RESUMEN
The role of dynamics in enzymatic function is a highly debated topic. Dihydrofolate reductase (DHFR), due to its universality and the depth with which it has been studied, is a model system in this debate. Myriad previous works have identified networks of residues in positions near to and remote from the active site that are involved in dynamics and others that are important for catalysis. For example, specific mutations on the Met20 loop in E. coli DHFR (N23PP/S148A) are known to disrupt millisecond-timescale motions and reduce catalytic activity. However, how and if networks of dynamically coupled residues influence the evolution of DHFR is still an unanswered question. In this study, we first identify, by statistical coupling analysis and molecular dynamic simulations, a network of coevolving residues, which possess increased correlated motions. We then go on to show that allosteric communication in this network is selectively knocked down in N23PP/S148A mutant E. coli DHFR. Finally, we identify two sites in the human DHFR sector which may accommodate the Met20 loop double proline mutation while preserving dynamics. These findings strongly implicate protein dynamics as a driving force for evolution.
RESUMEN
Time span in literature: 1985-early 2024Natural products play a key role in drug discovery, both as a direct source of drugs and as a starting point for the development of synthetic compounds. Most natural products are not suitable to be used as drugs without further modification due to insufficient activity or poor pharmacokinetic properties. Choosing what modifications to make requires an understanding of the compound's structure-activity relationships. Use of structure-activity relationships is commonplace and essential in medicinal chemistry campaigns applied to human-designed synthetic compounds. Structure-activity relationships have also been used to improve the properties of natural products, but several challenges still limit these efforts. Here, we review methods for studying the structure-activity relationships of natural products and their limitations. Specifically, we will discuss how synthesis, including total synthesis, late-stage derivatization, chemoenzymatic synthetic pathways, and engineering and genome mining of biosynthetic pathways can be used to produce natural product analogs and discuss the challenges of each of these approaches. Finally, we will discuss computational methods including machine learning methods for analyzing the relationship between biosynthetic genes and product activity, computer aided drug design techniques, and interpretable artificial intelligence approaches towards elucidating structure-activity relationships from models trained to predict bioactivity from chemical structure. Our focus will be on these latter topics as their applications for natural products have not been extensively reviewed. We suggest that these methods are all complementary to each other, and that only collaborative efforts using a combination of these techniques will result in a full understanding of the structure-activity relationships of natural products.
RESUMEN
Eleven novel acridone derivatives were synthesized and evaluated for their anticancer activity against 60 human cancer cell lines. Five compounds 8b, 8d, 8g, 8h, and 8k displayed very good in vitro antiproliferative activities well over 95% of the panels. The most active compound is 8k (5, 7-dibromo-3-phenyl-3,4-dihydroacridin-1 (2H)-one). In addition, 8k was the most sensitive agent in all 9 panels starting with prostate (0.075 µm), leukemia (0.116 µm), non-small cell lung cancer (0.164 µm), colon cancer (0.193 µm), CNS cancer (0.264 µm), melanoma (0.317 µm), renal cancer (0.403 µm), ovarian cancer (0.410 µm), and breast cancer (0.608 µm). Virtual screening studies also revealed that nine of the eleven compounds formed good binding interaction with the active site ATPase domain of human topoisomerase IIα (PDB: 1zxm). All nine derivatives exhibited binding affinities that ranged in values from -8.5 to -7.9 kcal/mol, indicating that they could be catalytic inhibitors of the nuclear enzyme, topoisomerase.
