Ensemble multiclassification model for predicting developmental toxicity in zebrafish.

In recent years, with the rapid development of society, organic compounds have been released into aquatic environments in various forms, posing a significant threat to the survival of aquatic organisms. The assessment of developmental toxicity is an important part of environmental safety risk systems, helping to identify the potential impacts of organic compounds on the embryonic development of aquatic organisms and enabling early detection and warning of potential ecological risks. Additionally, binary classification models cannot accurately classify organic compounds. Therefore, it is crucial to construct a multiclassification model for predicting the developmental toxicity of organic compounds. In this study, binary and multiclassification models were developed based on the ToxCast™ Phase I chemical library and literature data. The random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms, as well as 8 types of molecular fingerprint were used to establish a multiclassification base model for predicting developmental toxicity through 5-fold cross-validation and external validation. Ultimately, a multiclassification ensemble model was derived through a voting method. The performance of the binary ensemble model, as measured by the balanced accuracy, was 0.918, while that of the multiclassification model was 0.819. The developmental toxicity voting ensemble model (DT-VEM) achieved accuracies of 0.804, 0.834, and 0.855. Furthermore, by utilizing the XGBoost machine learning algorithm to construct separate models for molecular descriptors and substructure molecular fingerprints, we identified several substructures and physical properties related to developmental toxicity. Our research contributes to a more detailed classification of developmental toxicity, providing a new and valuable tool for predicting the developmental toxicity effects of unknown compounds. This supplement addresses the limitations of previous tools, as it offers an enhanced ability to predict potential developmental toxicity in novel compounds.

Ensemble multiclassification model for aquatic toxicity of organic compounds.

With environmental pollution becoming increasingly serious, organic compounds have become the main hazard of environmental pollution and exert substantial negative impacts on aquatic organisms. In research pertaining to the acute toxicity of organic compounds, traditional biological experimental methods are time-consuming and expensive. In addition, computer-aided binary classification models cannot accurately classify acute toxicity. Therefore, the multiclassication model is necessary for more accurate classification of acute toxicity. In this study, median lethal concentrations of 373 organic compounds in the environmental toxicology datasets ECOTOX and EAT5 were used. These chemicals were classified into four categories based on the European Economic Community criteria. Then the random forest, support vector machine, extreme gradient boosting, adaptive gradient boosting, and C5.0 decision tree algorithms and eight molecular fingerprints were used to build a multiclassification base model for the acute toxicity of organic compounds. The base models were repeated 100 times with fivefold cross-validation and external validation. The ensemble model was obtained by the voting method. The best base classifier was ExtendFP-C5.0, which had an accuracy, sensitivity and specificity values of 87.30%, 87.32% and 95.76% for external validation, and the voting ensemble model performance of 96.92%, 96.93% and 98.97%, respectively. The ensemble model achieved a higher accuracy than previously reported studies. Our study will help to further classify the acute toxicity of organic compounds to aquatic organisms and predict the hazard classes of organic compounds.

QSAR modelling study of the bioconcentration factor and toxicity of organic compounds to aquatic organisms using machine learning and ensemble methods.

Bioconcentration factors and median lethal concentrations (LC50s) are important when assessing risks posed by organic pollutants to aquatic ecosystems. Various quantitative structure-activity relationship models have been developed to predict bioconcentration factors and classify acute toxicity. In the study, we developed a regression model using Recursive Feature Elimination (RFE) method combined with the Support Vector Machine (SVM) algorithm. We calculated 2D molecular descriptors from a dataset containing 450 diverse chemicals in our regression model. Then we built three ensemble models using three machine learning algorithms and calculated 12 molecular fingerprints from a dataset containing 400 diverse chemicals in our classification models. In the regression model, the R2 and Rpred2 for the regression model were 0.860 and 0.757, respectively. Other parameters indicated that the regression model made good predictions and could efficiently predict a new set of compounds following standards set by Golbraikh, Tropsha, and Roy. In the classification models, the ensemble-SVM classification model gave an overall accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 92.2, 95.1, 86.0, and 0.965, respectively, in a five-fold cross-validation and of 87.3, 92.6, 76.0, and 0.940, respectively, in an external validation. These parameters indicated that our ensemble-SVM model was more stable and gave more accurate predictions than previous models. The model could therefore be used to effectively predict aquatic toxicity and assess risks posed to aquatic ecosystems. We identified several structures most relevant to acute aquatic toxicity through predictions made by the two types of models, and this information may be important to aquatic toxicology experiments and aquatic system management.

Predicting the cytotoxicity of chemicals using ensemble learning methods and molecular fingerprints.

