Extensive efforts have been devoted to the qualification of plant extracts as green corrosion inhibitors for industrial metals, but studies that demonstrate the active component(s) of these extracts remain scarce. We report here that piperine, the major pungent component of peppers, has the best corrosion inhibitive efficiency for copper in HCl among four analogous amide alkaloids isolated from a traditional Chinese medicine. This compound inhibited HCl corrosion more efficiently than cysteine, and did not exhibit markedly decreased efficiency under several harsh experimental conditions. Electrochemical and microscopic analyses suggested that piperine could form a protective layer on the metal surface via both physisorption and chemisorption, reducing the corrosion rate. The adsorption energies of all the test compounds were calculated using a hybrid density functional theory.

Aquatic toxicity is an important issue in pesticide development. In this study, using nine molecular fingerprints to describe pesticides, binary and ternary classification models were constructed to predict aquatic toxicity of pesticides via six machine learning methods: Naïve Bayes (NB), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Classification Tree (CT), Random Forest (RF) and Support Vector Machine (SVM). For the binary models, local models were obtained with 829 pesticides on rainbow trout (RT) and 151 pesticides on lepomis (LP), and global models were constructed on the basis of 1258 diverse pesticides on RT and LP and 278 on other fish species. After analyzing the local binary models, we found that fish species caused influence in terms of accuracy. Considering the data size and predictive range, the 1258 pesticides were also used to build global ternary models. The best local binary models were Maccs_ANN for RT and Maccs_SVM for LP, which exhibited accuracies of 0.90 and 0.90, respectively. For global binary models, the best model was Graph_SVM with an accuracy of 0.89. Accuracy of the best global ternary model Graph_SVM was 0.81, which was a little lower than that of the best global binary model. In addition, several substructural alerts were identified including nitrobenzene, chloroalkene and nitrile, which could significantly correlate with pesticide aquatic toxicity. This study provides a useful tool for an early evaluation of pesticide aquatic toxicity in environmental risk assessment.

Rapidly and correctly identifying eye irritants or corrosive chemicals is an important issue in health hazard assessment. The purpose of this study is to describe the development of in silico methods for the classification of chemicals into irritants/corrosives or non-irritants/non-corrosives. A total of 5220 chemicals for a serious eye irritation (EI) dataset and 2299 chemicals as an eye corrosion (EC) dataset were collected from available databases and literature. Structure–activity relationship (SAR) models were developed to separately predict serious EI or EC via machine learning methods. According to the overall prediction accuracy, the Pub-SVM model gave the best results for both serious EI (overall classification accuracy CA = 0.946) and EC (CA = 0.959). The sensitivity and specificity of serious EI were 97.3% and 86.7% for the training set, and 96.9% and 82.7% for the external validation set, respectively. Similarly, the sensitivity and specificity of EC were 95.5% and 96.2% for the training set, and 94.9% and 96.2% for the external validation set, respectively. The high specificity and sensitivity indicated that our models were reliable and robust, which can be used to predict the potential seriousness of EI/EC of compounds. Moreover, several structural alerts for characterizing serious EI/EC were identified using the combination of information gain and substructure frequency analysis.

Bicalutamide-bovine serum albumin (Bic-BSA) complexes were prepared by anti-solvent precipitation. Bovine serum albumin (BSA) was used as a stabilizer for particle growth. The physicochemical properties of Bic-BSA were analyzed by scanning electron microscopy, X-ray powder diffraction and differential scanning calorimetry. The interaction between Bic and BSA was characterized by Fourier transform infrared spectroscopy, Raman spectroscopy, fluorescence spectroscopy and molecular docking. The particle size could be easily reduced to 1–10 μm with a good lognormal distribution. The Bic-BSA complexes exhibited nonporous spherical morphology with a uniformly plicated surface. Moreover, the crystal form and thermostability of Bic were altered in the presence of BSA. Bic was found to make hydrogen bonding and hydrophobic interactions with BSA by spectroscopic studies and molecular docking. Results from the Van't Hoff equation and binding free energy calculations indicated that the improvement of physicochemical properties was the consequence of a variety of interactions in the Bic-BSA system. Bic-BSA tablets showed significantly enhanced dissolution. It was concluded that BSA plays an important role in improving the physicochemical properties of Bic due to strong multiple interactions between Bic and BSA.Download high-res image (285KB)Download full-size image

