Transcriptome Analysis Reveals Potential Regulators of DMI Fungicide Resistance in the Citrus Postharvest Pathogen Penicillium digitatum.

Green mold, caused by Penicillium digitatum, is the major cause of citrus postharvest decay. Currently, the application of sterol demethylation inhibitor (DMI) fungicide is one of the main control measures to prevent green mold. However, the fungicide-resistance problem in the pathogen P. digitatum is growing. The regulatory mechanism of DMI fungicide resistance in P. digitatum is poorly understood. Here, we first performed transcriptomic analysis of the P. digitatum strain Pdw03 treated with imazalil (IMZ) for 2 and 12 h. A total of 1338 genes were up-regulated and 1635 were down-regulated under IMZ treatment for 2 h compared to control while 1700 were up-regulated and 1661 down-regulated under IMZ treatment for 12 h. The expression of about half of the genes in the ergosterol biosynthesis pathway was affected during IMZ stress. Further analysis identified that 84 of 320 transcription factors (TFs) were differentially expressed at both conditions, making them potential regulators in DMI resistance. To confirm their roles, three differentially expressed TFs were selected to generate disruption mutants using the CRISPR/Cas9 technology. The results showed that two of them had no response to IMZ stress while ∆PdflbC was more sensitive compared with the wild type. However, disruption of PdflbC did not affect the ergosterol content. The defect in IMZ sensitivity of ∆PdflbC was restored by genetic complementation of the mutant with a functional copy of PdflbC. Taken together, our results offer a rich source of information to identify novel regulators in DMI resistance.

Highly Efficient Oxygen-Activated Self-Cleaning Membranes Prepared by Grafting a Metal-Organic Framework-Derived Catalyst.

In this study, an efficient oxygen-activated self-cleaning membrane was successfully prepared by grafting a metal-organic framework-devised catalyst (CuNi-C) onto a membrane surface, resulting in enhanced filtration performance and self-cleaning capability based on oxygen activation under mild conditions. The pore features, surface roughness, and surface hydrophilicity of the prepared membrane were analyzed and used to determine the causes of the enhanced filtration performance; the results showed that an increase in the porosity and surface roughness enhanced the permeate flux, and enhanced adsorption capacity and surface hydrophobicity improved the membrane removal efficiency. The self-cleaning mechanism was elucidated by identifying the reactive oxygen species (ROS) and detecting catalytic element valences. The results revealed that zero-valent Cu embedded into the membrane surface effectively activated natural dissolved oxygen (DO) to generate ROS that degraded organic pollutants. In this study, catalytic oxidation with DO as the oxidant was successively integrated with membrane separation to prevent membrane fouling, providing a novel direction for the development of multifunctional membranes.

Rapid toxicity prediction of organic chemicals to Chlorella vulgaris using quantitative structure-activity relationships methods.

This paper presents the results of an optimization study on the toxicity of 91 aliphatic and aromatic compounds as well as a small subset of pesticides to algae Chlorella vulgaris, which was accomplished by using quantitative structure-activity relationships (QSAR). The linear (HM) and the nonlinear method radial basis function neural networks (RBFNN) were used to develop the QSAR models and both of them can give satisfactory prediction results. At the same time, by interpreting the descriptors, we can get some insight into structural features (molecular surface area, electrostatic repulsion, and hydrogen bonds) related to the toxic action. Finally, a detailed analysis on the model application domain defined the compounds, whose estimation can be accepted with confidence. The results of this study suggest that the proposed approaches could be successfully used as a general tool for the estimate of novel toxic compounds.

First synthesis of two deoxy Lewis(x) pentaosyl glycosphingolipids.

The Lewis(x)-Lewis(x) interaction has been increasingly studied, using a variety of techniques including nuclear magnetic resonance spectroscopy, mass spectrometry, vesicle adhesion, atomic force microscopy, and surface plasmon resonance spectroscopy. However, the detailed molecular mechanism of these weak, divalent cation dependent interactions remains unclear, and new models are needed to probe the nature of this phenomenon in term of key roles of the different hydroxyl groups on Lewis(x) trisaccharide determinant involved in the Lewis(x)-Lewis(x) interaction. An interesting solution is to synthesize a series of Lewis(x) pentaosyl glycosphingolipid derivatives in which one of the eight hydroxyl groups of Lewis(x) trisaccharide is replaced by a hydrogen atom, and to test the adhesion induced by interaction of these derivatives, in order to gain insight into the functions played by the hydroxyl groups of the Lewis(x) trisaccharide. This article describes the synthesis of 3d-deoxy and 4d-deoxy Lewis(x) pentaosyl glycosphingolipids, to be used for study of the Lewis(x)-Lewis(x) interaction.

Molecular modeling studies of N-substituted pyrrole derivatives--potential HIV-1 gp41 inhibitors.

2D-, 3D-QSAR and docking studies were carried out on 23 pyrrole derivatives, to model their HIV-1 gp41 inhibitory activities. The 2D, 3D-QSAR studies were performed using CODESSA software package and comparative molecular field analysis (CoMFA) technique, respectively. The CODESSA five-descriptor model has a correlation coefficient R(2)=0.825 and a standard deviation s(2)=0.132. The 3D-QSAR CoMFA study allowed to obtain a model showing a good correlative and predictive capability which statistical results, provided by a eight-component regression equation, are: R(2)=0.984, q(2)=0.463, s=0.119. Docking studies were employed to determine probable binding conformation of these analogues into the gp41 active site using the AutoDock program whose results were found complementary with thus of 2D- and 3D-QSAR analysis. These findings provide guidance for the design and structural modifications of these derivatives for better anti-HIV-1 activity which is important for the development of a new class of entry inhibitors.

Quantitative structure-activity relationship studies of a series of non-benzodiazepine structural ligands binding to benzodiazepine receptor.

Heuristic method (HM) and radial basis function neural network (RBFNN) methods were proposed to generate QSAR models for a set of non-benzodiazepine ligands at the benzodiazepine receptor (BzR). Descriptors calculated from the molecular structures alone were used to represent the characteristics of the compounds. The six molecular descriptors selected by HM in CODESSA were used as inputs for RBFNN. Compared with the results of HM, more accurate prediction could be obtained from RBFNN. The correlation coefficients (R) of the nonlinear RBFNN model were 0.9113 and 0.9030 for the training and test sets, respectively. This paper proposed an effective method to design new ligands of BzR based on QSAR.

Flexible computational docking studies of new aminoglycosides targeting RNA 16S bacterial ribosome site.

Ribonucleic acids (RNAs) have only recently been viewed as a target for small-molecules drug discovery. Aminoglycoside compounds are antibiotics which bind the ribosomal A site (16S fragment) and cause misreading of the bacterial genetic code and inhibit translocation. In this work, a complete molecular modeling study is done for 16 newly derived aminoglycoside compounds where diverse nucleoside fragments are linked. Docking calculations are applied to 16S RNA target and a weak linear correlation, between experimental and calculated data, is obtained. However, one particularity of RNA is its high flexibility. To mimic this behavior, all docking calculations are followed by small molecular dynamic simulations. This last computational step improves significantly the correlation with experimental data and allowed us to establish structure-activity relationships. The overall results showed that the consideration of the RNA dynamic behavior is of great interest.

QSAR method for prediction of protein-peptide binding affinity: application to MHC class I molecule HLA-A*0201.

The support vector machine (SVM), which is a novel algorithm from the machine learning community, was used to develop quantitative structure-activity relationship (QSAR) models for predicting the binding affinity of 152 nonapeptides, which can bind to class I MHC HLA-A*201 molecule. Each peptide was represented by a large pool of descriptors including constitutional, topological descriptors and physical-chemical properties. The heuristic method (HM) was then used to search the descriptor space for selecting the proper ones responsible for binding affinity. The four descriptors were obtained to build linear models based on HM and nonlinear models based on SVM method. The best results are found using SVM: root mean-square (RMS) errors for training, test and whole data set were 0.383, 0.385 and 0.384, respectively. This paper allow the prediction of the binding affinity of new, untested peptides and, through the analysis of contribution of each parameter of different residue at specific position of peptidic ligands, to understand nature of the forces governing binding behavior and suggest new ideas for further synthesis of high-affinity peptides.

Study on adsorption behavior of volatile and semivolatile organic vapors to air-dry soils based on QSPR methods.

The accurate non-linear quantitive structure-property relationship model for predicting the adsorption constant of volatile and semivolatile organic vapors in soil was firstly developed based on support vector machine (SVM) by using the compounds' molecular descriptors calculated from the structure alone and the features of soil and air. Multiple linear regression (MLR) was used to build the linear QSPR model. Both the linear and non-linear models can give satisfactory prediction results: the correlation coefficient R was 0.953 and 0.995, the mean square error (MSE) was 0.0517 and 0.0057, respectively, for the whole dataset. The prediction result of the SVM model was better than that obtained by the MLR model, which proved non-linear model can simulate the relationship between the structural descriptors, the environmental condition and the soil/air distribution more accurately as well as SVM was a useful tool in the prediction of the adsorption constant of compounds.

"In vitro" protection of DNA from Fenton reaction by plant polyphenol verbascoside.

