Detection of Pb2+ in Tea Using Aptamer Labeled with AIEgen Nanospheres Based on MOFs Sensors.

Tea is an important economic crop and health beverage in China. The presence of heavy metal ions in tea poses a significant threat to public health. Here, we prepared an aptamer biosensor labelled with AIEgen nanospheres to detect Pb2+ in tea. The dsDNA modified by amino and phosphoric acid was combined with the carboxylated AIEgen NPs to form AIEgen-DNA with a fluorescence group, which was then fixed to the surface of Zr-MOFs to quench the fluorescence of AIEgen NPs. At the same time, PEG was added to remove nonspecific adsorption. Then Pb2+ was added to cut the DNA sequences containing the cutting sites, and AIEgen NPs and part of the DNA sequences were separated from the Zr-MOFs surface to recover the fluorescence. By comparing the fluorescence changes before and after adding Pb2+, the detection limit of Pb2+ can reach 1.70 nM. The fluorescence sensor was applied to detect Pb2+ in tea, and the detection results showed that the tea purchased on the market did not contain the concentration of Pb2+ within the detection range. This study provides new insights into monitoring food and agriculture-related pollutants based on fluorescent biosensors.

In silico Screening of Natural Phytocompounds Towards Identification of Potential Lead Compounds to Treat COVID-19.

COVID-19 is one of the members of the coronavirus family that can easily assail humans. As of now, 10 million people are infected and above two million people have died from COVID-19 globally. Over the past year, several researchers have made essential advances in discovering potential drugs. Up to now, no efficient drugs are available on the market. The present study aims to identify the potent phytocompounds from different medicinal plants (Zingiber officinale, Cuminum cyminum, Piper nigrum, Curcuma longa, and Allium sativum). In total, 227 phytocompounds were identified and screened against the proteins S-ACE2 and M pro through structure-based virtual screening approaches. Based on the binding affinity score, 30 active phytocompounds were selected. Amongst, the binding affinity for beta-sitosterol and beta-elemene against S-ACE2 showed -12.0 and -10.9 kcal/mol, respectively. Meanwhile, the binding affinity for beta-sitosterol and beta-chlorogenin against M pro was found to be -9.7 and -8.4 kcal/mol, respectively. Further, the selected compounds proceeded with molecular dynamics simulation, prime MM-GBSA analysis, and ADME/T property checks to understand the stability, interaction, conformational changes, binding free energy, and pharmaceutical relevant parameters. Moreover, the hotspot residues such as Lys31 and Lys353 for S-ACE2 and catalytic dyad His41 and Cys145 for M pro were actively involved in the inhibition of viral entry. From the in silico analyses, we anticipate that this work could be valuable to ongoing novel drug discovery with potential treatment for COVID-19.

Effect of HIV-1 Subtype C integrase mutations implied using molecular modeling and docking data.

The degree of sequence variation in HIV-1 integrase genes among infected patients and their impact on clinical response to Anti retroviral therapy (ART) is of interest. Therefore, we collected plasma samples from 161 HIV-1 infected individuals for subsequent integrase gene amplification (1087 bp). Thus, 102 complete integrase gene sequences identified as HIV-1 subtype-C was assembled. This sequence data was further used for sequence analysis and multiple sequence alignment (MSA) to assess position specific frequency of mutations within pol gene among infected individuals. We also used biophysical geometric optimization technique based molecular modeling and docking (Schrodinger suite) methods to infer differential function caused by position specific sequence mutations towards improved inhibitor selection. We thus identified accessory mutations (usually reduce susceptibility) leading to the resistance of some known integrase inhibitors in 14% of sequences in this data set. The Stanford HIV-1 drug resistance database provided complementary information on integrase resistance mutations to deduce molecular basis for such observation. Modeling and docking analysis show reduced binding by mutants for known compounds. The predicted binding values further reduced for models with combination of mutations among subtype C clinical strains. Thus, the molecular basis implied for the consequence of mutations in different variants of integrase genes of HIV-1 subtype C clinical strains from South India is reported. This data finds utility in the design, modification and development of a representative yet an improved inhibitor for HIV-1 integrase.