Ab Initio Structure Prediction of Polypeptides

多肽从头开始结构预测

• 批准号: 6915082
• 负责人: Christodoulos Achilleus Floudas
• 金额: $ 21.97万
• 依托单位: PRINCETON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1996
• 资助国家: 美国
• 起止时间: 1996-05-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6915082
• 关键词: computer simulation; disulfide bond; mathematics; nuclear magnetic resonance spectroscopy; oligopeptides; peptides; protein folding; protein sequence; protein structure; technology /technique development; thermodynamics

DESCRIPTION (provided by applicant): The overall aim of this project is to improve our understanding of the ab initio structure prediction of single chain polypeptides. Based upon our recent advances in (1) free energy calculations of solvated oligopeptines (Klepeis and Floudas, 1999); (b) deterministic global optimization through the alphaBB approach (Adjiman et al., 1998a,b; Floudas, 2000) and its application to solvated oligopeptides (Klepeis et aL, 1998; Klepeis and Floudas, 1999b); and (c) structure refinement of oligepeptides through sparse NMR restraints (Klepeis et al., 1999), the proposed research is directed at determining the secondary and tertiary structure of polypeptides from the sequence of amino acids without using information available from the databases. We plan to focus on the following objectives: (a) Study a new approach for the identification of the helical segments in monomeric polypeptides that is based on entropic and free energy calculations of overlapping pentapeptides, and treatment of the electrostatics through the nonlinear Poisson-Boltzmann equation. (b) Investigate a novel approach, based upon a mixed-integer optimization framework, for the determination of antiparallel, parallel and multistanded beta sheets, as well as the identification of the disulfide bridges. (c) Study a new approach, based on deterministic global optimization and torsion angle dynamics, for the structure refinement of polypeptides in the presence of sparse restraints that arise either from NmR experiments or through secondary structure information provided by approaches such as those proposed in (a) and (b). (d) Investigate a novel framework for the ab initio structure prediction of single chain polypep tides based on the advances in (a), (b) and (c). (e) Develop distributed computing algorithms for (a), (b), (c) and (d), apply them to polypeptides, and develop tools for the ab initio prediction of three-dimensional structures of polypeptides.

描述（由申请人提供）：该项目的总体目标是提高我们对单链多肽从头开始结构预测的理解。基于我们在 (1) 溶剂化寡肽自由能计算方面的最新进展（Klepeis 和 Floudas，1999）； (b) 通过 alphaBB 方法进行确定性全局优化（Adjiman 等人，1998a,b；Floudas，2000）及其在溶剂化寡肽中的应用（Klepeis 等人，1998；Klepeis 和 Floudas，1999b）； (c) 通过稀疏 NMR 限制对寡肽进行结构精炼（Klepeis 等人，1999），拟议的研究旨在从氨基酸序列中确定多肽的二级和三级结构，而不使用数据库中的可用信息。我们计划重点实现以下目标： (a) 研究一种识别单体多肽中螺旋片段的新方法，该方法基于重叠五肽的熵和自由能计算，并通过非线性泊松-玻尔兹曼方程处理静电。 (b) 研究一种基于混合整数优化框架的新方法，用于确定反平行、平行和多链β折叠，以及二硫键的识别。 (c) 研究一种基于确定性全局优化和扭转角动力学的新方法，用于在存在稀疏约束的情况下对多肽进行结构精修，这些稀疏约束要么来自核磁共振实验，要么通过诸如（中提出的方法）提供的二级结构信息a）和（b）。 (d) 基于(a)、(b)和(c)的进展，研究单链多肽从头开始结构预测的新框架。 (e) 开发(a)、(b)、(c)和(d)的分布式计算算法，并将其应用于多肽，并开发从头开始预测多肽三维结构的工具。