International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: The Dof1 protein is a member of the Dof transcription factor gene family in plants involved in the regulation of specific genes involved in C/N metabolism or in other words the genes influencing nitrogen use efficiency in plants. Dof1 proteins recognize the sequence 5’-AAAG-3’ present in the promoter regions of genes controlled by them. The present study was focussed to understand whether Dof1 simultaneously regulates all the genes of the target family of genes and whether number of Dof binding sites plays any role in driving the expression of its target genes. Correlation studies carried out between the Dof binding sites in the promoter region and the expression levels of genes specifically regulated by Dof1 TF in viz. rice and Arabidopsis and genes involved in glycolysis, TCA cycle and N metabolism. The analysis revealed a non-significant effect of number of Dof binding sites on gene the expression levels. Further, In silico interaction studies between Dof1 protein and its target promoters revealed that Dof1 protein specifically interacts with “(A\T\C) AAAG” sequence and does not interact with all the “AAAG” motifs present in the promoter. The results indicate that (a) number of Dof binding sites has no influence on the expression levels of target genes and (b) Probably Dof1 alone does not regulate the expression of genes involved in C/N metabolism.
KeyWords: Dof transcription factor, nitrogen use efficiency, gene expression, molecular modeling, docking.
How to cite: Alok Kumar Gupta et. al. Dof1 transcription factor interacts with only specific regions of the promoters driving the expression of genes involved in carbon and nitrogen metabolism. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 412-422
ABSTRACT: According to the folding pattern of a protein the organization of protein structures imposes a very useful logical structure on the entire in the Protein Data Bank. It affords a basis of structure-oriented information retrieval. There are several databases derived from the PDB that are built around the classifications of protein structure. The combination of different databases and signature types would produce a powerful protein classification tool and facilitate accurate prediction of protein function. Based on its description, the protein signature interface would use different method for protein signature derivation since databases such as CATH, SCOP, and FSSP. A single protein can be placed into different classes by different databases. One should therefore exercise caution while choosing the right database to use for the purpose of classification. The analysis of the existing protein structure classification databases reveals that there are consistencies among major databases such as CATH, SCOP, and FSSP. Additionally, new techniques for analysis and classification of proteins need to be tested for consistency before adoption. It would also be important to examine the classes and determine which groups of proteins remain in the same family because some proteins have been classified in the same class despite the fact they have less robust relationship.
KeyWords: McBASC, CMA, DCA, DMA, solvent accessibility, coordination number
How to cite: Manish Kumar and Ajay Prakash. Development of a New Protein Classification Scheme. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 423-425
ABSTRACT: Structural analysis of the inducible nitric oxide synthase (iNOS) model and a docking simulation between 6-(methylsulfinyl)hexyl isothiocyanate (6MITC) and the iNOS model were performed with a software package the Molecular Operating Environment (MOE). A human iNOS (PDB code: 3E7G) was selected for the 3-D structure modeling of the iNOS model. The Site Finder module of the MOE identified 18 possible ligand-binding sites in the modeled iNOS. The docking simulation revealed that 6MITC possibly inhibits functions of iNOS interferring with Gln263, Pro350 and Glu377. To the best of our knowledge, this is the first report of an iNOS model with 6MITC, and our data verify that the 6MITC-iNOS model can be utilized for application to target iNOS for the development of anticancer drugs.
KeyWords: 6-(Methylsulfinyl)hexyl isothiocyanate (6MITC), inducible nitric oxide synthase (iNOS), in silico
How to cite: Hideaki Yamaguchi et al. Structural basis for the interaction of 6-(methylsulfinyl)hexyl isothiocyanate with inducible nitric oxide synthase. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 426-432
ABSTRACT: Discovering highly interacting proteins and analysis of their pathways in the human proteome is important not only to decipher critical interactions but also to understand the connectedness within the proteome, which further provides insights toward drug development. However performing such a large scale analysis requires specific mathematical tools, such as graph theory, to handle and comprehend the study. In our study, we have obtained a dataset of 40,788 protein-protein interactions from HPRD and IntAct to generate the human interactome network.Largest connected components were extracted from this network to obtain a set of 89 highly connected clusters. Proteins that formed these clusters were further categorised to either normal or both(diseased and normal) pathways as provided in KEGG. A total of 1350 unique proteins were obtained, of which 374 proteins were in the normal pathways.Our findings suggest that 976 proteins belong to both classes. These proteins can be probed further to understand differences between the normal and disease states and therefore possibly to understand how disease is manifested, and to devise better treatments.
KeyWords: Human Interactome, HPRD, IntAct, KEEG, CCA, MCODE, Cytoscape
How to cite: Arinnia Anto and Padma Nambisan. Using multi level algorithmic methods for identifying highly interacting human protein complexes and various protein pathways. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 433-439
ABSTRACT: There has been growing interest in species identification in forensics, conservation and evolutionary biology, and food processing industry. DNA signatures are used to distinguish and compare presence of certain organisms from all other organisms. Considering the added advantages of mitochondrial DNA signature over genomic signature, the study envisaged in silico amplification mitochondrial DNA. Primers were designed over common conserve flanking regions of mitochondrial NADH dehydrogenase subunit 2 (mitochondrial-ND-2) gene. Several sets of primers were tested under the study, and among these, two set of universal primers -one amplifying full length copy of mitochondrial-ND-2 gene, amplicon size ~1352bp and the other primer- for a truncated portion of mitochondrial-ND-2 gene (≤600bp), amplicon size 564bp were used in the study. Single nucleotide polymorphism (SNP) were detected in mitochondrial-ND-2 gene for identification of six livestock species, Bos grunnines, Bos taurus, Bubalus bubalis, Capra hircus, Ovis aries and Camelus dromedaries that revealed species specific RFLP pattern and characteristic differences in the phylogenetic analysis.
