Combinatorial Chemistry & High Throughput Screening - Volume 20, Issue 2, 2017

Background: Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. Objective: To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. Materials and Methods: A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. Results: As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. Conclusion: This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences.

Aim and Objective: The disturbance of consciousness is one of the most common symptoms of those have alcoholism and may cause disability and mortality. Previous studies indicated that several single nucleotide polymorphisms (SNP) increase the susceptibility of alcoholism. In this study, we utilized the Ensemble Bayesian Network (EBN) method to identify causal SNPs of alcoholism based on the verified GAW14 data. Materials and Methods: We built a Bayesian network combining random process and greedy search by using Genetic Analysis Workshop 14 (GAW14) dataset to establish EBN of SNPs. Then we predicted the association between SNPs and alcoholism by determining Bayes’ prior probability. Results and Conclusion: Thirteen out of eighteen SNPs directly connected with alcoholism were found concordance with potential risk regions of alcoholism in OMIM database. As many SNPs were found contributing to alteration on gene expression, known as expression quantitative trait loci (eQTLs), we further sought to identify chemical compounds acting as regulators of alcoholism genes captured by causal SNPs. Chloroprene and valproic acid were identified as the expression regulators for genes C11orf66 and SALL3 which were captured by alcoholism SNPs, respectively.

Aim and Objective: The aim of this study was to evaluate the association between folate level and the risk of schizophrenia and to identify possible biomarker for schizophrenia. Materials and Methods: Data about folate were extracted from 16 high quality studies. The association of folate level in blood and schizophrenia was evaluated using standardized mean difference (SMD) and 95% confidence interval (CI). Results: Totally 1183 (52.1%) cases and 1089 (47.9%) controls were included in the current metaanalysis. Folate level in schizophrenia patients was significantly lower than that in healthy controls (SMD= −0.65; 95% CI: [−0.86, −0.45]; P <0.00001). Subgroup analysis demonstrated that the decreased folate level was found in both Asian and European patients (SMD=−0.86, P<0.00001; SMD=−0.44, P<0.00001, respectively), while there were no significant differences in patients from other areas (P>0.05). Sensitivity analysis confirmed that these results were stable and reliable, no publication bias existed in our meta-analysis based on Egger's and Begg's tests (P=0.48 and 0.30, respectively). Conclusion: These results suggest that decreased folate may be a risk factor for schizophrenia. More epidemiological and biochemistry studies are required to describe how folate or folate supplementation play roles in the progress of schizophrenia.

Background: Accurate diagnosis of chronic Hepatitis B virus (HBV) infection related diseases is crucial to guide the therapy and to understand the mechanisms of disease progression. Plasma microRNAs, as stable biomarkers, have drawn significant attentions for distinguishing HBVrelated diseases. Methods: In this study, a new HBV-related disease identification method based on a two-layer logistic regression model was presented. A total of nine effective plasma microRNA biomarkers were selected through sample collection, data processing, model selection, feature selection and model optimization to distinguish HBV-related chronic hepatitis and cirrhosis samples as well as healthy controls. The first layer utilized three microRNAs to distinguish HBV-related disease samples from healthy controls. Then the second layer divided the HBV-related disease samples into cirrhosis and chronic hepatitis samples by using eight microRNAs. Result: Test on two independent cohorts showed high accuracy and robustness of our model. Functional analysis of the selected microRNAs and their target genes confirmed that they were significantly associated with HBV-related diseases and related functional pathways. Conclusion: Compared with previous models, the two-layer model was more consistent with the underlying pathological progress of HBV related diseases from health to chronic hepatitis and further to liver cirrhosis. It could also take the results of other diagnostic tests into account, which could be useful in both physical examination and disease diagnosis.

Background: Esophageal Squamous Cell Carcinoma (ESCC) is a common malignant tumor in China, which causes about 200,000 deaths each year. Sensitive biomarkers are helpful to diagnose the disease in early stage. Methods: To identify biomarkers of ESCC and elucidate underlying mechanism of the disease, a targeted metabolomics strategy based on liquid chromatography-tandem mass spectrometry (LCMS/ MS) has been implemented to explore tyrosine metabolism from 40 ESCC patients and 27 healthy controls. Results: Four metabolites, i.e. phenylalanine, 4-hydroxyphenyllactic acid, 3,4-dihydroxyphenylalanine, and 3,4-dihydroxyphenylacetic acid were identified as diagnostic biomarkers for ESCC patients. Based on these biomarkers, a prediction model was constructed for ESCC diagnosis. The analysis of receiver operating characteristic (ROC) curve confirmed its effectiveness of the model. Conclusion: Our results reveal that tyrosine metabolism is disturbed in ESCC patients and the metabolites involved in tyrosine pathway can be used as diagnostic biomarkers of the disease. Findings of this study can help investigate pathogenesis of ESCC and facilitate understanding mechanism of the disease.

