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1.  Genomic Promoter Analysis Predicts Functional Transcription Factor Binding 
Advances in bioinformatics  2008;2008:3698301-3698309.
Background
The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology.
Results
We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt to predict in vivo occupancy of transcription factor binding sites, we developed a novel marginal effect isolator algorithm that builds upon Bayesian methods for multigroup TFBS filtering and predicted the in vivo occupancy of two transcription factors with an overall accuracy of 84%.
Conclusion
Our analyses show that integration of chromatin immunoprecipitation data with conserved TFBS analysis can be used to generate accurate predictions of functional TFBS. They also show that TFBS cooccurrence can be used to predict transcription factor binding to promoters in vivo.
doi:10.1155/2008/369830
PMCID: PMC2768302  PMID: 19865592
2.  Genomic Promoter Analysis Predicts Functional Transcription Factor Binding 
Advances in Bioinformatics  2008;2008:369830.
Background. The computational identification of functional transcription factor binding sites (TFBSs) remains a major challenge of computational biology. Results. We have analyzed the conserved promoter sequences for the complete set of human RefSeq genes using our conserved transcription factor binding site (CONFAC) software. CONFAC identified 16296 human-mouse ortholog gene pairs, and of those pairs, 9107 genes contained conserved TFBS in the 3 kb proximal promoter and first intron. To attempt to predict in vivo occupancy of transcription factor binding sites, we developed a novel marginal effect isolator algorithm that builds upon Bayesian methods for multigroup TFBS filtering and predicted the in vivo occupancy of two transcription factors with an overall accuracy of 84%. Conclusion. Our analyses show that integration of chromatin immunoprecipitation data with conserved TFBS analysis can be used to generate accurate predictions of functional TFBS. They also show that TFBS cooccurrence can be used to predict transcription factor binding to promoters in vivo.
doi:10.1155/2008/369830
PMCID: PMC2768302  PMID: 19865592
3.  A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data 
Advances in Bioinformatics  2015;2015:198363.
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.
doi:10.1155/2015/198363
PMCID: PMC4480804  PMID: 26170834
4.  Semantic Annotation for Biological Information Retrieval System 
Advances in Bioinformatics  2015;2015:597170.
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.
doi:10.1155/2015/597170
PMCID: PMC4337267  PMID: 25737720
5.  A Highly Conserved GEQYQQLR Epitope Has Been Identified in the Nucleoprotein of Ebola Virus by Using an In Silico Approach 
Advances in Bioinformatics  2015;2015:278197.
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.
doi:10.1155/2015/278197
PMCID: PMC4331325  PMID: 25709646
6.  Development of a Machine Learning Method to Predict Membrane Protein-Ligand Binding Residues Using Basic Sequence Information 
Advances in Bioinformatics  2015;2015:843030.
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.
doi:10.1155/2015/843030
PMCID: PMC4329842  PMID: 25802517
7.  PhosphoHunter: An Efficient Software Tool for Phosphopeptide Identification 
Advances in Bioinformatics  2015;2015:382869.
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.
doi:10.1155/2015/382869
PMCID: PMC4309027  PMID: 25653679
8.  CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences 
Advances in Bioinformatics  2015;2015:909765.
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.
doi:10.1155/2015/909765
PMCID: PMC4302972  PMID: 25632276
9.  A Computational Approach for Predicting Role of Human MicroRNAs in MERS-CoV Genome 
Advances in Bioinformatics  2014;2014:967946.
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.
doi:10.1155/2014/967946
PMCID: PMC4283225  PMID: 25610462
10.  Alternate Phosphorylation/O-GlcNAc Modification on Human Insulin IRSs: A Road towards Impaired Insulin Signaling in Alzheimer and Diabetes 
Advances in Bioinformatics  2014;2014:324753.
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.
doi:10.1155/2014/324753
PMCID: PMC4281456  PMID: 25580119
11.  An In Silico Approach towards the Prediction of Druglikeness Properties of Inhibitors of Plasminogen Activator Inhibitor1 
Advances in Bioinformatics  2014;2014:385418.
