International Journal of Computational Biology and Drug Design (11 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.
In Silico Neuroprotective Properties of Volatile Constituents of Grape (Vitis vinifera L.) Seed Extract Against Parkinsons Disease
by Venkatramanan Varadharajan
Abstract: Aggregation of ?-synuclein is one of the significant factors in the pathogenesis of Parkinsons disease. Many natural extracts demonstrate neuroprotective activity against PD by inhibiting ?-synuclein aggregation. In India, the grape extract is traditionally used as a brain tonic to boost memory power. Hence in this study, the neuroprotective activity of grape seed extract against Parkinsons disease under in silico conditions was investigated. Molecular docking studies indicated that volatile constituents of ethanolic extract of grape seed could bind to both C-terminal and N-terminal regions of ?-synuclein with more preference towards the formation of hydrogen bonds than hydrophobic interactions. Also, the compounds most commonly interacted with the residues Val40, Lys43 and Lys45 of N-terminal to form both hydrogen bonds and hydrophobic interactions. Among the compounds studied, Dasycarpidan-1-methanol, acetate (ester) molecule showed superior binding affinity, Blood-Brain Barrier penetration, drug-likeness and lead-likeness properties.
Keywords: Parkinson’s disease; ?-synuclein; Grape seed extract; Volatiles; Molecular docking; Neuroprotective properties; in silico; Interaction; Blood-Brain Barrier; Drug-likeness.
Identification of potential anti-obesity drug scaffolds using molecular modeling
by Amie Jobe, Bincy Baby, Amanat Ali, Ranjit Vijayan
Abstract: The prevalence of obesity has remarkably increased in recent decades. An important strategy to combat obesity is to reduce the imbalance between energy intake and expenditure. Pancreatic lipase (PL) and acetyl coenzyme A carboxylase 2 (ACC2) are two promising targets for therapeutic treatment of obesity. In silico techniques including high throughput virtual screening, binding free energy calculations, and molecular dynamics (MD) simulation were used to identify molecules with good potential to inhibit these targets. Derivatives of coumaran-3-one and dioxabicyclo[3.3.0]octane-2,6-diamine are likely to possess inhibitory potential against PL while acetamide and hexanamide derivatives showed inhibitory potential against ACC2. MD simulations of the top scoring molecules confirmed that the identified molecules bind strongly and consistently in the binding site of PL and ACC2. The shortlisted molecules exhibited better interactions and affinity when compared to control molecules and thus could be explored as scaffolds for the development of anti-obesity drugs.
Keywords: obesity; pancreatic lipase; acetyl coenzyme A carboxylase 2; molecular dynamics; molecular docking.
The Spreading of Covid-19 in India and its impact A Mathematical Analysis
by Bibhatsu Kuiri, Bubai Dutta, Saikat Santra, Paulomi Mandal, Khaleda Mallick, Ardhendu S. Patra
Abstract: The rapid spreading of the coronavirus in India and its behavior for the near future has been studied and analyzed as accurately as possible using the SEIR model as a fundamental tool. The official covid-19 data of infected and death cases in India up to 10th October, 2020 have been considered as raw data. The value of various parameters of the model is optimized by feeding the raw data in the simulation model. The various parameters are defined as infection rate, basic reproduction number, death rate, recovery time, exposure time, and other parameters to optimize the best fit model. The total population of India is considered 1.36 billion people. The simulation results that the number of recovered people will be 2.8X10^8 and number of deaths will be 4.2X10^6 after 800 days for the total population of India. In an ideal scenario, at the end of the pandemic total death count is expected to be of the order of 10^6 which is a big challenge.
Keywords: Coronavirus (Covid-19); Simulation; SEIR model; India.
Interaction Network of Insulin Resistance Proteins with Organo-Phosphorus and Chlorine Pesticides
by Amitha Joy, S. Balaji, Md.Afroz Alam
Abstract: The work focuses on deciphering the underlying mechanism behind insulin resistance through exposure to pesticides. The selected organochlorine and organophosphorus pesticides were analyzed for their interactions with protein targets having a regulatory role in glucose metabolism and the insulin signaling pathway. Their binding affinities were understood based on the docking studies using AutoDock. Nine pesticides gave the minimum binding energy values ranging from -5.17 to -9.79 kcal/mol. An interaction protein network and pesticide network was generated. The merged network, an interaction network showing the binding affinities of pesticides with protein targets is also generated using Cytoscape. An understanding of the molecular interactions between pesticides and various protein targets can help in designing new lead molecules to treat pesticide-driven insulin resistance.
