Studies on 8-tertbutyl Caffeine: An in silico approach to mechanistic studies

Amminecobalt (III) promoted aerial oxidation of alkyl hydrazines undergoing homolytic alkylation of xanthines selectively at C-8 position. No modeling studies have been done previously on these compounds. An attempt was made to predict the mechanism involved in this spontaneous reaction using molecular modeling. The predictions revealed that homolytic aromatic substitution of alkyl radical exhibits primary isotopic effect. We try to correlate the importance of in silico approaches towards mechanistic studies in such compounds

DPAAR: a Database of Perfect Amino Acid Repeat

Repeat of amino acids in a protein sequence has clinical and functional importance. Perfect Amino Acid Repeat (PAAR) is a kind of relational as well as flat file database which is created by the comprehensive analysis of  5,42,782 protein sequences of Swiss-Prot database (released on 19th March,2014) to know the association between repeated sequence and disease. It provides the search engine for rapid access of a particular repeated amino acid, or particular swissprot ID, or particular length of repeated amino acids in a protein sequence. It also provides the flat files for single, oligo, and tandem repeated sequence information to get the complete informaton about concerned amino acids repeat. It consists of the tables of repeated sequence and its associated disease in human being

Determination of protein-protein interaction through Artificial Neural Network and Support Vector Machine: A Comparative study

Protein-protein interactions (PPI) plays considerable role in most of the cellular processes and study of PPI enhances understanding of molecular mechanism of the cells. After emergence of proteomics, huge amount of protein sequences were generated but there interaction patterns are still unrevealed. Traditionally various techniques were used to predict PPI but are deficient in terms of accuracy. To overcome the limitations of experimental approaches numerous computational approaches were developed to find PPI. However previous computational approaches were based on descriptors, various external factors and protein sequences. In this article, a sequence based prediction model is proposed by using various machine learning approaches. A comparative study was done to understand efficiency of various machine learning approaches. Large amount of yeast PPI data have been analyzed. Same data has been incorporated for different classification approach like Artificial Neural Network (ANN) and Support Vector Machine (SVM), and compared their results. Existing methods with additional features were implemented to enhance the accuracy of the result. Thus it was concluded that efficiency of this model was more admirable than those existing sequence-based methods; therefore it can be effective for future proteomics research work

Recent Trends in In-silico Drug Discovery

A Drug designing is a process in which new leads (potential drugs) are discovered which have therapeutic benefits in diseased condition. With development of various computational tools and availability of databases (having information about 3D structure of various molecules) discovery of drugs became comparatively, a faster process. The two major drug development methods are structure based drug designing and ligand based drug designing. Structure based methods try to make predictions based on three dimensional structure of the target molecules. The major approach of structure based drug designing is Molecular docking, a method based on several sampling algorithms and scoring functions. Docking can be performed in several ways depending upon whether ligand and receptors are rigid or flexible. Hotspot grafting, is another method of drug designing. It is preferred when the structure of a native binding protein and target protein complex is available and the hotspots on the interface are known. In absence of information of three Dimensional structure of target molecule, Ligand based methods are used. Two common methods used in ligand based drug designing are Pharmacophore modelling and QSAR. Pharmacophore modelling explains only essential features of an active ligand whereas QSAR model determines effect of certain property on activity of ligand. Fragment based drug designing is a de novo approach of building new lead compounds using fragments within the active site of the protein. All the candidate leads obtained by various drug designing method need to satisfy ADMET properties for its development as a drug. In-silico ADMET prediction tools have made ADMET profiling an easier and faster process. In this review, various softwares available for drug designing and ADMET property predictions have also been listed

Unraveling long non-coding RNAs through analysis of high-throughput RNA-sequencing data

Extensive genome-wide transcriptome study mediated by high throughput sequencing technique has revolutionized the study of genetics and epigenetic at unprecedented resolution. The research has revealed that besides protein-coding RNAs, large proportions of mammalian transcriptome includes a heap of regulatory non protein-coding RNAs, the number encoded within human genome is enigmatic. Many taboos developed in the past categorized these non-coding RNAs as ââdark matterâ and âjunksâ. Breaking the myth, RNA-seq-- a recently developed experimental technique is widely being used for studying non-coding RNAs which has acquired the limelight due to their physiological and pathological significance. The longest member of the ncRNA family-- long non-coding RNAs, acts as stable and functional part of a genome, guiding towards the important clues about the varied biological events like cellular-, structural- processes governing the complexity of an organism. Here, we review the most recent and influential computational approach developed to identify and quantify the long non-coding RNAs serving as an assistant for the users to choose appropriate tools for their specific research. Keywords: Transcriptome, High throughput sequencing, Genetic and epigenetic, Long non-coding RNA, RNA-sequencing, RNA-se

Integrated analysis of dysregulated lncRNA expression in breast cancer cell identified by RNA-seq study

Among all the sequencing techniques, RNA sequencing (RNA-seq) has galloped with pace adopting the profiling of transcriptomic data in almost every biological analytics area like gene regulation study, development biology and clinical research. Recently the discovery of differentially expressed genes across different conditions has outshone the barrier of genetic & epigenetic regulations. The present work identified and analyzed differentially expressed novel long non-coding RNAs (lncRNAs) for breast cancer. A complex computational pipeline was adopted for the study which includes analysis of 18498 differentially expressed genes with 4114 up-regulated and 3475 down-regulated transcripts. The overexpression of lnc-MTAP (CDKN2B-AS1), lnc-PCP4 (DSCAM-S1), and lnc-FAM (H19) in breast cells suggests that these lncRNAs may have significant role to play in breast cancer. These results validated the relevance of the dysregulation pattern in cancer cells due to the presence of lncRNAs. The study further opens a new scope for experimental analysis to confirm the aberrant expression pattern of these lncRNAs which may act as potential bio-markers for the diagnosis and early detection of breast cancer. Keywords: RNA sequencing, Transcriptomics, Differentially expressed genes, Long non-coding RNAs (lncRNAs), Breast cancer, Dysregulatio