Epitope-Based Immunoinformatics and Molecular Docking Studies of Nucleocapsid Protein and Ovarian Tumor Domain of Crimean–Congo Hemorrhagic Fever Virus

Crimean–Congo hemorrhagic fever virus (CCHFV), the fatal human pathogen is transmitted to humans by tick bite, or exposure to infected blood or tissues of infected livestock. The CCHFV genome consists of three RNA segments namely, S, M, and L. The unusual large viral L protein has an ovarian tumor (OTU) protease domain located in the N terminus. It is likely that the protein may be autoproteolytically cleaved to generate the active virus L polymerase with additional functions. Identification of the epitope regions of the virus is important for the diagnosis, phylogeny studies, and drug discovery. Early diagnosis and treatment of CCHF infection is critical to the survival of patients and the control of the disease. In this study, we undertook different in silico approaches using molecular docking and immunoinformatics tools to predict epitopes which can be helpful for vaccine designing. Small molecule ligands against OTU domain and protein–protein interaction between a viral and a host protein have been studied using docking tools

Implementation of Integrated Learning Program in neurosciences during first year of traditional medical course: Perception of students and faculty

<p>Abstract</p> <p>Background</p> <p>Our college introduced an integrated learning program (ILP) for first year undergraduates with an aim to develop, implement and evaluate a module for CNS in basic sciences and to assess the feasibility of an ILP in phase I of medical education in a college following traditional medical curriculum.</p> <p>Methods</p> <p>The idea of implementing ILP for Central Nervous System (CNS) in phase one was conceived by curriculum development committee drawn from faculty of all phases. After a series of meetings of curriculum development committee, inputs from basic science and clinical departments, a time table was constructed. Various teaching learning methods, themes for integrated didactic lectures, case based learning and clinical exposure were decided. Basic science faculty were made to participate actively in both case based learning and hospital visits along with clinical experts. The completed program was evaluated based on structured questionnaire.</p> <p>Results</p> <p>Sixty percent students rated the program good to excellent with reference to appreciation, understanding and application of basic science knowledge in health and disease. Seventy eight percent felt that this program will help them perform better in later days of clinical training. However sixty percent students felt that ILP will not help them perform better at the first professional examination. Seventy two per cent of faculty agreed that this program improved understanding and application of basic science knowledge of students. Ninety percent of faculty felt that this program will help them perform better in later days of clinical training.</p> <p>Conclusion</p> <p>The adoption of present integrated module for CNS and the use of multiple teaching learning methods have been proven to be useful in acquisition of knowledge from the student satisfaction point of view. Students and faculty expressed an overall satisfaction towards ILP for CNS. The study showed that it is possible to adopt an integrated learning module in the first year of medical course under a conventional curriculum.</p

Comparative Genome analysis of Plasmodium sp. and identification of unique signature with Next Generation Sequencing Technology

Malaria is a malignant disease which is growing all over the world and its causative agent. Plasmodium species easily develops resistant to commonly used antimalarial drugs easily. These empower different strains of Plasmodium e.g. Plasmodium falciparum and Plasmodium vivax to infect humans with malaria. To get the deeper molecular insights, next generation sequencing data were used for further analysis as it has shifted the paradigm of genomics to address biological questions with high confidence and in timely manner. The short reads for above mentioned parasites were retrieved from SRA (Sequence read archive) and de novo assembly was performed. Several novel genes along with known genes were predicted from assembled contigs, Functional annotation followed by gene ontology and pathway analysis. Comparison between species gave structural and functional diversity of the specific genes responsible for disease condition which further can be studied for disease biology

2D-QSAR ANALYSIS OF DIHYDROFOLATE REDUCTASE (DHFR) INHIBITORS WITH ACTIVITY IN TOXOPLASMA GONDII AND LACTOBACILLUS CASEI

ABSTRACT: Methotrexate (MTX), an inhibitor of Dihydrofolate reductase (DHFR), is a well known drug given in the treatment of rheumatoid arthritis (RA). Due to its potential neurotoxicity, the patient has to discontinue the chemotherapy. In the present study, DHFR inhibitors which were structurally similar to MTX and had reported biological activity in model organisms such as Toxoplasma gondii and Lactobacillus casei was considered. A 2D-QSAR was modeled based on certain topological and constitutional descriptors along with its biological activity and found best 5 inhibitory molecules. in vitro validation of this inhibitors will be an alternative for effective drug development against RA

Evaluating changes in treeline position and land surface phenology in Sikkim Himalaya

Mountain ecosystems of the Himalaya have warmed more rapidly in recent decades than other areas of the globe. Landscape-level delineation of treeline position and its dynamics can give insight regarding climatic variability. Landsat-2 Multispectral Scanner (MSS), Resourcesat-2 Linear Imaging Self Scanning Sensor (LISS-III) and National Oceanic and Atmospheric Administration- Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived normalized difference vegetation index (NDVI) was used to study the long-term treeline dynamics. The treeline has shifted vertically 301 ± 66 m upward in 37 years at a rate of c. 81 m decade−1. The minimum air temperature has increased at a rate of 0.3 °C decade−1 (p < 0.001) depicting the favourable scenario for treeline growth in this temperature limited ecosystem. The annual cumulative precipitation has decreased at a rate of 206.5 mm decade−1. The length of growing season (LOS) has increased and the start (SOS) and end of growing season (EOS) has got earlier in the 1977 treeline. The elevation shifts and phenological changes of the treeline were observed in the warming scenario

