Advances in Bioinformatics The latest articles from Hindawi Publishing Corporation © 2016 , Hindawi Publishing Corporation . All rights reserved. In Silico Phylogenetic Analysis and Molecular Modelling Study of 2-Haloalkanoic Acid Dehalogenase Enzymes from Bacterial and Fungal Origin Wed, 06 Jan 2016 11:58:08 +0000 2-Haloalkanoic acid dehalogenase enzymes have broad range of applications, starting from bioremediation to chemical synthesis of useful compounds that are widely distributed in fungi and bacteria. In the present study, a total of 81 full-length protein sequences of 2-haloalkanoic acid dehalogenase from bacteria and fungi were retrieved from NCBI database. Sequence analysis such as multiple sequence alignment (MSA), conserved motif identification, computation of amino acid composition, and phylogenetic tree construction were performed on these primary sequences. From MSA analysis, it was observed that the sequences share conserved lysine (K) and aspartate (D) residues in them. Also, phylogenetic tree indicated a subcluster comprised of both fungal and bacterial species. Due to nonavailability of experimental 3D structure for fungal 2-haloalkanoic acid dehalogenase in the PDB, molecular modelling study was performed for both fungal and bacterial sources of enzymes present in the subcluster. Further structural analysis revealed a common evolutionary topology shared between both fungal and bacterial enzymes. Studies on the buried amino acids showed highly conserved Leu and Ser in the core, despite variation in their amino acid percentage. Additionally, a surface exposed tryptophan was conserved in all of these selected models. Raghunath Satpathy, V. B. Konkimalla, and Jagnyeswar Ratha Copyright © 2016 Raghunath Satpathy et al. All rights reserved. FN-Identify: Novel Restriction Enzymes-Based Method for Bacterial Identification in Absence of Genome Sequencing Thu, 31 Dec 2015 06:00:15 +0000 Sequencing and restriction analysis of genes like 16S rRNA and HSP60 are intensively used for molecular identification in the microbial communities. With aid of the rapid progress in bioinformatics, genome sequencing became the method of choice for bacterial identification. However, the genome sequencing technology is still out of reach in the developing countries. In this paper, we propose FN-Identify, a sequencing-free method for bacterial identification. FN-Identify exploits the gene sequences data available in GenBank and other databases and the two algorithms that we developed, CreateScheme and GeneIdentify, to create a restriction enzyme-based identification scheme. FN-Identify was tested using three different and diverse bacterial populations (members of Lactobacillus, Pseudomonas, and Mycobacterium groups) in an in silico analysis using restriction enzymes and sequences of 16S rRNA gene. The analysis of the restriction maps of the members of three groups using the fragment numbers information only or along with fragments sizes successfully identified all of the members of the three groups using a minimum of four and maximum of eight restriction enzymes. Our results demonstrate the utility and accuracy of FN-Identify method and its two algorithms as an alternative method that uses the standard microbiology laboratories techniques when the genome sequencing is not available. Mohamed Awad, Osama Ouda, Ali El-Refy, Fawzy A. El-Feky, Kareem A. Mosa, and Mohamed Helmy Copyright © 2015 Mohamed Awad et al. All rights reserved. Local Mutational Pressures in Genomes of Zaire Ebolavirus and Marburg Virus Sun, 20 Dec 2015 12:55:49 +0000 Heterogeneities in nucleotide content distribution along the length of Zaire ebolavirus and Marburg virus genomes have been analyzed. Results showed that there is asymmetric mutational A-pressure in the majority of Zaire ebolavirus genes; there is mutational AC-pressure in the coding region of the matrix protein VP40, probably, caused by its high expression at the end of the infection process; there is also AC-pressure in the 3′-part of the nucleoprotein (NP) coding gene associated with low amount of secondary structure formed by the 3′-part of its mRNA; in the middle of the glycoprotein (GP) coding gene that kind of mutational bias is linked with the high amount of secondary structure formed by the corresponding fragment of RNA negative (−) strand; there is relatively symmetric mutational AU-pressure in the polymerase (Pol) coding gene caused by its low expression level. In Marburg virus all genes, including C-rich fragment of GP coding region, demonstrate asymmetric mutational A-bias, while the last gene (Pol) demonstrates more symmetric mutational AU-pressure. The hypothesis of a newly synthesized RNA negative (−) strand shielding by complementary fragments of mRNAs has been described in this work: shielded fragments of RNA negative (−) strand should be better protected from oxidative damage and prone to ADAR-editing. Vladislav Victorovich Khrustalev, Eugene