International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Rice is the primary staple food for more than half of the world’s population but very sensitive to various abiotic stresses (submergence) causing crop loss. During abiotic stresses various genes are differentially expressed to cope up with the stress conditions. The identification of Sub1A gene was a major breakthrough for the submergence tolerance which often regulates other genes by binding to their consensus promoter motifs such as GCC box. It was observed that Ubiquinol Cytochrome C chaperone (UCCC) gene was among many up-regulated differentially co-expressed genes having GCC box as a conserved motif. The primary role of UCCC gene is oxidative respiration but also has imperative secondary functions in plants. Therefore, UCCC gene was selected for the identification of GCC Box in the promoter region using Molecular Beacon Probe based Real Time PCR and their interaction with Sub1A protein. Real Time PCR analysis confirmed the presence of GCC box. Subsequently, the interaction of Sub1A with GCC box was studied through HADDOCK server. Protein-DNA interaction thus, suggested significant binding affinity of Sub1A towards GCC box present in the promoter region of UCCC gene.
KeyWords: 3D-DART, Differentially expressed genes, HADDOCK, I-TASSER Oryza sativa, Sub1A, Ubiquinol Cytochrome C Chaperone gene.
How to cite: GK Prajapati et al. Int J Comput Bioinfo In Silico Model. 2(5) 2013: 213-222
ABSTRACT: The Topoisomerase I enzyme has become an attractive target for the treatment of cancer. In this paper molecular dynamics, 2D and 3D QSAR and molecular docking studies were performed on 90 naphthoquinone derivatives as Topoisomerase I inhibitors by using the human Topo I-DNA cleavable complex. This model has the drug intercalated with its planar pharmacophore between +1 and -1 bp flanking cleavage site. The docking analysis focuses the importance of Asn 722 and Thr 718 residues as necessary active site interaction residues. The 2D and 3D QSAR models also gave satisfactory results with r2 as 0.6298 and 0.7868 respectively. The docking analysis and the biological activity are also correlated by the QSAR equations, thus validating the binding analysis results. These results are also useful in order to understand the structural features required to improve the performance of naphthoquinone derivatives as Topoisomerase I inhibitors and include the pharmacophoric features to design and develop new better analogs.
KeyWords: Quantitative Structure Activity Relationship, Topoisomerase I, Simulation and docking
How to cite: S Kulkarni et. al. Int J Comput Bioinfo In Silico Model. 2(5) 2013: 223-233
ABSTRACT: Single nucleotide polymorphisms (SNPs) represent the most frequent type of genetic polymorphism and thus provide a high density of markers near the locus of interest. However, the mining of SNPs for their and significance of SNPs in organellar genomes has not been completely understood. In the present work, mitochondrial genomes were investigated for the distribution and pattern of SNPs. In recent past, the availability of organelle genome sequences has allowed us to understand the organization of SNPs in their genic and intergenic region. Most of the SNPs in mitochondrial genes are neutral with respect to protein structure therefore can be used to study divergence in closely related species. In this study, the SNPs were identified and categorized in six mitochondrial genes of five species of class Chlorophyceae of green algae and many of their properties such as DNA polymorphism, codon usage bias, conserved DNA regions, indels etc. were measured. The data revealed that nad2 gene exhibited highest degree of polymorphism and significantly the indels were also observed in great amount in same. This work constitutes the first report of an exhaustive comparison of mitochondrial SNPs in algal species and has revealed important information thereon.
KeyWords: SNP, Mitochondrial genomes, Chlorophytes
How to cite: Himani Kuntal and Vinay Sharma. Int J Comput Bioinfo In Silico Model. 2(5) 2013: 234-238
ABSTRACT: Protein function prediction is very important and challenging task in Bioinformatics. In this paper we have used proteins represented by a set of enzymes i.e. Oxidoreductase, Transferase, Hydrolase, Isomerase, Ligase, and Lyase, extracted from the Enzyme Commission (EC) classification to build the models. In this paper we have used Support vector machine to predict protein function which is more efficient for resolving linear and non linear classification problems. We have used protein dataset available at PDB using features such as primary structures, secondary structures , molecular weight, structural molecular weight, chain length, atom count, ligand molecular weight and residue count as training parameters and EC number as corresponding output . Here we used expert model of support vector machine, with RBF kernel function where width is 0.10 and parameter C is 10. The result in this paper using these parameters shows that the overall average accuracy is 84.07%.
KeyWords: Proteins, Function prediction, Support vector machine, classification, Enzyme
How to cite: AK Tiwari and RB Mishra. Int J Comput Bioinfo In Silico Model. 2(5) 2013: 239-244
ABSTRACT: The importance of influenza viruses as worldwide infectious agents is well recognized. Specific mutations and evolution in influenza viruses is difficult to predict. We studied specific mutations in Hemagglutinin (HA) of H5N1 influenza A virus together with properties associated with it using prediction tools developed in Bioinformatics. Changes in hydrophobicity, polarity and secondary structure at the site of mutations were noticed and documented to gain insight towards its infection.
KeyWords: hemagglutinin (HA), mutation, influenza, hydrophobicity
How to cite: Manish Kumar. Int J Comput Bioinfo In Silico Model. 2(5) 2013: 245-248
ABSTRACT: Sucrose non-fermenting1-related protein kinase 2 (SnRK2) is a serine/threonine protein kinase. The protein is of utmost importance because it has been found in all plants analyzed to date and is thought to play a significant role in stress and metabolic responses. Arabidopsis thaliana, a model plant, encodes ten members from three subclasses of SnRK2 family, but the least is known about 3D structure and physiological role. Comparative analysis among five different proteins of SnRK2 subclass I and subclass III was performed using bioinformatics tools. Members of subclass I and III were found to be highly similar regarding physicochemical properties, presence of antigenic determinant sites and structural behavior. The tertiary structure of one of the proteins of subclass I, namely SnRK2.1 was constructed based on homology modeling approach. Predicted structure was verified by Ramachandran plot and verify3D and was evaluated by superimposing on its three templates. The functional analysis of this protein revealed nucleotide binding activity, ATP-binding activity, transferase, catalytic activity as well as other cellular activities with high confidence level. This study may play role in revelation of crystallographic structure of SnRK2.1 protein based on this predicted three-dimensional conformation as well as in further laboratory studies to find out functional role of SnRK2 subclass I kinases in plant metabolic pathway.
KeyWords: SnRK2; Arabidopsis thaliana; Molecular characterization; Homology modeling
How to cite: Saleha Sultana et. al. Int J Comput Bioinfo In Silico Model. 2(5) 2013: 249-256