KeyWords: Livestock species, mitochondrial-ND2 gene, PCR-RFLP assay, SNP signatures, species discrimination
How to cite: Uday Singh et al. In silico analysis of mitochondrial-ND-2 gene as species signature and development of PCR-RFLP test for identification of six livestock species. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 440-446
ABSTRACT: HDACs are important anticancer drug targets, and their study is currently being actively pursued. A series of HDAC inhibitors were docked to the homology models to understand the similarities and differences between the binding modes, docking analysis was applied to clarify the binding modes between the ligands and the receptor HDAC. The docking studies of some newly designed hydroxamate analogues with HDAC8. The geometry of HDAC-8 was extracted from the Brookhaven PDB (entry code: 1T64,1T67) complex with the irreversible inhibitors TSA and SAHA respectively. A docking protocol using MVD software was developed to predict the interaction of histone deacetylase (HDAC) inhibitors. MVD Tool was employed to generate the docking input files and to analyze the docking results. To validate the use of the MVD, re-docking was performed on the reference compound TSA and SAHA. Molegrow virtual docker successfully reproduced the experimental binding conformations of the reference drugs TSA and SAHA with acceptable RMSD of 0.813849 Å and 0.563832Å followed by algorithm plant score grid and moledock optimizer.
KeyWords: protein data bank; Molegro virtual docker; root-mean-square deviation; trichostatin A, Suberoyl Anilde Hydroxamic acid
How to cite: Anubha Bajpai and Amit Mishra. Molecular Docking studies and ADME/T parameter values of a series of Hydroxamate analogues acting as HDACs inhibitors. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 447-453
|Himanshu Avashthi, Budhayash Gautam, Prashant Ankur Jain, Apoorv Tiwari, Rajesh Kumar Pathak, Ambuj Srivastava, Gohar Taj* and Anil Kumar
||Himanshu Avashthi et al. International Journal of Computational Bioinformatics and In Silico Modeling 3(4) 2014: 454-459
ABSTRACT: Mitogen activated protein kinases (MAPKs) are key proteins involved in the signal transduction of extracellular information to intracellular targets and plays essential role in the response to biotic and abiotic stresses. We have identified the important determinants or motif for substrate specificity of MAPK3/6 with six major transcription factors viz., AP2, bZIP, MYB, MYB-related, NAC and WRKY, SP and TP sites; which are serine and threonine specific phosphorylation sites. Netphos 2.0 server was used to predict 101 SP, 62 TP out of 147 substrates in AP2; 44 SP, 31 TP out of 70 substrates in bZIP; 98 SP, 81 TP out of 150 substrates in MYB; 38 SP, 27 TP out of 49 substrates in MYB-related; 64 SP, 29 TP out of 101 substrates in NAC and 53 SP, 32 TP out of 72 substrates in WRKY. We have also found proline residue is present at +1 position of all phosphorylation sites that means proline might play an important role to enhance the binding affinity of MAPK3/6 with different transcription factors. Although, further functional analysis through wet lab experimentation will be required, our study provides the basis for future research on the complex signaling pathway mediated by MAPK with its substrates in Arabidopsis thaliana and related plant species.
KeyWords: MAPK, Phosphorylation site, Transcription factor, WRKY, bZIP, MYB, MYB related, NAC, AP2
How to cite: Himanshu Avashthi et al. In silico identification of MAPK3/6 substrates in WRKY, bZIP, MYB, MYB- related, NAC and AP-2 transcription factor family in Arabidopsis thaliana. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 454-459
ABSTRACT: The advent of genome scale metabolic models of Escherichia coli coupled with limited successes in computational advancement could facilitate rapid advancement in the field of metabolic engineering and synthetic biology. E. coli has been subjected to various metabolic engineering approaches using established experimental methods to produce D-lactate under micro-aerobic conditions using glycerol as substrate. However, investigation on the in silico prediction and/or deletion of competing pathway genes on glycerol for the production of D-lactate by E. coli genome scale model using regulatory on or off minimization (ROOM) under the OptFlux software platform is yet to be elucidated. Here, we show that in silico metabolic engineering using this software platform by simulating the knocking out of pyruvate formate lyase (pflB/b0903), fumarate reductase (frdA/b4154), phosphoacetyltransferase (pta/b2297) and alcohol/acetaldehyde dehydrogenase (adhE/b1241) have been predicted to increase D-lactate production in E. coli. The mutant models constructed in this study exhibited growth rate that is 96 % of the wild-type model, and hence maintaining a significant flux for D-lactate production. The results reported herein, were found to be in conformity with previously established experimental studies. These findings indicates that the OptFlux software platform using ROOM as simulation algorithm hold great promise as potential software platform that can accurately predict metabolic engineering targets to guide future experimental studies not only for D-lactate production in E. coli but also for other microbial chemical compounds of interests.
KeyWords: Escherichia coli genome model, metabolic engineering, D-lactate, glycerol, OptFlux software, gene knockout simulation
How to cite: BS Mienda et al. In silico metabolic engineering prediction of Escherichia coli genome model for production of D-lactic acid from glycerol using the OptFlux software platform. Int J Comput Bioinfo In Silico Model. 3(4) 2014: 460-465