanism of MDR in bacteria is the pump drug efflux. In general this is accomplished by the presence of AcrAB-TolC efflux systems, which are responsible for the unidirectional pumping of a wide variety of lipophilic and amphiphilic compounds out of the cell. • FACTORS THAT INFLUENCES THE MULTI DRUG EFFLUX RATE • Pumps number • Substrate concentration • pH • Highly charged residues • Substrate charged groups • MDR PUMPS consist of 3 components: • 1- a resistance-nodulation-cell division transporter AcrB (trimeric) • 2- an outer membrane channel protein of the family TolC(trimeric) • 3- a membrane fusion lipoprotein AcrA (probably trimeric also)
General AcrAB-TolC bacterial pump. S. Murakami et al., Nature, 419 (2002) 587.
METHODOLOGY MICs for bacterial strains →Mass concentration MICs (from literature) for 16 b-lactams effluxed by bacterial strains S. typhimurium SH5014 (parent strain), SH7616 (an acr mutant) and HN891 (an overproducer of the Acr pump). Drugs Modeling →Molecular structures were refined or modeled by Spartan Pro using atomic coordinates from PPSD, CSD or 2D formula. Conformational search was done by Montecarlo method and the most stable conformers were optimized by the semiempirical method PM3. Lipophilicity Parameters → logarithm of the octanol-water partition coefficient logKOW was from Nikaido et al and several others were calculated using different approaches. wC, Sf → are the number fraction and surface fraction of hydrophobic carbon atoms, respectively. Other molecular descriptors: geometrical, electronic and Hydrogen bond molecular properties were calculated using 2D or 3D geometry of the antibiotics.
Molecules 16 antibiotics (penicillins and cephalosporins) as AcrB substrates
Correlation of pMICS Comparison among three pMICs: pHN891 and pSH5014 are highly correlated (right). pSH7616 shows different trend (left). The three bacterial strains are not distinguished when excreting highly charged antibiotics.
Chemometrics of pMICs PCA and HCA were performed using only pMIcs data. b-Lactams were classified as good, moderately good to poor, and bad AcrB substrates.Clustering of b-lactams with respect to the number of charged groups NCH and hydrophobic surface fraction Sf is visible.
Chemometrics of lipophilicity descriptors PCA (left) and HCA (right) analysis of 9 lipophilicity descriptors: logarithm of the octanol-water partition coefficient (logP) calculated by various methods, surface fraction (Sf) and number fraction (wC) of hydrophobic carbons. Two clusters and two isolated logPs are visible. The lipophilicity descriptors do not contain the same information (82.8% of the variance contained in PC1 + PC2).
Lipophilicity – pMIC relationships An example of lipophilicity – activity nonlinear relationship. Log Kow was linearized by GlogKOW = exp[–(logKOW – 1.1)2]; Other transformations : SlogPs = (logPs)2; SlogKWIN = (logKWIN)2.
PLS regression models for pMICs It is visible that the best PLS models are obtained when alltypes of parameters are used: lipophilic, electronic and hydrogen bonding.
Nafcillin (1) 2.607 2.531 Cloxacillin (2) 2.930 3.036 Penicillin G (3) 4.621 4.297 Cephalothin (4) 4.996 5.074 Cefoxitin (5) 5.029 4.919 Cephaloridin (6) 4.715 4.925 Carbenicillin (7) 4.675 4.470 Sulbenicillin (8) 4.714 4.594 Cefsulodin (9) 3.919 4.538 Latamoxef (10) 6.637 6.724 Cefotaxime (11) 6.579 6.567 Ceftriaxone (12) 6.665 6.647 Cefmetazole (13) 5.975 5.752 Cefazolin (14) 5.357 5.308 Penicillin N (15) 4.652 4.734 Cephalosporin C (16) 4.414 4.369 Experimentala and Predicted pMICSH5014b Except for sample 9, exp-cal differences are smaller than 10%. aH. Nikaido et al., J. Bacteriol.,180 (1998) 4686. bMIC are in mols per liter.
AcrB crystal structure S. Murakami et al., Nature, Nature419 (2002) 587. Science300 (2003) 976. Crystal structure of the AcrB trimer determined by X-ray diffraction: protein without (left) and with a ligand (right). Three distinctive units are visible: TolC docking domains, Pore domains and Transmembrane domains. The system of cavities and channels for drug efflux can be also noted: the three vestibules, the large central cavity, the narrow pore, and the cone-like funnel.
Left: efflux mechanism by AcrAB-TolC pump. Right: AcrB trimer in two views.
The vestibule structure Left: Electrostatic potential of the pore anf the transmembrane domains. The vestibule’s projection has functional surface through which the drug can pass without difficulty. This area is called BRAMLA, due to its resemblance with the map of Brazil (BRAzil Map-Like Area). The upper third of BRAMLA is surrounded by hydrophilic and the other two thirds by hydrophobic residues of the AcrB.
The vestibule-drug interactions Left: Schematic representation of drug-vestibule stereolectronic complementarity that was deduced from similarity of the 16 antibiotic structures and importance of lipophilic, electronic and hydrogen bonding molecular parameters. Molecular recognition is obvious, and it can be weaken or enhanced by the nature of R and R1 side chains. Right: 3D docking of nafcillin (1) to the vestibule. Interactions between hidrophilic AcrB residues (in rectangles) and nafcillin polar groups are visible.
