ABSTRACT
The interest in the fusion product of quinoxalinone skeletons and 1,2,3-triazole units has greatly increased in recent years since they are known to be agonists of G-protein-coupled Niacin receptor 109A and inhibitors of the benzodiazepine and adenosine receptors. Here, we unveil the mechanism for the photoredox catalyzed synthesis of those scaffolds by means of DFT calculations. The calculations indicate that the rate determining step of this transformation is the attack of the in situ generated radical intermediate on the CîN bond of the quinoxalinone species to form a new C-C bond. Predictive chemistry here reveals that the energy difference is so subtle, and gives the recipe of which substituents, sterically and electronically, can fit to perform the reaction at room temperature.
Subject(s)
Density Functional TheoryABSTRACT
A library of 66 cyclic decapeptides incorporating a Trp residue was synthesized on solid phase and screened against the phytopathogenic bacteria Pseudomonas syringae pv. syringae, Xanthomonas axonopodis pv. vesicatoria, and Erwinia amylovora. The hemolytic activity of these peptides was also evaluated. The results obtained were compared with those of a collection of Phe analogues previously reported. The analysis of the data showed that the presence of the Trp improved the antibacterial activity against these three pathogens. In particular, 40 to 46 Trp analogues displayed lower minimum inhibitory concentration (MIC) values than their corresponding Phe counterparts. Interestingly, 26 Trp-containing sequences exhibited MIC of 0.8 to 3.1 µM against X. axonopodis pv. vesicatoria, 21 peptides MIC of 1.6 to 6.2 µM against P. syringae pv. syringae and six peptides MIC of 6.2 to 12.5 µM against E. amylovora. Regarding the hemolysis, in general, Trp derivatives displayed a percentage of hemolysis comparable to that of their Phe analogues. Notably, 49 Trp-containing cyclic peptides showed a hemolysis ≤ 20% at 125 µM. The peptides with the best biological activity profile were c(LKKKLWKKLQ) (BPC086W) and c(LKKKKWLLKQ) (BPC108W), which displayed MIC values ranging from 0.8 to 12.5 µM and a hemolysis ≤ 8% at 125 µM. Therefore, it is evident that these Trp sequences constitute promising candidates for the development of new agents for use in plant protection.
Subject(s)
Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/pharmacology , Bacteria/drug effects , Peptides, Cyclic/chemistry , Peptides, Cyclic/pharmacology , Tryptophan/chemistry , Anti-Bacterial Agents/chemical synthesis , Chemistry Techniques, Synthetic , Drug Design , Microbial Sensitivity Tests , Peptides, Cyclic/chemical synthesis , Plant Diseases/microbiology , Plants/microbiologyABSTRACT
The Superposing Significant Interaction Rules (SSIR) method is described. It is a general combinatorial and symbolic procedure able to rank compounds belonging to combinatorial analogue series. The procedure generates structure-activity relationship (SAR) models and also serves as an inverse SAR tool. The method is fast and can deal with large databases. SSIR operates from statistical significances calculated from the available library of compounds and according to the previously attached molecular labels of interest or non-interest. The required symbolic codification allows dealing with almost any combinatorial data set, even in a confidential manner, if desired. The application example categorizes molecules as binding or non-binding, and consensus ranking SAR models are generated from training and two distinct cross-validation methods: leave-one-out and balanced leave-two-out (BL2O), the latter being suited for the treatment of binary properties.
Subject(s)
Algorithms , Data Mining/methods , Databases, Pharmaceutical , Models, Molecular , Molecular Structure , Structure-Activity RelationshipABSTRACT
Rearrangements and their control are a hot topic in supramolecular chemistry due to the possibilities that these phenomena open in the design of synthetic receptors and molecular machines. Macrocycle aza-scorpiands constitute an interesting system that can reorganize their spatial structure depending on pH variations or the presence of metal cations. In this study, the relative stabilities of these conformations were predicted computationally by semi-empirical and density functional theory approximations, and the reorganization from closed to open conformations was simulated by using the Monte Carlo multiple minimum method.
