Toward experimental quality protein structures: a synergistic approach

迈向实验性优质蛋白质结构：协同方法

• 批准号: 7584457
• 负责人: YONG DUAN
• 金额: $ 35.98万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-01 至 2013-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7584457
• 关键词: Amber; Amino Acid Sequence; Amino Acids; Area; Biological Models; Biological Sciences; Biotechnology; Cells; Communities; Computing Methodologies; Data; Dependence; Development; Drug Industry; Entropy; Evaluation; Event; Family; Free Energy; G-substrate; GTP-Binding Proteins; Gases; Goals; Health; Human; Individual; Investments; Knowledge; Life; Mediating; Methods; Modeling; Molecular; Peptide Sequence Determination; Performance; Physics; Play; Process; Proteins; Resolution; Role; Sampling; Side; Structural Models; Structure; Techniques; Testing; Water; Work; base; blind; drug discovery; flexibility; improved; knowledge base; method development; molecular dynamics; mutant; novel; novel therapeutics; polypeptide; protein folding; protein structure; protein structure prediction; public health relevance; research study; simulation; structural genomics; success; villin

DESCRIPTION (provided by applicant): Understanding the mechanisms of protein folding and misfolding and accurately predicting protein structures are two of the important challenges facing the scientific community. Detailed knowledge of the molecular events leading to the formation of both native and non-native states are the basis for a full elucidation of the protein folding mechanisms. With the rapid progress facilitated by the high-throughput structural characterization of representative protein sequence families, an essential need is the highly accurate computational methods that can reliably generate near-experimental quality structural models for capitalizing on the investment in the structural genomics. Availability of such methods would enable accurate modeling of protein structures which would have significant impact on a range of fields including biotechnology, pharmaceutical industry, drug discovery, and life sciences in general. Duan and Zhou propose to combine the strengths of the groups with complementary expertise to develop computational methods for protein structure modeling and refinement with the ultimate goal to produce highly accurate and reliable methods for protein structure prediction that have comparable accuracy to experimental techniques. Aim 1: Duan and Zhou propose to develop novel conformational sampling method for protein structure refinement in the first specific aim. A recently developed "Grow-to-Fit" method will be utilized and further developed to enable accurate identification of the near-native structures from a large ensemble of perspective protein structures. Further development of the methods will facilitate structural refinement which will help to improve the structures to be comparably accurate as those obtained from experimental techniques. Aim 2: Duan and Zhou propose to develop effective free energy (scoring) functions for accurate all-atom modeling of protein structures. This novel scoring function is based on the synergistic concept of integrating both knowledge-based statistical potential and the all-atom physics-based force field. Furthermore, comparison to the statistical potential will allow critical assessment of the force field parameters and solvation models. Aim 3: Duan and Zhou propose to examine the roles of protein native structure topology in protein folding using FSD1, Protein G and Protein L and their respective topologically distinct mutants as the model systems; to study the dependence of tertiary structure formation on secondary structures. Comparison with experiments, including direct tests on the predictive ability of our model will be an integral part of our study and will be instrumental for a close scrutiny on the approach. PUBLIC HEALTH RELEVANCE: To understand the basic rules of life, how cell works, it is necessary to know the protein structures that are critically important to understand how they work. This proposal is motivated by the need to develop computational method to reliably predict protein structures based on the primary sequence. Because protein structures are also enormously useful in drug discovery, a potential impact of the proposed work in human health is in the area of development of novel therapeutics.

描述（由申请人提供）：了解蛋白质折叠和错误折叠的机制以及准确预测蛋白质结构是科学界面临的两个重要挑战。详细的知识的分子事件，导致形成的天然和非天然状态的基础上充分阐明的蛋白质折叠机制。随着代表性蛋白质序列家族的高通量结构表征的快速进展，一个基本的需求是高度准确的计算方法，可以可靠地产生接近实验质量的结构模型，以利用结构基因组学的投资。这些方法的可用性将使得能够准确地建模蛋白质结构，这将对包括生物技术、制药工业、药物发现和一般生命科学在内的一系列领域产生重大影响。Duan和Zhou建议将联合收割机的优势与互补的专业知识相结合，以开发用于蛋白质结构建模和优化的计算方法，最终目标是产生高度准确和可靠的蛋白质结构预测方法，这些方法具有与实验技术相当的准确性。目的1：Duan和Zhou提出了一种新的构象采样方法，用于蛋白质结构精化。最近开发的“生长适应”方法将被利用和进一步开发，使近天然结构的准确识别从一个大的整体的角度蛋白质结构。这些方法的进一步发展将有助于结构的精细化，这将有助于改善结构，使其与从实验技术中获得的结构一样精确。目标2：Duan和Zhou提出开发有效的自由能（评分）函数，用于蛋白质结构的精确全原子建模。这种新的评分功能是基于集成的协同概念的知识为基础的统计势和全原子物理为基础的力场。此外，比较的统计潜力将允许关键的力场参数和溶剂化模型的评估。目标三：Duan和Zhou建议使用FSD 1，蛋白质G和蛋白质L及其各自的拓扑结构不同的突变体作为模型系统来检查蛋白质天然结构拓扑结构在蛋白质折叠中的作用;研究三级结构形成对二级结构的依赖性。与实验的比较，包括对我们模型的预测能力的直接测试，将是我们研究的一个组成部分，并将有助于对方法进行仔细审查。 公共卫生关系：为了了解生命的基本规则，细胞如何工作，有必要了解蛋白质结构，这对了解它们如何工作至关重要。该提议的动机是需要开发基于一级序列可靠地预测蛋白质结构的计算方法。由于蛋白质结构在药物发现中也非常有用，因此拟议工作对人类健康的潜在影响是在开发新疗法领域。