MULTIMINIMA PROBLEM IN PROTEIN FOLDING

蛋白质折叠中的多极小问题

• 批准号: 2172837
• 负责人: HONGZHI SUN
• 金额: $ 2.24万
• 依托单位: CORNELL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-03-18 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/2172837
• 关键词: MULTIMINIMA; PROBLEM; PROTEIN; FOLDING

Protein secondary and tertiary structures are essential determinants of molecular function. Thus, the problem of developing correct predictions of secondary and tertiary structures of proteins of known amino acid sequences is central to modern protein physical chemistry. The Monte Carlo method is one of the most popular techniques used to predict protein secondary and tertiary structure. In this proposal, two new Monte Carlo simulation algorithms specially tailored for the prediction of peptide and protein secondary and tertiary structures are proposed. These algorithms will use an extended formulation of Markov chain Monte Carlo sampling method. This formulation shows that a correct sample can be obtained when different types of random moves are performed according to some pattern in a Monte Carlo algorithm. The first algorithm is Entropy Sampling Monte Carlo (ESMC) and the second is standard Monte Carlo (SMC). Two types of random moves will be included in both algorithms. The first type of move is guided by local intramolecular interactions; therefore, it is computationally cheap and efficient. The second type of move mimics energy minimization in a probabilistic way. These moves can help to find low-energy conformations in simulations. ESMC is better in overcoming energy barriers, while SMC is more efficient for simulations of short peptides.

蛋白质的二级和三级结构是决定蛋白质的主要因素 分子功能。因此，制定正确的预测的问题 已知氨基酸的蛋白质的二级和三级结构 序列是现代蛋白质物理化学的核心。《蒙特》 卡罗方法是最常用的预测方法之一。 蛋白质的二级和三级结构。在这项提案中，有两个新的 专门为预测量身定做的蒙特卡罗模拟算法 提出了多肽和蛋白质的二级结构和三级结构。 这些算法将使用马尔科夫链蒙特卡罗的扩展公式 卡罗抽样法。这一公式表明，一个正确的样本可以 当执行不同类型的随机移动时，根据 蒙特卡罗算法中的某种模式。第一种算法是 第二种是标准蒙特卡罗方法 卡洛(SMC)。这两种类型的随机移动都将包括在内 算法。第一种运动是由局部分子内引导的 交互；因此，它在计算上是廉价和高效的。这个 第二类动作以概率的方式模仿能量最小化。 这些动作有助于在模拟中找到低能构象。 ESMC在克服能源障碍方面做得更好，而SMC效率更高 用于模拟短肽。