Background: The study of metabolic pathway is one of the most important fields in biochemistry. Good comprehension of the metabolic pathway system is helpful to uncover the mechanism of some fundamental biological processes. Because chemicals are part of the main components of the metabolic pathway, correct identification of which metabolic pathways a given chemical can participate in is an important step for understanding the metabolic pathway system. Most previous methods only considered the chemical information, which tried to deal with a multilabel classification problem of assigning chemicals to proper metabolic pathways. Methods: In this study, the pathway information was also employed, thereby transforming the problem into a binary classification problem of identifying the pair of chemicals and metabolic pathways, i.e., a chemical and a metabolic pathway was paired as a sample to be considered in this study. To construct the prediction model, the association between chemical pathway type pairs was evaluated by integrating the association between chemicals and association between pathway types. The support vector machine was adopted as the prediction engine. Results: The extensive tests show that the constructed model yields good performance with total prediction accuracy around 0.878. Conclusion: The comparison results indicate that our model is quite effective and suitable for the identification of whether a given chemical can participate in a given metabolic pathway.

Aim and Objective: Mosquitoes can transmit many types of viruses such as West Nile virus and Zika virus and are responsible for a number of virus-causing diseases including malaria, dengue fever, yellow fever, lymphatic filariasis, and Japanese B encephalitis. On January 19, 2016, the first case of Zika virus infection was identified in Taiwan, which presents the need for studying the mosquito species in the Taiwan Strait and evaluating the risk of the outbreak of this infection. Materials and Method: In this study, we have collected 144 mosquito specimens from 42 species belonging to nine genera from both sides of the Taiwan Strait during 2013 and 2014. We then applied the COI DNA Barcoding technique to classify the specimens and performed a phylogenetic analysis to infer the evolutionary history of these mosquitoes. Based on the analyses, we found that though the mosquitoes from different sides of the Taiwan Strait share a lot of commonality, they have a few regional specificities. Results: Our results also suggested a very small divergences (1%~9%) between specimens from the same mosquito species and relatively large divergences (8%~25%) between specimens from different mosquito species. Within the same species, the divergence of specimens from the same region is significantly smaller than that between two regions. A few highly divergent species between Fujian and Taiwan (e.g., An.maculatus and Ae.elsiae) might be formed due to the so-called “cryptic evolutionary events”, in which the species has differentiation into cryptic species due to geographical differences without changing morphological characteristics. Conclusion: In conclusion, the phylogenetic analyses showed a very similar taxonomy to the historical one based on morphological characteristics, validating again the application of COI DNA Barcoding technique in classifying mosquito species. However, there are also some inconsistencies between COI DNA Barcoding and historical taxonomy, which points out the differences between mosquito DNA and morphological characteristics and suggests the possibility to improve mosquito taxonomy based on DNA techniques.

Aim and Objective: Protein tyrosine phosphatases (PTPs) are responsible for protein phosphorylation. Because the level of protein phosphorylation is correlated with tumor transformation, PTPs have been considered as candidate transformation suppressors. In this study, we developed a novel PTP site prediction model, DephosSitePred, based on bi-profile sequence features. Materials and Method: A dataset which contains 63-, 50- and 51-positive samples, and 868-, 856-, and 731-negative samples with less than 70% sequence identity for the three phosphatases was constructed in this study. Based on the dataset, a predictor model DephosSitePred was constructed, by applying the sequence-based bi-profile Bayes feature extraction technique to identify three phosphatases, PTP1B, SHP-1, and SHP-2. Concerning the imbalance of datasets used in our study, the weight parameters (W1 and W-1) of the support vector machine (SVM) were selected according to jackknife cross-validation. Results: DephosSitePred yielded Matthews correlation coefficients of 0.686 for protein tyrosine phosphatase 1B (PTP1B), 0.668 for Src homology region 2 domain-containing phosphatase (SHP)-1, and 0.748 for SHP-2 substrate sites, which significantly outperformed other existing predictors. Moreover, 30 times of 5-fold cross-validations showed that DephosSitePred achieved average area under the curve values of 0.968, 0.968, and 0.982 for PTP1B, SHP-1 and SHP-2, respectively, which were 0.115, 0.105 and 0.105 higher than those of the second best model, MGPS-DEPHOS, respectively. Conclusion: DephosSitePred is indeed an effective auxiliary tool for in silico identification of dephosphorylation sites and may help to reveal the physiological and pathological role of dephosphorylation protein.