Subject(s)
Aza Compounds/chemistry , Computational Biology , Macrocyclic Compounds/chemistry , Monte Carlo Method , Quantum Theory , Hydrogen-Ion Concentration , Models, MolecularABSTRACT
Even though there are many photocurable compositions that are cured by cationic photopolymerization mechanisms, UV curing generally consists of the formation of cross-linking covalent bonds between a resin and monomers via a photoinitiated free radical polymerization reaction, obtaining a three-dimensional polymer network. One of its many applications is in the refinish coatings market, where putties, primers and clear coats can be cured faster and more efficiently than with traditional curing. All these products contain the same essential components, which are resin, monomers and photoinitiators, the latter being the source of free radicals. They may also include additives used to achieve a certain consistency, but always taking into account the avoidance of damage to the UV curing-for example, by removing light from the innermost layers. Surface curing also has its challenges since it can be easily inhibited by oxygen, although this can be solved by adding scavengers such as amines or thiols, able to react with the otherwise inactive peroxy radicals and continue the propagation of the polymerization reaction. In this review article, we cover a broad analysis from the organic point of view to the industrial applications of this line of research, with a wide current and future range of uses.
ABSTRACT
The cis-trans isomerization of (thio)amides was studied by DFT calculations to get the model for the higher preference for the cis conformation by guided predictive chemistry, suggesting how to select the alkyl/aryl substituents on the C/N atoms that lead to the trans isomer. Multilinear analysis, together with cross-validation analysis, helped to select the best fitting parameters to achieve the energy barriers of the cis to trans interconversion, as well as the relative stability between both isomers. Double experimental check led to the synthesis of the best trans candidate with sterically demanding t-butyl substituents, confirming the utility of predictive chemistry, bridging organic and computational chemistry.
Subject(s)
Amides , Thioamides , Amides/chemistry , Kinetics , Molecular Conformation , StereoisomerismABSTRACT
This work describes a new procedure to obtain optimal molecular superposition based on quantum similarity (QS): the geometric-quantum similarity molecular superposition (GQSMS) algorithm. It has been inspired by the QS Aufbau principle, already described in a previous work, to build up coherently quantum similarity matrices (QSMs). The cornerstone of the present superposition technique relies upon the fact that quantum similarity integrals (QSIs), defined using a GTO basis set, depend on the squared intermolecular atomic distances. The resulting QSM structure, constructed under the GQSMS algorithm, becomes not only optimal in terms of its QSI elements but can also be arranged to produce a positive definite matrix global structure. Kruskal minimum spanning trees are also discussed as a device to order molecular sets described in turn by means of QSM. Besides the main subject of this work, focused on MS and QS, other practical considerations are also included in this study: essentially the use of elementary Jacobi rotations as QSM refinement tools and inward functions as QSM scaling methods.
ABSTRACT
The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses.
ABSTRACT
This study describes a new chemometric tool for the identification of relevant volatile compounds in cork by untargeted headspace solid phase microextraction and gas chromatography mass spectrometry (HS-SPME/GC-MS) analysis. The production process in cork industries commonly includes a washing procedure based on water and temperature cycles in order to reduce off-flavors and decrease the amount of trichloroanisole (TCA) in cork samples. The treatment has been demonstrated to be effective for the designed purpose, but chemical changes in the volatile fraction of the cork sample are produced, which need to be further investigated through the chemometric examination of data obtained from the headspace. Ordinary principal component analysis (PCA) based on the numerical description provided by the chromatographic area of several target compounds was inconclusive. This led us to consider a new tool, which is presented here for the first time for an application in the chromatographic field. The superposing significant interaction rules (SSIR) method is a variable selector which directly analyses the raw internal data coming from the spectrophotometer software and, combined with PCA and discriminant analysis, has been able to separate a group of 56 cork samples into two groups: treated and non-treated. This procedure revealed the presence of two compounds, furfural and 5-methylfurfural, which are increased in the case of treated samples. These compounds explain the sweet notes found in the sensory evaluation of the treated corks. The model that is obtained is robust; the overall sensitivity and specificity are 96% and 100%, respectively. Furthermore, a leave-one-out cross-validation calculation revealed that all of the samples can be correctly classified one at a time if three or more PCA descriptors are considered.
