Peckbrantley1816

The application of bioinformatics to vaccine research and drug discovery has never been so essential in the fight against infectious diseases. The greatest combat of the 21st century against a debilitating disease agent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus discovered in Wuhan, China, December 2019, has piqued an unprecedented usage of bioinformatics tools in deciphering the molecular characterizations of infectious pathogens. With the viral genome data of SARS-COV-2 been made available barely weeks after the reported outbreak, bioinformatics platforms have become an all-time critical tool to gain time in the fight against the disease pandemic. Before the outbreak, different platforms have been developed to explore antigenic epitopes, predict peptide-protein docking and antibody structures, and simulate antigen-antibody reactions and lots more. However, the advent of the pandemic witnessed an upsurge in the application of these pipelines with the development of newer ones such as the Coronavirus Explorer in the development of efficacious vaccines, drug repurposing, and/or discovery. In this review, we have explored the various pipelines available for use, their relevance, and limitations in the timely development of useful therapeutic candidates from genomic data knowledge to clinical therapy.

Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor.

Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in then applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.A high level of mutation enables the influenza A virus to resist antibiotics previously effective against the influenza A virus. A portion of the structure of hemagglutinin HA is assumed to be well-conserved to maintain its role in cellular fusion, and the structure tends to be more conserved than sequence. Vafidemstat nmr We designed peptide inhibitors to target the conserved residues on the HA surface, which were identified based on structural alignment. Most of the conserved and strongly similar residues are located in the receptor-binding and esterase regions on the HA1 domain In a later step, fragments of anti-HA antibodies were gathered and screened for the binding ability to the found conserved residues. As a result, Methionine amino acid got the best docking score within the -2.8 Å radius of Van der Waals when it is interacting with Tyrosine, Arginine, and Glutamic acid. Then, the binding affinity and spectrum of the fragments were enhanced by grafting hotspot amino acid into the fragments to form peptide inhibitors. Our peptide inhibitor was able to form in silico contact with a structurally conserved region across H1, H2, and H3 HA, with the binding site at the boundary between HA1 and HA2 domains, spreading across different monomers, suggesting a new target for designing broad-spectrum antibody and vaccine. This research presents an affordable method to design broad-spectrum peptide inhibitors using fragments of an antibody as a scaffold.ORF8 is a highly variable genomic region of SARS-CoV-2. Although non-essential and the precise functions are unknown, it has been suggested that this protein assists in SARS-CoV-2 replication in the early secretory pathway and in immune evasion. We utilized the binding partners of SARS-CoV-2 proteins in human HEK293T cells and performed genome-wide phylogenetic profiling and clustering analyses in 446 eukaryotic species to predict and discover ORF8 binding partners that share associated functional mechanisms based on co-evolution. Results classified 47 ORF8 binding partner proteins into 3 clusters (groups 1-3), which were conserved in vertebrates (group 1), metazoan (group 2), and eukaryotes (group 3). Gene ontology analysis indicated that group 1 had no significant associated biological processes, while groups 2 and 3 were associated with glycoprotein biosynthesis process and ubiquitin-dependent endoplasmic reticulum-associated degradation pathways, respectively. Collectively, our results classified potential genes that might be associated with SARS-CoV-2 viral pathogenesis, specifically related to acute respiratory distress syndrome, and the secretory pathway. Here, we discuss the possible role of ORF8 in viral pathogenesis and in assisting viral replication and immune evasion via secretory pathway, as well as the possible factors associated with the rapid evolution of ORF8.Androgen-deprivation therapy (ADT) is only a palliative measure, and prostate cancer invariably recurs in a lethal, castration-resistant form (CRPC). Prostate cancer resists ADT by metabolizing weak, adrenal androgens to growth-promoting 5α-dihydrotestosterone (DHT), the preferred ligand for the androgen receptor (AR). Developing small-molecule inhibitors for the final steps in androgen metabolic pathways that utilize 17-oxidoreductases required probes that possess fluorescent groups at C-3 and intact, naturally occurring functionality at C-17. Application of the Pictet-Spengler condensation to substituted 4-(2-aminoethyl)coumarins and 5α-androstane-3-ones furnished spirocyclic, fluorescent androgens at the desired C-3 position. Condensations required the presence of activating C-7 amino or N,N-dialkylamino groups in the 4-(2-aminoethyl)coumarin component of these condensation reactions. Successful Pictet-Spengler condensation, for example, of DHT with 9-(2-aminoethyl)-2,3,6,7-tetrahydro-1H,5H,11H-pyrano[2,3-f]pyrido[3,2,1-ij]quinolin-11-one led to a spirocyclic androgen, (3R,5S,10S,13S,17S)-17-hydroxy-10,13-dimethyl-1,2,2',3',4,5,6,7,8,8',9,9',10,11,12,12',13,13',14,15,16,17-docosahydro-7'H,11'H-spiro-[cyclopenta[a]phenanthrene-3,4'-pyrido[3,2,1-ij]pyrido[4',3'4,5]pyrano[2,3-f]quinolin]-5'(1'H)-one.