The protection effect of verbascoside (Ver) against Fenton reaction on plasmid pBR322 DNA was studied using agarose gel electrophoresis and UV-visible spectroscopy. The pBR322 plasmid DNA is damaged by hydroxyl radical (OH*) generated from the Fenton reaction with H2O2 and Fe(II) or Fe(III). This DNA damage is characterized by the diminution of supercoiled DNA forms or by the increase of relaxed or linear DNA forms after oxidative attack. The UV spectrum study showed that verbascoside can form complexes with Fe(II) or Fe(III), and the complexation can be reversed by the addition of EDTA. The formation constants of verbascoside-Fe complexes were estimated as 10(21.03) and 10(31.94) M(-2) for Fe(II) and Fe(III) respectively. The inhibition of Fenton reaction by verbascoside could be partially explained by the sequestration of Fe ions.

Quantitative structure-property relationships for pesticides in biopartitioning micellar chromatography.

The retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides was studied by quantitative structure-property relationships (QSPR) method. Heuristic method (HM) and support vector machine (SVM) method were used to build linear and nonlinear models, respectively. Compared the results of these two methods, those obtained by the SVM model are much better. For the test set, a predictive correlation coefficient (R) of 0.9755 and root-mean-square (RMS) error of 0.1403 were obtained. The proposed QSPR models, both by HM and SVM, contain the same descriptors that agree with the classical Abraham parameters of well-known linear solvation energy relationships (LSER).

The accurate QSPR models for the prediction of nonionic surfactant cloud point.

Quantitative structure-property relationship models were developed to predict cloud points and study the cloud phenomena of nonionic surfactants in aqueous solution. Four descriptors were selected by the heuristic method as the inputs of multiplier linear regression and support vector machine (SVM) models. Very satisfactory results were obtained. SVM models performed better both in fitness and in prediction capacity. For the test set, they gave a predictive correlation coefficient (R) of 0.9882, root mean squared error of 4.2727, and absolute average relative deviation of 9.5490, respectively. The proposed models can identify and provide some insight into what structural features are related to the cloud points of compounds, i.e., the molecular size, structure, and isomerism of the hydrocarbon moiety and the degree of oxyethylation. They can also help to understand the cloud phenomena of nonionic surfactants in aqueous solution. Additionally, this paper provides two simple, practical, and effective methods for analytical chemists to predict the cloud points of nonionic surfactants in aqueous solution.

The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine.

The heuristic method (HM) and support vector machine (SVM) were used to build the linear and nonlinear quantitive structure-property relationship (QSPR) models for the prediction of the fish bioconcentration factors (BCF) for 122 diverse nonionic organic chemicals using the three descriptors calculated from the molecular structure alone and selected by HM. Both the linear and nonlinear model can give very satisfactory prediction results: the square of correlation coefficient R(2) was 0.929 and 0.953, the root mean square (RMS) error was 0.404 and 0.331, respectively for the whole dataset. The prediction result of the SVM model is better than that obtained by heuristic method, which proved SVM was a useful tool in the prediction of the BCF. At the same time, the HM model showed the influencing degree of different molecular descriptors on bioconcentration factors and then could improve the understanding for the bioconcentration mechanism of organic pollutants from molecular level.

Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine.

A least-squares support vector machine (LSSVM) was used for the first time as a novel machine-learning technique for the prediction of the solubility of C60 in a large number of diverse solvents using calculated molecular descriptors from the molecular structure alone and on the basis of the software CODESSA as inputs. The heuristic method of CODESSA was used to select the correlated descriptors and build the linear model. Both the linear and the nonlinear models can give very satisfactory prediction results: the square of the correlation coefficient R(2) was 0.892 and 0.903, and the root-mean-square error was 0.126 and 0.116, respectively, for the whole data set. The prediction result of the LSSVM model is better than that obtained by the heuristic method and the reference, which proved LSSVM was a useful tool in the prediction of the solubility of C60. In addition, this paper provided a new and effective method for predicting the solubility of C60 from its structures and gave some insight into the structural features related to the solubility of C60 in different solvents.

Fast repair of deoxythymidine radical anions by two polyphenols: rutin and quercetin.

The effects of rutin and quercetin on the repair of the deoxythemindine radical anion (dT*) were studied using the technique of pulse radiolysis. The radical anion of dT was formed by the reaction of hydrated electron with dT. After pulse irradiation of nitrogen-saturated aqueous solutions containing dT, 0.2M t-BuOH and either rutin or quercetin, the initially formed dT*(-), detected spectrophotometrically, rapidly decayed with the concurrent formation of the radical anion of rutin or quercetin. The results indicated that dT*(-) can be rapidly repaired by rutin or quercetin. The rate constants of the repair reactions were determined to be 3.1 and 4.1 x 10(9)M(-1)s(-1) for rutin and quercetin, respectively. With substitution by glycosyl groups at C(3)-OH bond being neighbor to C(4) keto group, which is the active site for electron transfer, rutin has a lower repair reaction rate constant toward dT*(-) than quercetin. Together with findings from our previous studies, the present results demonstrated that nonenzymatic fast repair may be a universal form of repair involving phenolic antioxidants.