Subject(s)
Elastomers/chemistry , Volatile Organic Compounds/analysis , Anisoles/analysis , Anisoles/pharmacokinetics , Discriminant Analysis , Elastomers/pharmacokinetics , Equipment Design , Food Packaging/instrumentation , Gas Chromatography-Mass Spectrometry , Principal Component Analysis , Solid Phase Microextraction , Spectrophotometry/instrumentation , Spectrophotometry/methods , Temperature , Volatile Organic Compounds/pharmacokinetics , WineABSTRACT
The Superposing Significant Interaction Rules (SSIR) method is a combinatorial procedure that deals with symbolic descriptors of samples. It is able to rank the series of samples when those items are classified into two classes. The method selects preferential descriptors and, with them, generates rules that make up the rank by means of a simple voting procedure. Here, two application examples are provided. In both cases, binary or multilevel strings encoding gene expressions are considered as descriptors. It is shown how the SSIR procedure is useful for ranking the series of patient transcription data to diagnose two types of cancer (leukemia and prostate cancer) obtaining Area Under Receiver Operating Characteristic (AU-ROC) values of 0.95 (leukemia prediction) and 0.80-0.90 (prostate). The preferential selected descriptors here are specific gene expressions, and this is potentially useful to point to possible key genes.
Subject(s)
Data Mining/methods , Leukemia/genetics , Oligonucleotide Array Sequence Analysis/methods , Pattern Recognition, Automated/methods , Prostatic Neoplasms/genetics , Algorithms , Data Interpretation, Statistical , Gene Expression Profiling , Humans , Leukemia/classification , Male , Prostatic Neoplasms/classification , ROC Curve , Structure-Activity RelationshipABSTRACT
A 125-member library of synthetic linear undecapeptides was prepared based on a previously described peptide H-K(1)KLFKKILKF(10)L-NH(2) (BP76) that inhibited in vitro growth of the plant pathogenic bacteria Erwinia amylovora, Xanthomonas axonopodis pv. vesicatoria, and Pseudomonas syringae pv. syringae at low micromolar concentrations. Peptides were designed using a combinatorial chemistry approach by incorporating amino acids possessing various degrees of hydrophobicity and hydrophilicity at positions 1 and 10 and by varying the N-terminus. Library screening for in vitro growth inhibition identified 27, 40 and 113 sequences with MIC values below 7.5 microM against E. amylovora, P. syringae and X. axonopodis, respectively. Cytotoxicity, bactericidal activity and stability towards protease degradation of the most active peptides were also determined. Seven peptides with a good balance between antibacterial and hemolytic activities were identified. Several analogues displayed a bactericidal effect and low susceptibility to protease degradation. The most promising peptides were tested in vivo by evaluating their preventive effect of inhibition of E. amylovora infection in detached apple and pear flowers. The peptide H-KKLFKKILKYL-NH(2) (BP100) showed efficacies in flowers of 63-76% at 100 microM, being more potent than BP76 and only less effective than streptomycin, currently used for fire blight control.
Subject(s)
Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/pharmacology , Bacteria/drug effects , Bacteria/pathogenicity , Oligopeptides/chemistry , Oligopeptides/pharmacology , Plants/microbiology , Amino Acid Sequence , Drug Design , Endopeptidase K , Erwinia amylovora/drug effects , Erwinia amylovora/pathogenicity , Hemolysis/drug effects , Humans , In Vitro Techniques , Peptide Library , Plant Diseases/microbiology , Pseudomonas syringae/drug effects , Pseudomonas syringae/pathogenicity , Xanthomonas axonopodis/drug effects , Xanthomonas axonopodis/pathogenicityABSTRACT
Major and minor (K, P, Ca, Mg, Na, Fe, Mn, Zn, and Sr) and trace (Ba, Ni, Pb, V, Co, Cd, and Sb) elements from wine samples from the Denomination of Origin (DO) Empordà-Costa Brava (Catalonia, Spain) were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES) and mass spectrometry (ICP-MS) respectively. Previously, a comparison of different calibration methodologies and sample digestion treatments had been carried out using ANOVA statistical tool. The obtained results demonstrated that internal standardization provides reliable results with the advantage that no further manipulation of the sample is needed. A principal component analysis of the concentration data was performed to differentiate the samples of DO Empordà-Costa Brava from wine samples from other wine-producing regions in Spain (i.e., Penedès, Somontano, and Rioja). It was found that Sr and Ba contents discriminate the two DO groups. Moreover, a discriminant analysis function involving both variables distinguishes the two groups with a 100% classification rate. At the level of the leave-one-out cross-validation, all of the Empordà-Costa Brava samples were well classified, whereas the other DOs presented two borderline misclassifications.
