Project titles for:
Project titles for: 2006-08
- Detection of putative miRNA sequences in rice genome
- QSAR Studies on Tuberculosis Inhibitors
- Eigenvalue analysis and its applications in Bioinformatics
- Parallel Implementation of DOCK Algorithm
- Comparative Analysis of Protein Classification Methods
- Identification of Neuraminidase inhibitors of Influenza virus
- Identification of Transcription factor binding sites in yeast
- Restriction site analysis of Rice genome
- Docking Studies on Tuberculosis Inhibitors
- Determination of sequence homology in promoter region for abiotic stress responsive genes in rice
- SNP mining in Chromosome no.8 of rice
- Annotation of EST Sequences present on Chromosome no. 1, 4 & 8 of Rice
- Function Prediction for genes near BAD-2 locus on Chromosome no. 8 of Rice
- Computational Prediction of Putative miRNA Candidates in Malarial Parasite Genome
- Multimedia database of fungal diseases in Rice and Wheat
- GePre: A Gene Prediction Tool
- SEALI: A Sequence Alignment Tool
- Tannase Structure Prediction
- Algorithms on PAM and BLOSUM Matrices
Prediction of the biological activity of a molecule from a set of structure-based descriptors is very useful for drug discovery. These types of quantitative structureactivity relationships (QSAR) can be captured in statistical models relating structure to activity in a particular reaction. The QSAR procedure begins with the identification of a molecule set and measured activities by chemical analysis. The target is the Tuberculosis AccD5 against which 3-D QSAR analysis was performed using a set of Nitroimdazole inhibitors. The alignment of molecules and “active” conformation selection are the key to a successful 3D-QSAR model. The 3D QSAR models demonstrate good ability to predict activity of studied compounds (r2 = 0.616, 0.655, q2 = 0.150, 0.025). Successful QSAR models were then used to predict the activity of novel compounds and provide information about the mechanism of action in the reaction that was being examined. A ligand-based approach is used in rational drug design to build activity models, which provide important information on possible improvements in ligand structure to increase activity. Meanwhile receptor-based modeling provides an insight into the interaction model of a ligand in its receptor and aids in new ligand design. Both approaches provide a powerful approach in building 3D-QSAR models.