Study of quantitative structure-mobility relationship of carboxylic and sulphonic acids in capillary electrophoresis.

A quantitative structure-mobility relationship (QSMR) was developed for the absolute mobilities of 115 carboxylic and sulphonic acids in capillary electrophoresis based on the descriptors calculated from the structure alone. The heuristic method (HM) and radial basis function neural networks (RBFNN) were utilized to construct the linear and nonlinear prediction models, respectively. The prediction results were in agreement with the experimental values. The HM model gave an root-mean-square (RMS) error of 3.76 electrophoretic mobility units for the training set, 5.59 for the test set, and 4.19 for the whole data set, while the RBFNN gave an RMS error of 1.78, 2.04, and 1.83, respectively. The heuristic linear model could give some insights into the factors that are likely to govern the mobilities of the compounds, however, the prediction results of the RBFNN model seem to be better than that of the heuristic method.

CoMFA/CoMSIA/HQSAR and Docking Study of the Binding Mode of Selective Cyclooxygenase (COX-2) Inhibitors.

The intermolecular interaction between four types of anti-inflammatory inhibitors (oxazoles, pyrazoles, pyrroles and imidazoles) and COX-2 receptor was studied. The results of docking suggest that they have similar interaction mechanism. The most active compounds of these four types of inhibitors could both form several hydrogen bonds with residues His90, Arg513, Leu352 and Arg120, and develop hydrophobic interaction with residues Phe518, Leu352 and Leu359. This is consistent with the investigation reported by R. G. Kurumbail et al. (Nature. 1996, 384, 644-648). A common 3D-QSAR model could be constructed with these four categories of COX-2 inhibitors using the method of docking- guided conformer selection. The cross-validated q2 values are found as 0.741 and 0.632 for CoMFA and CoMSIA respectively. And the non-cross-validated r2 values are 0.887 and 0.885. 54 inhibitors constitute the test set used to validate the model. The results show that this model possesses good predictive ability for diverse COX-2 inhibitors. Furthermore, a HQSAR model was used to evaluate the influence of substituents on anti-inflammatory activity. Compared with the results of previous works, our model possesses significantly better prediction ability. It could help us to well understand the interaction mechanism between inhibitors and COX-2 receptor, and to make quantitative prediction of their inhibitory activities.

MASSIS: a mass spectrum simulation system 1. Principle and method.

A mass spectrum simulation system was developed. The simulated spectrum for a given target structure is computed based on the cleavage knowledge and statistical rules established and stocked in pivot databases: cleavage rule knowledge, function groups, small fragments and fragment-intensity relationships. These databases were constructed from correlation charts and statistical analysis of large population of organic mass spectra using data mining techniques. Since 1980, several systems were proposed for mass spectrum simulation, but in present there is no any commercial software available. This shows the complexity and difficulties in the development of a such system. The reported mass spectral simulation system in this paper could be the first general software for organic chemistry use

MASSIS: a mass spectrum simulation system. 2: Procedures and performance.

The use of the mass spectral simulation system, MASSIS, is reported and its performance has been evaluated. The search for substructures matching with fragments stored in four pivot databases was realised using the Ullmann algorithm. Special cleavage rules, such as the McLafferty rearrangement, the retro-Diels-Alder reaction, elimination of a neutral small molecule and oxygen migration, are processed through shortest path and depth-first search algorithms. For a search in the database of small fragments, the key step is to determine the tautomeric fragments; then a match can be obtained using a subgraph isomorphism algorithm. A string match is used to determine peak intensity. If the limited environment of an atom is the same as that found in the database of relationships between fragment and intensity, this intensity value is assigned to the query atom. Performance in a set of tests is very important in evaluating the system performance. A comparison of peaks with an intensity greater than 5% (relative) shows that our system has a very high performance figure (> 90% ) for routine organic compounds.

Screening rules for leads of fungicides, herbicides, and insecticides.

To increase efficiency of finding leads in pesticide design, reasonable screening rules for leads of fungicide, herbicide, and insecticide, respectively, are desired. Previous works showed that "Rule 5" of Lipinski is not a suitable screening rule for leads of pesticide and proposed rules for leads of fungicide, insecticide, and herbicide, which were combined by logarithmic ratio of octanol-water partition coefficient (log P), number of hydrogen bond donors, molecular weight, number of hydrogen bond acceptors, polar surface area, carcinogenic toxicity, and mutagenic toxicity. Herein, three sets of screening rules for leads of fungicide, insecticide, and herbicide, respectively, are presented. Each set of screening rules involves seven descriptors, which were selected by Kolmogorov-Smirnov test, ANOVA, Kruskal-Wallis test, and Pearson product-moment correlation, from more than 450 descriptors calculated by Codessa. Their accuracies are about 82, 83, and 89%, respectively.