Major Article Contributions by some of the Indian Authors in Bentham Science Publishers Journal; Current Bioinformatics:

Article Title: A Computational Prediction of Conserved MicroRNA Targets of Ion Channels in Vertebrates

Author(s): Priyadarshan Kathirvel, Gopal Ramesh Kumar and Kavitha

Sankaranarayanan Abstract: Ion channels are integral membrane proteins that are responsible for most physiological functions including the electrical activity of excitable cells. Their expression and function are regulated by a variety of means including microRNAs (miRNAs). MicroRNAs are small non coding RNAs, 22 nucleotides in length, involved in regulation of gene expression. In this study, we attempt to predict the miRNA targets of ion channel genes conserved across 4 species using TargetScan algorithm. From the results, many miRNA targets were found to be conserved among mammals. The expression profile of identified miRNA targets of certain genes that are implicated in channelopathies of brain, heart and skeletal muscle in Homo sapiens are explored. Further a final correlation of channel genes and miRNAs expressed in specific tissues is obtained by comparison of expression profiles using mimiRNA. miR-302a and miR-9 were found to be positively correlated with many channelopathy associated genes, while miR-143 and miR-29a were predicted to exhibit negative correlation. In this paper we summarize a list of all possible miRNAs linked with channelopathies. For details, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=106272

Article Title: Mangrove Infoline Database: A Database of Mangrove Plants with Protein Sequence Information Author(s): Sambhaji B. Thakar and Kailas D. Sonawane Abstract: Mangrove Infoline Database contains information about the medicinal uses of mangrove plants with protein/enzyme sequences. Mangrove Infoline Database is a comprehensive dynamic web-based database, used to facilitate retrieval of information related to the mangrove medicinal species. Most of the enzyme sequences extracted from mangroves are taken from NCBI’s protein sequence database, whereas the information related to physical characteristics, geographical distribution, common/vernacular names, taxonomy IDs, medicinal uses, parts used, chemical components extracted from various mangrove plants which can be used as a drug molecules have been collected from various literatures and scientific journals available in the text form. NCBI’s BLAST link has also been provided for the further comparative study. So there was a need to build database where users could get all specific information about mangrove medicinal plants at one place. The current database contains information about 100 Mangrove medicinal species out of which 40 are True mangroves, 30 Minor mangroves and, 30 Associate mangroves. This database would be useful to explore mangrove medicinal plant information through Web based database which would be helpful to derive the information for Researchers, Scientists, Pharmacologists, Biologists, Chemists, Doctors/Pharmacists, Teaching (Students and Teachers from universities and schools), Home-User, Botanical interested Persons, Farmers, and finally mangrove lovers. This attempt would create social awareness among the users about the important applications, uses and conservation of mangrove species around the world. For more information, kindly visit:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=114022

Article Title: Credential Role of van der Waal Volumes and Atomic Masses in Modeling Hepatitis C Virus NS5B Polymerase Inhibition by Tetrahydrobenzo- Thiophenes Using SVM and MLR Aided QSAR Studies Author(s): Kirti Khuntwal, Mukesh Yadav, Anuraj Nayarisseri, Shobha Joshi, Deepika Sharma and Smita Suhane Abstract: Chronic hepatitis C virus (HCV) infections are a significant health problem worldwide. The NS5B Polymerase of HCV plays a central role in virus replication and is a prime target for the discovery of new treatment options. The urgent need to develop novel anti-HCV agents has provided an impetus for understanding the structure-activity relationship of novel Hepatitis C virus (HCV) NS5B polymerase inhibitors. Towards this objective, multiple linear regression (MLR) and support vector machine (SVM) were used to develop quantitative structure-activity relationship (QSAR) models for a dataset of 34 Tetrahydrobenzothiophene derivatives. The statistical analysis showed that the models derived from both SVM (R2 = 0.9784, SE=0.2982, R2 cv = 0.92) and MLR (R2=0.9684, SE=0.1171, R2 cv= 0.955) have a good internal predictivity. The models were also validated using external test set validation and Y-scrambling, the results demonstrated that MLR has a significant predictive ability for the external dataset as compared to SVM. Also the model is found to yield reliable clues for further optimization of Tetrahydrobenzothiophene derivatives in the data set. For more details, click: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=114016

Article Title: Development of an Engineering Method to Optimize Polyamine Metabolic Pathways. Author(s): Mouli Das, Subhasis Mukhopadhyay and Rajat K. De Abstract: In this article, we determine an optimal set of enzymes which is needed to be expressed at a specific level, in order to maximize the production of the target metabolite, spermine, from the substrate, ornithine, in the polyamine metabolic pathway. The pathway thus obtained is compared with the optimal pathway obtained using the existing extreme pathway analysis method. The results are appropriately validated through literature. Finally, we discuss potential applications of this approach in the field of metabolic and genetic engineering. It is hoped that the engineering of this pathway can produce secondary metabolites having commercial values, and finally drugs. For more details click: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=116191

Article Title: PredictFold-PSS-3D1D: A Protein Fold Recognition Server for Predicting Folds from the Twilight Zone Sequences. Author(s): Kaliappan Ganesan and Subbiah Parthasarathy Abstract: The PredictFold-PSS-3D1D is an online protein fold recognition web server used to predict the possible folds from the twilight zone protein sequences. In this server, an improved 3D1D profile method (Ganesan and Parthasarathy, J. Struct. Funct. Genomics, 12, 181-189, 2011) is employed, wherein, the inclusion of predicted secondary structure information improves fold recognition. The PredictFold-PSS-3D1D server accepts amino acid sequences and their predicted secondary structure data as input and aligns them with the 3D1D profiles of known SCOP folds in a database. The alignments are ranked by the z-values and P-values. The top 5 ranks of the SCOP folds from the database are listed along with a link to ‘View SCOP details’. The folds with z-values ≥3.0 and P-values ≤0.05 are indicated as ‘Predicted Fold’ for the given query twilight zone protein sequence. This server is available in our PredictFold web server athttp://bioinfo.bdu.ac.in/pss3d1d/. For more information, visit: http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=116188

Article Title: An In Silico Identification of Human Promoters: A Soft Computing Based Approach Author(s): Sutapa Datta and Subhasis Mukhopadhyay Abstract: Promoter region of a gene sequence of Eukaryotes is very important as it helps us to understand the mechanism of transcription regulation. The identification of this region is a complex problem as the signature for identification turns out to be fuzzy. Several in silico methods are available for identifying the promoter region, but the scope for new methods still exists. Reasonable prediction of promoter sequence (that can be tested by comparing with the wet-lab data) from a mixed database of promoters and nonpromoters is thus a challenge that any new method would have to face. In this communication we propose a composite method that utilizes clustering of known promoter and non-promoter sequences in their respective clusters based on their relative distances, and then classifying the max similarity scores obtained from a group of new sequences and the clusters, to predict the true promoters among the new set of sequences. The in silico experiment is carried out on different databases constructed by us from the available primary sequence databanks to demonstrate the advantage of the proposed approach. For more information on this journal, click:http://benthamscience.com/journal/abstracts.php?journalID=cbio&articleID=111148

courtesy by : Bentham Insight