The prediction of compound cytotoxicity is an important part of the drug discovery process. However, it usually appears as poor predictive performance because the datasets are high-throughput and have a class-imbalance problem. In this study, several strategies of performing a structure-activity relationship study for a cytotoxic endpoint in the AID364 dataset were explored to solve the class-imbalance problem. Random forest adaboost was used as the base learners for 10 types of molecular fingerprints and an ensemble method and six data-balancing methods were applied to balance the classes. As a result, the ensemble model using MACCS fingerprint was found to be the best, giving area under the curve of 85.2% ± 0.35%, sensitivity of 81.8% ± 0.65%, and specificity of 76.0% ± 0.12% in fivefold cross-validation and area under the curve of 78.8%, sensitivity of 55.5% and specificity of 78.5% in external validation. Good performance also appeared on other datasets with different sizes/degrees of imbalance. To explore the structural commonality of cytotoxic compounds, several substructures were identified as an important reference for substructure alerts. The convincing results indicate that the proposed models are helpful in predicting the cytotoxicity of chemicals.

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.

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/).

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.

HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy.

LncRNA plays an important role in many biological and disease progression by binding to related proteins. However, the experimental methods for studying lncRNA-protein interactions are time-consuming and expensive. Although there are a few models designed to predict the interactions of ncRNA-protein, they all have some common drawbacks that limit their predictive performance. In this study, we present a model called HLPI-Ensemble designed specifically for human lncRNA-protein interactions. HLPI-Ensemble adopts the ensemble strategy based on three mainstream machine learning algorithms of Support Vector Machines (SVM), Random Forests (RF) and Extreme Gradient Boosting (XGB) to generate HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble, respectively. The results of 10-fold cross-validation show that HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble achieved AUCs of 0.95, 0.96 and 0.96, respectively, in the test dataset. Furthermore, we compared the performance of the HLPI-Ensemble models with the previous models through external validation dataset. The results show that the false positives (FPs) of HLPI-Ensemble models are much lower than that of the previous models, and other evaluation indicators of HLPI-Ensemble models are also higher than those of the previous models. It is further showed that HLPI-Ensemble models are superior in predicting human lncRNA-protein interaction compared with previous models. The HLPI-Ensemble is publicly available at: http://ccsipb.lnu.edu.cn/hlpiensemble/ .

LPI-NRLMF: lncRNA-protein interaction prediction by neighborhood regularized logistic matrix factorization.

LncRNA-protein interactions play important roles in many important cellular processes including signaling, transcriptional regulation, and even the generation and progression of complex diseases. However, experimental methods for determining proteins bound by a specific lncRNA remain expensive, difficult and time-consuming, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. In this study, we developed a novel matrix factorization computational approach to uncover lncRNA-protein relationships, namely lncRNA-protein interactions prediction by neighborhood regularized logistic matrix factorization (LPI-NRLMF). Moreover, it is a semi-supervised and does not need negative samples. As a result, new model obtained reliable performance in the leave-one-out cross validation (the AUC of 0.9025 and AUPR of 0.6924), which significantly improved the prediction performance of previous models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is anticipated that LPI-NRLMF could serve as a useful resource for potential lncRNA-protein association identification.

Study on the interaction of paeoniflorin with human serum albumin (HSA) by spectroscopic and molecular docking techniques.

The interaction of paeoniflorin with human serum albumin (HSA) was investigated using fluorescence, UV-vis absorption, circular dichroism (CD) spectra and molecular docking techniques under simulative physiological conditions. The results clarified that the fluorescence quenching of HSA by paeoniflorin was a static quenching process and energy transfer as a result of a newly formed complex (1:1). Paeoniflorin spontaneously bound to HSA in site I (subdomain IIA), which was primarily driven by hydrophobic forces and hydrogen bonds (ΔH° = - 9.98 kJ mol-1, ΔS° = 28.18 J mol-1 K-1). The binding constant was calculated to be 1.909 × 103 L mol-1 at 288 K and it decreased with the increase of the temperature. The binding distance was estimated to be 1.74 nm at 288 K, showing the occurrence of fluorescence energy transfer. The results of CD and three-dimensional fluorescence spectra showed that paeoniflorin induced the conformational changes of HSA. Meanwhile, the study of molecular docking also indicated that paeoniflorin could bind to the site I of HSA mainly by hydrophobic and hydrogen bond interactions.

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.

LPI-ETSLP: lncRNA-protein interaction prediction using eigenvalue transformation-based semi-supervised link prediction.

