ABSTRACT
Ebola virus is a virulent pathogen that causes highly lethal hemorrhagic fever in human and non-human species. The rapid growth of this virus infection has made the scenario increasingly complicated to control the disease. Receptor viral matrix protein (VP40) is highly responsible for the replication and budding of progeny virus. The binding of RNA to VP40 could be the crucial factor for the successful lifecycle of the Ebola virus. In this study, we aimed to identify the potential drug that could inhibit VP40. Sugar alcohols were enrich with antiviral properties used to inhibit VP40. Virtual screening analysis was perform for the 48 sugar alcohol compounds, of which the following three compounds show the best binding affinity: Sorbitol, Mannitol and Galactitol. To understand the perfect binding orientation and the strength of non-bonded interactions, individual molecular docking studies were perform for the best hits. Further molecular dynamics studies were conduct to analyze the efficacy between the protein-ligand complexes and it was identify that Sorbitol obtains the highest efficacy. The best-screened compounds obtained drug-like property and were less toxic, which could be use as a potential lead compound to develop anti-Ebola drugs.
Subject(s)
Antiviral Agents/pharmacology , Ebolavirus/metabolism , Sugar Alcohols/pharmacology , Viral Matrix Proteins/antagonists & inhibitors , Antiviral Agents/chemistry , Computer Simulation , Galactitol/pharmacology , HEK293 Cells , Hemorrhagic Fever, Ebola/drug therapy , Hemorrhagic Fever, Ebola/metabolism , Hemorrhagic Fever, Ebola/virology , Humans , Ligands , Mannitol/pharmacology , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Sorbitol/pharmacology , Sugar Alcohols/metabolism , Viral Matrix Proteins/metabolism , Viral Matrix Proteins/ultrastructureABSTRACT
Artificial intelligence (AI) proves to have enormous potential in many areas of healthcare including research and chemical discoveries. Using large amounts of aggregated data, the AI can discover and learn further transforming these data into "usable" knowledge. Being well aware of this, the world's leading pharmaceutical companies have already begun to use artificial intelligence to improve their research regarding new drugs. The goal is to exploit modern computational biology and machine learning systems to predict the molecular behaviour and the likelihood of getting a useful drug, thus saving time and money on unnecessary tests. Clinical studies, electronic medical records, high-resolution medical images, and genomic profiles can be used as resources to aid drug development. Pharmaceutical and medical researchers have extensive data sets that can be analyzed by strong AI systems. This review focused on how computational biology and artificial intelligence technologies can be implemented by integrating the knowledge of cancer drugs, drug resistance, next-generation sequencing, genetic variants, and structural biology in the cancer precision drug discovery.