ABSTRACT
Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.
ABSTRACT
COVID-19 pandemic caused by the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has become the world's largest public health emergency of the past few decades. Thousands of mutations were identified in the SARS-CoV-2 genome. Some mutants are more infectious and may replace the original strains. Recently, B.1.1.7(Alpha), B1.351(Beta), and B.1.617.2(Delta) strains, which appear to have increased transmissibility, were detected. These strains accounting for the high proportion of newly diagnosed cases spread rapidly over the world. Particularly, the Delta variant has been reported to account for a vast majority of the infections in several countries over the last few weeks. The application of biosensors in the detection of SARS-CoV-2 is important for the control of the COVID-19 pandemic. Due to high demand for SARS-CoV-2 genotyping, it is urgent to develop reliable and efficient systems based on integrated multiple biosensor technology for rapid detection of multiple SARS-CoV-2 mutations simultaneously. This is important not only for the detection and analysis of the current but also for future mutations. Novel biosensors combined with other technologies can be used for the reliable and effective detection of SARS-CoV-2 mutants.
ABSTRACT
The emergence of a mutant strain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with an amino acid change from aspartate to a glycine residue at position 614 (D614G) has been reported and this mutant appears to be now dominant in the pandemic. Efficient detection of the SARS-CoV-2 D614G mutant by biosensing technologies is therefore crucial for the control of the pandemic.
Subject(s)
Biosensing Techniques , Mutation , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Amino Acid SubstitutionABSTRACT
G-quadruplex is a non-canonical nucleic acid structure formed by the folding of guanine rich DNA or RNA. The conformation and function of G-quadruplex are determined by a number of factors, including the number and polarity of nucleotide strands, the type of cations and the binding targets. Recent studies led to the discovery of additional advantageous attributes of G-quadruplex with the potential to be used in novel biosensors, such as improved ligand binding and unique folding properties. G-quadruplex based biosensor can detect various substances, such as metal ions, organic macromolecules, proteins and nucleic acids with improved affinity and specificity compared to standard biosensors. The recently developed G-quadruplex based biosensors include electrochemical and optical biosensors. A novel G-quadruplex based biosensors also show better performance and broader applications in the detection of a wide spectrum of pathogens, including SARS-CoV-2, the causative agent of COVID-19 disease. This review highlights the latest developments in the field of G-quadruplex based biosensors, with particular focus on the G-quadruplex sequences and recent applications and the potential of G-quadruplex based biosensors in SARS-CoV-2 detection.