[Open, multicenter, phase â £ clinical trial of Shenbei Guchang capsules in treatment of diarrhea type irritable bowel syndrome].

To evaluate the safety and effectiveness of Shenbei Guchang capsules in treatment of diarrhea type irritable bowel syndrome (yang deficiency of spleen and kidney) under widely used conditions, an open, multicenter, controlled, phase Ⅳ clinical trial was conducted in the drug clinical trial centers of 16 domestic hospitals. 2 123 patients from June 10, 2011 to November 29, 2012 were enrolled in the trial. Drug clinical trial was approved by Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital Ethics Committee before implementation. Before the start of trial, subjects were selected according to the research scheme and inclusion criteria, then they would step into the 14 d study after signing Informed Consent Form. All subjects were treated according to the research scheme, evaluated the conditions and filled in CFR sheet, to provide the evaluation data and information on safety and efficacy of Shenbei Guchang capsules. Shenbei Guchang capsules were used to treat diarrhea type irritable bowel syndrome in widely used conditions (2 123 cases), and 2 029 cases of them entered FAS set, cure+markedly effective in 1 921 cases, with a comprehensive curative effect rate of 94.68%; 2 010 cases of them entered PPS set, cure+markedly effective in 1 906 cases, with a comprehensive curative effect rate of 94.83%. The primary symptoms of IBS were abdominal pain and diarrhea. After treatment, both abdominal pain and diarrhea were improved, with significant differences (P<0.000 1). There were significant differences in traditional Chinese medicine symptom scores on both post-treatment day 7 and day 14 as compared with the conditions before treatment (P<0.000 1). 35 cases of adverse events occurred during the trial with an incidence of 1.65%, including 12 cases of drug-related adverse events (adverse reaction) with an incidence of 0.57%, mainly manifested as nausea, abdominal distension and dry mouth, most of which would be spontaneously relieved without any measures. No serious adverse events occurred. The commercially available Shenbei Guchang capsules are proved safe and effective for the treatment of diarrhea type irritable bowel syndrome (yang deficiency of spleen and kidney) under widely used conditions (2 123 cases), and can be continued for clinical promotion and application.

Identification of efflux proteins using efficient radial basis function networks with position-specific scoring matrices and biochemical properties.

Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.

ETMB-RBF: discrimination of metal-binding sites in electron transporters based on RBF networks with PSSM profiles and significant amino acid pairs.

BACKGROUND: Cellular respiration is the process by which cells obtain energy from glucose and is a very important biological process in living cell. As cells do cellular respiration, they need a pathway to store and transport electrons, the electron transport chain. The function of the electron transport chain is to produce a trans-membrane proton electrochemical gradient as a result of oxidation-reduction reactions. In these oxidation-reduction reactions in electron transport chains, metal ions play very important role as electron donor and acceptor. For example, Fe ions are in complex I and complex II, and Cu ions are in complex IV. Therefore, to identify metal-binding sites in electron transporters is an important issue in helping biologists better understand the workings of the electron transport chain. METHODS: We propose a method based on Position Specific Scoring Matrix (PSSM) profiles and significant amino acid pairs to identify metal-binding residues in electron transport proteins. RESULTS: We have selected a non-redundant set of 55 metal-binding electron transport proteins as our dataset. The proposed method can predict metal-binding sites in electron transport proteins with an average 10-fold cross-validation accuracy of 93.2% and 93.1% for metal-binding cysteine and histidine, respectively. Compared with the general metal-binding predictor from A. Passerini et al., the proposed method can improve over 9% of sensitivity, and 14% specificity on the independent dataset in identifying metal-binding cysteines. The proposed method can also improve almost 76% sensitivity with same specificity in metal-binding histidine, and MCC is also improved from 0.28 to 0.88. CONCLUSIONS: We have developed a novel approach based on PSSM profiles and significant amino acid pairs for identifying metal-binding sites from electron transport proteins. The proposed approach achieved a significant improvement with independent test set of metal-binding electron transport proteins.

Catellibacterium nanjingense sp. nov., a propanil-degrading bacterium isolated from activated sludge, and emended description of the genus Catellibacterium.

