Mobile genetic elements (MGEs) represent a large portion of the human genome. Its ability tochange their position within the genome has contributed to evolution, however, the same has alsoresulted in several mutations. Many of such mutations are known to cause exon skipping orpremature truncation that result in non-functional or dysfunctional protein, leading to cancer. Here,in this study we aim to determine the distribution of MGEs in cancer-associated genes as comparedto non-cancer associated genes. We curated a list of genes for both the categories and downloadedthe nucleotide sequences of these genes and ran on RepeatMasker to identify the MGEs in eachgene. All the data generated with respect to each gene was parsed and compared. The resultsshowed that the number and the sequence length covered by the identified MGEs in cancer-associated genes were comparatively high. The abundance of MGEs may be correlated with thehigh risk of deletion/insertion of large DNA segments in these genes, that results in higher risk ofcancer. Further studies need to be performed for better clarity on these associations.
Trypanosomiasis or Chagas disease is a disease quite prevalent in Central America and other South American countries. With the high rate of infection spread over 7 million people in America, along with high number of mortality rate, this disease requires a good preventive vaccine to get eradicated. And for the same, there has been research on various kind of proteins to find the right vaccine candidate. In this paper, we are evaluating the protein DnaJ Chaperone to find out if it is a potential vaccine candidate or not. For evaluation, we procure DnaJ Chaperone protein sequence from NCBI and run through the Vax-Elan pipeline to get the result values. These values are further compared with the prediction tools cut-off values (Prediction tool is a table of tools which has different parameters for deciding the vaccine candidacy). And based on the cut-off value of different tools, we get to find the potential vaccine candidate.
Background: Millions of people have been infected and thousands of people have died as a result of the COVID-19 pandemic. B.1.1.529 (Omicron) is a new variant of SARS-CoV-2, and on November 26th, WHO designated B.1.1.529 as a variant of concern. The search for an effective and appropriate drug to treat COVID-19 continues to be a major challenge. In this study, we look into whether a mycophenolic acid drug can be repurposed for COVID-19. Mycophenolic acid (MPA), the active immunosuppressive form of the prodrug mycophenolate mofetil (MMF), is a common component of immunosuppressive regimens for organ transplant recipients. Mycophenolic acid inhibits the coronaviral papain-like protease, and a deeper understanding of how it works could aid in the development of new anti-SARS-CoV-2 medicines.Methods: The CoV-DrugX pipeline contains 13 distinct sorts of modules that specify 13 different properties to explain whether this medicine can be repurposed for COVID-19 or not; these modules were built using several methodologies such as biological and chemical information, target-based, docking-based, symptom-based, target-based, and circuit-based. Based on an analysis of modules in the DrugX pipeline, we describe the effectiveness of mycophenolic acid for repurposing in COVID-19.Results and conclusions: We found that the mycophenolic acid had the highest binding affinity (-8.2 Kcal/Mol) with Nucleocapsid protein (Npro). Based on deep learning modules that utilize chemo-informatics properties, we reported that the mycophenolic acid drug had similar features to COVID-19. Mycophenolic acid interacted with COVID-19 targets as well, and it caused symptoms similar to COVID-19. The mycophenolic acid drug received a SI score of 7 (sum of all categorical values of all modules) and a Pi score of 0.56 (total executed tools run/SI score) from the DrugX pipeline. Mycophenolic acid received a score of 0 in four modules, a score of 1 in seven modules (100 percent), and a score between 0 and 1 in two modules out of a total of thirteen modules. Mycophenolic acid predicts a high score, indicating its potential repurposing for COVID-19.
Background:The novel coronavirus disease, COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused catastrophic effects resulting in over 5.6 million deaths worldwide as of February 2022 and approximately 3.6 million new cases have resulted. The traditional drug discovery methodology is a risky, lengthy and expensive process, and due to the urgency to discover new therapies and treatments, the drug repositioning strategy has been driven by its potential to identify compounds that could be used to treat the symptoms. Viral infection attracts attention.Method:It was discovered that the convolutional neural network (CNN) and its modified models were mainly used for COVID-19 pandemic prediction, whereas in the case of machine learning (ML), the support vector machine (SVM), and random forest (RF) was largely utilized for COVID-19 pandemic combat.Result:In the case of COVID-19, modern technologies such as AI and ML have been used effectively to identify remdesivir alongside other drugs to treat COVID-19. It has shown promise in treating COVID-19, prompting the FDA to issue emergency use authorization, although it is only limited to severe conditions. The FDA made this decision based on early research showing the drug could help speed recovery in hospitalized patients with COVID-19.