Keywords: pesticides; insulin resistance; targets; docking; organochlorine; organophosphate; STITCH.
A New Edge Effect Correction for Sequence Alignment
by Amirhossein Karami, Afshin Fayyaz Movaghar
Abstract: The edge effect correction is an appropriate way to improve the sequences alignment. In this paper, the edge effect correction is applied to the h−tuple method. Then, the results of the corrected h−tuple methodrnare compared with the ones based on the extreme value theory in a real database. The Receiver Operating Characteristic (ROC) curve reveals that the corrected h−tuple method has an advantage over another one.
Keywords: Edge Effect Correction; h−tuple method; Extreme Value Theory; Sequence Alignment; Local Score.
Identification and optimization of novel selective inhibitors against human Regulator of G protein signaling 2 (RGS2) protein for type 2 diabetes mellitus: An in silico approach
by Goverdhan Lanka, Revanth Bathula, Mahendar Dasari, Manan Bhargavi, Sarita Rajender Potlapally
Abstract: Regulator G protein Signaling 2 (RGS2) protein negatively modulates the GPCR signaling pathway thereby causing type 2 diabetes and is considered as potential drug target against type 2 diabetes mellitus for the identification of potential inhibitors as drug candidates. In this present work, the 3D homology model of RGS2 protein was constructed using MODELLER9.9 software to understand its structural and functional features. The energy optimization of RGS2 was carried out by NAMD-VMD interface followed by validation of the 3D model for the reliability assessment with standard validation protocols. The potential binding site of RGS2 protein was identified by literature studies, manual correlation technique (Ligplot), and SiteMap tools. The structure-based virtual screening was carried out using the Asinex-signature library of 21859 minimized small molecules at the RGS2 active site by GLIDE module of Schrodinger suite for the hit identification. A total of 14 molecules were identified as final hits and are prioritized using glide score, glide energy, and intermolecular interactions. The potential leads were identified by optimization of 14 hit molecules using AutoDock (binding energies), Prime-MM/GBSA, percent human oral absorption, and ligand-residue interactions of RGS2-ligand complexes. The drug-likeness of newly identified lead inhibitors was verified through ADME assessment by QikProp module and all ligands molecules were showed permissible ranges of pharmacokinetic properties. The comparative binding affinity study of current drugs of diabetes to that of newly identified leads was carried out to justify the potency of new leads against RGS2 protein. Thus, the results of the present study may provide the potential strategy in the identification of new selective chemical entities as potential type 2 diabetes mellitus drug candidates.
Keywords: RGS2; NAMD-VMD; Virtual Screening; Prime MM/GBSA; AutoDock; Type 2 diabetes mellitus.
Genes to Drug: An in-silico approach to design a drug for Huntington disease (HD) in Homo sapiens
by Sachin Kumar, Shivalika Panwar, Manoj Kumar Sharma, Manoj Kumar Sharma
Abstract: The current study is carried out using in silico approach to find some potent drug molecule against Huntington's disease. Mutant Huntingtin protein (HTT), HTT-interacting protein 1 (HIP-1) were used as the target protein for drug designing. Swiss PDB Viewer (SPDBW) was used for active site determination. Ligands were prepared through MolinspirationCheminformatics following Lipinski rule of five (RO5). Bioinformatics software Molegro Virtual Docker (MVD) and HEX were used for docking between the ligands and target proteins.
Eleven ligands were prepared and compared with drugs based on MolDock score and hydrogen bond score. Four ligands - named [1-benzyl-6bromo-8-((4-ethyl-2hydroxy-2H- pyran-6y1)methyl)-4-hydroxy-7,8dihydro-1,8-naphthydrin-2(1H)-one], [4-((3-amino- 5bromocyclohexyl)methoxy)-5-butyl-6hydroxydechydronaphthalene-2-carboxylic acid], [3- amino-2-chloro-13-(2-hydroxybutyl)-5-nitro-2,3-dihydro-7H-benzo[c][1,4]dioxino[2,3- h]xanthen-7-one] and [3-(3-bromo-[1,1-bi(cyclohexan)]-3-y1)-6-(4-methoxy-4- oxobutanamido)hexanoic acid] respectively with most appropriate values as -731.31 for MolDock score and -33.51 for H-Bond score are found to be potential drug candidate for Huntington's Disease.