A novel facultatively anaerobic, non-spore-forming, non-motile, catalase- and oxidase-positive, Gram-negative and rod-shaped bacterial strain, designated Y12(T), was isolated from activated sludge of a wastewater bio-treatment facility. The strain was able to degrade about 90% of added propanil (100 mg l(-1)) within 3 days of incubation. Growth occurred in the presence of 0-4.5% (w/v) NaCl (optimum 0.5%), at 10-40 °C (optimum 28 °C) and at pH 5.5-10.0 (optimum pH 7.0). Vesicular internal membrane structures and photoheterotrophic growth were not observed. The major respiratory quinone was ubiquinone-10 and the major cellular fatty acid was summed feature 8 (C(18:1)ω6c and/or C(18:1)ω7c). The genomic DNA G+C content of strain Y12(T) was 63.7 mol%. Phylogenetic analysis based on 16S rRNA gene sequence comparison revealed that strain Y12(T) was a member of the genus Catellibacterium, as it showed highest sequence similarities to Catellibacterium caeni DCA-1(T) (99.1%) and <96.0% similarities with other species of the genus Catellibacterium. Strain Y12(T) showed low DNA-DNA relatedness values with C. caeni DCA-1(T). Based on phenotypic, genotypic and phylogenetic properties, strain Y12(T) represents a novel species of the genus Catellibacterium, for which the name Catellibacterium nanjingense sp. nov. is proposed. The type strain is Y12(T) (=CCTCC AB 2010218(T) =KCTC 23298(T)). An emended description of the genus Catellibacterium is also presented.

Carboxylator: incorporating solvent-accessible surface area for identifying protein carboxylation sites.

In proteins, glutamate (Glu) residues are transformed into γ-carboxyglutamate (Gla) residues in a process called carboxylation. The process of protein carboxylation catalyzed by γ-glutamyl carboxylase is deemed to be important due to its involvement in biological processes such as blood clotting cascade and bone growth. There is an increasing interest within the scientific community to identify protein carboxylation sites. However, experimental identification of carboxylation sites via mass spectrometry-based methods is observed to be expensive, time-consuming, and labor-intensive. Thus, we were motivated to design a computational method for identifying protein carboxylation sites. This work aims to investigate the protein carboxylation by considering the composition of amino acids that surround modification sites. With the implication of a modified residue prefers to be accessible on the surface of a protein, the solvent-accessible surface area (ASA) around carboxylation sites is also investigated. Radial basis function network is then employed to build a predictive model using various features for identifying carboxylation sites. Based on a five-fold cross-validation evaluation, a predictive model trained using the combined features of amino acid sequence (AA20D), amino acid composition, and ASA, yields the highest accuracy at 0.874. Furthermore, an independent test done involving data not included in the cross-validation process indicates that in silico identification is a feasible means of preliminary analysis. Additionally, the predictive method presented in this work is implemented as Carboxylator ( http://csb.cse.yzu.edu.tw/Carboxylator/ ), a web-based tool for identifying carboxylated proteins with modification sites in order to help users in investigating γ-glutamyl carboxylation.

Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties.

SUMMARY: Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Currently, our knowledge about the functions of transporters is limited due to the paucity of their 3D structures. Hence, computational techniques are necessary to annotate the functions of transporters. In this work, we focused on an important functional aspect of transporters, namely annotation of targets for transport proteins. We have systematically analyzed four major classes of transporters with different transporter targets: (i) electron, (ii) protein/mRNA, (iii) ion and (iv) others, using amino acid properties. We have developed a radial basis function network-based method for predicting transport targets with amino acid properties and position specific scoring matrix profiles. Our method showed a 10-fold cross-validation accuracy of 90.1, 80.1, 70.3 and 82.3% for electron transporters, protein/mRNA transporters, ion transporters and others, respectively, in a dataset of 543 transporters. We have also evaluated the performance of the method with an independent dataset of 108 proteins and we obtained similar accuracy. We suggest that our method could be an effective tool for functional annotation of transport proteins. AVAILABILITY: http://rbf.bioinfo.tw/~sachen/ttrbf.html

Incorporating distant sequence features and radial basis function networks to identify ubiquitin conjugation sites.