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.
doi:10.1155/2014/385418
PMCID: PMC4279271  PMID: 25580120
12.  A Framework for Prediction of Response to HCV Therapy Using Different Data Mining Techniques 
Advances in Bioinformatics  2014;2014:181056.
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.
doi:10.1155/2014/181056
PMCID: PMC4279177  PMID: 25580118
13.  Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors 
Advances in Bioinformatics  2014;2014:812148.
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.
doi:10.1155/2014/812148
PMCID: PMC4265523  PMID: 25525429
14.  Comparative Genomics of Ten Solanaceous Plastomes 
Advances in Bioinformatics  2014;2014:424873.
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.
doi:10.1155/2014/424873
PMCID: PMC4248371  PMID: 25477958
15.  Binding Energy Calculation of Patchouli Alcohol Isomer Cyclooxygenase Complexes Suggested as COX-1/COX-2 Selective Inhibitor 
Advances in Bioinformatics  2014;2014:850628.
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.
doi:10.1155/2014/850628
PMCID: PMC4251649  PMID: 25484897
16.  Computational Studies of Beta Amyloid (Aβ42) with p75NTR Receptor: A Novel Therapeutic Target in Alzheimer's Disease 
Advances in Bioinformatics  2014;2014:736378.
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.
doi:10.1155/2014/736378
PMCID: PMC4244936  PMID: 25477959
17.  Computational Analysis Reveals the Association of Threonine 118 Methionine Mutation in PMP22 Resulting in CMT-1A 
Advances in Bioinformatics  2014;2014:502618.
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.
doi:10.1155/2014/502618
PMCID: PMC4220619  PMID: 25400662
18.  A Hybrid Method for Endocardial Contour Extraction of Right Ventricle in 4-Slices from 3D Echocardiography Dataset 
Advances in Bioinformatics  2014;2014:207149.
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.
doi:10.1155/2014/207149
PMCID: PMC4209758  PMID: 25371675
19.  In Silico Screening of Mutated K-Ras Inhibitors from Malaysian Typhonium flagelliforme for Non-Small Cell Lung Cancer 
Advances in Bioinformatics  2014;2014:431696.
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.
doi:10.1155/2014/431696
PMCID: PMC4189522  PMID: 25309590
20.  Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images 
Advances in Bioinformatics  2014;2014:708279.
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.
doi:10.1155/2014/708279
PMCID: PMC4181903  PMID: 25332717
21.  Breast Cancer Nodes Detection Using Ultrasonic Microscale Subarrayed MIMO RADAR 
Advances in Bioinformatics  2014;2014:797013.
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.
doi:10.1155/2014/797013
PMCID: PMC4181940  PMID: 25309591
22.  Utilization of Boron Compounds for the Modification of Suberoyl Anilide Hydroxamic Acid as Inhibitor of Histone Deacetylase Class II Homo sapiens 
Advances in Bioinformatics  2014;2014:104823.
Histone deacetylase (HDAC) has a critical function in regulating gene expression. The inhibition of HDAC has developed as an interesting anticancer research area that targets biological processes such as cell cycle, apoptosis, and cell differentiation. In this study, an HDAC inhibitor that is available commercially, suberoyl anilide hydroxamic acid (SAHA), has been modified to improve its efficacy and reduce the side effects of the compound. Hydrophobic cap and zinc-binding group of these compounds were substituted with boron-based compounds, whereas the linker region was substituted with p-aminobenzoic acid. The molecular docking analysis resulted in 8 ligands with ΔGbinding value more negative than the standards, SAHA and trichostatin A (TSA). That ligands were analyzed based on the nature of QSAR, pharmacological properties, and ADME-Tox. It is conducted to obtain a potent inhibitor of HDAC class II Homo sapiens. The screening process result gave one best ligand, Nova2 (513246-99-6), which was then further studied by molecular dynamics simulations.
doi:10.1155/2014/104823
PMCID: PMC4158260  PMID: 25214833
23.  AUTO-MUTE 2.0: A Portable Framework with Enhanced Capabilities for Predicting Protein Functional Consequences upon Mutation 
Advances in Bioinformatics  2014;2014:278385.
