The emerging monkeypox virus (MPXV) is a zoonotic orthopoxvirus that causes infections in humans similar to smallpox. Since May 2022, cases of monkeypox (MPX) have been increasingly reported by the World Health Organization (WHO) worldwide. Currently, there are no clinically validated treatments for MPX infections. In this study, an immunoinformatics approach was used to identify potential vaccine targets against MPXV. A total of 190 MPXV-2022 proteins were retrieved from the ViPR database and subjected to various analyses including antigenicity, allergenicity, toxicity, solubility, IFN-γ, and virulence. Three outer membrane and extracellular proteins were selected based on their respective parameters to predict B-cell and T-cell epitopes. The epitopes are conserved among different strains of MPXV and the population coverage is 100% worldwide, which will provide broader protection against various strains of the virus globally. Nine overlapping MHC-I, MHC-II, and B-cell epitopes were selected to design multi-epitope vaccine constructs linked with suitable linkers in combination with different adjuvants to enhance the immune responses of the vaccine constructs. Molecular modeling and structural validation ensured high-quality 3D structures of vaccine constructs. Based on various immunological and physiochemical properties and docking scores, MPXV-V2 was selected for further investigation. In silico cloning revealed a high level of gene expression for the MPXV-V2 vaccine within the bacterial expression system. Immune and MD simulations confirmed the molecular stability of the MPXV-V2 construct, with high immune responses within the host cell. These results may aid in the development of experimental vaccines against MPXV with increased potency and improved safety.

Human multidrug resistance protein 1 (hMRP1) is an important member of the ATPbinding cassette (ABC) transporter superfamily. It can extrude a variety of anticancer drugs and physiological organic anions across the plasma membrane, which is activated by substrate binding, and is accompanied by large-scale cooperative movements between different domains. Currently, it remains unclear completely about how the specific interactions between hMRP1 and its substrate are and which critical residues are responsible for allosteric signal transduction. To the end, we first construct an inward-facing state of hMRP1 using homology modeling method, and then dock substrate proinflammatory agent leukotriene C4 (LTC4) to hMRP1 pocket. The result manifests LTC4 interacts with two parts of hMRP1 pocket, namely the positively charged pocket (P pocket) and hydrophobic pocket (H pocket), similar to its binding mode with bMRP1 (bovine MRP1). Additionally, we use the Gaussian network model (GNM)-based thermodynamic method proposed by us to identify the key residues whose perturbations markedly alter their binding free energy. Here the conventional GNM is improved with covalent/non-covalent interactions and secondary structure information considered (denoted as sscGNM). In the result, sscGNM improves the flexibility prediction, especially for the nucleotide binding domains with rich kinds of secondary structures. The 46 key residue clusters located in different subdomains are identified which are highly consistent with experimental observations. Furtherly, we explore the long-range cooperation within the transporter. This study is helpful for strengthening the understanding of the work mechanism in ABC exporters and can provide important information to scientists in drug design studies.binding mode, Gaussian network model, hMRP1, key residues, thermodynamic cycle | INTRODUCTIONMultidrug resistance (MDR) to chemotherapy is a major obstacle in the treatment of cancer patients. Human multidrug resistance protein 1 (hMRP1), a member of the ATP-binding cassette (ABC) transporter superfamily, can export a wide variety of anticancer drugs such as vincristine, etoposide, anthracyclines, methotrexate, and physiological organic anions antioxidants GSH and proinflammatory agent leukotriene C4 (LTC4) across the cell membrane utilizing the energy from ATP binding and hydrolysis. 1 Studies have shown that the process of substrate transport is accompanied by large-scale cooperative motions between spatially separated subdomains. 2 Thus, there must exist a network of key residues mediating long-range signal transmission and allosteric coupling. 2,3 Identification of key functional residues is very important not only for an "in-depth" understanding of the mechanism of signal transmission but also for the structure-based drug design.

Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HpVs. Study on protein interaction maps of pathogens with their host is a recent trend in 'omics' era and has been practiced by researchers to find novel drug targets. in current study, we construct an integrated protein interaction map of HpV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HpV infections. it can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies. Human papilloma virus (HPV) is associated with approximately 5% of all human cancers affecting 0.6 million people worldwide with cervical, anal, oropharyngeal, penile and vulvovaginal cancers 1-3. Among these cancers, cervical cancer ranks 4th in affecting women worldwide 4 while in developing countries it ranks second 5. According to World Health Organization (WHO) current factsheets, there are more than 100 genotypes of HPV, out of which 14 strains are high-risk. The most talked about high-risk HPV strains are HPV 6, 11, 16, 18, 31, 33, 35, 45, 52 and 58 with type 16 and 18 responsible for 70% of cervical cancer cases 6-8. HPV is a serious threat to human life and it is causing 250,000 deaths annually, among which 85% of cases are occurring in low and middle-income countries 9. HPV is a small ~8 kb in size, non-enveloped circular dsDNA virus 5,10. The HPV genome encodes 8 proteins among which 2 are structural viral capsid proteins (L1 and L2) while 6 are non-structural viral proteins (E1, E2, E4, E5, E6, E7) 10,11. Besides these 8 proteins, there are a few other macromolecules found in literature which are actually the transcripts made by the fusion of two existing HPV proteins. E8∧E2, a transcript, is created by the fusion of E8 with carboxy terminal of E2 12 , and E1∧E4 is generated by the fusion of E1 to the Open Reading Frame (ORF) of E4 13. Protein interaction network provides a plethora of information when it comes to virus-host relationship because viruses entirely depend upon the host factors for their survival 14,15. Viruses tend to regulate host biological processes by manipulating its cell proteome. Researchers have been using network biology for designing novel antiviral drug therapies 16. ...

Background Hepatitis C Virus is becoming a major health problem in Asia and across the globe since it is causing serious liver diseases including liver cirrhosis, chronic hepatitis and hepatocarcinoma (HCC). Protein interaction networks presents us innumerable novel insights into functional constitution of proteome and helps us finding potential candidates for targeting the drugs. Methods Here we present a comprehensive protein interaction network of Hepatitis C Virus with its host, constructed by literature curated interactions. The network was constructed and explored using Cytoscape and the results were further analyzed using KEGG pathway, Gene Ontology enrichment analysis and MCODE. Results We found 1325 interactions between 12 HCV proteins and 940 human genes, among which 21 were intraviral and 1304 were HCV-Human. By analyzing the network, we found potential human gene list with their number of interactions with HCV proteins. ANXA2 and NR4A1 were interacting with 6 HCV proteins while we found 11 human genes which were interacting with 5 HCV proteins. Furthermore, the enrichment analysis and Gene Ontology of the top genes to find the pathways and the biological processes enriched with those genes. Among the viral proteins, NS3 was interacting with most number of interactors followed by NS5A and so on. KEGG pathway analysis of three set of most HCV- associated human genes was performed to find out which gene products are involved in certain disease pathways. Top 5, 10 and 20 human genes with most interactions were analyzed which revealed some striking results among which the top 10 host genes came up to be significant because they were more related to Influenza A viral infection previously. This insight provides us with a clue that the set of genes are highly enriched in HCV but are not well studied in its infection pathway. Conclusions We found out a group of proteins which were rich in HCV viral pathway but there were no drugs targeting them according to the drug repurposing hub. It can be concluded that the cluster we obtained from MCODE contains potential targets for HCV treatment and could be implemented for molecular docking and drug designing further by the scientists.

Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation.