High-Accuracy Protein Models Derived from Lower Resolution Data

已经组建了一支杰出的国际跨学科团队，将带来各种各样的 蛋白质模型构建方面的专业知识，汇集了化学、物理、计算机等领域的研究人员 科学、数学、结构生物学和生物信息学。专业知识从量子化学到 到机器学习，从数据挖掘到高性能计算。来自协作的核磁共振和 结晶学家将是验证蛋白质模型的关键。提高对蛋白质建模的能力可以 通过加强我们对蛋白质行为的基本了解，并通过 促进更有效地选择用于药物设计的蛋白质靶标。总的目标是提高一个广泛的 一系列蛋白质建模方法，既通过开发新方法，也通过结合这些方法 以前已经开发了。具体目标是：1)改进现有的比较(同源)建模 2)改进折叠识别和从头算过程得到的模型，使其适用于 分子置换。将会有一些新的方法开发。在四个方面做出了努力--数据库， 相互作用势、构象采样和组合方法的优化。我们将发展 使用新雇用包括从亚原子分辨率蛋白质结构中挖掘约束的方法 数据库(包括分辨率为0.85A的结构)。这些将包括结构片段数据库， 以及短程距离分布。这些数据可用于比较模型化结构 与收集到的数据进行对比。使用高分辨率数据将不包括选择更高质量的碎片来 替换模型中质量较差的部分，以挖掘交互潜力，并作为各种 关于蛋白质结构的其他高质量信息。更好地评估蛋白质结构模型 将制定，包括评估蛋白质结构中个别片段的质量； 开发的新指标将用于评估计算机建立的模型、晶体结构的质量 和核磁共振结构，并提供了整个蛋白质模型的预期质量以及其 细分市场。对蛋白质运动进行采样的新方法将被探索。结合不同的方法将导致 在蛋白质结构的计算机建模方面取得了重大进展。将进行广泛的测试和验证 在项目的每个阶段和每个部分都进行了评估，以确保在模型准确性方面有很大的提高。

An outstanding international interdisciplinary team has been assembled that will bring a broad variety of expertise to bear on protein model building, bringing together researchers from chemistry, physics, computer science, mathematics, structural biology, and bioinformatics. The expertise ranges from quantum chemistry to machine learning, and from datamining to high performance computing. Input from collaborating NMR and crystallographers will be essential for validating the protein models. Improving abilities to model proteins can impact public health in important ways by enhancing our basic understanding of protein behavior and by facilitating a more efficient selection of protein targets for drug design. The overall goal is to improve a wide range of protein modeling approaches, both by developing new approaches, and by combining those previously been developed. The specific aims are to: 1) Improve existing comparative (homology) modeling and 2) Improve models obtained by fold-recognition and ab initio procedures to make them useful for molecular replacement. There will be some new methods development. Efforts are in four areas - databases, interaction potentials, conformational sampling, and optimization for combining approaches. We will develop ways to include constraints mined from sub-atomic resolution protein structures using a new HIRES Database (to include structures with resolution < 0.85 A). These will include a structure fragment database, as well as short-range distance distributions. These data can be used to compare modeled structures against the collected data. Uses of the high resolution data will ilclude selecting higher quality fragments to replace poor quality segments in the models, for mining interaction potentials, and as a source of a variety of other high quality information regarding protein structures. Better assessments of protein structural models will be developed, including the assessment of the quality of individual segments within a protein structure; the new metrics developed will be used for assessing the quality of computer-built models, crystal structures and NMR structures, and provide indicators of the expected quality of whole protein models as well as of its segments. New ways to sample protein motions will be pursued. Combining diverse methods will lead to significant gains in the computer modeling of protein structures. Extensive testing and validation will be carried out at each stage and in each part of the project to ensure large gains in model accuracy.