Haixin Ai

Virtual screening of potential inhibitors from TCM for the CPSF30 binding site on the NS1A protein of influenza A virus

Inhibition of CPSF30 function by the effector domain of influenza A virus of non-structural protein 1 (NS1A) protein plays a critical role in the suppression of host key antiviral response. The CPSF30-binding site of NS1A appears to be a very attractive target for the development of new drugs against influenza A virus. In this study, structure-based molecular docking was utilized to screen more than 30,000 compounds from a Traditional Chinese Medicine (TCM) database. Four drug-like compounds were selected as potential inhibitors for the CPSF30-binding site of NS1A. Docking conformation analysis results showed that these potential inhibitors could bind to the CPSF30-binding site with strong hydrophobic interactions and weak hydrogen bonds. Molecular dynamics simulations and MM-PBSA calculations suggested that two of the inhibitors, compounds 32056 and 31674, could stably bind to the CPSF30-binding site with high binding free energy. These two compounds could be modified to achieve higher binding affinity, so that they may be used as potential leads in the development of new anti-influenza drugs.

Discovery of novel influenza inhibitors targeting the interaction of dsRNA with the NS1 protein by structure-based virtual screening

Influenza A Non-structural protein 1 (NS1A) RNA-Binding Domain (RBD) bound to a double-stranded RNA (dsRNA), which can inhibit the activation of antiviral pathway. The chemical compound binding sites at this pocket have abilities to block NS1 protein to inhibit dsRNA-dependent activation transfected beta interferon promoter construct. The molecular docking program AUTODOCK was used for virtual screening of about 200,000 compounds. Two more typical compounds were selected as the starting point for predicting binding modes. Further analysis shows that these compounds candidates of antiinfluenza drug, which provide an important reference for discovering new influenza virus drugs.

Probing the binding reaction of cytarabine to human serum albumin using multispectroscopic techniques with the aid of molecular docking

Cytarabine is a kind of chemotherapy medication. In the present study, the molecular interaction between cytarabine and human serum albumin (HSA) was investigated via fluorescence, UV-vis absorption, circular dichroism (CD) spectroscopy and molecular docking method under simulative physiological conditions. It was found that cytarabine could effectively quench the intrinsic fluorescence of HSA through a static quenching process. The apparent binding constants between drug and HSA at 288, 293 and 298K were estimated to be in the order of 103L·mol-1. The thermodynamic parameters ΔH°, ΔG°and ΔS° were calculated, in which the negative ΔG°suggested that the binding of cytarabine to HSA was spontaneous, moreover the negative ΔS°and negative ΔH°revealed that van der Waals force and hydrogen bonds were the major forces to stabilize the protein-cytarabine (1:1) complex. The competitive binding experiments showed that the primary binding site of cytarabine was located in the site I (subdomain IIA) of HSA. In addition, the binding distance was calculated to be 3.4nm according to the Förster no-radiation energy transfer theory. The analysis of CD and three-dimensional (3D) fluorescence spectra demonstrated that the binding of drug to HSA induced some conformational changes in HSA. The molecular docking study also led to the same conclusion obtained from the spectral results.

Study on the Mechanisms of Active Compounds in Traditional Chinese Medicine for the Treatment of Influenza Virus by Virtual Screening

In recent years, new strains of influenza virus such as H7N9, H10N8, H5N6 and H5N8 had continued to emerge. There was an urgent need for discovery of new anti-influenza virus drugs as well as accurate and efficient large-scale inhibitor screening methods. In this study, we focused on six influenza virus proteins that could be anti-influenza drug targets, including neuraminidase (NA), hemagglutinin (HA), matrix protein 1 (M1), M2 proton channel (M2), nucleoprotein (NP) and non-structural protein 1 (NS1). Structure-based molecular docking was utilized to identify potential inhibitors for these drug targets from 13144 compounds in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The results showed that 56 compounds could inhibit more than two drug targets simultaneously. Further, we utilized reverse docking to study the interaction of these compounds with host targets. Finally, the 22 compound inhibitors could stably bind to host targets with high binding free energy. The results showed that the Chinese herbal medicines had a multi-target effect, which could directly inhibit influenza virus by the target viral protein and indirectly inhibit virus by the human target protein. This method was of great value for large-scale virtual screening of new anti-influenza virus compounds.