Victorovich Barkovsky, and Tatyana Aleksandrovna Khrustaleva Copyright © 2015 Vladislav Victorovich Khrustalev et al. All rights reserved. HBS-Tools for Hairpin Bisulfite Sequencing Data Processing and Analysis Sun, 20 Dec 2015 08:55:29 +0000 The emerging genome-wide hairpin bisulfite sequencing (hairpin-BS-Seq) technique enables the determination of the methylation pattern for DNA double strands simultaneously. Compared with traditional bisulfite sequencing (BS-Seq) techniques, hairpin-BS-Seq can determine methylation fidelity and increase mapping efficiency. However, no computational tool has been designed for the analysis of hairpin-BS-Seq data yet. Here we present HBS-tools, a set of command line based tools for the preprocessing, mapping, methylation calling, and summarizing of genome-wide hairpin-BS-Seq data. It accepts paired-end hairpin-BS-Seq reads to recover the original (pre-bisulfite-converted) sequences using global alignment and then calls the methylation statuses for cytosines on both DNA strands after mapping the original sequences to the reference genome. After applying to hairpin-BS-Seq datasets, we found that HBS-tools have a reduced mapping time and improved mapping efficiency compared with state-of-the-art mapping tools. The HBS-tools source scripts, along with user guide and testing data, are freely available for download. Ming-an Sun, Karthik Raja Velmurugan, David Keimig, and Hehuang Xie Copyright © 2015 Ming-an Sun et al. All rights reserved. Developing of the Computer Method for Annotation of Bacterial Genes Sun, 06 Dec 2015 13:58:51 +0000 Over the last years a great number of bacterial genomes were sequenced. Now one of the most important challenges of computational genomics is the functional annotation of nucleic acid sequences. In this study we presented the computational method and the annotation system for predicting biological functions using phylogenetic profiles. The phylogenetic profile of a gene was created by way of searching for similarities between the nucleotide sequence of the gene and 1204 reference genomes, with further estimation of the statistical significance of found similarities. The profiles of the genes with known functions were used for prediction of possible functions and functional groups for the new genes. We conducted the functional annotation for genes from 104 bacterial genomes and compared the functions predicted by our system with the already known functions. For the genes that have already been annotated, the known function matched the function we predicted in 63% of the time, and in 86% of the time the known function was found within the top five predicted functions. Besides, our system increased the share of annotated genes by 19%. The developed system may be used as an alternative or complementary system to the current annotation systems. Mikhail A. Golyshev and Eugene V. Korotkov Copyright © 2015 Mikhail A. Golyshev and Eugene V. Korotkov. All rights reserved. Evaluation of Docking Target Functions by the Comprehensive Investigation of Protein-Ligand Energy Minima Thu, 26 Nov 2015 12:56:29 +0000 The adequate choice of the docking target function impacts the accuracy of the ligand positioning as well as the accuracy of the protein-ligand binding energy calculation. To evaluate a docking target function we compared positions of its minima with the experimentally known pose of the ligand in the protein active site. We evaluated five docking target functions based on either the MMFF94 force field or the PM7 quantum-chemical method with or without implicit solvent models: PCM, COSMO, and SGB. Each function was tested on the same set of 16 protein-ligand complexes. For exhaustive low-energy minima search the novel MPI parallelized docking program FLM and large supercomputer resources were used. Protein-ligand binding energies calculated using low-energy minima were compared with experimental values. It was demonstrated that the docking target function on the base of the MMFF94 force field in vacuo can be used for discovery of native or near native ligand positions by finding the low-energy local minima spectrum of the target function. The importance of solute-solvent interaction for the correct ligand positioning is demonstrated. It is shown that docking accuracy can be improved by replacement of the MMFF94 force field by the new semiempirical quantum-chemical PM7 method. Igor V. Oferkin, Ekaterina V. Katkova, Alexey V. Sulimov, Danil C. Kutov, Sergey I. Sobolev, Vladimir V. Voevodin, and Vladimir B. Sulimov Copyright © 2015 Igor V. Oferkin et al. All rights reserved. In Silico Investigation of Flavonoids as Potential Trypanosomal Nucleoside Hydrolase Inhibitors Thu, 12 Nov 2015 06:37:53 +0000 Human African Trypanosomiasis is endemic to 37 countries of sub-Saharan Africa. It is caused by two related species of Trypanosoma brucei. Current therapies suffer from resistance and public accessibility of expensive medicines. Finding