UNLABELLED: S-sulfenylation (S-sulphenylation, or sulfenic acid), the covalent attachment of S-hydroxyl (-SOH) to cysteine thiol, plays a significant role in redox regulation of protein functions. Although sulfenic acid is transient and labile, most of its physiological activities occur under control of S-hydroxylation. Therefore, discriminating the substrate site of S-sulfenylated proteins is an essential task in computational biology for the furtherance of protein structures and functions. Research into S-sulfenylated protein is currently very limited, and no dedicated tools are available for the computational identification of SOH sites. Given a total of 1096 experimentally verified S-sulfenylated proteins from humans, this study carries out a bioinformatics investigation on SOH sites based on amino acid composition and solvent-accessible surface area. A TwoSampleLogo indicates that the positively and negatively charged amino acids flanking the SOH sites may impact the formulation of S-sulfenylation in closed three-dimensional environments. In addition, the substrate motifs of SOH sites are studied using the maximal dependence decomposition (MDD). Based on the concept of binary classification between SOH and non-SOH sites, Support vector machine (SVM) is applied to learn the predictive model from MDD-identified substrate motifs. According to the evaluation results of 5-fold cross-validation, the integrated SVM model learned from substrate motifs yields an average accuracy of 0.87, significantly improving the prediction of SOH sites. Furthermore, the integrated SVM model also effectively improves the predictive performance in an independent testing set. Finally, the integrated SVM model is applied to implement an effective web resource, named MDD-SOH, to identify SOH sites with their corresponding substrate motifs. AVAILABILITY AND IMPLEMENTATION: The MDD-SOH is now freely available to all interested users at http://csb.cse.yzu.edu.tw/MDDSOH/. All of the data set used in this work is also available for download in the website. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: [email protected].

Protein O-GlcNAcylation, involving the β-attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues, is an O-linked glycosylation catalyzed by O-GlcNAc transferase (OGT). Molecular level investigation of the basis for OGT's substrate specificity should aid understanding how O-GlcNAc contributes to diverse cellular processes. Due to an increasing number of O-GlcNAcylated peptides with site-specific information identified by mass spectrometry (MS)-based proteomics, we were motivated to characterize substrate site motifs of O-GlcNAc transferases. In this investigation, a non-redundant dataset of 410 experimentally verified O-GlcNAcylation sites were manually extracted from dbOGAP, OGlycBase and UniProtKB. After detection of conserved motifs by using maximal dependence decomposition, profile hidden Markov model (profile HMM) was adopted to learn a first-layered model for each identified OGT substrate motif. Support Vector Machine (SVM) was then used to generate a second-layered model learned from the output values of profile HMMs in first layer. The two-layered predictive model was evaluated using a five-fold cross validation which yielded a sensitivity of 85.4%, a specificity of 84.1%, and an accuracy of 84.7%. Additionally, an independent testing set from PhosphoSitePlus, which was really non-homologous to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (84.05%) and outperform other O-GlcNAcylation site prediction tools. A case study indicated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation and has been implemented as a web-based system, OGTSite, which is now freely available at http://csb.cse.yzu.edu.tw/OGTSite/.

UNLABELLED: S-glutathionylation, the reversible protein posttranslational modification (PTM) that generates a mixed disulfide bond between glutathione and cysteine residue, critically regulates protein activity, stability and redox regulation. Due to its importance in regulating oxidative/nitrosative stress and balance in cellular response, a number of methods have been rapidly developed to study S-glutathionylation, thus expanding the dataset of experimentally determined glutathionylation sites. However, there is currently no database dedicated to the integration of all experimentally verified S-glutathionylation sites along with their characteristics or structural or functional information. Thus, the dbGSH database has been created to integrate all available datasets and to provide the relevant structural analysis. As of January 31, 2014, dbGSH has manually collected >2200 experimentally verified S-glutathionylated peptides from 169 research articles using a text-mining approach. To solve the problem of heterogeneity of the data collected from different sources, the sequence identity of the reported S-glutathionylated peptides is mapped to UniProtKB protein entries. To delineate the structural correlations and consensus motifs of these S-glutathionylation sites, the dbGSH database also provides structural and functional analyses, including the motifs of substrate sites, solvent accessibility, protein secondary and tertiary structures, protein domains and gene ontology. AVAILABILITY AND IMPLEMENTATION: dbGSH is now freely accessible at http://csb.cse.yzu.edu.tw/dbGSH/. The database content is regularly updated with new data collected by the continuous survey of research articles.

BACKGROUND: Protein O-GlcNAcylation, involving the attachment of single N-acetylglucosamine (GlcNAc) to the hydroxyl group of serine or threonine residues. Elucidation of O-GlcNAcylation sites on proteins is required in order to decipher its crucial roles in regulating cellular processes and aid in drug design. With an increasing number of O-GlcNAcylation sites identified by mass spectrometry (MS)-based proteomics, several methods have been proposed for the computational identification of O-GlcNAcylation sites. However, no development that focuses on the investigation of O-GlcNAcylated substrate motifs has existed. Thus, we were motivated to design a new method for the identification of protein O-GlcNAcylation sites with the consideration of substrate site specificity. RESULTS: In this study, 375 experimentally verified O-GlcNAcylation sites were collected from dbOGAP, which is an integrated resource for protein O-GlcNAcylation. Due to the difficulty in characterizing the substrate motifs by conventional sequence logo analysis, a recursively statistical method has been applied to obtain significant conserved motifs. To construct the predictive models learned from the identified substrate motifs, we adopted Support Vector Machines (SVMs). A five-fold cross validation was used to evaluate the predictive model, achieving sensitivity, specificity, and accuracy of 0.76, 0.80, and 0.78, respectively. Additionally, an independent testing set, which was really blind to the training data of predictive model, was used to demonstrate that the proposed method could provide a promising accuracy (0.94) and outperform three other O-GlcNAcylation site prediction tools. CONCLUSION: This work proposed a computational method to identify informative substrate motifs for O-GlcNAcylation sites. The evaluation of cross validation and independent testing indicated that the identified motifs were effective in the identification of O-GlcNAcylation sites. A case study demonstrated that the proposed method could be a feasible means of conducting preliminary analyses of protein O-GlcNAcylation. We also anticipated that the revealed substrate motif may facilitate the study of extensive crosstalk between O-GlcNAcylation and phosphorylation. This method may help unravel their mechanisms and roles in signaling, transcription, chronic disease, and cancer.

