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通过蛋白质结构的细化。详细的结构信息对于详细理解生物学过程以及允许对各种疾病的治疗策略的合理发展至关重要。实验方法允许准确确定高分辨率结构，但受到巨大的努力和实验约束的影响。作为替代方案，计算方法可以在一定程度的准确性上预测蛋白质结构。但是，以近实验的准确性预测蛋白质结构一直是一个挑战。可以通过完善初始模型来达到高水平的分辨率。成功的蛋白质结构细化需要采样方法，该方法可以生成类似天然的构象和得分方法，这些方法能够从一组候选者中识别出最大的天然结构，而无需任何真正的实验结构。为了实现这些目标，开发了新的蛋白质结构预测和改进方案。特别是，引入了基于现有和新方法的有效构象抽样策略，以减少构象搜索空间的限制；开发了在选择一组诱饵的精制模型中增强和结合现有评分功能的新型统计方法；开发了中间分辨率模型，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。这些新方法合并为一种集成的改进策略，并在自动蛋白质结构管道的背景下应用。 公共卫生相关性：用于准确预测蛋白质结构的新计算方法是为实验方法的替代而开发的。这样的结构信息是 在理解详细的生物学机制以及允许针对各种疾病的治疗策略中发展至关重要。