Keywords: Huntington's Disease; HIP-1; HTT; ligands; neurodegenerative disorder; Haloperidol; GSK356278.
Bottomhat Kernel Analysis based on Different Shape and Size using Colonies Extraction for Counting of Bacterial Colonies in Petri Dish
by Fitri Utaminingrum, Abdurrohman Hidayat
Abstract: Research involving microorganisms such as bacteria mostly were done in many areas such as health, medicine, biology, chemistry, and another particle science. The counting process to calculate the number of bacteria in the petri dish is manually using the colony counter. It is possible to obtain error calculation because of human fatigue, inaccuracy, and neglect. Counting the number of bacterial colonies is one of the crucial steps in microbiology testing. The manual process is time-consuming, labor-intensive, and imprecise. An automatic system counted off the number of microbial colonies by using digital image processing is proposed. The proposed method aims to calculate the number of bacterial colonies more quickly, precisely, and effectively. Comparisons show that the proposed method can provide excellent F-measure with the mean value of F-measure is 93.02%. An embed-ready program is implemented in the embedded system to do the automatic bacterial colony counting. This system could give contribution value to the world of microbiology.
Keywords: colony extraction; colony counting; petri dish; bacterial colonies.
A novel formulation with potential for improving osteoarthritis mediated through COX2 and MMP9 receptors
by Mohit M. Jain, Geeta Rai
Abstract: Osteoarthritis is the most common form of arthritis, affecting the synovial joints and specifically characterized by loss of cartilage within the joint. The present line of treatment mostly includes non-steroidal anti-inflammatory drugs which have limited advantage and high associated cytotoxicity. In this study, we evaluated a formulation joint joy containing ingredients from natural sources like Withania somnifera, Curcumin-C3-complex along with Glucosamine and Chondroitin Sulfate, in-silico for mechanism involved in improvement of osteoarthritis. We conducted molecular docking and ADMET analysis of these compounds on COX-2R and MMP-9 receptors, which are implicated in inflammation and joint degradation and are also key therapeutic targets for drugs against joint degradation. Binding free energy was calculated and information on hydrogen bonds, interacting residues and drug-likeness properties were generated. Molecular docking and pharmacokinetics analysis showed docking scores of joint joy ingredients comparable to that of reference drug celecoxib, suggesting that the formulation has a potential to improve the joint pain and inflammation of osteoarthritis patients.
Keywords: Joint Joy; COX-2R; MMP-9R; Molecular docking; ADMET analysis.
IN SILICO characterization and phylogenetic analysis of Arabidopsis homologues of the human antiretroviral SERINC proteins
by Siarhei Dabravolski, Yury Kavalionak
Abstract: Serine incorporator (SERINC) family proteins are well-known for their antiretroviral properties strong inhibition of the HIV-1 replication in human T-cells. SERINC proteins are highly conserved in all eukaryotes, representing 10-12 transmembrane domains. In the present study, for the first time, five SERINC homologues from the model plant Arabidopsis thaliana were analysed. We primarily focused on the comprehensive computational, structural and functional characterization, phylogeny reconstruction. Overall, our analyses revealed an evolutionally conserved nature of the plant serinc domain-containing proteins, sharing physiochemical and structural properties with yeast and human homologues. The study found that Arabidopsis SERINC proteins are divided into cytoplasmic and secreted/chloroplast groups, distinguished by the physiochemical properties, localization and phylogeny. Analysis of the protein-protein interaction databases suggests that SERINC proteins could play an important regulatory and physiological role in plant metabolism. Further investigation on the SERINC proteins on plants will help to define their role in plant development, exact biochemical function and affected physiological processes. Also, it would enhance our comprehension of the evolutionally conserved mechanisms of antiretroviral protection, shared between yeast, plants and human. Potentially, plant serinc domain-containing proteins could be used as a model to study antiretroviral response. Such knowledge could provide important insight into the creation of the effective antiretroviral vaccine for medical use.
Keywords: serinc; retrovirus; HIV; Arabidopsis; in silico; phylogeny.