RNA-protein interactions are essential for understanding many important cellular processes. In particular, lncRNA-protein interactions play important roles in post-transcriptional gene regulation, such as splicing, translation, signaling and even the progression of complex diseases. However, the experimental validation of lncRNA-protein interactions remains time-consuming and expensive, and only a few theoretical approaches are available for predicting potential lncRNA-protein associations. Here, we presented eigenvalue transformation-based semi-supervised link prediction (LPI-ETSLP) to uncover the relationship between lncRNAs and proteins. Moreover, it is semi-supervised and does not need negative samples. Based on 5-fold cross validation, an AUC of 0.8876 and an AUPR of 0.6438 have demonstrated its reliable performance compared with three other computational models. Furthermore, the case study demonstrated that many lncRNA-protein interactions predicted by our method can be successfully confirmed by experiments. It is indicated that LPI-ETSLP would be a useful bioinformatics resource for biomedical research studies.

pSuc-PseRat: Predicting Lysine Succinylation in Proteins by Exploiting the Ratios of Sequence Coupling and Properties.

Lysine succinylation is an extremely important protein post-translational modification that plays a fundamental role in regulating various biological reactions, and dysfunction of this process is associated with a number of diseases. Thus, determining which Lys residues in an uncharacterized protein sequence are succinylated underpins both basic research and drug development endeavors. To solve this problem, we have developed a predictor called pSuc-PseRat. The features of the pSuc-PseRat predictor are derived from two aspects: (1) the binary encoding from succinylated sites and non-succinylated sites; (2) the sequence-coupling effects between succinylated sites and non-succinylated sites. Eleven gradient boosting machine classifiers were trained with these features to build the predictor. The pSuc-PseRat predictor achieved an average ACU (area under the receiver operating characteristic curve) score of 0.805 in the fivefold cross-validation set and performed better than existing predictors on two comprehensive independent test sets. A freely available web server has been developed for pSuc-PseRat.

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.

CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods.

Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).

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).

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.

Responses of a novel salt-tolerant Streptomyces albidoflavus DUT_AHX capable of degrading nitrobenzene to salinity stress.

A novel salt-tolerant strain DUT_AHX, which was capable of utilizing nitrobenzene (NB) as the sole carbon source, was isolated from NB-contaminated soil. Furthermore, it was identified as Streptomyces albidoflavus on the basis of physiological and biochemical tests and 16S ribosomal DNA (rDNA) sequence analysis. It can grow in the presence of NaCl up to 12% (w/v) or NB up to 900 mg/l in mineral salts basal (MSB) medium. The exogenously added osmoprotectants such as glycin, glutamic acid, proline, betaine and ectoine can improve growth of strain DUT_AHX in the presence of 10% (w/v) NaCl. NB-grown cells of strain DUT_AHX in modified MSB medium can degrade NB with the concomitant release of ammonia. Moreover, crude extracts of NB-grown strain DUT_AHX mainly contained 2-aminophenol 1,6-dioxygenase activity. These indicate that NB degradation by strain DUT_AHX might involve a partial reductive pathway. The proteins induced by salinity stress or NB were analyzed by native-gradient polyacrylamide gel electrophoresis (PAGE) and sodium dodecyl sulfate (SDS)-PAGE. In NB-induced proteins de novo, 141 kDa protein on the native-gradient PAGE gel was excised and electroeluted. Furthermore, enzyme tests exhibit the 2-aminophenol 1,6-dioxygenase activity of purified 141 kDa protein is 11-fold that of the cell-free extracts. The exploitation of strain DUT_AHX in salinity stress will be a remarkable improvement in NB bioremediation and wastewater treatment in high salinity.

Location and PCR analysis of catabolic genes in a novel Streptomyces sp. DUT_AHX capable of degrading nitrobenzene.

A novel strain of Streptomyces sp. DUT_AHX was isolated from sludge contaminated with nitrobenzene and identified on the basis of physiological and biochemical tests and 16S ribosomal DNA (rDNA) sequence analysis. The optimal degradation conditions were as follows: temperature 30 degrees C, pH 7.0-8.0, shaking speed 150-180 r/min, and inocula 10% (V/V). The strain, which possessed a partial reductive pathway with the release of ammonia, was also able to grow on mineral salts basal (MSB) medium plates with 2-aminophenol, phenol, or toluene as the sole carbon source. Furthermore, the enzyme activity tests showed crude extracts of nitrobenzene-grown DUTAHX contained 2-aminophenol 1,6-dioxygenase activity. The 17-kb plasmid was isolated by the modified alkaline lysis method and was further cured by sodium dodecyl sulphate (SDS) together with 37 degrees C. As a result, the cured derivative strain DUTAHX-4 lost the 2-aminophenol 1,6-dioxygenase activity. The results suggested that the catabolic genes encoding the nitrobenzene-degrading enzymes were plasmid-associated. Moreover, the plasmid DNA was amplified with degenerate primers by touchdown PCR and an expected size fragment (471 bp) was generated. The Blast results revealed that the gene encoding a 157 amino acid polypeptide was 39%-76% identical to YHS domain protein. The further examination of the plasmid would demonstrate the molecular basis of nitrobenzene catabolism in Streptomyces, such as regulation and genetic organization of the catabolic genes.