safer and effective therapies of natural origin is being extensively explored worldwide. Pentamidine is the only available therapy for inhibiting the P2 adenosine transporter involved in the purine salvage pathway of the trypanosomatids. The objective of the present study is to use computational studies for the investigation of the probable trypanocidal mechanism of flavonoids. Docking experiments were carried out on eight flavonoids of varying level of hydroxylation, namely, flavone, 5-hydroxyflavone, 7-hydroxyflavone, chrysin, apigenin, kaempferol, fisetin, and quercetin. Using AutoDock 4.2, these compounds were tested for their affinity towards inosine-adenosine-guanosine nucleoside hydrolase and the inosine-guanosine nucleoside hydrolase, the major enzymes of the purine salvage pathway. Our results showed that all of the eight tested flavonoids showed high affinities for both hydrolases (lowest free binding energy ranging from −10.23 to −7.14 kcal/mol). These compounds, especially the hydroxylated derivatives, could be further studied as potential inhibitors of the nucleoside hydrolases. Christina Hung Hung Ha, Ayesha Fatima, and Anand Gaurav Copyright © 2015 Christina Hung Hung Ha et al. All rights reserved. High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features Tue, 20 Oct 2015 11:56:39 +0000 Statistical features are widely used in radiology for tumor heterogeneity assessment using magnetic resonance (MR) imaging technique. In this paper, feature selection based on decision tree is examined to determine the relevant subset of glioblastoma (GBM) phenotypes in the statistical domain. To discriminate between active tumor (vAT) and edema/invasion (vE) phenotype, we selected the significant features using analysis of variance (ANOVA) with p value < 0.01. Then, we implemented the decision tree to define the optimal subset features of phenotype classifier. Naïve Bayes (NB), support vector machine (SVM), and decision tree (DT) classifier were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate vAT from vE. Whole nine features were statistically significant to classify the vAT from vE with p value < 0.01. Feature selection based on decision tree showed the best performance by the comparative study using full feature set. The feature selected showed that the two features Kurtosis and Skewness achieved a highest range value of 58.33–75.00% accuracy classifier and 73.88–92.50% AUC. This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression. Ahmad Chaddad and Camel Tanougast Copyright © 2015 Ahmad Chaddad and Camel Tanougast. All rights reserved. Identification of Novel Inhibitors for Tobacco Mosaic Virus Infection in Solanaceae Plants Sun, 18 Oct 2015 14:29:45 +0000 Tobacco mosaic virus (TMV) infects several crops of economic importance (e.g., tomato) and remains as one of the major concerns to the farmers. TMV enters the host cell and produces the capping enzyme RNA polymerase. The viral genome replicates further to produce multiple mRNAs which encodes several proteins, including the coat protein and an RNA-dependent RNA polymerase (RdRp), as well as the movement protein. TMV replicase domain was chosen for the virtual screening studies against small molecules derived from ligand databases such as PubChem and ChemBank. Catalytic sites of the RdRp domain were identified and subjected to docking analysis with screened ligands derived from virtual screening LigandFit. Small molecules that interact with the target molecule at the catalytic domain region amino acids, GDD, were chosen as the best inhibitors for controlling the TMV replicase activity. Archana Prabahar, Subashini Swaminathan, Arul Loganathan, and Ramalingam Jegadeesan Copyright © 2015 Archana Prabahar et al. All rights reserved. Discovering Alzheimer Genetic Biomarkers Using Bayesian Networks Sun, 23 Aug 2015 09:30:44 +0000 Single nucleotide polymorphisms (SNPs) contribute most of the genetic variation to the human genome. SNPs associate with many complex and common diseases like Alzheimer’s disease (AD). Discovering SNP biomarkers at different loci can improve early diagnosis and treatment of these diseases. Bayesian network provides a comprehensible and modular framework for representing interactions between genes or single SNPs. Here, different Bayesian network structure learning algorithms have been applied in whole genome sequencing (WGS) data for detecting the causal AD SNPs and gene-SNP interactions. We focused on polymorphisms in the top ten genes associated with AD and identified by genome-wide association (GWA) studies. New SNP biomarkers were observed to be significantly associated with Alzheimer’s disease. These SNPs are rs7530069, rs113464261, rs114506298, rs73504429, rs7929589, rs76306710, and rs668134. The obtained results demonstrated the effectiveness of using BN for identifying AD causal SNPs with acceptable accuracy. The results guarantee that the SNP set detected by Markov blanket