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

Green mold, caused by , 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 is growing.

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.

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.

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.

QSPR prediction of GC retention indices for nitrogen-containing polycyclic aromatic compounds from heuristically computed molecular descriptors.

Gas chromatographic retention indices of nitrogen-containing polycyclic aromatic compounds (N-PACs) have been predicted by quantitative structure-property relationship (QSPR) analysis based on heuristic method (HM) implemented in CODESSA. In order to indicate the influence of different molecular descriptors on retention indices and well understand the important structural factors affecting the experimental values, three multivariable linear models derived from three groups of different molecular descriptors were built. Moreover, each molecular descriptor in these models was discussed to well understand the relationship between molecular structures and their retention indices.

The prediction for gas chromatographic retention index of disulfides on stationary phases of different polarity.

Quantitative structure-retention relationship (QSRR) models for the gas chromatographic (GC) Kaváts indices of disulfides on four different polarity stationary phase have been developed. Semi-empirical quantum chemical method (AM1) implemented in hyperchem 4.0 was employed to calculate a set of molecular descriptors of 50 disulfides.

The prediction for gas chromatographic retention indices of saturated esters on stationary phases of different polarity.

A quantitative structure-retention relationship (QSRR) model has been developed for the gas chromatographic Kováts indices of 98 saturated esters on seven different polar stationary phases by multiple linear regression analysis (MLR). The seven stationary phases are: SE-30, OV-7, DC-710, OV-25, 100% phenyl, DC-230 and DC-530. Chemical descriptors were calculated from the molecular structures by PM3 of Hyperchem 4.

Prediction of gas chromatographic retention indices by the use of radial basis function neural networks.

A new method for the prediction of retention indices for a diverse set of compounds from their physicochemical parameters has been proposed. The two used input parameters for representing molecular properties are boiling point and molar volume. Models relating relationships between physicochemical parameters and retention indices of compounds are constructed by means of radial basis function neural networks.

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.

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.

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.

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.

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.

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.

Classification study of skin sensitizers based on support vector machine and linear discriminant analysis.

The support vector machine (SVM), recently developed from machine learning community, was used to develop a nonlinear binary classification model of skin sensitization for a diverse set of 131 organic compounds. Six descriptors were selected by stepwise forward discriminant analysis (LDA) from a diverse set of molecular descriptors calculated from molecular structures alone. These six descriptors could reflect the mechanic relevance to skin sensitization and were used as inputs of the SVM model.

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.

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.

[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.

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.

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.

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.

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.

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.

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.

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.