Improving Modeling by Learning from Details of High Accuracy Protein Structures

描述(申请人提供)：蛋白质的功能精确地依赖于它们的结构，在0.1？范围内的细节影响酶催化、致病突变和药物识别。因此，拥有详细和准确的蛋白质结构是现代生物医学研究的基石，美国国立卫生研究院资助蛋白质结构倡议的目标是准确地获得每种蛋白质结构的模型 接近高分辨率的晶体结构。目前基于模板的建模技术非常强大，但还不能提供“接近晶体结构”的质量。测试表明，最好的最小化例程仍然不能始终如一地产生接近晶体结构最终揭示的‘天然’结构的~1？RMSD范围内的紧密同系物的蛋白质模型。为了帮助突破这一障碍，在之前的支持期间，我们使用超高分辨率结构为蛋白质骨架创建了一个构象依赖的理想几何函数库，并表明它的使用提高了蛋白质晶体结构的质量，并有望改善基于模板的模型精细化。我们还发现，超高分辨率晶体结构是蛋白质结构细节的丰富来源，这些细节不能从~1.5-2？分辨率范围的结构中准确获得，因此在当前的能量函数中尚未被完全解释。这里，我们的中心假设是，基于模板的建模精度的主要进步将来自识别和明确考虑蛋白质共价几何的详细特征， 构象和非共价堆积相互作用尚未被表征，现在可以从对高精度超高分辨率蛋白质结构的研究中收集到。我们建议的总体目标是挖掘这些信息，以便将其用于提高预测建模的准确性。现在有了许多超高分辨率结构，实现这一目标的时机已经成熟，可以通过追求与以下三个具体目标相关的目标来实现这一目标：(1)通过创建、优化和实施构象依赖文库来扩大“理想几何函数”范式的影响，其中包括肽的平面性、侧链和顺式多肽；(2)挖掘超高分辨率晶体结构以收集下一代经验能量函数的信息；以及(3)分析在不同环境中求解的超高分辨率蛋白质结构，以产生一套基准测试用例，并开发残基水平评估工具以用于这些测试用例，以评估和完善基于模板的建模优化应用。这项拟议的工作是低成本和低风险的，并且具有很高的实质性影响的可能性，因为它提供的基本信息可以广泛地结合到预测和实验建模应用中，以提高其准确性。它也不同于投入到基于模板的建模中的主要工作。引入这种更高水平的现实主义是改进基于模板的建模的细化步骤和实现蛋白质结构倡议的目标的先决条件。

DESCRIPTION (provided by applicant): The functions of proteins depend exquisitely on their structure, with details at the 0.1 ¿ scale influencing enzyme catalysis, disease-causing mutations, and drug recognition. For this reason, having detailed and accurate structures of proteins is a cornerstone of modern biomedical research, and the NIH funded the Protein Structure Initiative with the goal of obtaining models for every protein structure with an accuracy approaching that of a high-resolution crystal structure. Current technology for template-based modeling is powerful, but cannot yet deliver "near-crystal-structure" quality. Tests show that the best minimization routines still fall short of consistently producing protein models for close homologs that approach within ~1 ¿ rmsd of the 'native' structure as ultimately revealed by crystal structures. To help break through this 1 ¿ barrier, during the previous period of support we used ultrahigh-resolution structures to create a library of conformation- dependent ideal geometry functions for the protein backbone, and showed that its use improves the quality of protein crystal structures and holds promise to improve template-based model refinement. We also discovered that ultrahigh-resolution crystal structures are a rich source of details about protein structure that are not accurately attainable from structures in the ~1.5-2 ¿ resolution range and thus have not yet been fully accounted for in current energy functions. Here, our central hypothesis is that a major step forward in template-based modeling accuracy will come from identifying and explicitly taking into account detailed features of protein covalent geometry, conformation and non-covalent packing interactions that have not yet been characterized, and can now be gleaned from the study of highly accurate ultrahigh-resolution protein structures. The overall goal of our proposal is to mine such information so it can be used to improve the accuracy of predictive modeling. With many ultrahigh-resolution structures now available, the time is ripe to achieve this goal by pursuing three specific aims related to (1) extending the impact of the 'ideal geometry function' paradigm by creating, optimizing, and implementing conformation- dependent libraries accounting for peptide planarity, side chains, and cis-peptides, (2) mining ultrahigh- resolution crystal structures to glean information for next-generation empirical energy functions, and (3) analyzing ultrahigh-resolution protein structures solved in varying environments to produce a set of benchmark test cases and developing residue level assessment tools to use with these test cases to evaluate and hone template-based modeling refinement applications. This proposed work is low cost and low risk, and has a high likelihood of substantial impact as it provides basic information that can be widely incorporated into predictive and experimental modeling applications to improve their accuracy. It is also distinct from major efforts being invested into template-based modeling. Introducing this greater level of realism is a prerequisite to improving the refinement step of template-based modeling and achieving the goals of the Protein Structure Initiative.