2D docking of selected AcrB substrates to the BRAMLA area, using maximum and minimum (right) stereoelectronic fit approach for some antibiotics. It can be noticed that the antibiotic molecules differ in how well then can fit sterically and electronically to the vestibule. These fittings correspond to biological activities for the presented antibiotics.
The pore structure The structure of the pore channell (left figures) and the pore recognition site (right figures) viewed perpendicularly to or along the three-fold axis of the AcrB protein. The pore channel consists of three short a-helices and three random coils. The pore recognition site contains highly hydrophobic (yellow) and hydrophilic (red or pink) residues: these residues are selective with respect to drugs due to hydrophobic, polar and hydrogen bond interactions.
The pore-drug interactions Some drugs docked to the pore recognition site. Lipophilic drugs enter the pore channel easier than hydrophilic ones due to: 1) weaker intermolecular interactions; 2) more favourable drug-pore recognition. These conclusions, based on 3D docking of the presented drugs, are in agreement to chemometric results.
Substrate 14 bound to a portion of a pore from a protomer (left) and its electrostatic potential at molecular surface in free and bound state (right). There are four drug – pore hydrogen bonds involving residues Ala100, Ala103 and Gln104. This illustrates why hydrogen bonding and electronic descriptors are important in the PLS models.
CONCLUSIONS PLS models of good quality were obtained using lipophilic, electronic and hydrogen bond descriptors for 16 b-lactams. Proposed efflux mechanismbased on chemometrics and molecular graphics and modeling methods: 1) a drug molecule comes from periplasmic space and interacts with a vestibule through a mechanism of molecular recognition large and highly hydrophilic molecules hardly enter the vestibule and come to the central cavity of AcrB protein. 2) a drug molecule from the central cavity comes to the pore recognition site and through a mechanism of molecular recognition enters the pore channel again large and highly hydrophilic molecules hardly enter the pore channel to be excreted from the cell.
A METHODOLOGY FOR IDENTIFICATION OF ATMOSPHERIC POLLUTION SOURCES Edilton de S. Barcellos
This work introduces a methodology to identify the principal emission pollution sources in the Região Metropolitana de São Paulo. • The analysis covered the primary pollutants CO, NO, NO2 and CH4, and the secondary one O3. • The data (kindly provided by the Sanitation Department of the State of São Paulo, CETESB), are time series consisting of concentrations measured hourly throughout the year of 1999 for each compound, in the site of P. D. Pedro II. • To capture the systematic variations for each compound, the data was firstly arranged as matrices 24 (hours of the day) 365 (days of the year) and submitted to a Principal Component Analysis (PCA). • To extract simultaneously the daily and weekly systematic variations, the data was rearranged in a multiway structure (24 hours of the day 7 days a week 52 weeks of the year) and the Tucker model was applied.
METHODOLOGY Outliers Outliers were identified from a visual inspection of the original data matrix and considered as missing values in the case their values were ten times larger than the mean value for that pollutant. Missing Data The matrices containing missing data were subjected to the mdpca routine of the PLS-Toolbox (Eigenvector Research, Inc.) (MATLAB software version 5.1) was used. Data Preprocessing For individual pollutants: no preprocessing was applied. In order to minimize the local (hourly) sudden variations, when the analysis was carried out for more than one pollutant simultaneously, the data set was standardized by the mean concentration of the pollutant in question, i.e., for a given pollutant, each entry value was divided by the mean value taken from all the data relative to that compound. Constraints Non-negativity was the only constraint used. The multiway analyses were performed by using the N-way Toolbox 1.02 for MATLAB (http://newton.foodsci.kvl.dk/Matlab/nwaytoolbox).
Construção da matriz 365 (dias) 24 (horas) para o CO e sua decomposição em matrizes de “scores” e de “loadings”. Threeway representation for CO (array X) and its decomposition by PARAFAC model into component matrices A, B and C.
Construção da estrutura multimodo mostrando a disposição dos dias, horas e semanas nas matrizes.
Carbon Monoxide PCA showing daily systematic variation throughout the year
CO, NO and NO2 have a profile that is not similar to the profile of CH4. This result indicates that there are at least two emission sources. One of them is a road traffic source. The profile for CH4 in this site may be due to industries that uses natural gas, and sludge treatment plants, among others less important.
The major sources of ozone in the troposphere are the photochemical reactions and the necessary conditions are: a source of carbon (CO and/or hydrocarbons), nitrogen oxides (NO, NO2), and sunlight . Carbon Monoxide Cycle The chain of chemical reactions that occurs in the troposphere has the real starting point with the photochemical decomposition of ozone, resulting in molecules and oxygen atoms in the excited O3 + h O2 + O O + H2O 2 HO Depending on the concentration of nitrogen oxides (especially NO) in the environment, the reaction can follow two different paths. Low NOx conc. High NOx conc. CO + HO CO2 + H CO + HO CO2 + H H + O2 + M HO2 + M H + O2 + M HO2 + M HO2 + O3 HO + 2 O2 HO2 + NO HO +NO2 Net: CO + O3 CO2 + O2 NO2 + h NO + O O + O2 + M O3 + M Net: CO + 2 O2 CO2 + O3
(a) (b) (d) (c) TUCKER MODEL (a) Daily emission source profile for the primary vehicular pollutants (PC1), for other primary pollutants (PC2), and for O3 (PC3); (b) Weekly systematic variations for the primary vehicular emissions (PC1), other primary sources (PC2) and O3 (PC3); (c) Annual variations for the primary pollutants (PC1) and for the secondary ones (PC2); (d) Traffic density profile in the RMSP.