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.

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.

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.

Design, synthesis, and biological evaluation of N-carboxyphenylpyrrole derivatives as potent HIV fusion inhibitors targeting gp41.

On the basis of the structures of small-molecule hits targeting the HIV-1 gp41, N-(4-carboxy-3-hydroxy)phenyl-2,5-dimethylpyrrole (2, NB-2), and N-(3-carboxy-4-chloro)phenylpyrrole (A(1), NB-64), 42 N-carboxyphenylpyrrole derivatives in two categories (A and B series) were designed and synthesized. We found that 11 compounds exhibited promising anti-HIV-1 activity at micromolar level and their antiviral activity was correlated with their inhibitory activity on gp41 six-helix bundle formation, suggesting that these compounds block HIV fusion and entry by disrupting gp41 core formation. The structure-activity relationship and molecular docking analysis revealed that the carboxyl group could interact with either Arg579 or Lys574 to form salt bridges and two methyl groups on the pyrrole ring were favorable for interaction with the residues in gp41 pocket. The most active compound, N-(3-carboxy-4-hydroxy)phenyl-2,5-dimethylpyrrole (A(12)), partially occupied the deep hydrophobic pocket, suggesting that enlarging the molecular size of A(12) could improve its binding affinity and anti-HIV-1 activity for further development as a small-molecule HIV fusion and entry inhibitor.

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.

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.

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.

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.

MolDiA: a novel molecular diversity analysis tool. 1. Principles and architecture.

We introduce the principles and the architecture of a user-friendly software named MOLDIA (Molecular Diversity Analysis) which aims to the comparison of diverse molecular data sets through an XML structured database of predefined fragments. The MOLDIA descriptors are composed of complex fingerprint-like structures, which enclose not only structural information but also physicochemical property data. The system architecture includes the use of customizable weights on molecular descriptors and different choices of similarity/diversity measures to analyze the given data sets. Intermolecular comparisons using Ullmann's algorithm were optimized by the use of fuzzy logic, generic atoms, and a whole system of chemical graph analysis. We have found that customizing the similarity/diversity computation using structural and/or properties weights and choosing the level of fuzziness of the molecular comparison allow the user to adapt the tool to particular needs and increases the possibilities of MolDiA applications. The implementation of XML Web technologies has proven to improve and ease the extraction, processing, and analysis of chemical information.

Quantitative structure-retention relationships for organic pollutants in biopartitioning micellar chromatography.

Quantitative structure-retention relationship (QSRR) models have been successfully developed for the prediction of the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 66 organic pollutants. Heuristic method (HM) and radial basis function neural networks (RBFNN) were utilized to construct the linear and non-linear QSRR models, respectively. The optimal QSRR model was developed based on a 6-17-1 radial basis function neural network architecture using molecular descriptors calculated from molecular structure alone. The RBFNN model gave a correlation coefficient (R2) of 0.8464 and root-mean-square error (RMSE) of 0.1925 for the test set. This paper provided a useful model for the predicting the log k of other organic compounds when experiment data are unknown.

[Quantitative relationship between gas chromatographic retention time and structural parameters of alkylphenols].

Alkylphenols are a group of permanent pollutants in the environment and could adversely disturb the human endocrine system. It is therefore important to effectively separate and measure the alkylphenols. To guide the chromatographic analysis of these compounds in practice, the development of quantitative relationship between the molecular structure and the retention time of alkylphenols becomes necessary. In this study, topological, constitutional, geometrical, electrostatic and quantum-chemical descriptors of 44 alkylphenols were calculated using a software, CODESSA, and these descriptors were pre-selected using the heuristic method. As a result, three-descriptor linear model (LM) was developed to describe the relationship between the molecular structure and the retention time of alkylphenols. Meanwhile, the non-linear regression model was also developed based on support vector machine (SVM) using the same three descriptors. The correlation coefficient (R(2)) for the LM and SVM was 0.98 and 0. 92, and the corresponding root-mean-square error was 0. 99 and 2. 77, respectively. By comparing the stability and prediction ability of the two models, it was found that the linear model was a better method for describing the quantitative relationship between the retention time of alkylphenols and the molecular structure. The results obtained suggested that the linear model could be applied for the chromatographic analysis of alkylphenols with known molecular structural parameters.

Structure-activity relationship study of oxindole-based inhibitors of cyclin-dependent kinases based on least-squares support vector machines.

The least-squares support vector machines (LS-SVMs), as an effective modified algorithm of support vector machine, was used to build structure-activity relationship (SAR) models to classify the oxindole-based inhibitors of cyclin-dependent kinases (CDKs) based on their activity. Each compound was depicted by the structural descriptors that encode constitutional, topological, geometrical, electrostatic and quantum-chemical features. The forward-step-wise linear discriminate analysis method was used to search the descriptor space and select the structural descriptors responsible for activity. The linear discriminant analysis (LDA) and nonlinear LS-SVMs method were employed to build classification models, and the best results were obtained by the LS-SVMs method with prediction accuracy of 100% on the test set and 90.91% for CDK1 and CDK2, respectively, as well as that of LDA models 95.45% and 86.36%. This paper provides an effective method to screen CDKs inhibitors.

Prediction of T-cell epitopes based on least squares support vector machines and amino acid properties.