The glucagon-like peptide-1 receptor (GLP-1R) has captivated researchers because of its tremendous therapeutic effects for the treatment of type 2 diabetes mellitus (T2DM). However, since the full-length crystal structure of GLP-1R has not been revealed yet, the molecular binding mode and the activation mechanism remain unclear, which will be the obstacle for the discovery of novel potent GLP-1R agonists. In the present study, we constructed the model of GLP-1R in its full length and explored the binding modes between GLP-1 and GLP-1R by means of a bunch of computational methods including homology modeling, protein–protein docking, and molecular dynamics simulations. Our model is in agreement with previous experiment and the results from our MD simulations that verified the binding modes between GLP-1 and GLP-1R are reasonable. What's more, we found the absence or presence of GLP-1 significantly affected the conformation of extracellular domain (ECD) of GLP-1R. The GLP-1R in the apo form stabilized in a ‘closed’ state which is unfavorable to the binding of GLP-1, resembling as the GCGR. By contrast, in the GLP-1/GLP-1R complex, GLP-1R maintained an ‘open’ state.

Mutagenicity is one of the most important end points of toxicity. Due to high cost and laboriousness in experimental tests, it is necessary to develop robust in silico methods to predict chemical mutagenicity. In this paper, a comprehensive database containing 7617 diverse compounds, including 4252 mutagens and 3365 nonmutagens, was constructed. On the basis of this data set, high predictive models were then built using five machine learning methods, namely support vector machine (SVM), C4.5 decision tree (C4.5 DT), artificial neural network (ANN), k-nearest neighbors (kNN), and naïve Bayes (NB), along with five fingerprints, namely CDK fingerprint (FP), Estate fingerprint (Estate), MACCS keys (MACCS), PubChem fingerprint (PubChem), and Substructure fingerprint (SubFP). Performances were measured by cross validation and an external test set containing 831 diverse chemicals. Information gain and substructure analysis were used to interpret the models. The accuracies of fivefold cross validation were from 0.808 to 0.841 for top five models. The range of accuracy for the external validation set was from 0.904 to 0.980, which outperformed that of Toxtree. Three models (PubChem-kNN, MACCS-kNN, and PubChem-SVM) showed high and reliable predictive accuracy for the mutagens and nonmutagens and, hence, could be used in prediction of chemical Ames mutagenicity.

Despite natural amino acids having been proposed as the green surrogate of currently used corrosion inhibitors that are generally toxic to both nature and human body during the everyday industrial processing of metallic equipments, their structural simplicity yet lowers the inhibitive potency, thereby hampering their further industrialization. We disclose here that a concise chemical ligation (CuI-catalyzed azide–alkyne 1,3-dipolar cycloaddition reaction [Cue-AAC]) between two l-amino acids that are weak or noncorrosion inhibitors may result in their largely improved protective effect for mild steel in HCl. A series of 1,4-disubstituted 1,2,3-triazolyl bis-amino acid derivatives constituted by l-serine, l-threonine, l-phenylalanine, and l-tyrosine were efficiently synthesized via Cue-AAC and deprotection reactions in high yields. Subsequently performed electrochemical impedance spectroscopy (EIS) evidenced that the inhibitive effect of these compounds for mild steel in 1 M HCl is markedly better than that of their natural amino acid counterparts. The inhibitive modality of the most potent inhibitor was interpreted in detail by potentiodynamic polarization and thermodynamic calculations. Furthermore, quantum chemical calculations suggest that the triazole ring formed by the Cue-AAC has contribution to their metal surface adsorption. This study would offer unique insights into the facile development of potency-enhanced green corrosion inhibitors based on the concise Cue-AAC ligation of natural amino acids.

Dipeptidyl peptidase IV (DPP4) is an important target for the treatment of type II diabetes mellitus. Inhibition of DPP4 will improve glycemic control in such patients by preventing the rapid breakdown and thereby prolonging the physiological actions of incretin hormones. Known DPP4 inhibitors (including marketed drugs and those drug candidates) appear to share similar structural features: the cyanopyrrolidine moieties, the xanthenes/pyrimidine parts and amino-like linkages. In this study, a multi-step virtual screening strategy including both rigid and flexible docking was employed to search for novel structures with DPP4 inhibition. From SPECS database, consisting of over 190,000 commercially available compounds, 99 virtual hits were picked up and 15 of them were eventually identified to have DPP4 inhibitory activities at 5 ~ 50 μM. Diverse structures of our compounds were out of usual structural categories. Hence a pharmacophore model was built to further explore their common binding features on the enzyme. The results provided a new pathway for the discovery of DPP4 inhibitors and would be helpful for further optimization of DPP4 inhibitors.