based methods has a strong association with AD disease and achieves better performance than both naïve Bayes and tree augmented naïve Bayes. Minimal augmented Markov blanket reaches accuracy of 66.13% and sensitivity of 88.87% versus 61.58% and 59.43% in naïve Bayes, respectively. Fayroz F. Sherif, Nourhan Zayed, and Mahmoud Fakhr Copyright © 2015 Fayroz F. Sherif et al. All rights reserved. Tyrosine Kinase Ligand-Receptor Pair Prediction by Using Support Vector Machine Tue, 11 Aug 2015 11:22:47 +0000 Receptor tyrosine kinases are essential proteins involved in cellular differentiation and proliferation in vivo and are heavily involved in allergic diseases, diabetes, and onset/proliferation of cancerous cells. Identifying the interacting partner of this protein, a growth factor ligand, will provide a deeper understanding of cellular proliferation/differentiation and other cell processes. In this study, we developed a method for predicting tyrosine kinase ligand-receptor pairs from their amino acid sequences. We collected tyrosine kinase ligand-receptor pairs from the Database of Interacting Proteins (DIP) and UniProtKB, filtered them by removing sequence redundancy, and used them as a dataset for machine learning and assessment of predictive performance. Our prediction method is based on support vector machines (SVMs), and we evaluated several input features suitable for tyrosine kinase for machine learning and compared and analyzed the results. Using sequence pattern information and domain information extracted from sequences as input features, we obtained 0.996 of the area under the receiver operating characteristic curve. This accuracy is higher than that obtained from general protein-protein interaction pair predictions. Masayuki Yarimizu, Cao Wei, Yusuke Komiyama, Kokoro Ueki, Shugo Nakamura, Kazuya Sumikoshi, Tohru Terada, and Kentaro Shimizu Copyright © 2015 Masayuki Yarimizu et al. All rights reserved. A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data Thu, 11 Jun 2015 13:48:13 +0000 We summarise various ways of performing dimensionality reduction on high-dimensional microarray data. Many different feature selection and feature extraction methods exist and they are being widely used. All these methods aim to remove redundant and irrelevant features so that classification of new instances will be more accurate. A popular source of data is microarrays, a biological platform for gathering gene expressions. Analysing microarrays can be difficult due to the size of the data they provide. In addition the complicated relations among the different genes make analysis more difficult and removing excess features can improve the quality of the results. We present some of the most popular methods for selecting significant features and provide a comparison between them. Their advantages and disadvantages are outlined in order to provide a clearer idea of when to use each one of them for saving computational time and resources. Zena M. Hira and Duncan F. Gillies Copyright © 2015 Zena M. Hira and Duncan F. Gillies. All rights reserved. Semantic Annotation for Biological Information Retrieval System Mon, 09 Feb 2015 06:37:38 +0000 Online literatures are increasing in a tremendous rate. Biological domain is one of the fast growing domains. Biological researchers face a problem finding what they are searching for effectively and efficiently. The aim of this research is to find documents that contain any combination of biological process and/or molecular function and/or cellular component. This research proposes a framework that helps researchers to retrieve meaningful documents related to their asserted terms based on gene ontology (GO). The system utilizes GO by semantically decomposing it into three subontologies (cellular component, biological process, and molecular function). Researcher has the flexibility to choose searching terms from any combination of the three subontologies. Document annotation is taking a place in this research to create an index of biological terms in documents to speed the searching process. Query expansion is used to infer semantically related terms to asserted terms. It increases the search meaningful results using the term synonyms and term relationships. The system uses a ranking method to order the retrieved documents based on the ranking weights. The proposed system achieves researchers’ needs to find documents that fit the asserted terms semantically. Mohamed Marouf Z. Oshaiba, Enas M. F. El Houby, and Akram Salah Copyright © 2015 Mohamed Marouf Z. Oshaiba et al. All rights reserved. A Highly Conserved GEQYQQLR Epitope Has Been Identified in the Nucleoprotein of Ebola Virus by Using an In Silico Approach Sun, 01 Feb 2015 09:50:26 +0000 Ebola virus (EBOV) is a deadly virus that has caused several fatal outbreaks. Recently it caused another outbreak and resulted in thousands afflicted cases. Effective and approved vaccine or therapeutic treatment