The outbreak of severe pneumonia at the end of 2019 was proved to be caused by the SARS-CoV-2 virus spreading out the world. And COVID-19 spread rapidly through a terrible transmission way by human-to-human, which led to many suspected cases waiting to be diagnosed and huge daily samples needed to be tested by an effective and rapid detection method. With an increasing number of COVID-19 infections, medical pressure is severe. Therefore, more efficient and accurate diagnosis methods were keen urgently established. In this review, we summarized several methods that can rapidly and sensitively identify COVID-19; some of them are widely used as the diagnostic techniques for SARS-CoV-2 in various countries, some diagnostic technologies refer to SARS (Severe Acute Respiratory Syndrome) or/and MERS (Middle East Respiratory Syndrome) detection, which may provide potential diagnosis ideas.
This investigation was performed to assess the importance of interaction in the binding of blockers to KCNQ1 potassium using molecular modeling. This work could be considered made up by three main steps: (1) the construction of closed-state structure of KCNQ1 through homology modeling; (2) the automated docking of three blockers: IKS-142, L-735821, and BMS-IKS, using DOCK program; (3) the generation and validation of pharmacophore for KCNQ1 ligands using Catalyst/HypoGen. The obtained results highlight the hydrophobic or aromatic residues involved in S6 transmembrane domain and the base of the pore helix of KCNQ1, confirming the mutagenesis data and pharmacophore model, and giving new suggestions for the rational design of novel KCNQ1 ligands.
Potassium Channels, Voltage-Gated/chemistry , Amino Acid Sequence , Benzodiazepines/pharmacology , Binding Sites , Drug Design , Humans , In Vitro Techniques , KCNQ Potassium Channels , KCNQ1 Potassium Channel , Ligands , Models, Molecular , Molecular Sequence Data , Molecular Structure , Potassium Channels, Voltage-Gated/antagonists & inhibitors , Potassium Channels, Voltage-Gated/genetics , Potassium Channels, Voltage-Gated/metabolism , Protein Conformation , Sequence Homology, Amino Acid
Predictive pharmacophore models were developed for a large series of I(Kr) potassium channel blockers as class III antiarrhythmic agents using HypoGen in Catalyst software. The pharmacophore hypotheses were generated using a training set consisting of 34 compounds carefully selected from documents. Their biological data, expressed as IC(50), spanned from 1.5 nM to 2.8 mM with 7 orders difference. The most predictive hypothesis (Hypo1), consisting of four features (one positive ionizable feature, two aromatic rings and one hydrophobic group), had a best correlation coefficient of 0.825, a lowest rms deviation of 1.612, and a highest cost difference (null cost-total cost) of 77.552, which represents a true correlation and a good predictivity. The hypothesis Hypo1 was then validated by a test set consisting of 21 compounds and by a cross-validation of 95% confidence level with randomizing the data using CatScramble program. Accordingly, our model has strong predictivity to identify structural diverse I(Kr) potassium channel blockers with desired biological activity by virtual screening
Anti-Arrhythmia Agents/chemistry , Potassium Channel Blockers/chemistry , Anti-Arrhythmia Agents/chemical synthesis , Anti-Arrhythmia Agents/classification , Models, Biological , Models, Molecular , Potassium Channel Blockers/chemical synthesis , Potassium Channels/chemistry , Quantitative Structure-Activity Relationship , Technology, Pharmaceutical/methods
