Application of Quantitative Structure-Activity Relationships (QSAR) to Biological Systems

该职业进步奖由有机和大分子化学项目支持。德克萨斯理工大学化学系的Allan D. Headley教授将使用定量构效关系（QSAR）来确定和量化中性粒细胞受体位点与构象移动的二肽化学引诱剂之间存在的相互作用类型。他将与德克萨斯理工大学动物科学系的John McGlone合作进行潜在趋化二肽的合成，确定溶液中的构象，研究其生物活性，然后计算MOPAC中MNDO的各种分子参数。最后，他将把这些结果应用于数据的QSAR分析，以设计更有效的趋化剂。对单个氨基酸构象的了解提供了多肽可能采用的构象的信息。肽和蛋白质必须采用特定的构象才能具有生物活性。这些研究的目标是建立理论模型来预测小肽的生物活性构象。这些都是基础性研究，可能最终为开发新的抗癌药物奠定基础。

This Career Advancement Award is supported by the Organic and Macromolecular Chemistry Program. Professor Allan D. Headley of the Department of Chemistry at Texas Tech University will use quantitative structure-activity relationships (QSAR) to determine and quantify the type of interactions that exist between the receptor site of neutrophil and conformationally mobile dipeptide chemoattractants. He will carry out the synthesis of potential chemotactic dipeptides, determine conformations in solution, study their biological activities in collaboration with John McGlone of the Animal Sciences Department at Texas Tech, and then calculate a variety of MNDO molecular parameters in MOPAC. Finally, he will apply these results to a QSAR analysis of the data in efforts to design more potent chemotactic agents. Knowledge of the conformation of individual amino acids gives information on possible conformation that peptides adopt. Peptides and proteins must adopt specific conformations in order to be biologically active. A goal of these studies is to develop theoretical models to predict the biologically-active conformation of small peptides. These are fundamental studies which may ultimately lay the groundwork for the development of new anti-cancer agents.