Empirical conformation-dependent covalent geometry variation in proteins

描述(由申请人提供)：对蛋白质结构的详细和准确的了解是现代生物医学研究的基石之一，而NIH的一个明确目标是通过准确的实验测定或比较模型建立来确定所有蛋白质的结构。最成功的结构预测方法使用了基于经验知识的能量项，这些能量项来自已知蛋白质结构的特征--最明显的是单残基？？分布、主干相关的侧链旋转体偏好和紧密堆积标准。这些预测程序的一个已知的不切实际的特点是假设主干有一个固定的理想几何形状。这一建议背后的驱动假说是，主链键角和多肽平面性存在作为主链扭角的函数的基本上未被认识到的、但真实的、系统的、显著的和普遍的变化，为了达到比较模型的X射线晶体结构质量，需要适当地考虑这种变化。这项工作的总体目标是为这种共价变化产生准确的经验值，这将导致比较建模和从头结构预测以及X射线结晶学产生的结构的准确性得到明显改善。我们建议通过追求以下三个具体目标来实现这一总体目标：1)设计、开发并提供一个可灵活搜索的数据库，其中包含分辨率高于1.75？(目前为~500,000个残基)的所有已知结构的键长、键角和扭角；2)使用传统的基于查询的和现代机器学习方法从数据库中获得关于局部构象与共价几何变化的系统相关性的准确经验信息；以及3)创建依赖于模构象的预期共价几何库，并促进其纳入比较和晶体蛋白质结构建模的领先应用程序。随着目前已知的超高分辨率晶体结构数量的急剧增加，构建这个蛋白质几何数据库的时机已经成熟，它将提供对关于蛋白质结构的可靠和详细的经验信息的巨大宝库的便捷访问。为了做好这项工作，这项工作将需要对细节的艰苦关注，并熟悉晶体细化的局限性和蛋白质结构的原理。Karplus博士非常适合领导这项工作，因为他在高质量晶体结构测定方面有20多年的跟踪记录，并对蛋白质结构做出了更广泛的见解，其中包括作为该项目基础的共价几何构象依赖于构象变化的开创性表征。在结构预测、晶体精炼和结构验证以及基于知识的库开发方面与世界领先的团体合作，确保将收集到的信息快速有效地转化为蛋白质建模的改进。与公共健康相关：蛋白质负责执行生命的大部分过程，它们的功能微妙地依赖于它们的结构，即使是最微小的结构细节。因此，确定蛋白质的准确结构是现代生物医学研究的基石。这项工作的目的是普遍提高构建蛋白质结构的精确度。

DESCRIPTION (provided by applicant): A detailed and accurate understanding of the structure of proteins is one cornerstone of modern biomedical research, and an explicit goal of the NIH is to define the structure of all proteins either by accurate experimental determination or comparative model-building. The most successful structure prediction approaches employ empirical knowledge-based energy terms derived from features of known protein structures - most notably single-residue ???-distributions, backbone-dependent side chain rotamer preferences, and tight packing criteria. One known unrealistic feature of these prediction programs is the assumption of a fixed ideal geometry for the backbone. The driving hypothesis behind this proposal is that there exists a largely unappreciated but real, systematic, significant and pervasive variation in backbone bond angles and peptide planarity that occurs as a function of backbone torsion angles, and accounting properly for this variation will be required to achieve X-ray crystal structure quality for comparative models. The overall goal of this work is to generate accurate empirical values for this covalent variation that will lead to tangible improvements in the accuracy of structures produced by comparative modeling and de novo structure prediction as well as by X-ray crystallography. We propose to achieve this overall goal by pursuing the following three specific aims: 1) to design, develop, and make available a flexibly-searchable database containing bond lengths, bond angles, and torsion angles for all structures known at better than 1.75 ¿ resolution (currently ~500,000 residues); 2) to use conventional query-based and modern machine learning approaches to derive accurate empirical information from the database about the systematic correlation of local conformation with variations in covalent geometry; and 3) to create a modular conformation-dependent expected covalent geometry library and to facilitate its incorporation into leading applications for comparative and crystallographic protein structure modeling. With the dramatically increased number of ultrahigh-resolution resolution crystal structures now known, the time is ripe for construction of this Protein Geometry Database that will provide facile access to a massive treasure trove of reliable and detailed empirical information about protein structure. To be done well, this work will require painstaking attention to detail and an intimate familiarity with the limitations of crystallographic refinement and the principles of protein structure. Dr. Karplus is well-suited to lead this work as he has a 20+-year track record of quality crystallographic structure determinations combined with contributions of more general insights into protein structure, among them being the pioneering characterization of the conformation-dependent variations in covalent geometry that serves as this project's foundation. Collaborations with world-leading groups in structure prediction, in crystallographic refinement and structure validation, and in knowledge-based library development ensure a rapid and effective translation of the gleaned information into improvements in protein modeling. PUBLIC HEALTH RELEVANCE: Proteins are responsible for carrying out most of the processes of life and their function depends exquisitely on their structure, even on the tiniest structural details. For this reason, determining accurate structures of proteins is a cornerstone of modern biomedical research. This work is aimed at leading to a universal improvement in the accuracy with which protein structure can be built.