against this virus is still absent. In this study, we aimed to predict B-cell epitopes from several EBOV encoded proteins which may aid in developing new antibody-based therapeutics or viral antigen detection method against this virus. Multiple sequence alignment (MSA) was performed for the identification of conserved region among glycoprotein (GP), nucleoprotein (NP), and viral structural proteins (VP40, VP35, and VP24) of EBOV. Next, different consensus immunogenic and conserved sites were predicted from the conserved region(s) using various computational tools which are available in Immune Epitope Database (IEDB). Among GP, NP, VP40, VP35, and VP30 protein, only NP gave a 100% conserved GEQYQQLR B-cell epitope that fulfills the ideal features of an effective B-cell epitope and could lead a way in the milieu of Ebola treatment. However, successful in vivo and in vitro studies are prerequisite to determine the actual potency of our predicted epitope and establishing it as a preventing medication against all the fatal strains of EBOV. Mohammad Tuhin Ali and Md Ohedul Islam Copyright © 2015 Mohammad Tuhin Ali and Md Ohedul Islam. All rights reserved. Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information Sat, 31 Jan 2015 08:20:58 +0000 Locating ligand binding sites and finding the functionally important residues from protein sequences as well as structures became one of the challenges in understanding their function. Hence a Naïve Bayes classifier has been trained to predict whether a given amino acid residue in membrane protein sequence is a ligand binding residue or not using only sequence based information. The input to the classifier consists of the features of the target residue and two sequence neighbors on each side of the target residue. The classifier is trained and evaluated on a nonredundant set of 42 sequences (chains with at least one transmembrane domain) from 31 alpha-helical membrane proteins. The classifier achieves an overall accuracy of 70.7% with 72.5% specificity and 61.1% sensitivity in identifying ligand binding residues from sequence. The classifier performs better when the sequence is encoded by psi-blast generated PSSM profiles. Assessment of the predictions in the context of three-dimensional structures of proteins reveals the effectiveness of this method in identifying ligand binding sites from sequence information. In 83.3% (35 out of 42) of the proteins, the classifier identifies the ligand binding sites by correctly recognizing more than half of the binding residues. This will be useful to protein engineers in exploiting potential residues for functional assessment. M. Xavier Suresh, M. Michael Gromiha, and Makiko Suwa Copyright © 2015 M. Xavier Suresh et al. All rights reserved. PhosphoHunter: An Efficient Software Tool for Phosphopeptide Identification Mon, 12 Jan 2015 12:58:05 +0000 Phosphorylation is a protein posttranslational modification. It is responsible of the activation/inactivation of disease-related pathways, thanks to its role of “molecular switch.” The study of phosphorylated proteins becomes a key point for the proteomic analyses focused on the identification of diagnostic/therapeutic targets. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the most widely used analytical approach. Although unmodified peptides are automatically identified by consolidated algorithms, phosphopeptides still require automated tools to avoid time-consuming manual interpretation. To improve phosphopeptide identification efficiency, a novel procedure was developed and implemented in a Perl/C tool called PhosphoHunter, here proposed and evaluated. It includes a preliminary heuristic step for filtering out the MS/MS spectra produced by nonphosphorylated peptides before sequence identification. A method to assess the statistical significance of identified phosphopeptides was also formulated. PhosphoHunter performance was tested on a dataset of 1500 MS/MS spectra and it was compared with two other tools: Mascot and Inspect. Comparisons demonstrated that a strong point of PhosphoHunter is sensitivity, suggesting that it is able to identify real phosphopeptides with superior performance. Performance indexes depend on a single parameter (intensity threshold) that users can tune according to the study aim. All the three tools localized >90% of phosphosites. Alessandra Tiengo, Lorenzo Pasotti, Nicola Barbarini, and Paolo Magni Copyright © 2015 Alessandra Tiengo et al. All rights reserved. CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences Tue, 06 Jan 2015 07:20:06 +0000 Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work. Charalambos Chrysostomou, Huseyin Seker, and Nizamettin Aydin Copyright © 2015 Charalambos Chrysostomou et al. All rights reserved. A Computational Approach for Predicting Role of Human MicroRNAs in MERS-CoV Genome Tue, 23 Dec 2014 00:10:06 +0000 The new epidemic Middle East Respiratory Syndrome (MERS) is caused by a type of