T-lymphocyte (T-cell) is a very important component in human immune system. It possesses a receptor (TCR) that is specific for the foreign epitopes which are in a form of short peptides bound to the major histocompatibility complex (MHC). When T-cell receives the message about the peptides bound to MHC, it makes the immune system active and results in the disposal of the immunogen. The antigenic determinants recognized and bound by the T-cell receptor is known as T-cell epitope. The accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. For the first time we developed new models using least squares support vector machine (LSSVM) and amino acid properties for T-cell epitopes prediction. A dataset including 203 short peptides (167 non-epitopes and 36 epitopes) was used as the input dataset and it was randomly divided into a training set and a test set. The models based on LSSVM and amino acid properties were evaluated using leave-one-out cross-validation method and the predictive ability of the test set, and obtained the results of 0.9875 and 0.9734 under the ROC curves, respectively. This result is more satisfactory than that were reported before. Especially, the accuracy of true positive gets a marked enhancement.

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.

Prediction of gas-phase reduced ion mobility constants (K(0)) based on the multiple linear regression and projection pursuit regression.

Multiple linear regression and projection pursuit regression were used to develop the linear and nonlinear models for predicting the gas-phase reduced ion mobility constant (K(0)) of 159 diverse compounds. The six descriptors selected by heuristic method were used as the inputs of the linear and nonlinear models. The linear and nonlinear models gave very satisfactory results; the square of correlation coefficient was 0.9082 and 0.9379, the squared standard error was 0.0043 and 0.0030, respectively for the whole data set. The proposed models can identify and provide some insight into what structural features are related to the K(0) of compounds. They can also help to understand the separation mechanism in ion mobility spectrometry. Additionally, this paper provided two simple, practical and effective methods for analytical chemists to predict the K(0) of compounds in ion mobility spectrometry.

Prediction of surface tension for common compounds based on novel methods using heuristic method and support vector machine.

As a novel type of learning machine method a support vector machine (SVM) was first used to develop a quantitative structure-property relationship (QSPR) model for the latest surface tension data of common diversity liquid compounds. Each compound was represented by structural descriptors, which were calculated from the molecular structure by the CODESSA program. The heuristic method (HM) was used to search the descriptor space, select the descriptors responsible for surface tension, and give the best linear regression model using the selected descriptors. Using the same descriptors, the non-linear regression model was built based on the support vector machine. Comparing the results of the two methods, the non-linear regression model gave a better prediction result than the heuristic method. Some insights into the factors that were likely to govern the surface tension of the diversity compounds could be gained by interpreting the molecular descriptors, which were selected by the heuristic model. This paper proposes a new effective way of researching interface chemistry, and can be very helpful to industry.

Accurate quantitative structure-property relationship model of mobilities of peptides in capillary zone electrophoresis.

The aim of this work was to predict electrophoretic mobilities of peptides in capillary zone electrophoresis (CZE) using the linear heuristic method (HM) and a nonlinear radial basis function neural network (RBFNN). Two data sets, consisting of 125 peptides ranging in size between 2 and 14 amino acids and 58 peptides ranging in size between 2 and 39 amino acids, are researched to test applicability of the QSPR methods. In this study, the root mean squared (RMS) errors of the training set, the test set and the whole set of data set 1 are 1.3766, 1.5608 and 1.4157 and the correlation coefficients (R2) are 0.9740, 0.9671 and 0.9724 predicted by RBFNN, respectively. While the RMS errors of the training set, the test set and the whole set of data set 2 are 0.6279, 0.8145 and 0.6673 and the correlation coefficients (R2) are 0.9773, 0.9489 and 0.9732, respectively. So the Offord charge-over-mass term (Q/M(2/3)) combined with descriptors calculated by CODESSA represents the structural features of the peptides appropriately. The electrophoretic mobilities of peptides can be accurately predicted by the linear and nonlinear model. Furthermore, the results of nonlinear model are closer to the experimental data than those of linear model.

Cytotoxicity of cantharidin analogues targeting protein phosphatase 2A.

Cantharidin is a natural toxin that possesses potent anti-tumor properties. Its clinical application, however, is limited due to severe side-effects. Its cytotoxicity is believed to be mediated by the inhibition of serine/threonine protein phosphatase 2A. In order to identify new compounds with potential clinical therapeutic use, a series of cantharidin analogues, including those with skeletal modifications at 1-C position (analogues 1-6) and those with anhydride modifications (analogues 7-13), were synthesized, and tested for their inhibitory effects on protein phosphatase 2A and their cytotoxicity to a panel of cancer cell lines. In addition, the mode of inhibition of cantharidin and analogue 13 on protein phosphatase 2A was determined by enzymatic kinetics assay. The data indicated that analogue 13 exhibited potent cytotoxicity to all cancer cell lines, and analogues 9, 11 and 12 showed relatively weak cytotoxicity to one or more cell lines, while other analogues showed little cytotoxicity. Accordingly, analogue 13 exhibited potent inhibitory activity on protein phosphatase 2A, and analogues 9, 11 and 12 showed weak inhibitory activity, while other analogues did not show any inhibitory activity. The findings indicate that the cytotoxicity of synthetic cantharidin analogues is likely to be associated with their protein phosphatase 2A inhibitory activity. The mode of inhibition of cantharidin and analogue 13 on protein phosphatase 2A is identified as noncompetitive inhibition by the Lineweaver-Burk plot.