The crisis of the COVID-19 pandemic around the world has been devastating as many lives have been lost. There is an urgent need for the right therapeutic drug to control the disease. Drug development is a time-consuming process, hence the need to approach drug repurposing. Bafilomycin A1 is a drug that was used against many viruses hence used for the analysis using the developed pipeline. The drug which will be repurposed should be analyzed for its efficiency against the COVID-19. The CoV- DrugX Pipeline is developed for drug repurposing. The CoV-DrugX pipeline is available on (http://drugx.kamalrawal.in/drugx/) which integrates that should be considered for the repurposing of drugs against COVID-19. Bafilomycin A1 is a drug that was used against many viruses hence used for the analysis using the developed pipeline. The pipeline predicted and resulted in scores for the individual modules. The CoV-DrugX pipeline provides key parameters indicating the suitability of Bafilomycin A1 as a potential drug candidate for the treatment of COVID-19. The CoV-DrugX pipeline provides appropriate SI (sensitivity index) and PI (predictive index) scores which predicts that the drug Bafilomycin A1 has an efficiency of the drug repurposing.
Chagas' disease is anthropozoonosis caused by flagellated protozoan Trypanosoma cruzi. American trypanosomiasis earned the name in honor of Dr. Carlos Chagas, described in his work, in 1909, the etiologic agent of the disease, the evolutionary cycle of parasites and vectors, and clinical manifestations of the critical stage (Chagas, 1909). Chagas disease causes significant morbidity and mortality in Latin America. The disease has recently become a public health concern in non-endemic areas such as the United States and Europe. MASPs are glycosylphosphatidylinositol (GPI)-linked glycoproteins encoded by a multigene family with hundreds of members. MASPs are among the most abundant antigens set up on the exterior of the pestilent trypomastigote stage of T.cruzi, making them an alluring target to vaccine development. In this study, the assessment of the suitability of MASP for Chagas disease as a potential vaccine candidate is done with the application of VaxELAN and VaxiDL. The entire proteome of TC-CLB (T. cruzi CL Brener) was screened using a computational pipeline to identify sets of PVCs. Following that, the B-Cell and T-cell epitopes were analyzed for various parameters, and the highest-scoring ones were shortlisted based on their antigenicities. Cross-protection studies with other Trypanosoma species and strains were also conducted to investigate the cross-protection potential of the selected epitopes. Several vaccine constructs were created by combining the various shortlisted epitopes with appropriate adjuvant and linker sequences. These were then evaluated for their physicochemical properties and subjected to structural studies, codon optimization, and immune modulation studies. In summary, the suitability of the MASP is checked using VaxELAN and VaxiDL as a potential vaccine candidate for Chagas disease.
Immunoproliferative small intestine disease (IPSID) is a collective name for a range of diseases caused by various microorganisms but the major and persistent organism is Campylobacter Jejuni. IPSID can lead to minor symptoms like diarrhea, nausea, imbalance of electrolytes in the body etc. to major consequences that may lead to death in case of prolonged untreated condition. IPSID leads to infiltration of lymphocytes as a consequence of an immune response to invasion by microbes, which eventually leads to the evolvement of IgA producing bodies and to the selection of a body that produces α heavy chains. Hence, it is also called “α- Heavy chain disease”. Until now there has been no successful development of a vaccine for this disease. N-acetylmuramoyl-L-alanine amidase is one of the proteins in Campylobacter Jejuni ssp. Jejuni which is also a Potential vaccine candidate (PVC) against IPSID as identified by Vaxigen. Here, we are utilizing deep learning softwares i.e, Vaxi-DL and VaxELAN for analyzing the given protein in terms of adhesion, secretory nature, trans-membrane helices, cleavage sites, MHC-I binding, CTL epitope prediction, essential genes, molecular weight, non-bacterial pathogen, non-homology with human genome, virulence factors, allergenicity, cellular localization and probability of being a PVC.
Despite mass level vaccinations and the launch of several repurposed drugs, the recently emerged SARS CoV-2 Omicron (B.1.1.529) is a variant of concern. New drugs must be discovered with artificial intelligence (AI) assistance. Artificial intelligence (AI) enabled drug repurposing reduces the time and costs of drug discovery. Ruxolitinib (formerly known as INCB018424; Jakavi; Jakafi) is an oral inhibitor of JAK 1 & 2. Ruxolitinib has been approved to treat primary myelofibrosis, polycythemia vera, and hemophagocytic lymphohistiocytosis by the Food and Drug Administration and European Medicines Agency. The analysis of ruxolitinib is done via the CoV-DrugX pipeline. We find that the Ruxolitinib has a 75% probability of being considered a COVID-19 repurposed drug with the help of the CoV-DrugX pipeline. In addition, there is a clinical trial assessing the safety and efficacy of ruxolitinib. Therefore, we believe that Ruxolitinib could be a potent drug against the treatment of COVID-19.
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