human coronavirus called MERS-CoV which has global fatality rate of about 30%. We are investigating potential antiviral therapeutics against MERS-CoV by using host microRNAs (miRNAs) which may downregulate viral gene expression to quell viral replication. We computationally predicted potential 13 cellular miRNAs from 11 potential hairpin sequences of MERS-CoV genome. Our study provided an interesting hypothesis that those miRNAs, that is, hsa-miR-628-5p, hsa-miR-6804-3p, hsa-miR-4289, hsa-miR-208a-3p, hsa-miR-510-3p, hsa-miR-18a-3p, hsa-miR-329-3p, hsa-miR-548ax, hsa-miR-3934-5p, hsa-miR-4474-5p, hsa-miR-7974, hsa-miR-6865-5p, and hsa-miR-342-3p, would be antiviral therapeutics against MERS-CoV infection. Md Mahmudul Hasan, Rozina Akter, Md. Shahin Ullah, Md. Jaynul Abedin, G. M. Ahsan Ullah, and Md. Zakir Hossain Copyright © 2014 Md Mahmudul Hasan et al. All rights reserved. Alternate Phosphorylation/O-GlcNAc Modification on Human Insulin IRSs: A Road towards Impaired Insulin Signaling in Alzheimer and Diabetes Wed, 17 Dec 2014 13:26:37 +0000 Impaired insulin signaling has been thought of as important step in both Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM). Posttranslational modifications (PTMs) regulate functions and interaction of insulin with insulin receptors substrates (IRSs) and activate insulin signaling downstream pathways via autophosphorylation on several tyrosine (TYR) residues on IRSs. Two important insulin receptor substrates 1 and 2 are widely expressed in human, and alternative phosphorylation on their serine (Ser) and threonine (Thr) residues has been known to block the Tyr phosphorylation of IRSs, thus inhibiting insulin signaling and promoting insulin resistance. Like phosphorylation, O-glycosylation modification is important PTM and inhibits phosphorylation on same or neighboring Ser/Thr residues, often called Yin Yang sites. Both IRS-1 and IRS-2 have been shown to be O-glycosylated; however exact sites are not determined yet. In this study, by using neuronal network based prediction methods, we found more than 50 Ser/Thr residues that have potential to be O-glycosylated and may act as possible sites as well. Moreover, alternative phosphorylation and O-glycosylation on IRS-1 Ser-312, 984, 1037, and 1101 may act as possible therapeutic targets to minimize the risk of AD and T2DM. Zainab Jahangir, Waqar Ahmad, and Khadija Shabbiri Copyright © 2014 Zainab Jahangir et al. All rights reserved. An In Silico Approach towards the Prediction of Druglikeness Properties of Inhibitors of Plasminogen Activator Inhibitor1 Mon, 15 Dec 2014 11:28:23 +0000 Diabetic retinopathy is the leading cause of blindness worldwide. It is caused by the abnormal growth of the retinal blood vessels. Plasminogen activator inhibitor1 (PAI1) is the key growth factor and the inhibition of PAI1 can reduce the angiogenesis. In this study, currently available inhibitors are taken and tested for the toxicity, binding affinity, and bioactivities of the compounds by in silico approach. Five toxic free inhibitors were identified, among which N-acetyl-D-glucosamine shows the significant binding affinity and two of the molecules are having the better bioactivity properties. The molecular optimization of 2-(acetylamino)-2-deoxy-A-D-glucopyranose and alpha-L-fucose can be used for the treatment of diabetic retinopathy. Umadevi Subramanian, Ashok Sivapunniyam, Ayyasamy Pudukadu Munusamy, and Rajakumar Sundaram Copyright © 2014 Umadevi Subramanian et al. All rights reserved. A Framework for Prediction of Response to HCV Therapy Using Different Data Mining Techniques Thu, 11 Dec 2014 11:37:33 +0000 Hepatitis C which is a widely spread disease all over the world is a fatal liver disease caused by Hepatitis C Virus (HCV). The only approved therapy is interferon plus ribavirin. The number of responders to this treatment is low, while its cost is high and side effects are undesirable. Treatment response prediction will help in reducing the patients who suffer from the side effects and high costs without achieving recovery. The aim of this research is to develop a framework which can select the best model to predict HCV patients’ response to the treatment of HCV from clinical information. The framework contains three phases which are preprocessing phase to prepare the data for applying Data Mining (DM) techniques, DM phase to apply different DM techniques, and evaluation phase to evaluate and compare the performance of the built models and select the best model as the recommended one. Different DM techniques had been applied which are associative classification, artificial neural network, and decision tree to evaluate the framework. The experimental results showed the effectiveness of the framework in selecting the best model which is the model built by associative classification using histology activity index, fibrosis stage, and alanine amino transferase. Enas M. F. El Houby Copyright © 2014 Enas M. F. El Houby. All rights reserved. Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors Wed, 26 Nov 2014 12:59:09 +0000 Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski’s rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors. Naresh Kandakatla and Geetha Ramakrishnan Copyright © 2014 Naresh Kandakatla and Geetha Ramakrishnan. All rights reserved. Binding Energy Calculation of Patchouli Alcohol Isomer Cyclooxygenase Complexes Suggested as COX-1/COX-2 Selective Inhibitor Mon, 17 Nov 2014 09:58:17 +0000 To understand the structural features that dictate the selectivity of the two isoforms of the prostaglandin H2 synthase (PGHS/COX), the three-dimensional (3D) structure of COX-1/COX-2 was assessed by means of binding energy calculation of virtual molecular dynamic with using ligand alpha-Patchouli alcohol isomers. Molecular interaction studies with COX-1 and COX-2 were done using the molecular docking tools by Hex 8.0. Interactions were further visualized by using Discovery Studio Client 3.5 software tool. The binding energy of molecular interaction was calculated by AMBER12 and Virtual Molecular Dynamic 1.9.1 software. The analysis of the alpha-Patchouli alcohol isomer compounds showed that all alpha-Patchouli alcohol isomers were suggested as inhibitor of COX-1 and COX-2. Collectively, the scoring binding energy calculation (with PBSA Model Solvent) of alpha-Patchouli alcohol isomer compounds (CID442384, CID6432585, CID3080622, CID10955174, and CID56928117) was suggested as candidate for a selective COX-1 inhibitor and CID521903 as nonselective COX-1/COX-2. Sentot Joko Raharjo, Chanif Mahdi, Nurdiana Nurdiana, Takheshi Kikuchi, and Fatchiyah Fatchiyah Copyright © 2014 Sentot Joko Raharjo et al. All rights reserved. Comparative Genomics of Ten Solanaceous Plastomes Mon, 17 Nov 2014 00:00:00 +0000 Availability of complete plastid genomes of ten solanaceous species, Atropa belladonna, Capsicum annuum, Datura stramonium, Nicotiana sylvestris, Nicotiana tabacum, Nicotiana tomentosiformis, Nicotiana undulata, Solanum bulbocastanum, Solanum lycopersicum, and Solanum tuberosum provided us with an opportunity to conduct their in silico comparative analysis in depth. The size of complete chloroplast genomes and LSC and SSC regions of three species of Solanum is comparatively smaller than that of any other species studied till date (exception: SSC region of A. belladonna). AT content of coding regions was found to be less than noncoding regions. A duplicate copy of trnH gene in C. annuum and two alternative tRNA genes for proline in D. stramonium were observed for the first time in this analysis. Further, homology search revealed the presence of rps19 pseudogene and infA genes in A. belladonna and D. stramonium, a region identical to rps19 pseudogene in C. annum and orthologues of sprA gene in another six species. Among the eighteen intron-containing genes, 3 genes have two introns and 15 genes have one intron. The longest insertion was found in accD gene in C. annuum. Phylogenetic analysis using concatenated protein coding sequences gave two clades, one for Nicotiana species and another for Solanum, Capsicum, Atropa, and Datura. Harpreet Kaur, Bhupinder Pal Singh, Harpreet Singh, and Avinash Kaur Nagpal Copyright © 2014 Harpreet Kaur et al. All rights reserved. Computational Studies of Beta Amyloid (Aβ42) with p75NTR Receptor: A Novel Therapeutic Target in Alzheimer’s Disease Tue, 11 Nov 2014 13:14:00 +0000 Alzheimer’s disease is a neurodegenerative disorder characterized by the accumulation of beta amyloid plaques (Aβ) which can induce neurite degeneration and progressive dementia. It has been identified that neuronal apoptosis is induced by binding of Aβ42 to pan neurotrophin receptor (p75NTR) and gave the possibility that beta amyloid oligomer is a ligand for p75NTR. However, the atomic contact point responsible for molecular interactions and conformational changes of the protein upon binding was not studied in detail. In view of this, we conducted a molecular docking and simulation study to investigate the binding behaviour of Aβ42 monomer with p75NTR ectodomain. Furthermore, we proposed a p75NTR-ectodomain-Aβ42 complex model. Our data revealed that, Aβ42 specifically recognizes CRD1 and CRD2 domains of the receptor and formed a “cap” like structure at the N-terminal of receptor which is stabilized by a network of hydrogen bonds. These findings are supported by molecular dynamics simulation that Aβ42 showed distinct structural alterations at N- and C-terminal regions due to the influence of the receptor binding site. Overall, the present study gives more structural insight on the molecular interactions of beta amyloid protein involved in the activation of p75NTR receptor. Shine Devarajan and Jeya Sundara Sharmila Copyright © 2014 Shine Devarajan and Jeya Sundara Sharmila. All rights reserved. Computational Analysis Reveals the Association of Threonine 118 Methionine Mutation