To probe the selective mechanism of agonists binding to three opioid receptor subtypes, ligand-based and receptor-based methods were implemented together and subtype characteristics of opioid agonists were clearly described. Three pharmacophore models of opioid agonists were generated by the Catalyst/HypoGen program. The best pharmacophore models for μ, δ and κ agonists contained four, five and five features, respectively. Meanwhile, the three-dimensional structures of three receptor subtypes were modeled on the basis of the crystal structure of β2-adrenergic receptor, and molecular docking was conducted further. According to these pharmacophore models and docking results, the similarities and differences among agonists of three subtypes were identified. μ or δ agonists, for example, could form one hydrogen bond separately with Tyr129 and Tyr150 at TMIII, whereas κ ones formed a π-π interaction in that place. These findings may be crucial for the development of novel selective analgesic drugs.

5-hydroxytryptamine-2c (5-HT2c) receptor antagonists have clinical utility in the management of nervous system. In this work, ligand-based and receptor-based methods were used to investigate the binding mode of h5-HT2c receptor antagonists. First, the pharmacophore modeling of the h5-HT2c receptor antagonists was carried out by CATALYST. Then, the h5-HT2c antagonists were docked to the h5-HT2c receptor model. Subsequently, the comprehensive analysis of the pharmacophore and docking results revealed the structure-activity relationship of 5-HT2c receptor antagonists and the key residues involved in the interactions. For example, three hydrophobic points in the ligands corresponded to the region surrounded by Val135, Val208, Phe214, Ala222, Phe327, Phe328 and Val354 of the h5-HT2c receptor. The carbonyl group of compound 1 formed a hydrogen bond with Asn331. The nitrogen atom in the piperidine of compound 1 corresponding to the positive ionizable position of the best pharmacophore formed the electrostatic interactions with the carbonyl of Asp134, Asn331 and Val354, and with the hydroxyl group of Ser334. In addition, a predictive CoMFA model was developed based on the 24 compounds that were used as the training set in the pharmacophore modeling. Our results were not only useful to explore the detailed mechanism of the interactions between the h5-HT2c receptor and antagonists, but also provided suggestions in the discovery of novel 5-HT2c receptor antagonists.

Agonists of β3-adrenergic receptor (AR) have been thought as potential drugs for the treatment of obesity, type II diabetes, and overactive bladder. In order to clarify the essential structure–activity relationship and the detailed binding modes of β3-AR agonists as well as to identify new lead compounds activating β3-AR, ligand-based and receptor-based methods were applied. The pharmacophore models were developed based on 144 β3-AR agonists. Meanwhile, the homology model of the β3-AR was built based on the crystal structure of β2-AR. The pharmacophore model and the homology model mapped with each other very well, and some important information was obtained from the docking result. For example, agonists formed similar hydrogen-bonding interactions with residues Asp117, Arg315, and Asn332, π–π stacking interaction with residues Phe308, and hydrophobic interactions with residues Val118, Val121, Ala197, Phe198, Ala199, Phe309, and Phe328 of β3-AR. And the major difference about binding mode from the crystal structures of β1- and β2-ARs is the hydrogen-bonding interaction with the residue Arg315, which corresponds to the residue Asn313 of β1-AR and the residue His296 of β2-AR, respectively. Our findings may be crucial for the design and development of novel selective and potent β3-AR agonists.

Two chemical function-based pharmacophore models of selective κ-opioid receptor agonists were generated by using two different programs: Catalyst/HypoGen and Phase. The best output hypothesis (Hypo1) of HypoGen consisted of five features: one hydrogen-bond acceptor (HA), three hydrophobic points (HY), and one positive ionizable function (PI). The highest scoring model (Hypo2) produced by Phase comprised four features: one acceptor (A), one positive ionizable function (P), and two aromatic ring features (R). These two models (Hypo1 and Hypo2) were then validated by test set prediction and enrichment factors. They were shown to be able to identify highly potent κ-agonists within a certain range, and satisfactory enrichments were achieved. The features of these two pharmacophore models were similar and consistent with experiment data. The models produced here were also generally in accord with other reported models. Therefore, our pharmacophore models were considered as valuable tools for 3D virtual screening, and could be useful for designing novel κ-agonists.

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analyses using CoMFA and CoMSIA methods were conducted on a series of fluoropyrrolidine amides as dipeptidyl peptidase IV (DP-IV) inhibitors. The selected ligands were docked into the binding site of the 3D model of DP-IV using the GOLD software, and the possible interaction models between DP-IV and the inhibitors were obtained. Based on the binding conformations of these fluoropyrrolidine amides and their alignment inside the binding pocket of DP-IV, predictive 3D-QSAR models were established by CoMFA and CoMSIA analyses, which had conventional r2 and cross-validated coefficient values (\( r^{2}_{{{\text{cv}}}} \)) up to 0.982 and 0.555 for CoMFA and 0.953 and 0.613 for CoMSIA, respectively. The predictive ability of these models was validated by six compounds that were in the testing set. Structure-based investigations and the final 3D-QSAR results provide the guide for designing new potent inhibitors.