Subject(s)
Spectrophotometry, Atomic/methods , Spectrophotometry, Atomic/standards , Wine/analysis , Wine/classification , Mass Spectrometry , Metals/analysis , Microwaves , Sensitivity and Specificity , SpainABSTRACT
Cyclic decapeptides were developed based on the previously reported peptide c(LysLeuLysLeuLysPheLysLeuLysGln). These compounds were active against the economically important plant pathogenic bacteria Erwinia amylovora, Pseudomonas syringae and Xanthomonas vesicatoria. A library of 56 cyclic decapeptides was prepared and screened for antibacterial activity and eukaryotic cytotoxicity, and led to the identification of peptides with improved minimum inhibitory concentration (MIC) against P. syringae (3.1-6.2 microM) and X. vesicatoria (1.6-3.1 microM). Notably, peptides active against E. amylovora (MIC of 12.5-25 microM) were found, constituting the first report of cyclic peptides with activity towards this bacteria. A second library based on the structure c(X(1)X(2)X(3)X(4)LysPheLysLysLeuGln) with X being Lys or Leu yielded peptides with optimized activity profiles. The activity against E. amylovora was further improved (MIC of 6.2-12.5 microM) and the best peptides displayed a low eukaryotic cytotoxicity at concentrations 30-120 times higher than the MIC values. A design of experiments permitted to define rules for high antibacterial activity and low cytotoxicity, being the main rule X(2) not equal X(3), and the secondary rule X(4)=Lys. The best analogs can be considered as good candidates for the development of effective antibacterial agents for use in plant protection.
Subject(s)
Bacteria/drug effects , Drug Design , Peptides, Cyclic/chemistry , Peptides, Cyclic/pharmacology , Plant Diseases , Amino Acid Sequence , Anti-Bacterial Agents/chemistry , Anti-Bacterial Agents/pharmacology , Combinatorial Chemistry Techniques , Hemolysis/drug effects , Humans , Microbial Sensitivity Tests , Molecular Sequence Data , Peptide Library , Plant Diseases/microbiologyABSTRACT
The appearance of 2,4,6-trichloroanisole (TCA) in cork stoppers is of great concern because it can cause off-flavors in bottled wine. To prevent this sensorial defect, there should not be any traces of 2,4,6-trichlorophenol (TCP), 2,3,4,6-tetrachlorophenol (TeCP), or pentachlorophenol (PCP) in the finished corks, because they are the direct precursors of TCA. In the course of this study two methodologies based upon an extraction with ethanol/water mixtures to determine the chlorophenolic content in cork matrices were developed. The cork extract is preconcentrated using both solid-phase extraction and solid-phase microextraction methodologies. The latter was optimized by applying a full two-level factorial design. Finally, spiked ground corks at nanogram per gram levels of each chlorophenol were analyzed under optimal conditions and by applying both procedures. The obtained results demonstrate that chlorophenols can be detected in corks contaminated at the nanogram per gram level and, thus, these approaches can be successfully applied as quality control measures in the cork industry.
Subject(s)
Chlorophenols/analysis , Food Packaging/instrumentation , Wine/analysis , Chromatography, Gas/methods , Ethanol , Osmolar Concentration , Quality Control , Temperature , Time Factors , WaterABSTRACT
The search for new drug candidates in databases is of paramount importance in pharmaceutical chemistry. The selection of molecular subsets is greatly optimized and much more promising when potential drug-like molecules are detected a priori. In this work, about one hundred thousand molecules are ranked following a new methodology: a drug/non-drug classifier constructed by a consensual set of classification trees. The classification trees arise from the stochastic generation of training sets, which in turn are used to estimate probability factors of test molecules to be drug-like compounds. Molecules were represented by Topological Quantum Similarity Indices and their Graph Theoretical counterparts. The contribution of the present paper consists of presenting an effective ranking method able to improve the probability of finding drug-like substances by using these types of molecular descriptors.
