Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation.

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

Characterization and structure prediction of partial length protein sequences of pcoA, pcoR and chrB genes from heavy metal resistant bacteria from the Klip River, South Africa.

The Klip River has suffered from severe anthropogenic effects from industrial activities such as mining. Long-term exposure to heavy metal pollution has led to the development of heavy metal resistant strains of Pseudomonas sp. KR23, Lysinibacillus sp. KR25, and E. coli KR29. The objectives of this study were to characterize the genetics of copper and chromate resistance of the isolates. Copper and chromate resistance determinants were cloned and sequenced. Open reading frames (ORFs) related to the genes CopA and CopR were identified in E. coli KR29, PcoA in Lysinibacillus sp. KR25 and none related to chromate resistance were detected. The 3D-models predicted by I-TASSER disclose that the PcoA proteins consist of ß-sheets, which form a part of the cupredoxin domain of the CopA copper resistance family of genes. The model for PcoR_29 revealed the presence of a helix turn helix; this forms part of a DNA binding protein, which is part of a heavy metal transcriptional regulator. The bacterial strains were cured using ethidium bromide. The genes encoding for heavy metal resistance and antibiotic resistance were found to be located on the chromosome for both Pseudomonas sp. (KR23) and E. coli (KR29). For Lysinibacillus (KR25) the heavy metal resistance determinants are suspected to be located on a mobile genetic element, which was not detected using gel electrophoresis.