Ubiquitin (Ub) is a small protein that consists of 76 amino acids about 8.5 kDa. In ubiquitin conjugation, the ubiquitin is majorly conjugated on the lysine residue of protein by Ub-ligating (E3) enzymes. Three major enzymes participate in ubiquitin conjugation. They are E1, E2 and E3 which are responsible for activating, conjugating and ligating ubiquitin, respectively. Ubiquitin conjugation in eukaryotes is an important mechanism of the proteasome-mediated degradation of a protein and regulating the activity of transcription factors. Motivated by the importance of ubiquitin conjugation in biological processes, this investigation develops a method, UbSite, which uses utilizes an efficient radial basis function (RBF) network to identify protein ubiquitin conjugation (ubiquitylation) sites. This work not only investigates the amino acid composition but also the structural characteristics, physicochemical properties, and evolutionary information of amino acids around ubiquitylation (Ub) sites. With reference to the pathway of ubiquitin conjugation, the substrate sites for E3 recognition, which are distant from ubiquitylation sites, are investigated. The measurement of F-score in a large window size (-20∼+20) revealed a statistically significant amino acid composition and position-specific scoring matrix (evolutionary information), which are mainly located distant from Ub sites. The distant information can be used effectively to differentiate Ub sites from non-Ub sites. As determined by five-fold cross-validation, the model that was trained using the combination of amino acid composition and evolutionary information performs best in identifying ubiquitin conjugation sites. The prediction sensitivity, specificity, and accuracy are 65.5%, 74.8%, and 74.5%, respectively. Although the amino acid sequences around the ubiquitin conjugation sites do not contain conserved motifs, the cross-validation result indicates that the integration of distant sequence features of Ub sites can improve predictive performance. Additionally, the independent test demonstrates that the proposed method can outperform other ubiquitylation prediction tools.

Investigation and identification of protein γ-glutamyl carboxylation sites.

BACKGROUND: Carboxylation is a modification of glutamate (Glu) residues which occurs post-translation that is catalyzed by γ-glutamyl carboxylase in the lumen of the endoplasmic reticulum. Vitamin K is a critical co-factor in the post-translational conversion of Glu residues to γ-carboxyglutamate (Gla) residues. It has been shown that the process of carboxylation is involved in the blood clotting cascade, bone growth, and extraosseous calcification. However, studies in this field have been limited by the difficulty of experimentally studying substrate site specificity in γ-glutamyl carboxylation. In silico investigations have the potential for characterizing carboxylated sites before experiments are carried out. RESULTS: Because of the importance of γ-glutamyl carboxylation in biological mechanisms, this study investigates the substrate site specificity in carboxylation sites. It considers not only the composition of amino acids that surround carboxylation sites, but also the structural characteristics of these sites, including secondary structure and solvent-accessible surface area (ASA). The explored features are used to establish a predictive model for differentiating between carboxylation sites and non-carboxylation sites. A support vector machine (SVM) is employed to establish a predictive model with various features. A five-fold cross-validation evaluation reveals that the SVM model, trained with the combined features of positional weighted matrix (PWM), amino acid composition (AAC), and ASA, yields the highest accuracy (0.892). Furthermore, an independent testing set is constructed to evaluate whether the predictive model is over-fitted to the training set. CONCLUSIONS: Independent testing data that did not undergo the cross-validation process shows that the proposed model can differentiate between carboxylation sites and non-carboxylation sites. This investigation is the first to study carboxylation sites and to develop a system for identifying them. The proposed method is a practical means of preliminary analysis and greatly diminishes the total number of potential carboxylation sites requiring further experimental confirmation.

Incorporating significant amino acid pairs to identify O-linked glycosylation sites on transmembrane proteins and non-transmembrane proteins.

BACKGROUND: While occurring enzymatically in biological systems, O-linked glycosylation affects protein folding, localization and trafficking, protein solubility, antigenicity, biological activity, as well as cell-cell interactions on membrane proteins. Catalytic enzymes involve glycotransferases, sugar-transferring enzymes and glycosidases which trim specific monosaccharides from precursors to form intermediate structures. Due to the difficulty of experimental identification, several works have used computational methods to identify glycosylation sites. RESULTS: By investigating glycosylated sites that contain various motifs between Transmembrane (TM) and non-Transmembrane (non-TM) proteins, this work presents a novel method, GlycoRBF, that implements radial basis function (RBF) networks with significant amino acid pairs (SAAPs) for identifying O-linked glycosylated serine and threonine on TM proteins and non-TM proteins. Additionally, a membrane topology is considered for reducing the false positives on glycosylated TM proteins. Based on an evaluation using five-fold cross-validation, the consideration of a membrane topology can reduce 31.4% of the false positives when identifying O-linked glycosylation sites on TM proteins. Via an independent test, GlycoRBF outperforms previous O-linked glycosylation site prediction schemes. CONCLUSION: A case study of Cyclic AMP-dependent transcription factor ATF-6 alpha was presented to demonstrate the effectiveness of GlycoRBF. Web-based GlycoRBF, which can be accessed at http://GlycoRBF.bioinfo.tw, can identify O-linked glycosylated serine and threonine effectively and efficiently. Moreover, the structural topology of Transmembrane (TM) proteins with glycosylation sites is provided to users. The stand-alone version of GlycoRBF is also available for high throughput data analysis.