Subject(s)
Algorithms , Pharmaceutical Preparations/chemistry , Quantum Theory , Chemistry, Pharmaceutical , Databases, FactualABSTRACT
A methodology based on headspace solid phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS) determination was developed for the monitoring and evaluation of the removal efficiency of 16 common fragrance allergens and two polycyclic musks in wastewater treatment plants (WWTPs). An experimental design with a full factorial model was applied to evaluate the effects of the experimental parameters on the extraction (e.g., salt content, time and extraction temperature). After determining the optimum conditions (2.4 g NaCl, 45 min at 90 °C), an external calibration was performed and quality parameters of the proposed method were evaluated. Method detection limits in the range of 0.01-1.7 µg L(-1) were obtained. Satisfactory inter-day precision values between 4% and 23% (n=5) were obtained for most compounds. The method was applied to the monitoring of the target analytes in samples from two WWTPs. Seven target compounds were detected at the primary effluent of both plants at µg L(-1) levels. Limonene, linalool and eugenol were quantitatively eliminated during the secondary treatments of both WWTPs, while lilial, benzyl salicylate, galaxolide, and tonalide were still detected at the effluent waters.
Subject(s)
Allergens/analysis , Fatty Acids, Monounsaturated/analysis , Perfume/analysis , Water Pollutants, Chemical/analysis , Acyclic Monoterpenes , Aldehydes/analysis , Benzopyrans/analysis , Cyclohexenes/analysis , Eugenol/analysis , Gas Chromatography-Mass Spectrometry/methods , Limonene , Monoterpenes , Odorants/analysis , Salicylates/analysis , Solid Phase Microextraction/methods , Terpenes/analysis , Tetrahydronaphthalenes/analysis , Wastewater/analysisABSTRACT
The use of biomarkers permits the detection of smoking having taken place in an environment. However, no single biomarker is able to differentiate clearly between different types of environments. Multivariate classification models have helped us to differentiate between outdoors, non-smoking indoors, well ventilated smoking indoors, and smoking environments without good air exchange. We found that the variables that enabled us to classify environments most accurately were indoor temperature, 2,5-dimethylfuran and ethyltoluene. A successful prediction rate of 86.5% was obtained by applying both direct fitting and cross validation discriminant (leave-one-out) analyses. Our results show that although a good air exchange ratio decreases the levels of volatile organic compounds in indoor air due to tobacco smoke, significant contamination still remains.
Subject(s)
Environmental Exposure/statistics & numerical data , Environmental Monitoring , Tobacco Smoke Pollution/analysis , Air Pollution, Indoor/analysis , Air Pollution, Indoor/statistics & numerical data , Environmental Exposure/analysis , Humans , Tobacco Smoke Pollution/statistics & numerical dataABSTRACT
Discerning between the concepts of difficulty and usefulness of a molecular ranking classification is of significant importance in virtual design chemistry. Here, both concepts are viewed from the statistical and practical point of view according to the standard definitions of enrichment and statistical significance p-values. These parameters are useful not only to compare distinct rankings obtained for the same molecular database, but also in order to compare the ones established in distinct molecular sets from an objective point of view.
Subject(s)
Drug Design , Models, Statistical , Pharmaceutical Preparations/chemistry , Algorithms , Computer-Aided DesignABSTRACT
Within the context of the simple classical linear calibration procedure (regression of y on x), here it is shown how a distinction between the distributions of the observed dependent variable (yobs) and the calculated (fitted) one (ycalc) leads to the following counterintuitive approach: in order to get the independent x values with lesser systematic deviations, do not identify as direct inputs in the classical calibration equation the new y observed ones (experimentally acquired), but instead the transformed ones by means of a regression towards the mean effect correction. It is shown how the conjunction of both steps, i.e., first the transformation of observed values and then the ulterior use in the classical calibration equation, corresponds to an operation totally equivalent to the direct implementation of the inverse calibration equation (regression of x on y in a single step). The reasoning given here explains in a simple manner why the inverse calibration numerically performs usually better for predictions of interpolated x values. Results are accompanied with the analysis of both theoretical and experimental data.
ABSTRACT
Electrostatic molecular potentials (EMPs) are studied from two points of view. First, a softened EMP (SEMP) approach is proposed, consisting in the substitution of a positive point charge as the entity with which an electronic density function (DF) interacts electrostatically to generate a classical EMP for a Gaussian charge distribution. Second, the performance of this SEMP approach under the Atomic Shell Approximation (ASA) is described and compared with classical EMP at the same ASA level. Several sample applications are presented to describe the general features of this new method of studying electrostatic interactions in molecules. The net result is a family of SEMPs that encompass EMPs as special cases but do not possess their infinite discontinuities. The features of SEMPs are quite similar to those of EMPs distant from nuclei, and the absence of infinity values makes them good candidates to be employed in molecular similarity calculations.