International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: Tuberculosis (TB) is considered as a fatal disease and globally its mortality rate is about 2 million per year, this disease is caused by Mycobacterium tuberculosis (MTB). According to the previous reports, latent infection is prevalent in one-third of population. In the current study an efforts was made to predict an effective drug, as there is still no drug that efficiently kills these resting bacilli. During this study, three MTB_sirR inhibitors were identified using computer-aided drug design approach. Molecular Operating Environment (MOE 2011-10) software was utilized to generate three dimensional structures of MTB_sirR protein, and the predicted model was verified using RAMPAGE and ERRAT programs. Structural-Base Virtual screening was performed by docking inhibitors obtained from the Chembridge Database to the active site of the MTB_sirR protein using MOE-Dock 2011-10 software. Based on dock-score, Hydrogen bonds and interaction energy calculated in our computational approach, three compounds (Chembridge ID: 10293776, 10279567 and 10294460) might be the potent inhibitor of the MTB_sirR protein.
KeyWords: MTB_sirR, Homology modeling, Structure-base Virtual screening, Molecular Docking, Inhibitor.
How to cite: Ashfaq Ur Rehman et. al. Exploring the binding pattern of predicted potent inhibitor to sirR_mtb; Modeling and structure-base virtual screening of sirR protein of Mycobacterium Tuberculosis (MTB). Int J Comput Bioinfo In Silico Model. 4(3) 2015: 651-658
ABSTRACT: HIV-1 protease is a 99 amino acid aspartyl protease which is responsible for the cleavage of the viral polyprotein into functional constituent proteins. Inhibition of HIV protease causes the release of immature and noninfectious particles. Cyclic Urea inhibitors are seven member ring structure of cyclic urea compound. These inhibitors strongly bind with HIV-1 protease and are responsible for the formation of mature HIV which stops the cleavage of long viral protein into small peptide segment. Computational approaches were used to dock ligand (cyclic urea inhibitors) with HIV-1 proteases (mutated and wild type) to study their binding interaction and other properties. An ADME Toxicity property shows the best possible choice of drug against HIV-1 protease. Our in silico results approach that the mutated protein can bind with some of the Cyclic Urea inhibitors derivatives, which can block the functioning of HIV-1 protease, can be taken into consideration for further studies.
KeyWords: HIV-1 proteases; Cyclic Urea inhibitors; in silico study; virtual screening; Docking; Drug Designing.
How to cite: Saba Sheikh et. al. Study of Interaction between Wild type and Mutant HIV-1 protease and Cyclic Urea Inhibitor Using In Silico Techniques. Int J Comput Bioinfo In Silico Model. 4(3) 2015: 659-666
ABSTRACT: Due to the availability of large scale experimental data of protein-protein interaction, it remains a challenging task to predict the protein’s function from those high-throughput datasets. Widespread presence of false positive interaction and annotation error in public protein databases are the primary barrier in this case. Considering this fact, we work on network-based statistical approach to predict protein function. Our work is based on the insight that if two proteins have significantly larger numbers of common interaction partners in the experimental network than what is expected from a random network, they have close functional associations and most frequently occurred function in the network has higher probability to be found in the neighbourhood of a protein. First we pre-process both BioGRID and DIP interaction datasets, and then employ the chi-square scoring method with and without considering hypergeometric distance of each interacting pairs. We performed leave-one-out validation to evaluate prediction performance in both cases and find that considering hypergeometric distance improves the performance significantly.
KeyWords: Protein Interaction Network, Protein Function Prediction, Hypergeometric Distance, Chi Square.
How to cite: Md. Khaled Ben Islam et. al. Protein Function Prediction Using Hypergeometric Distance with Background Frequency from Protein-Protein Interaction Network. Int J Comput Bioinfo In Silico Model. 4(3) 2015: 667-672
ABSTRACT: The protein-protein Interaction study was used to understand the complex mechanism of the Mycobacterium bacterium in host. The literature scanning has been performed to investigate proteins involved in causing virulence, growth (up regulated genes) and survival of the pathogen in the diverse host environment by using web databases. The network of protein interactions was constructed by searching the primary interactions of seed proteins. The constructed network was analyzed by mathematical models to extract the biological significance of selected proteins. The results provide hub proteins such as Pyk and rpoB along with neighboring important nodes in the network. These proteins were used to understand their role in specific pathways to study biological significance to identify them as Bottleneck proteins. The hubs can be further analyzed biologically to identify them as targets to knockdown particular mechanism that stops survival and growth of the bacilli in the human host.
KeyWords: Mycobacterium tuberculosis, Protein-Protein interactions, Pyruvate Kinase, RNA polymerase Beta, Betweenness Centrality (BC), Closeness centrality (CC).
How to cite: Pavan Gollapalli et. al. In Silico protein-protein interaction studies of Mycobacterium tuberculosis during host infection. Int J Comput Bioinfo In Silico Model. 4(3) 2015: 673-682