The AUTO-MUTE 2.0 stand-alone software package includes a collection of programs for predicting functional changes to proteins upon single residue substitutions, developed by combining structure-based features with trained statistical learning models. Three of the predictors evaluate changes to protein stability upon mutation, each complementing a distinct experimental approach. Two additional classifiers are available, one for predicting activity changes due to residue replacements and the other for determining the disease potential of mutations associated with nonsynonymous single nucleotide polymorphisms (nsSNPs) in human proteins. These five command-line driven tools, as well as all the supporting programs, complement those that run our AUTO-MUTE web-based server. Nevertheless, all the codes have been rewritten and substantially altered for the new portable software, and they incorporate several new features based on user feedback. Included among these upgrades is the ability to perform three highly requested tasks: to run “big data” batch jobs; to generate predictions using modified protein data bank (PDB) structures, and unpublished personal models prepared using standard PDB file formatting; and to utilize NMR structure files that contain multiple models.
doi:10.1155/2014/278385
PMCID: PMC4150472  PMID: 25197272
24.  Multiplex Degenerate Primer Design for Targeted Whole Genome Amplification of Many Viral Genomes 
Advances in Bioinformatics  2014;2014:101894.
Background. Targeted enrichment improves coverage of highly mutable viruses at low concentration in complex samples. Degenerate primers that anneal to conserved regions can facilitate amplification of divergent, low concentration variants, even when the strain present is unknown. Results. A tool for designing multiplex sets of degenerate sequencing primers to tile overlapping amplicons across multiple whole genomes is described. The new script, run_tiled_primers, is part of the PriMux software. Primers were designed for each segment of South American hemorrhagic fever viruses, tick-borne encephalitis, Henipaviruses, Arenaviruses, Filoviruses, Crimean-Congo hemorrhagic fever virus, Rift Valley fever virus, and Japanese encephalitis virus. Each group is highly diverse with as little as 5% genome consensus. Primer sets were computationally checked for nontarget cross reactions against the NCBI nucleotide sequence database. Primers for murine hepatitis virus were demonstrated in the lab to specifically amplify selected genes from a laboratory cultured strain that had undergone extensive passage in vitro and in vivo. Conclusions. This software should help researchers design multiplex sets of primers for targeted whole genome enrichment prior to sequencing to obtain better coverage of low titer, divergent viruses. Applications include viral discovery from a complex background and improved sensitivity and coverage of rapidly evolving strains or variants in a gene family.
doi:10.1155/2014/101894
PMCID: PMC4137498  PMID: 25157264
25.  Prediction of Epitope-Based Peptides for the Utility of Vaccine Development from Fusion and Glycoprotein of Nipah Virus Using In Silico Approach 
Advances in Bioinformatics  2014;2014:402492.
This study aims to design epitope-based peptides for the utility of vaccine development by targeting glycoprotein G and envelope protein F of Nipah virus (NiV) that, respectively, facilitate attachment and fusion of NiV with host cells. Using various databases and tools, immune parameters of conserved sequence(s) from G and F proteins of different isolates of NiV were tested to predict probable epitope(s). Binding analyses of the peptides with MHC class-I and class-II molecules, epitope conservancy, population coverage, and linear B cell epitope prediction were analyzed. Predicted peptides interacted with seven or more MHC alleles and illustrated population coverage of more than 99% and 95%, for G and F proteins, respectively. The predicted class-I nonamers, SLIDTSSTI and EWISIVPNF, superimposed on the putative decameric B cell epitopes, were also identified as core sequences of the most probable class-II 15-mer peptides GPKVSLIDTSSTITI and EWISIVPNFILVRNT. These peptides were further validated for their binding to specific HLA alleles using in silico docking technique. Our in silico analysis suggested that the predicted epitopes, either GPKVSLIDTSSTITI or EWISIVPNFILVRNT, could be a better choice as universal vaccine component against NiV irrespective of different isolates which may elicit both humoral and cell-mediated immunity.
doi:10.1155/2014/402492
PMCID: PMC4131549  PMID: 25147564

Results 1-25 (131)