Protein structure refinement through effective sampling and scoring

通过有效采样和评分实现蛋白质结构的精细化 详细的结构信息对于理解生物过程和 为开发针对各种疾病的治疗策略提供了基础 疾病。实验方法允许高分辨率的准确测定 结构，但它们受制于巨大的努力和实验限制。 作为另一种选择，现代计算方法可以预测蛋白质的结构 准确度很好。然而，计算模型往往达不到 实验准确度。计算结构优化旨在改进初始 模型迈向实验质量。成功的蛋白质结构精炼需要 可生成类似原生构象的采样方法和评分方法 能够从一组候选结构中识别最自然的结构，而不需要 对真实的实验结构的了解。在过去进展的基础上与 结构细化方法的发展，新的方法被开发出来，以进一步 提高抽样的质量和效率，选择原生的结构。 一个关键的组成部分是结合使用中间分辨率模型Primo 使用完全原子化的模型。这些方法将应用于对两者的精化 可溶性蛋白和膜结合蛋白。开发一种实用的协议，可以 通过Web服务提供给社区是这项提议的中心目标。

Protein structure refinement through effective sampling and scoring Detailed structural information is essential for understanding biological processes and for providing the basis for the development of therapeutic strategies against a variety of diseases. Experimental methods allow the accurate determination of high-resolution structures, but they are encumbered by significant effort and experimental constraints. As an alternative, modern computational methods can predict protein structures to a good degree of accuracy. However, computational models often do not reach experimental accuracy. Computational structure refinement aims at improving initial models towards experimental quality. 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 knowledge of the true experimental structure. Building on past progress with the development of structure refinement methods, new methods are developed to further improve the quality and efficiency of sampling and the selection of native-like structures. A key component is the use of the intermediate resolution model PRIMO in conjunction with fully atomistic models. These methods will be applied to the refinement of both soluble and membrane-bound proteins. The development of a practical protocol that can be made available to the community via a web service is a central goal of this proposal.