Classification of transporters using efficient radial basis function networks with position-specific scoring matrices and biochemical properties.

Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Transporters are generally classified into channels/pores, electrochemical transporters, and active transporters. Discriminating the specific class of transporters and their subfamilies are essential tasks in computational biology for the advancement of structural and functional genomics. We have systematically analyzed the amino acid composition, residue pair preference and amino acid properties in six different families of transporters. Utilizing the information, we have developed a radial basis function (RBF) network method based on profiles obtained with position specific scoring matrices for discriminating transporters belonging to three different classes and six families. Our method showed a fivefold cross validation accuracy of 76%, 73%, and 69% for discriminating transporters and nontransporters, three different classes and six different families of transporters, respectively. Further, the method was tested with independent datasets, which showed similar level of accuracy. A web server has been developed for discriminating transporters based on three classes and six families, and it is available at http://rbf.bioinfo.tw/ approximately sachen/tcrbf.html. We suggest that our method could be effectively used to identify transporters and discriminating them into different classes and families.

Prediction of membrane spanning segments and topology in beta-barrel membrane proteins at better accuracy.

Prediction of membrane spanning segments in beta-barrel outer membrane proteins (OMP) and their topology is an important problem in structural and functional genomics. In this work, we propose a method based on radial basis networks for predicting the number of beta-strands in OMPs and identifying their membrane spanning segments. Our method showed a leave-one-out cross validation accuracy of 96% in a set of 28 OMPs, which have the range of 8-22 beta-strand segments. The beta-strand segments in OMPs and the residues in membrane spanning segments are correctly predicted with the accuracy of 96% and 87%, respectively. We have developed a web server, TMBETAPRED-RBF for predicting the transmembrane beta-strands from amino acid sequence and it is available at http://rbf.bioinfo.tw/~sachen/tmrbf.html. We suggest that our method could be an effective tool for predicting the membrane spanning regions and topology of beta-barrel membrane proteins.

TMBETADISC-RBF: Discrimination of beta-barrel membrane proteins using RBF networks and PSSM profiles.

Discriminating outer membrane proteins (OMPs) from other folding types of globular and membrane proteins is an important task both for identifying OMPs from genomic sequences and for the successful prediction of their secondary and tertiary structures. We have developed a method based on radial basis function networks and position specific scoring matrix (PSSM) profiles generated by PSI-BLAST and non-redundant protein database. Our approach with PSSM profiles has correctly predicted the OMPs with a cross-validated accuracy of 96.4% in a set of 1251 proteins, which contain 206 OMPs, 667 globular proteins and 378 alpha-helical inner membrane proteins. Furthermore, we applied our method on a dataset containing 114 OMPs, 187 TMH proteins and 195 globular proteins obtained with less than 20% sequence identity and obtained the cross-validated accuracy of 95%. This accuracy of discriminating OMPs is higher than other methods in the literature and our method could be used as an effective tool for dissecting OMPs from genomic sequences. We have developed a prediction server, TMBETADISC-RBF, which is available at http://rbf.bioinfo.tw/~sachen/OMP.html.

Promotion of the growth of Crocus sativus cells and the production of crocin by rare earth elements.

La3+ and Ce3+, either singly or a mixture, promoted crocin production of Crocus sativus callus but Nd3+ had little effect and all metal ions were toxic above 100 microM. La3+ (60 microM) promoted growth of callus significantly but increased crocin only slightly. Ce3+ (40 microM) significantly promoted crocin production but had little effect on cell growth. La3+ (60 microM) and Ce3+ (20 microM) together gave the highest dry weight biomass (20.4 g l(-1)), crocin content (4.4 mg g(-1)) and crocin production (90 mg l(-1)) which were, respectively, 1.7-fold, 4.2-fold and 7.1-fold of those without additions. Nd3+ (40 microM) only slightly promoted cell growth and crocin production.

Production of crocin using Crocus sativus callus by two-stage culture system.

Saffron callus was grown in a two-stage culture on B5 medium supplemented with casein hydrolysate (300 mg l(-1)) at 22 degrees C in dark with naphthalene acetic acid (2 mg l(-1)) and 6-benzyladenine (1 mg l(-1)) to give maximum biomass (16 g dry wt l(-1)), and with indole 3-acetic acid (2 mg l(-1)) and 6-benzyladenine (0.5 mg l(-1)) for crocin formation. The maximum crocin production (0.43 g l(-1)) was achieved by this two-stage culture method, which was three times that by a one-stage method.