International Journal of Computational Bioinformatics and In Silico Modeling
ABSTRACT: The present molecular docking study stands useful for the design and development of novel compound having better inhibitory activity against the selective proteins of human hepatocellular carcinoma cell line. The docking scores were highest for Transferrin with –6.13 kcal/mol with the stronger interaction followed by Plasminogen (-1.69 kcal/mol having least score, the LogP, lower hydrogen bond counts, confirming the capability of Stearic acid for binding at the active site of the receptor. This potential drug candidate can further be validated by wet lab studies for its proper function.
KeyWords: Stearic acid, HepG2 cell line and Hex.
How to cite: S. Rajesh et al. In Silico Docking of Selected Compound from Cardiospermum halicacabum Linn. Leaf against Human Hepatocellular Carcinoma (HepG2) Cell Line. Int J Comput Bioinfo In Silico Model. 5(2) 2016: 780-786
ABSTRACT: The aim of present study is to identify anti-lung cancer compounds against EGFR 696-1022 T790M mutant from five medicinal plants. In house library of 70 phytochemicals was screened by molecular docking using AutoDock Vina and by CDRUG server. 11 phytochemicals were screened with efficient range of negative binding energy and high probability to act as anticancer drugs. Finally 8 phytochemicals were obtained as hit with accepted results in terms of drug likeness (FAF-Drug3 server) and cytotoxicity on lung cancer cell lines (CLC-Pred). Thus, it can be concluded that out of 70, 8 hits has potential therapeutic activity against lung cancer.
KeyWords: Lung cancer inhibitors, Docking, CDRUG, FAF-Drug3, CLC-Pred.
How to cite: Priyanka Maiti et al. Molecular docking analysis and screening of plant compounds against lung cancer target EGFR T790M mutant. Int J Comput Bioinfo In Silico Model. 5(2) 2016: 787-792
ABSTRACT: Metalloproteases from nine photosynthetic bacterial species were analyzed and presented in this communication. The composition of alanine, glycine and leucine, valine was high while low concentrations of asparagines, histidine, tyrosine, tryptophan, cysteine and lysine residues were seen. The composition of other aminoacids was intermediate. The numbers of negative charged residues are more than positively charged residues. The relative volume of protein occupied by aliphatic side chains was found to span within a range of 99 to 113. Secondary structural analysis of the metalloproteases showed the dominance of α-helices followed by random coils. Less number of beta strands was found in all metalloproteases. The sequences were aligned and PIM was identified with Clustal analysis. Swiss modeling was performed to predict the structure of the proteases.
KeyWords: Photosynthetic bacteria, metalloproteases, in silico analysis, Swiss modeling.
How to cite: Karli Geethanjali et al. Bioinformatics analysis of few Zinc metalloproteases from anoxygenic phototrophic bacteria. Int J Comput Bioinfo In Silico Model. 5(2) 2016: 793-798
ABSTRACT: Flowering locus is a key integrator whose induction leads to activation of flowering in plants. The flowering hormone florigen is a universal protein found in flowering plants. In S. tuberosum FLOWERING LOCUS acts as both ‘florigen’ and ‘tuberigen’ which initiates flowering and triggers tuber formation respectively. We have determined the homology model of FLOWERING LOCUS T from S. tuberosum. This structure confirms the StFT is a subset of phosphatidylethanolamine-binding proteins (PEBP) family as predicted from sequence homology. The model of StFT protein share a similar topology dominated by a large central β-sheet like PEBP's. The homology model of StFT was verified for their stereochemical quality and accuracy. The 3D structure was validated using Structure Validation Server in which 99.3% of residues present in the favoured regions of the Ramachandran Plot. A better understanding of the 3D structure of StFT is helpful in understanding the mechanisms underlying flowering in S. tuberosum and in designing mutagenesis strategy to understanding the possible manipulation for future improvement in potato.
KeyWords: phosphatidylethanolamine-binding protein; flowering locus; modelling; three dimensional; homology modeling.
How to cite: Ritu Singh et al. Homology modelling and Structural analysis of FLOWERING LOCUS T protein from Solanum tuberosum. Int J Comput Bioinfo In Silico Model. 5(2) 2016: 799-807
ABSTRACT: Mortality among HIV-infected individuals is much higher as the CD4 cell count declines. There is evidence that the CD4 cell count is a strong predictor of the subsequent risk of AIDS or death in HIV-infected patients. There is limited information regarding the application of statistical models to predict AIDS diseases progression using longitudinal CD4 counts. This study applied a semi-markov process to predict AIDS disease progression and death using longitudinal CD4 count measurements. A five-year retrospective study was conducted in Jimma University Specialized Hospital, Southwest Ethiopia. A total of 456 HIV-infected adult patients were enrolled in this study from 2005 to 2010. CD4 cell counts were measured every 6 months among HIV-infected study participants using FACS Count Machine (Becton Dickinson, San Jose, California, USA). CD4 count was classified as: state I (CD4 > 500 cells/mm3), state II (350 cells/mm3 < CD4 < 500 cells/mm3), state III (200 cells/mm3 < CD4 < 350 cells/mm3) and state IV (CD4 <=200cells/mm3) in order to estimate the transition probabilities from one state to another using a semi-markov chain concept. The last absorbing state is ‘death’. Discrete Time Homogeneous Semi Markov (DTHSM) model was used to predict the evolution of CD4 count in time. The number of death observed from the state I, II, III, and IV was 3, 4, 15, and 40 respectively during the study period. The probability of dying was increased from the worse transition states. The probability of being found in state I after started the treatment at any other working state is higher. Reliability plot revealed that the probability of surviving 200 month, from state I, state II, state III and state IV, estimated as 0.71, 0.68, 0.63 and 0.58 respectively. The probability of remaining at the starting CD4 count state was decreased when time increased, patients from the state I has higher probability to remain in the ART starting state. The survival probability of a patient depends on the seriousness of a disease that measured by CD4 cell count. Attention should be given for patients with lower CD4 count to reduce mortality. Including risk factors which accelerate or deaccelerate the transition of patients in different state should be considered in the future researches.
KeyWords: ART, CD4, HIV/AIDS, Semi-markov, Stochastic.
How to cite: Dinberu Seyoum et al. Predicting AIDS disease progression using longitudinal CD4 count among adult HIV/AIDS patients in Southwest Ethiopia: Application of semi-markov process. Int J Comput Bioinfo In Silico Model. 5(2) 2016: 808-814