International Journal of Computational Biology and Drug Design
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International Journal of Computational Biology and Drug Design (5 papers in press)
Importance of safety maintenance of the survived with recent former infection experience during a pandemic syndrome episode: A Study by Difference Equation Approach by Subhasis Bhattacharya, Suman Paul, Sudip Mukherjee Abstract: During the outbreak of a highly infectious disease conceded by a virus, handling of healthcare catastrophe is the most momentous part. Any type of known or unknown relaxation may generate enormous loss in terms of population. Present study consider the concern that survived one who has some fresh former infection history can be fingered with appropriate care throughout the syndrome period otherwise a huge harm can be advent by the state. The study follow difference equation modelling considering two aspects where the survived with former infection history handled with care and not reckoned as a part of sustained population and the other is they encompassed with the general population category. The study considers an example of a hypothetical state with some give infection rate, death rate and quarantine rate. By using R- programme language the study observes that proper care for such group of population is very significant to reduce the situation like human loss. Keywords: Infectious disease; SARS-CoV-2; 2019-nCov; Difference Equation; Survived from the infected; Quarantine rate; Death Rate.
Generation of 2D-QSAR and pharmacophore models for fishing better anti-leishmanial therapeutics by Clayton Fernando Rencilin, Joseph Christina Rosy, Krishnan Sundar Abstract: Leishmaniasis, a life-threatening tropical disease, is caused by 20 different species of Leishmania parasites. The disease, that is endemic in nearly 100 countries, contributes to millions of deaths each year. However, very few anti-leishmanial compounds are available in the market and that too possess many drawbacks. Hence, the therapeutic arsenal requires potential and novel anti-leishmanial compounds to treat Leishmaniasis. In the present study, Quantitative Structure Activity Relationship (QSAR) model and Pharmacophore model were developed with a set of anti-leishmanial compounds collected from literature and commercial anti-leishmanial drugs. Novel compounds matching the Pharmacophore was screened which can be potentially be used to treat Leishmaniasis. The compounds were segregated into training and test sets and, QSAR models were developed by Multiple Linear Regression (MLR) method using EasyQSAR. Further, pharmacophore model was derived by physio-chemical features of the selected compounds using PharmaGist. Using MLR, different QSAR models were developed using various molecular descriptors. Further, the percentage contribution of descriptors on each model was studied. The models were validated using the test sets with statistical measures. A ligand-based pharmacophore model was developed using active compound as template. The pharmacophore model was used for searching the purchasable compound dataset of ZINC database for matching compounds. The theoretical activities, ADME and drug likeness properties of top compounds were analyzed. Thirteen novel, readily purchasable compounds were obtained from this approach, which shows good predicted activity, ADME and druglikeness. These compounds can be regarded as potential candidates to be developed as novel anti-leishmanial drugs with improved activity and reduced side effects. Keywords: Antileishmanial compounds; descriptor; pharmacophore; ZINCPharmar; Pharmacophore search and QSAR.
Computational Approach against Dengue Virus Type 2 Nonstructural Protein (NS1) form using Hepatoprotective Plant Secondary Metabolites by Krishn Kumar Agrawal, Yogesh Murti Abstract: Background: Dengue virus (DENV) causes dengue fever, dengue hemorrhagic illness, and dengue shock syndrome.
Objective: This investigation was intended to evaluate secondary metabolites of hepatoprotective plant against DENV type 2 nonstructural protein (NS1) formutilizing a molecular docking method.
Methodology: The three dimensional structure ofDENV NS1form was fetched from the protein data bank. The ligands structure were fetched from the Pubchem data base in sdf format and converted in to mol2 format using OpenBabel. Finally the docking was performed by using iGEMDOCK software tool.
Result & Discussion: The binding energy of kaempferol-3-O-rutinoside, lithospermic acid, hesperidin and rutin were found to be -108.73, -108.59, -103.72, and -102.5 kcal/mol respectively.
Conclusion: On the basis of result of the present research, DENV protein inhibitors may now be identified by using this knowledge, and some have already been identified. Clinical trials are being conducted to verify the result of in-silico studies. Keywords: Dengue virus; kaempferol-3-O-rutinoside; NS1; computational approach; in-silico.
Determination of relationships among cancer related genes using Bayesian Networks by Michael Kofi Ahenkan, Emmanuel S. Adabor, Kwaku F. Darkwah Abstract: The invention of Deoxyribonucleic acid (DNA) micro-array technologies has been a major breakthrough in biomedical research. It serves as a systematic means of extracting data of several gene expressions for further analysis to understand complex biological processes. For instance, network of relationships among cancer related genes can be constructed from high throughput datasets obtained by these technologies. Inferring such relationships in networks provide biological insights into the etiology of cancer. However, modeling such biological networks is challenged by nature of data and complexities of relationships among biological variables such as genes. While it is desirable for the methods of modeling to handle randomness in experimental data, they are also expected to convey the dynamic interactions among genes that accompany the diseases. In respect of these, Bayesian Network, a probabilistic graphical modeling technique, is applied to predict relationships among genes accompanying cancer. Particularly, genomic data obtained from previous studies was analyzed to decipher the new gene regulatory relationships in cancer using Bayesian Networks. The performance of the methods were assessed by standard metrics such as sensitivities and specificities. Furthermore, in order to validate the predictions and verify the reliability of the novel relationships among genes mined from the data, some of the results of the Bayesian Networks were examined with experimentally confirmed relationships found by previous research. Interestingly, some predicted regulatory relationships among the cancer genes were also found in the literature. These enhance confidence in the newly predicted network of regulatory relationships which could become hypotheses for further research. Thus, this paper identifies new relationships among genes to provide insights into cancer that will eventually advance efforts to finding new targeted therapies in complex diseases such as breast cancer. Keywords: gene relationships; Bayesian network; search techniques; cancer.
Modelling and Molecular dynamics simulation of novel anticancer ligand for restructuring mutant P53 into wild type by Ashik Chhetri, Moloy Roy, Aditi Gangopadhyay, Achintya Saha, Puja Mishra, Amit Kumar Halder, Souvik Basak Abstract: Mutant p53 is one key factor of cancer or cellular onchogenesis as mutant P53 losses its functions due to unfolding of its loos, losing its dynamics, DNA-protein interaction and thus losing its function. Thus one of the key strategies of developing anti-cancer drugs may be to design leads that can revert mutant P53 to wild type P53. In this regard, we have designed two nitrogen based heterocyclic ligands that are computationally revealed to revert mutant P53 to wild type. In addition, molecular dynamics simulation revealed stable complex of the ligands with mutant P53, conformational flexibility of the ligand with the binding cleft and the loop and structural reversal of the mutant P53 into protein that mimic wild type P53. We claim these may be the promising lead of broad spectrum anticancer drugs since it has the potential for functional reversal of mutant P53 in carcinogenic cells. Keywords: Mutant P53; LEA3D; Genetic Algorithm; Molecular Dynamics; Binding Cleft.