ABSTRACT
In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.
Subject(s)
Epigenomics , Genomics , Neoplasms/etiology , Cell Transformation, Neoplastic/genetics , Cell Transformation, Neoplastic/immunology , Cell Transformation, Neoplastic/metabolism , Chromosome Aberrations , Computational Biology/methods , Disease Susceptibility , Epigenomics/methods , Genetic Predisposition to Disease , Genomics/methods , Humans , Machine Learning , Precision Medicine , Proteomics/methods , TranscriptomeABSTRACT
The cardiac action potential is regulated by several ion channels. Drugs capable to block these channels, in particular the human ether-à-go-go-related gene (hERG) channel, also known as KV11.1 channel, may lead to a potentially lethal ventricular tachyarrhythmia called "Torsades de Pointes". Thus, evaluation of the hERG channel off-target activity of novel chemical entities is nowadays required to safeguard patients as well as to avoid attrition in drug development. Flavonoids, a large class of natural compounds abundantly present in food, beverages, herbal medicines, and dietary food supplements, generally escape this assessment, though consumed in consistent amounts. Continuously growing evidence indicates that these compounds may interact with the hERG channel and block it. The present review, by examining numerous studies, summarizes the state-of-the-art in this field, describing the most significant examples of direct and indirect inhibition of the hERG channel current operated by flavonoids. A description of the molecular interactions between a few of these natural molecules and the Rattus norvegicus channel protein, achieved by an in silico approach, is also presented.
Subject(s)
ERG1 Potassium Channel/antagonists & inhibitors , Flavonoids/toxicity , Heart Rate/drug effects , Long QT Syndrome/chemically induced , Myocytes, Cardiac/drug effects , Potassium Channel Blockers/toxicity , Torsades de Pointes/chemically induced , Action Potentials , Animals , ERG1 Potassium Channel/chemistry , ERG1 Potassium Channel/metabolism , Humans , Long QT Syndrome/metabolism , Long QT Syndrome/physiopathology , Myocytes, Cardiac/metabolism , Protein Conformation , Risk Assessment , Risk Factors , Structure-Activity Relationship , Torsades de Pointes/metabolism , Torsades de Pointes/physiopathologyABSTRACT
The Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2). The virus has rapidly spread in humans, causing the ongoing Coronavirus pandemic. Recent studies have shown that, similarly to SARS-CoV, SARS-CoV-2 utilises the Spike glycoprotein on the envelope to recognise and bind the human receptor ACE2. This event initiates the fusion of viral and host cell membranes and then the viral entry into the host cell. Despite several ongoing clinical studies, there are currently no approved vaccines or drugs that specifically target SARS-CoV-2. Until an effective vaccine is available, repurposing FDA approved drugs could significantly shorten the time and reduce the cost compared to de novo drug discovery. In this study we attempted to overcome the limitation of in silico virtual screening by applying a robust in silico drug repurposing strategy. We combined and integrated docking simulations, with molecular dynamics (MD), Supervised MD (SuMD) and Steered MD (SMD) simulations to identify a Spike protein - ACE2 interaction inhibitor. Our data showed that Simeprevir and Lumacaftor bind the receptor-binding domain of the Spike protein with high affinity and prevent ACE2 interaction.