Identification of Potential Binders of the SARS-Cov-2 Spike Protein via Molecular Docking, Dynamics Simulation and Binding Free Energy Calculation

The pandemic outbreak of COVID-19 virus (SARS-CoV-2) has become critical global health issue. The biophysical and structural evidence shows that SARS-CoV-2 spike protein possesses higher binding affinity towards angiotensin-converting enzyme 2 (ACE2) and hemagglutinin-acetylesterase (HE) glycoprotein receptor. Hence, it was selected as a target to generate the potential candidates for the inhibition of HE glycoprotein. The present study focuses on extensive computational approaches which contains molecular docking, ADMET prediction followed by molecular dynamics simulations and free energy calculations. Furthermore, virtual screening of NPACT compounds identified 3,4,5-Trihydroxy-1,8-bis[(2R,3R)-3,5,7-trihydroxy-3,4-dihydro-2H-chromen-2-yl]benzo[7]annulen-6-one, Silymarin, Withanolide D, Spirosolane and Oridonin were interact with high affinity. The ADMET prediction revealed pharmacokinetics and drug-likeness properties of top-ranked compounds. Molecular dynamics simulations and binding free energy calculations affirmed that these five NPACT compounds were robust HE inhibitor.</p

Pharmacophore-similarity-based QSAR (PS-QSAR) for group-specific biological activity predictions

<div><p>Recent technological breakthroughs in medicinal chemistry arena had ameliorated the perspectives of quantitative structure–activity relationship (QSAR) methods. In this direction, we developed a group-based QSAR method based on pharmacophore-similarity concept which takes into account the 2D topological pharmacophoric descriptors and predicts the group-specific biological activities. This activity prediction may assist the contribution of certain pharmacophore features encoded by respective fragments toward activity improvement and/or detrimental effects. We termed this method as pharmacophore-similarity-based QSAR (PS-QSAR) and studied the activity contribution of fragments from 3-hydroxypyridinones derivatives possessing antimalarial activities.</p></div

The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models

<p>The estimation of atomic partial charges of the small molecules to calculate molecular interaction fields (MIFs) is an important process in field-based quantitative structure–activity relationship (QSAR). Several studies showed the influence of partial charge schemes that drastically affects the prediction accuracy of the QSAR model and focused on the selection of appropriate charge models that provide highest cross-validated correlation coefficient ( or <i>q</i>^<i>2</i>) to explain the variation in chemical structures against biological endpoints. This study shift this focus in a direction to understand the molecular regions deemed to explain SAR in various charge models and recognize a consensus picture of activity-correlating molecular regions. We selected eleven diverse dataset and developed MIF-based QSAR models using various charge schemes including Gasteiger–Marsili, Del Re, Merck Molecular Force Field, Hückel, Gasteiger–Hückel, and Pullman. The generalized resultant QSAR models were then compared with Open3DQSAR model to interpret the MIF descriptors decisively. We suggest the regions of activity contribution or optimization can be effectively determined by studying various charge-based models to understand SAR precisely.</p

A multiparametric organ toxicity predictor for drug discovery

The assessment of major organ toxicities through in silico predictive models plays a crucial role in drug discovery. Computational tools can predict chemical toxicities using the knowledge gained from experimental studies which drastically reduces the attrition rate of compounds during drug discovery and developmental stages. The purpose of in silico predictions for drug leads and anticipating toxicological endpoints of absorption, distribution, metabolism, excretion and toxicity, clinical adverse impacts and metabolism of pharmaceutically active substances has gained widespread acceptance in academia and pharmaceutical industries. With unrestricted accessibility to powerful biomarkers, researchers have an opportunity to contemplate the most accurate predictive scores to evaluate drug's adverse impact on various organs. A multiparametric model involving physico-chemical properties, quantitative structure-activity relationship predictions and docking score was found to be a more reliable predictor for estimating chemical toxicities with potential to reflect atomic-level insights. These in silico models provide informed decisions to carry out in vitro and in vivo studies and subsequently confirms the molecules clues deciphering the cytotoxicity, pharmacokinetics, and pharmacodynamics and organ toxicity properties of compounds. Even though the drugs withdrawn by USFDA at later phases of drug discovery which should have passed all the state-of-the-art experimental approaches and currently acceptable toxicity filters, there is a dire need to interconnect all these molecular key properties to enhance our knowledge and guide in the identification of leads to drug optimization phases. Current computational tools can predict ADMET and organ toxicities based on pharmacophore fingerprint, toxicophores and advanced machine-learning techniques