Virtual screening approach to identifying influenza virus neuraminidase inhibitors using molecular docking combined with machine-learning-based scoring function

In recent years, an epidemic of the highly pathogenic avian influenza H7N9 virus has persisted in China, with a high mortality rate. To develop novel anti-influenza therapies, we have constructed a machine-learning-based scoring function (RF-NA-Score) for the effective virtual screening of lead compounds targeting the viral neuraminidase (NA) protein. RF-NA-Score is more accurate than RF-Score, with a root-mean-square error of 1.46, Pearson’s correlation coefficient of 0.707, and Spearman’s rank correlation coefficient of 0.707 in a 5-fold cross-validation study. The performance of RF-NA-Score in a docking-based virtual screening of NA inhibitors was evaluated with a dataset containing 281 NA inhibitors and 322 noninhibitors. Compared with other docking–rescoring virtual screening strategies, rescoring with RF-NA-Score significantly improved the efficiency of virtual screening, and a strategy that averaged the scores given by RF-NA-Score, based on the binding conformations predicted with AutoDock, AutoDock Vina, and LeDock, was shown to be the best strategy. This strategy was then applied to the virtual screening of NA inhibitors in the SPECS database. The 100 selected compounds were tested in an in vitro H7N9 NA inhibition assay, and two compounds with novel scaffolds showed moderate inhibitory activities. These results indicate that RF-NA-Score improves the efficiency of virtual screening for NA inhibitors, and can be used successfully to identify new NA inhibitor scaffolds. Scoring functions specific for other drug targets could also be established with the same method.

Paenibacillusliaoningensis sp. nov., isolated from soil.

A novel bacterial strain, designated as LNUB461T, was isolated from soil sample taken from the countryside of Shenyang, Liaoning Province, China. The isolate was a Gram-stain-positive, aerobiotic, motile, endospore-forming and rod-shaped bacterium. The organism grew optimally at 30-33 °C, pH 6.5-7.0 and in the absence of NaCl. Phylogenetic analysis based on the nearly full-length 16S rRNA gene sequence revealed high sequence similarity with Paenibacillus algorifonticola XJ259T (98.5 %), Paenibacillus xinjiangensis B538T (96.8 %), Paenibacillus glycanilyticus DS-1T (96.1 %) and Paenibacillus lupini RLAHU15T (96.1 %). The predominant cellular fatty acid and the only menaquinone were anteiso-C15:0 and MK-7, respectively. The main polar lipids of LNUB461T included phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylcholine (PC) and two unknown amino phospholipids (APL), and the cell-wall peptidoglycan was meso-diaminopimelic acid (A1γ). The DNA G+C content of LNUB461T was 49.1 mol%. The DNA-DNA hybridization value between LNUB461T and the most closely related species (P. algorifonticola) was 41.8 %. On the basis of these data, LNUB461T was classified as representing a novel species of the genus Paenibacillus, for which the name Paenibacillus liaoningensis sp. nov was proposed. The type strain is LNUB461T (=JCM 30712T=CGMCC 1.15101T).

Paenibacillus liaoningensis sp. nov., isolated from Liaoning province in China.

