The objective of this project is to analyze the physiochemical properties that enable natural products to permeate the cell membrane and bind to their targets despite their size and polarity. As many as half of the currently marketed pharmaceuticals were derived from natural products, with a significant subset lying outside modern physiochemical parameters for drug-like compounds. It is commonly assumed that natural products operate outside these parameters because they are actively transported across the cell membrane. However, research has shown that cyclosporine, a cyclic nonribosomal peptide with a molecular weight just over 1200, is passively permeable. Studies of cyclosporine and other cyclic peptides have demonstrated that their passive permeability is correlated to intramolecular hydrogen bond formation in a membrane-mimicking low dielectric solvent. A large class of natural products with known in vivo bioactivity is the nonribosomal peptide-polyketides (NRPPKs);however, the origin of their bioactivity is incompletely characterized. We propose to investigate the underlying physical properties related to natural product bioactivity by focusing on the membrane permeability, bioavailability, and cellular targets of NRP-PKs. Here, we use novel computational methods to probe the determinants of natural product permeability and examine the hypothesis that NRP-PKs will bind cyclophilin peptidylprolylisomerases, due to a conserved piperazic acid moiety. Through a combination of experimental and theoretical methods, including biochemical assays, X-ray crystallography, and computational modeling, we will test our hypothesis and explore the key physical characteristics related to permeability and bioactivity.
Many complex natural products have become successful pharmaceutical agents. However, the relationship between their molecular structure and biological activity is not fully understood. The key physical properties that determine how these compounds cross the cell membrane and interact with their targets will be studied.
|Lexa, Katrina W; Dolghih, Elena; Jacobson, Matthew P (2014) A structure-based model for predicting serum albumin binding. PLoS One 9:e93323|