Tourette Syndrome as an Independent Risk Factor for Subsequent Sleep Disorders in Children: A Nationwide Population-Based Case-Control Study.

Study objectives: Tourette syndrome (TS) is associated with a variety of neuropsychiatric comorbidities. However, the relationship between TS and sleep disorders in children is less investigated. This nationwide population-based case-control study aimed to determine the correlation of TS and sleep disorders in children. Methods: Patients aged less than 18 years with newly diagnosed TS from 2001 to 2007 were collected (n = 1124) using data from Taiwan's National Health Insurance Research Database and were compared with a comparison cohort (n = 3372). The adjusted hazard ratio (aHR) for developing sleep disorders was calculated by multivariate Cox proportional hazards model. Results: TS was more prevalent in boys, with a male to female ratio of 3.16:1. TS group also had significantly higher urbanization level of residence than controls (p < .001). The overall incidence rate of sleep disorders was 7.24‰ in children with TS, compared to 3.53‰ in controls. The TS group was associated with a significantly higher rate of sleep disorders, with a crude HR of 2.05 (95% confidence inerval [CI] = 1.43-2.95, p < .001). Among the comorbidities of TS, anxiety disorder was associated with the highest risk for sleep disorders (crude HR = 3.26, 95% CI = 1.52-7.00, p < .001). The aHR for TS cohort to develop sleep disorders was 1.72 (95% CI = 1.16-2.53, p = .007). Conclusions: The increased risk of sleep disorders in children with TS cannot be fully attributed to its comorbidities, and TS is an independent risk factor for sleep disorders in children.

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.

BACKGROUND: Bacterial tyrosine-kinases (BY-kinases), which play an important role in numerous cellular processes, are characterized as a separate class of enzymes and share no structural similarity with their eukaryotic counterparts. However, in silico methods for predicting BY-kinases have not been developed yet. Since these enzymes are involved in key regulatory processes, and are promising targets for anti-bacterial drug design, it is desirable to develop a simple and easily interpretable predictor to gain new insights into bacterial tyrosine phosphorylation. This study proposes a novel SCMBYK method for predicting and characterizing BY-kinases. RESULTS: A dataset consisting of 797 BY-kinases and 783 non-BY-kinases was established to design the SCMBYK predictor, which achieved training and test accuracies of 97.55 and 96.73%, respectively. Furthermore, the leave-one-phylum-out method was used to predict specific bacterial phyla hosts of target sequences, gaining 97.39% average test accuracy. After analyzing SCMBYK-derived propensity scores, four characteristics of BY-kinases were determined: 1) BY-kinases tend to be composed of α-helices; 2) the amino-acid content of extracellular regions of BY-kinases is expected to be dominated by residues such as Val, Ile, Phe and Tyr; 3) BY-kinases structurally resemble nuclear proteins; 4) different domains play different roles in triggering BY-kinase activity. CONCLUSIONS: The SCMBYK predictor is an effective method for identification of possible BY-kinases. Furthermore, it can be used as a part of a novel drug repurposing method, which recognizes putative BY-kinases and matches them to approved drugs. Among other results, our analysis revealed that azathioprine could suppress the virulence of M. tuberculosis, and thus be considered as a potential antibiotic for tuberculosis treatment.

SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides.

BACKGROUND: Identifying putative membrane transport proteins (MTPs) and understanding the transport mechanisms involved remain important challenges for the advancement of structural and functional genomics. However, the transporter characters are mainly acquired from MTP crystal structures which are hard to crystalize. Therefore, it is desirable to develop bioinformatics tools for the effective large-scale analysis of available sequences to identify novel transporters and characterize such transporters. RESULTS: This work proposes a novel method (SCMMTP) based on the scoring card method (SCM) using dipeptide composition to identify and characterize MTPs from an existing dataset containing 900 MTPs and 660 non-MTPs which are separated into a training dataset consisting 1,380 proteins and an independent dataset consisting 180 proteins. The SCMMTP produced estimating propensity scores for amino acids and dipeptides as MTPs. The SCMMTP training and test accuracy levels respectively reached 83.81% and 76.11%. The test accuracy of support vector machine (SVM) using a complicated classification method with a low possibility for biological interpretation and position-specific substitution matrix (PSSM) as a protein feature is 80.56%, thus SCMMTP is comparable to SVM-PSSM. To identify MTPs, SCMMTP is applied to three datasets including: 1) human transmembrane proteins, 2) a photosynthetic protein dataset, and 3) a human protein database. MTPs showing α-helix rich structure is agreed with previous studies. The MTPs used residues with low hydration energy. It is hypothesized that, after filtering substrates, the hydrated water molecules need to be released from the pore regions. CONCLUSIONS: SCMMTP yields estimating propensity scores for amino acids and dipeptides as MTPs, which can be used to identify novel MTPs and characterize transport mechanisms for use in further experiments. AVAILABILITY: http://iclab.life.nctu.edu.tw/iclab_webtools/SCMMTP/.

SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method.

BACKGROUND: Photosynthetic proteins (PSPs) greatly differ in their structure and function as they are involved in numerous subprocesses that take place inside an organelle called a chloroplast. Few studies predict PSPs from sequences due to their high variety of sequences and structues. This work aims to predict and characterize PSPs by establishing the datasets of PSP and non-PSP sequences and developing prediction methods. RESULTS: A novel bioinformatics method of predicting and characterizing PSPs based on scoring card method (SCMPSP) was used. First, a dataset consisting of 649 PSPs was established by using a Gene Ontology term GO:0015979 and 649 non-PSPs from the SwissProt database with sequence identity <= 25%.- Several prediction methods are presented based on support vector machine (SVM), decision tree J48, Bayes, BLAST, and SCM. The SVM method using dipeptide features-performed well and yielded - a test accuracy of 72.31%. The SCMPSP method uses the estimated propensity scores of 400 dipeptides - as PSPs and has a test accuracy of 71.54%, which is comparable to that of the SVM method. The derived propensity scores of 20 amino acids were further used to identify informative physicochemical properties for characterizing PSPs. The analytical results reveal the following four characteristics of PSPs: 1) PSPs favour hydrophobic side chain amino acids; 2) PSPs are composed of the amino acids prone to form helices in membrane environments; 3) PSPs have low interaction with water; and 4) PSPs prefer to be composed of the amino acids of electron-reactive side chains. CONCLUSIONS: The SCMPSP method not only estimates the propensity of a sequence to be PSPs, it also discovers characteristics that further improve understanding of PSPs. The SCMPSP source code and the datasets used in this study are available at http://iclab.life.nctu.edu.tw/SCMPSP/.

SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.

BACKGROUND: Heme binding proteins (HBPs) are metalloproteins that contain a heme ligand (an iron-porphyrin complex) as the prosthetic group. Several computational methods have been proposed to predict heme binding residues and thereby to understand the interactions between heme and its host proteins. However, few in silico methods for identifying HBPs have been proposed. RESULTS: This work proposes a scoring card method (SCM) based method (named SCMHBP) for predicting and analyzing HBPs from sequences. A balanced dataset of 747 HBPs (selected using a Gene Ontology term GO:0020037) and 747 non-HBPs (selected from 91,414 putative non-HBPs) with an identity of 25% was firstly established. Consequently, a set of scores that quantified the propensity of amino acids and dipeptides to be HBPs is estimated using SCM to maximize the predictive accuracy of SCMHBP. Finally, the informative physicochemical properties of 20 amino acids are identified by utilizing the estimated propensity scores to be used to categorize HBPs. The training and mean test accuracies of SCMHBP applied to three independent test datasets are 85.90% and 71.57%, respectively. SCMHBP performs well relative to comparison with such methods as support vector machine (SVM), decision tree J48, and Bayes classifiers. The putative non-HBPs with high sequence propensity scores are potential HBPs, which can be further validated by experimental confirmation. The propensity scores of individual amino acids and dipeptides are examined to elucidate the interactions between heme and its host proteins. The following characteristics of HBPs are derived from the propensity scores: 1) aromatic side chains are important to the effectiveness of specific HBP functions; 2) a hydrophobic environment is important in the interaction between heme and binding sites; and 3) the whole HBP has low flexibility whereas the heme binding residues are relatively flexible. CONCLUSIONS: SCMHBP yields knowledge that improves our understanding of HBPs rather than merely improves the prediction accuracy in predicting HBPs.