A novel bacterial strain, designated as LNUB461T, was isolated from soil sample taken from the countryside of Shenyang, Liaoning Province, China. The isolate was a Gram-stain-positive, aerobiotic, motile, endospore-forming and rod-shaped bacterium. The organism grew optimally at 30-33 °C, pH 6.5-7.0 and in the absence of NaCl. Phylogenetic analysis based on the nearly full-length 16S rRNA gene sequence revealed high sequence similarity with Paenibacillus algorifonticola XJ259T (98.5 %), Paenibacillus xinjiangensis B538T (96.8 %), Paenibacillus glycanilyticus DS-1T (96.1 %) and Paenibacillus lupini RLAHU15T (96.1 %). The predominant cellular fatty acid and the only menaquinone were anteiso-C15:0 and MK-7, respectively. The main polar lipids of LNUB461T included phosphatidylethanolamine (PE), phosphatidylglycerol (PG), phosphatidylcholine (PC) and two unknown amino phospholipids (APL), and the cell-wall peptidoglycan was meso-diaminopimelic acid (A1γ). The DNA G+C content of LNUB461T was 49.1 mol%. The DNA-DNA hybridization value between LNUB461T and the most closely related species (P. algorifonticola) was 41.8 %. On the basis of these data, LNUB461T was classified as representing a novel species of the genus Paenibacillus, for which the name Paenibacillus liaoningensis sp. nov was proposed. The type strain is LNUB461T (=JCM 30712T=CGMCC 1.15101T).

Structure-Based Virtual Screening for Potential Inhibitors of Influenza A Virus RNA Polymerase PA Subunit

The amino terminus of RNA polymerase A (PA-N) of influenza virus is an important target for the design of new antiviral agents. In this study, molecular docking was used to screen for compounds that specifically target the deep cleft at the endonuclease active site in N-terminus of the RNA polymerase. Four potential compounds (NCI100226, NCI122653, NCI625583, and NCI403587) with high binding affinity for the active site were identified. Structural analysis of the binding conformation of each of these compound-PA-N complexes revealed that hydrophobic interaction and manganese ion chelation comprised the main interaction between the compounds and enzyme. The binding configuration stability and the number of hydrogen and ionic bonds were investigated by molecular dynamic simulations. The results indicated that NCI403587 could be a promising PA-N inhibitor, and may represent a potential new agent for the treatment of influenza.

Predicting Drug-Induced Liver Injury Using Ensemble Learning Methods and Molecular Fingerprints

Drug-induced liver injury (DILI) is a major safety concern in the drug-development process, and various methods have been proposed to predict the hepatotoxicity of compounds during the early stages of drug trials. In this study, we developed an ensemble model using 3 machine learning algorithms and 12 molecular fingerprints from a dataset containing 1241 diverse compounds. The ensemble model achieved an average accuracy of 71.1 ± 2.6%, sensitivity (SE) of 79.9 ± 3.6%, specificity (SP) of 60.3 ± 4.8%, and area under the receiver-operating characteristic curve (AUC) of 0.764 ± 0.026 in 5-fold cross-validation and an accuracy of 84.3%, SE of 86.9%, SP of 75.4%, and AUC of 0.904 in an external validation dataset of 286 compounds collected from the Liver Toxicity Knowledge Base. Compared with previous methods, the ensemble model achieved relatively high accuracy and SE. We also identified several substructures related to DILI. In addition, we provide a web server offering access to our models (http://ccsipb.lnu.edu.cn/toxicity/HepatoPred-EL/).

Applications of Machine Learning Methods in Drug Toxicity Prediction.

Toxicity evaluation is an important part of the preclinical safety assessment of new drugs, which is directly related to human health and the fate of drugs. It is of importance to study how to evaluate drug toxicity accurately and economically. The traditional in vitro and in vivo toxicity tests are laborious, time-consuming, highly expensive, and even involve animal welfare issues. Computational methods developed for drug toxicity prediction can compensate for the shortcomings of traditional methods and have been considered useful in the early stages of drug development. Numerous drug toxicity prediction models have been developed using a variety of computational methods. With the advance of the theory of machine learning and molecular representation, more and more drug toxicity prediction models are developed using a variety of machine learning methods, such as support vector machine, random forest, naive Bayesian, back propagation neural network. And significant advances have been made in many toxicity endpoints, such as carcinogenicity, mutagenicity, and hepatotoxicity. In this review, we aimed to provide a comprehensive overview of the machine learning based drug toxicity prediction studies conducted in recent years. In addition, we compared the performance of the models proposed in these studies in terms of accuracy, sensitivity, and specificity, providing a view of the current state-of-the-art in this field and highlighting the issues in the current studies.