Accurate Modeling in Structural Genomics

结构基因组学的精确建模

• 批准号: 8118955
• 负责人: MICHAEL LEVITT
• 金额: $ 33.17万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8118955
• 关键词: Amino Acid Sequence; Area; Binding; Binding Proteins; CASP7 gene; CASP8 gene; Complement; Computer Simulation; Data; Data Set; Dimensions; Drug Formulations; Equilibrium; Face; Family; Funding; Generations; Genomics; Homology Modeling; Lead; Ligand Binding; Methodology; Methods; Modeling; Molecular; Molecular Structure; Motion; Nature; Noise; Organism; Peptide Sequence Determination; Personal Communication; Pharmaceutical Preparations; Philosophy; Physics; Plant Roots; Play; Proteins; Role; Sampling; Shapes; Solvents; Staging; Structural Protein; Structure; System; Testing; Theoretical Studies; Time; Torsion; Variant; Weight; Work; base; density; design; improved; knowledge base; method development; molecular dynamics; molecular mechanics; novel; polypeptide; protein function; protein protein interaction; public health relevance; rapid growth; research study; satisfaction; simulation; single bond; small molecule; stereochemistry; structural biology; structural genomics; three dimensional structure

DESCRIPTION (provided by applicant): Modeling three-dimensional structure of protein molecules is of clear biomedical importance, driven by two powerful forces. First is the realization that proteins carry out almost all essential functional and structural tasks in living systems by virtue of their folded shape; almost all drugs depend on a small molecule inhibiting a malfunctioning protein through shape complementarity in three-dimensions. Second is the rapid determination of genomic protein sequence data, doubling in the past 28 months, and complemented by equally rapid determination of novel protein structural data; structural coverage of sequences (percentage of sequences with some structural information) is over 50% and is increasing thanks to structural genomics initiatives. This proposal continues previous aims by developing and improving methods for accurate homology modeling (have known structure of a related sequence). Current aims extend to the general problem of ab initio structure prediction (no structure of any related sequence). Such extension is possible due to recent progress and a realization that both homology modeling and ab initio structure prediction share a common philosophy rooted in the decoy / discriminate paradigm we pioneered in 1995. Specifically, both protein modeling and structure prediction have four inter-related stages: (a) Formulation of energy functions, (b) Application of move sets, (c) Generation of decoy structures and (d) Assessment of predicted structures. These four steps are iterated to improve both decoys and energy functions so as to obtain ever better predicted structures. Analysis of experimentally determined sequences and structures goes hand in hand with this planned modeling to give as an over-view of the extent of the problem and the progress made in the field. Drawn to such an analysis in the previous funding period, we expect to continue this activity with particular focus on the 'dark matter', those sequences for which we have least information. We are well-aware that these are ambitious aims but are encouraged by recent progress. Our methodology uses knowledge-based or statistical energy functions, but our philosophy is very rooted in the physical nature of the systems. As such, our work will have far-reaching applications to theoretical studies of molecular function including ligand binding modeling, protein-protein interaction modeling and more general simulation of protein function. Our five specific aims are: (1) Better knowledge-based energy functions, (2) General and novel move sets, (3) Decoy generation by uniform sampling and powerful search and (4) Assessment of structures to reveal deficiencies and (5) Analysis of uncharacterized sequence in terms of clustering sequence domains into new families. Achieving these aims will advance our fundamental understanding of the molecular structure: predicted molecular structure can guide experiments and lead to further understanding of molecular mechanisms. PUBLIC HEALTH RELEVANCE: Modeling three-dimensional structures of protein molecules is of clear biomedical importance: (1) proteins carry out almost all essential functional and structural tasks in living systems by virtue of their folded shape (almost all drugs depend on a small molecule binding to and inhibiting a malfunctioning protein through shape complementarity in three-dimensions); and (2) the rapid growth of genomic protein sequence data, doubling in the past 28 months. This proposal continues previous aims by developing improved methods for accurate homology modeling (have known structure of a related sequence) and also extends the aims to the general problem of ab initio structure prediction (no structure of any related sequence).

描述（由申请人提供）：蛋白质分子的三维结构建模具有明显的生物医学重要性，由两种强大的力量驱动。首先是认识到蛋白质凭借其折叠形状在生命系统中执行几乎所有基本的功能和结构任务;几乎所有药物都依赖于通过三维形状互补性抑制故障蛋白质的小分子。其次是快速确定基因组蛋白质序列数据，在过去28个月内翻了一番，并通过同样快速确定新的蛋白质结构数据来补充;序列的结构覆盖率（具有一些结构信息的序列的百分比）超过50%，并且由于结构基因组学倡议而不断增加。该提案通过开发和改进用于精确同源性建模（具有相关序列的已知结构）的方法来继续先前的目标。目前的目标扩展到从头计算结构预测的一般问题（没有任何相关序列的结构）。这样的扩展是可能的，由于最近的进展和实现，同源建模和从头计算结构预测共享一个共同的哲学植根于诱饵/歧视范式，我们在1995年开创。具体而言，蛋白质建模和结构预测都有四个相互关联的阶段：（a）能量函数的公式化，（B）移动集的应用，（c）诱饵结构的生成和（d）预测结构的评估。迭代这四个步骤以改进诱饵和能量函数，从而获得更好的预测结构。实验确定的序列和结构的分析与此计划的建模齐头并进，以概述问题的程度和在该领域取得的进展。在前一个资助期进行了这样的分析，我们希望继续这项活动，特别关注“暗物质”，这些序列，我们有最少的信息。我们清楚地意识到这些目标是雄心勃勃的，但最近的进展使我们感到鼓舞。我们的方法使用基于知识的或统计的能量函数，但我们的哲学是非常植根于系统的物理性质。因此，我们的工作将有深远的应用，包括配体结合建模，蛋白质-蛋白质相互作用建模和更一般的模拟蛋白质功能的分子功能的理论研究。我们的五个具体目标是：（1）更好的基于知识的能量函数，（2）通用和新颖的移动集合，（3）通过均匀采样和强大的搜索产生诱饵，（4）评估结构以揭示缺陷，以及（5）根据将序列域聚类到新的家族中来分析未表征的序列。实现这些目标将推进我们对分子结构的基本理解：预测的分子结构可以指导实验，并导致对分子机制的进一步理解。 公共卫生相关性：蛋白质分子的三维结构建模具有重要的生物医学意义：（1）蛋白质在生命系统中几乎所有的基本功能和结构任务都是依靠其折叠的形状完成的（几乎所有的药物都依赖于小分子通过三维形状互补性结合并抑制故障蛋白）;以及（2）基因组蛋白质序列数据的快速增长，在过去28个月内翻了一番。该提案通过开发用于精确同源性建模（具有相关序列的已知结构）的改进方法来延续先前的目标，并且还将目标扩展到从头计算结构预测（没有任何相关序列的结构）的一般问题。