Protein structure refinement through effective sampling and scoring

描述（由申请人提供）：通过有效采样和评分进行蛋白质结构细化。详细的结构信息是必不可少的，在详细了解生物学过程，并允许合理的发展，对各种疾病的治疗策略。实验方法允许精确测定高分辨率结构，但受到重大努力和实验限制的阻碍。作为替代，计算方法可以预测蛋白质结构到一定程度的准确性。然而，以接近实验的精度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。成功的蛋白质结构优化需要能够产生类似天然构象的采样方法和能够在不了解真实实验结构的情况下从一组候选物中识别最天然结构的评分方法。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，有效的构象采样策略的基础上，现有的和新的方法与限制，以减少构象搜索空间的介绍;新的统计方法，以提高和联合收割机现有的评分功能，从一组诱饵的精细模型的选择开发;和一个中间分辨率模型PRIMO的开发，以获得更好的平衡之间的精力充沛的准确性，模型分辨率和采样效率。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：新的计算方法，准确预测蛋白质结构的开发作为一种替代实验方法。这种结构信息是 这对于理解详细的生物学机制和允许开发针对各种疾病的治疗策略至关重要。

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.