Aim and Objective: Gene selection method as an important data preprocessing work has been followed. The criteria Maximum relevance and minimum redundancy (MRMR) has been commonly used for gene selection, which has a satisfactory performance in evaluating the correlation between two genes. However, for viewing genes in isolation, it ignores the influence of other genes. Methods: In this study, we propose a new method based on sparse representation and MRMR algorithm (SRCMRM), using the sparse representation coefficient to represent the relevance of genes and correlation between genes and categories. The SRCMRMR algorithm contains two steps. Firstly, the genes irrelevant to the classification target are removed by using sparse representation coefficient. Secondly, sparse representation coefficient is used to calculate the correlation between genes and the most representative gene with the highest evaluation. Results: To validate the performance of the SRCMRM, our method is compared with various algorithms. The proposed method achieves better classification accuracy for all datasets. Conclusion: The effectiveness and stability of our method have been proven through various experiments, which means that our method has practical significance.

Background: As one of essential post-translational modifications (PTMs), the citrullination or deimination on an arginine residue would change the molecular weight and electrostatic charge of its side-chain. And it has been found that the citrullination in protein sequences was catalyzed by a type of Ca2+-dependent enzyme family called peptidylarginine deiminase (PAD), which include five isotypes: PAD1, 2, 3, 4/5, and 6. Citrullinated proteins participate in many biological processes, e.g. the citrullination of myelin basic protein (MBP) assists the early development of central nervous system. However, abnormal modifications on citrullinated proteins would also lead to some severe human diseases including multiple sclerosis and rheumatoid arthritis. Objective: Therefore, it is necessary and important to identify the citrullination sites in protein sequences. The information about the location of citrulliantion sites in protein sequences will be useful to investigate the molecular functions and disease mechanisms related to citrullinated proteins. Materials and Methods: In this study, we investigated the peptide segments that contain the citrullination sites in the centers, which were encoded into numeric digits from four aspects. Thus, we yielded a training set with 116 positive samples and 232 negative samples. Then, a reliable feature selection technique, called maximum-relevance-minimum-redundancy (mRMR), was applied to analyze these features, and four algorithms, including random forest (RF), Dagging, nearest neighbor algorithm (NNA), and support vector machine (SVM), together with the incremental feature selection (IFS) method were adopted to extract important features. Results: Finally an optimal classifier derived from RF algorithm was constructed to predict citrullination sites. 44 most prominent features were comprehensively analyzed and their biological characteristics in citrullination catalysis were also revealed. Conclusion: We believed that the biological features obtained in this pioneering work would provide some useful insights into the formation and function of citrullination and the optimal classifier could be a useful tool to identify citrullination sites in protein sequences.

Aim and Objective: Gastric cancer is one of the most common cancers and has very high incidence and mortality rate in Asian population. To tackle the problems of infiltration and heterogeneity, more accurate biomarkers for diagnosis and prognosis as well as effective targets for treatment are needed to achieve better outcomes of gastric cancer patients. Recently, methods and algorithms for analyzing high-throughput sequencing data have greatly facilitated the molecular profiling of gastric cancer. Nevertheless, prognostic biomarkers for gastric cancer that can be potentially applied in clinic are still lacking. Materials and Methods: In this study, we performed differential regulatory analysis based on gene co-expression network for four different cohorts of Asian gastric cancer samples and their clinical data. Results: We identified a 36-gene prognostic signature specific for gastric cancer, particularly for Asian population. We further analyzed differential regulatory patterns related to these featured genes, such as C1S, and suggested hypotheses for investigating their roles in gastric cancer pathogenesis. Conclusion: Findings from present study suggest a 36-gene signature which is based on differential regulatory analysis and can predict the prognosis of gastric cancer. Our research explores molecular mechanism of gastric cancer at transcriptional regulation level and provides potential drug targets. This integrated biomarker searching scheme is extendable to other cancer study for not only prognostic prediction, but also pathogenesis.