This study integrated theory of planning behavior (TPB) and technology acceptance model (TAM) to investigate the determinants affecting taxpayers' on-line tax filing. According to the Global e-Government Study of Brown University in the United States, Taiwan's ranking was the world's first in 2010. Hence, this study sampled 422 online taxpayers in Taiwan and questionnaire-based empirical study was used to collect data. The empirical results showed that: (1) Attitude was the primary factor affecting on-line tax filing, but “attitude” was also affected by “perceived usefulness”, “perceived ease of use”, “tax equity”, “social norm”, and “moral norm”. (2) Both TAM and TPB could be successfully integrated to explain online tax filing behaviors, and correlations were found between TAM factors and TPB factors. (3) Finally, we built a best-fitted model for on-line tax filing. This model would be the reference for establishing e-government.

BACKGROUND: The phosphorylation of virus proteins by host kinases is linked to viral replication. This leads to an inhibition of normal host-cell functions. Further elucidation of phosphorylation in virus proteins is required in order to aid in drug design and treatment. However, only a few studies have investigated substrate motifs in identifying virus phosphorylation sites. Additionally, existing bioinformatics tool do not consider potential host kinases that may initiate the phosphorylation of a virus protein. RESULTS: 329 experimentally verified phosphorylation fragments on 111 virus proteins were collected from virPTM. These were clustered into subgroups of significantly conserved motifs using a recursively statistical method. Two-layered Support Vector Machines (SVMs) were then applied to train a predictive model for the identified substrate motifs. The SVM models were evaluated using a five-fold cross validation which yields an average accuracy of 0.86 for serine, and 0.81 for threonine. Furthermore, the proposed method is shown to perform at par with three other phosphorylation site prediction tools: PPSP, KinasePhos 2.0 and GPS 2.1. CONCLUSION: In this study, we propose a computational method, ViralPhos, which aims to investigate virus substrate site motifs and identify potential phosphorylation sites on virus proteins. We identified informative substrate motifs that matched with several well-studied kinase groups as potential catalytic kinases for virus protein substrates. The identified substrate motifs were further exploited to identify potential virus phosphorylation sites. The proposed method is shown to be capable of predicting virus phosphorylation sites and has been implemented as a web server http://csb.cse.yzu.edu.tw/ViralPhos/.

UNLABELLED: S-nitrosylation (SNO), a selective and reversible protein post-translational modification that involves the covalent attachment of nitric oxide (NO) to the sulfur atom of cysteine, critically regulates protein activity, localization and stability. Due to its importance in regulating protein functions and cell signaling, a mass spectrometry-based proteomics method rapidly evolved to increase the dataset of experimentally determined SNO sites. However, there is currently no database dedicated to the integration of all experimentally verified S-nitrosylation sites with their structural or functional information. Thus, the dbSNO database is created to integrate all available datasets and to provide their structural analysis. Up to April 15, 2012, the dbSNO has manually accumulated >3000 experimentally verified S-nitrosylated peptides from 219 research articles using a text mining approach. To solve the heterogeneity among the data collected from different sources, the sequence identity of these reported S-nitrosylated peptides are mapped to the UniProtKB protein entries. To delineate the structural correlation and consensus motif of these SNO sites, the dbSNO database also provides structural and functional analyses, including the motifs of substrate sites, solvent accessibility, protein secondary and tertiary structures, protein domains and gene ontology. AVAILABILITY: The dbSNO is now freely accessible via http://dbSNO.mbc.nctu.edu.tw. The database content is regularly updated upon collecting new data obtained from continuously surveying research articles.

UNLABELLED: Bioinformatics research often requires conservative analyses of a group of sequences associated with a specific biological function (e.g. transcription factor binding sites, micro RNA target sites or protein post-translational modification sites). Due to the difficulty in exploring conserved motifs on a large-scale sequence data involved with various signals, a new method, MDDLogo, is developed. MDDLogo applies maximal dependence decomposition (MDD) to cluster a group of aligned signal sequences into subgroups containing statistically significant motifs. In order to extract motifs that contain a conserved biochemical property of amino acids in protein sequences, the set of 20 amino acids is further categorized according to their physicochemical properties, e.g. hydrophobicity, charge or molecular size. MDDLogo has been demonstrated to accurately identify the kinase-specific substrate motifs in 1221 human phosphorylation sites associated with seven well-known kinase families from Phospho.ELM. Moreover, in a set of plant phosphorylation data-lacking kinase information, MDDLogo has been applied to help in the investigation of substrate motifs of potential kinases and in the improvement of the identification of plant phosphorylation sites with various substrate specificities. In this study, MDDLogo is comparable with another well-known motif discover tool, Motif-X. CONTACT: [email protected]

BACKGROUND: Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding in silico prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites. RESULTS: Experimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species Arabidopsis thaliana. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using Arabidopsis thaliana phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms. CONCLUSIONS: This work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos http://csb.cse.yzu.edu.tw/PlantPhos/. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos.

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.