ABSTRACT
Drug repurposing is a major approach used by researchers to tackle the COVID-19 pandemic which has been worsened by the current surge of delta variant in many countries. Though drugs like Remdesivir and Hydroxychloroquine have been repurposed, studies prove these drugs have insignificant effect in treatment. So, in this study, we use the already FDA approved database of 1615 drugs to apply semi-flexible and flexible molecular docking methods to calculate the docking scores and identify the best 20 potential inhibitors for our modelled delta variant spike protein RBD. Then, we calculate 2325 1-D and 2-D molecular descriptors and use machine-learning algorithms like K-Nearest Neighbor, Random Forest, Support Vector Machine and ensemble stacking method to build regression-based prediction models. We identify 15 best descriptors for the dataset all of which were found to be inversely correlated with ligand binding. With only these few descriptors, the models performed excellently with an area under curve (AUC) value of 0.952 in Regression Error Characteristic curve for ensemble stacking. Therefore, we comment that these 15 descriptors are the most important features for the binding of inhibitors to the spike protein and hence these should be studied properly in terms of drug repurposing and drug discovery.
ABSTRACT
Drug repurposing is a major approach used by researchers to tackle the COVID-19 pandemic which has been worsened by the current surge of delta variant in many countries. Though drugs like Remdesivir and Hydroxychloroquine have been repurposed, studies prove these drugs have insignificant effect in treatment. So, in this study, we use the already FDA approved database of 1615 drugs to apply semi-flexible and flexible molecular docking methods to calculate the docking scores and identify the best 20 potential inhibitors for our modelled delta variant spike protein RBD. Then, we calculate 2325 1-D and 2-D molecular descriptors and use machine-learning algorithms like K-Nearest Neighbor, Random Forest, Support Vector Machine and ensemble stacking method to build regression-based prediction models. We identify 15 best descriptors for the dataset all of which were found to be inversely correlated with ligand binding. With only these few descriptors, the models performed excellently with an area under curve (AUC) value of 0.952 in Regression Error Characteristic curve for ensemble stacking. Therefore, we comment that these 15 descriptors are the most important features for the binding of inhibitors to the spike protein and hence these should be studied properly in terms of drug repurposing and drug discovery.
ABSTRACT
Biofilms are dense population of sessile bacterial cells that adhere to the surface, forming a matrix composed ofexopolysaccharide, proteins and DNA. This matrix is termed as extracellular polymeric substance and provides stability tothe cells adhering to it to form biofilms. It also provides nutrients and thus helps in the pathogenesis of biofilm-associatedinfections and resistance. Biofilms promote bacterial persistence by resisting host immune responses and antibiotic treatment. Antibiotics are rendered ineffective when biofilms form due to their relative impermeability, the variable physiological status of microorganisms, and subpopulations of persistent strains. Another factor that results in the development ofantibiotic resistance within the biofilm is the adaptations that take place within the genes present in the cells dwelling withinthe biofilm. These adaptations decrease the sensitivity of the bacterial cells toward the antibiotics and develop resistance.Hence, an alternative antimicrobial strategy of making use of plant-based products has been observed to be useful to curevarious ailments, as compared to conventional therapy. In this review, we have listed the various biofilm-forming bacteriaand the bioactive compounds being produced from the aerial parts of plants having antibiofilm activity and evaluated themagainst different biofilm-producing bacterial strains.