Characterization of Amino Acid Based Molecular Micelles with Molecular Modeling

• 作者: Alexander Billiot Kevin F. Morris Yayin Fang
• 来源期刊: 物理化学期刊(英文)
• 学科: 化学
• 关键词: AMINO Acid BASED MOLECULAR Micelles MOLECULAR Modeling COMPUTATIONAL CHEMISTRY
• 年，卷（期）: 2019,（4）
• 页码范围: 221-240
• 页数: 20页
• 分类号: O62

摘要

The enantiomers of chiral drugs often have different potencies, toxicities, and biochemical properties. Therefore, the FDA and other worldwide regulatory agencies require manufactures to test and prove the enantiomeric purity of chiral drugs. Amino acid based molecular micelles (AABMM) have been used in chiral CE separations since the 1990’s because of their low environmental impact and because their properties can easily be tuned by changing the amino acids in the chiral surfactant head groups. Using molecular dynamics simulations to investigate the structures and properties of AABMM is part of an ongoing study focusing on investigating and elucidating the factors responsible for chiral recognition with AABMM. The results will be useful for the proper design and selection of more efficient chiral selectors. The micelles investigated contained approximately twenty covalently linked surfactant monomers. Each monomer was in turn composed of an undecyl hydrocarbon chain bound to a dipeptide headgroup containing of all combinations of L-Alanine, L-Valine, and L-Leucine. These materials are of interest because they are effective chiral selectors in capillary electrophoresis separations. Molecular dynamics simulation analyses were used to investigate how the sizes and positions of the headgroup amino acid R-groups affected the solvent accessible surface areas of each AABMM chiral center. In addition, headgroup dihedral angle analyses were used to investigate how amino acid R-group size and position affected the overall headgroup conformations. Finally, distance measurements were used to study the structural and conformational flexibilities of each AABMM headgroup. All analyses were performed in the context of a broader study focused on developing structure-based predictive tools to identify the factors responsible for a) self-assembly, b) function, c) higher ordered structure and d) molecular recognition of these amino acid based molecular micelles.