Empirical Potential Functions for Prediction of Protein Folding

预测蛋白质折叠的经验势函数

• 批准号: 9614074
• 负责人: Gordon Crippen
• 金额: $ 20.34万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1997
• 资助国家: 美国
• 起止时间: 1997-08-01 至 2000-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9614074&HistoricalAwards=false
• 关键词: Empirical; Potential; Functions; Prediction; Protein

One of the grand challenges in biophysics is the prediction of protein folding from the amino acid sequence. The investigator and coworkers have determined an empirical mathematical function that can recognize the native conformation for many proteins compared to many thousands of decoy conformation constructed from pieces of other protein crystal structures. They are now improving this function by testing different functional forms, training against more natives, and challenging it with wider classes of nonnative folds. If the function can be made smooth enough, it could quide a search toward the native structure as part of a structure prediction algorithm. In the current excitement over the human genome project, it is easy to forget that gene sequences are like blueprints for a house, and we are more concerned with the house than the blueprints. At the moment, we have no idea about the biological and medical role that about a third of the sequences play once they are translated into proteins, the three-dimensional molecular machines that make cells work. How proteins function depends critically on how these long chains fold up into particular shapes. This project is concerned with making sense of those thousands of mysterious sequences by trying to predict their three-dimensional structures. Workers in this laboratory have developed computer methods to choose the correct structure for a given sequence out of a lineup of many incorrect ones, as in a huge multiple choice exam. They are now moving toward answering an essay exam, where the sequence is given, and the structure must not merely be recognized, but constructed. Until this can be done, much of the value of the massive sequencing projects cannot be realized.

生物物理学的重大挑战之一是从氨基酸序列预测蛋白质折叠。 研究人员和同事已经确定了一个经验数学函数，该函数可以识别许多蛋白质的天然构象，而不是由其他蛋白质晶体结构构建的数千种诱饵构象。 他们现在正在通过测试不同的功能形式来改善这种功能，训练更多的本地人，并用更广泛的非本地折叠来挑战它。 如果函数足够平滑，它可以引导对天然结构的搜索，作为结构预测算法的一部分。 在目前人类基因组计划的兴奋中，人们很容易忘记基因序列就像房子的蓝图，我们更关心的是房子而不是蓝图。 目前，我们还不知道大约三分之一的序列一旦被翻译成蛋白质（使细胞工作的三维分子机器）后所起的生物学和医学作用。 蛋白质的功能主要取决于这些长链如何折叠成特定的形状。 这个项目关注的是通过试图预测它们的三维结构来理解这数千个神秘的序列。 这个实验室的工作人员已经开发出了计算机方法，可以从一系列不正确的序列中为给定的序列选择正确的结构，就像在一个巨大的多项选择题考试中一样。他们现在正朝着回答作文考试的方向发展，在作文考试中，序列是给定的，结构不仅要被识别，还要被构造出来。 在此之前，大规模测序项目的大部分价值无法实现。