in PMP22 Resulting in CMT-1A Mon, 20 Oct 2014 06:47:05 +0000 The T118M mutation in PMP22 gene is associated with Charcot Marie Tooth, type 1A (CMT1A). CMT1A is a form of Charcot-Marie-Tooth disease, the most common inherited disorder of the peripheral nervous system. Mutations in CMT related disorder are seen to increase the stability of the protein resulting in the diseased state. We performed SNP analysis for all the nsSNPs of PMP22 protein and carried out molecular dynamics simulation for T118M mutation to compare the stability difference between the wild type protein structure and the mutant protein structure. The mutation T118M resulted in the overall increase in the stability of the mutant protein. The superimposed structure shows marked structural variation between the wild type and the mutant protein structures. Chundi Vinay Kumar, Rayapadi G. Swetha, Anand Anbarasu, and Sudha Ramaiah Copyright © 2014 Chundi Vinay Kumar et al. All rights reserved. A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset Sun, 12 Oct 2014 13:38:16 +0000 This paper presents a hybrid method to extract endocardial contour of the right ventricular (RV) in 4-slices from 3D echocardiography dataset. The overall framework comprises four processing phases. In Phase I, the region of interest (ROI) is identified by estimating the cavity boundary. Speckle noise reduction and contrast enhancement were implemented in Phase II as preprocessing tasks. In Phase III, the RV cavity region was segmented by generating intensity threshold which was used for once for all frames. Finally, Phase IV is proposed to extract the RV endocardial contour in a complete cardiac cycle using a combination of shape-based contour detection and improved radial search algorithm. The proposed method was applied to 16 datasets of 3D echocardiography encompassing the RV in long-axis view. The accuracy of experimental results obtained by the proposed method was evaluated qualitatively and quantitatively. It has been done by comparing the segmentation results of RV cavity based on endocardial contour extraction with the ground truth. The comparative analysis results show that the proposed method performs efficiently in all datasets with overall performance of 95% and the root mean square distances (RMSD) measure in terms of mean ± SD was found to be 2.21 ± 0.35 mm for RV endocardial contours. Faten A. Dawood, Rahmita W. Rahmat, Suhaini B. Kadiman, Lili N. Abdullah, and Mohd D. Zamrin Copyright © 2014 Faten A. Dawood et al. All rights reserved. In Silico Screening of Mutated K-Ras Inhibitors from Malaysian Typhonium flagelliforme for Non-Small Cell Lung Cancer Sun, 21 Sep 2014 00:00:00 +0000 K-ras is an oncogenic GTPase responsible for at least 15–25% of all non-small cell lung cancer cases worldwide. Lung cancer of both types is increasing with an alarming rate due to smoking habits in Malaysia among men and women. Natural products always offer alternate treatment therapies that are safe and effective. Typhonium flagelliforme or Keladi Tikus is a local plant known to possess anticancer properties. The whole extract is considered more potent than individual constituents. Since K-ras is the key protein in lung cancer, our aim was to identify the constituents of the plant that could target the mutated K-ras. Using docking strategies, reported potentially active compounds of Typhonium flagelliforme were docked into the allosteric surface pockets and switch regions of the K-ras protein to identify possible inhibitors. The selected ligands were found to have a high binding affinity for the switch II and the interphase region of the ras-SOS binding surface. Ayesha Fatima and H. F. Yee Copyright © 2014 Ayesha Fatima and H. F. Yee. All rights reserved. Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images Tue, 16 Sep 2014 00:00:00 +0000 The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data. Karthik Kalyan, Binal Jakhia, Ramachandra Dattatraya Lele, Mukund Joshi, and Abhay Chowdhary Copyright © 2014 Karthik Kalyan et al. All rights reserved. Breast Cancer Nodes Detection Using Ultrasonic Microscale Subarrayed MIMO RADAR Mon, 15 Sep 2014 08:37:25 +0000 This paper proposes the use of ultrasonic microscale subarrayed MIMO RADARs to estimate the position of breast cancer nodes. The transmit and receive antenna arrays are divided into subarrays. In order to increase the signal diversity each subarray is assigned a different waveform from an orthogonal set. High-frequency ultrasonic transducers are used since a breast is considered to be a superficial structure. Closed form expressions for the optimal Neyman-Pearson detector are derived. The combination of the waveform diversity present in the subarrayed deployment and traditional phased-array RADAR techniques provides promising results. Attaphongse Taparugssanagorn, Siwaruk Siwamogsatham, and Carlos Pomalaza-Ráez Copyright © 2014 Attaphongse Taparugssanagorn et al. All rights reserved.