Protein structure refinement through effective sampling and scoring

通过有效采样和评分细化蛋白质结构

• 批准号: 8142756
• 负责人: Michael Feig
• 金额: $ 24.76万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8142756
• 关键词: Biological; Biological Process; Computing Methodologies; Development; Disease; Drug Design; Equilibrium; Goals; Knowledge; Methods; Modeling; Molecular Conformation; Nucleic Acids; Plant Roots; Proteins; Protocols documentation; Resolution; Sampling; Scoring Method; Sequence Homology; Statistical Methods; Structure; Testing; Time; base; comparative; fight against; improved; insight; novel; protein structure; protein structure prediction; public health relevance; therapeutic development

DESCRIPTION (provided by applicant): Protein structure refinement through effective sampling and scoring. Detailed structural information is essential in understanding biological processes in detail and in allowing the rational development of therapeutic strategies against a variety of diseases. Experimental methods allow the accurate determination of high-resolution structures, but are encumbered by significant effort and experimental constraints. As an alternative, computational methods can predict protein structures to some degree of accuracy. However, it has remained a challenge to routinely predict protein structures at near-experimental accuracy. A high level of resolution may be reached through refinement of initial models. Successful protein structure refinement requires sampling methods that can generate native-like conformations and scoring methods that are able to identify the most native structures from a set of candidates without any knowledge of the true experimental structure. In order to achieve these goals novel protein structure prediction and refinement protocols are developed. In particular, effective conformational sampling strategies based on existing and new methods with constraints to reduce conformational search space are introduced; novel statistical methods to enhance and combine existing scoring functions in the selection of refined models from a set of decoys are developed; and an intermediate resolution model PRIMO is developed to obtain a better balance between energetic accuracy, model resolution, and sampling efficiency. These new methods are combined into an integrated refinement strategy and applied in the context of an automated protein structure pipeline. PUBLIC HEALTH RELEVANCE: New computational methods for the accurate prediction of protein structures are developed as an alternative to experimental approaches. Such structural information is crucial in understanding detailed biological mechanisms and allowing the development of therapeutic strategies against a variety of diseases.

描述（由申请人提供）：通过有效采样和评分来细化蛋白质结构。详细的结构信息对于详细了解生物过程以及合理开发针对各种疾病的治疗策略至关重要。实验方法可以准确确定高分辨率结构，但受到大量工作和实验限制的阻碍。作为替代方案，计算方法可以在一定程度上准确地预测蛋白质结构。然而，以接近实验精度的方式常规预测蛋白质结构仍然是一个挑战。通过改进初始模型可以达到高水平的分辨率。成功的蛋白质结构精炼需要能够生成类似天然构象的采样方法和能够从一组候选物中识别出最天然的结构的评分方法，而无需了解真实的实验结构。为了实现这些目标，开发了新的蛋白质结构预测和细化方案。特别是，介绍了基于现有方法和新方法的有效构象采样策略，并具有减少构象搜索空间的约束；开发了新的统计方法，以增强和结合现有的评分函数，以从一组诱饵中选择精炼模型；并开发了中间分辨率模型PRIMO，以在能量精度、模型分辨率和采样效率之间获得更好的平衡。这些新方法被组合成一个集成的细化策略，并应用于自动化蛋白质结构管道的背景下。 公共卫生相关性：开发了用于准确预测蛋白质结构的新计算方法，作为实验方法的替代方法。这样的结构信息是 对于理解详细的生物学机制和制定针对各种疾病的治疗策略至关重要。