Empirical conformation-dependent covalent geometry variation in proteins

蛋白质中经验构象依赖性共价几何变化

• 批准号: 7525973
• 负责人: Paul Andrew KARPLUS
• 金额: $ 21.38万
• 依托单位: OREGON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7525973
• 关键词: Accounting; Attention; Automobile Driving; Biomedical Research; Catalysis; Classification; Collaborations; Communities; Crystallography; Databases; Development; Ensure; Enzymes; Facility Construction Funding Category; Familiarity; Foundations; Glean; Goals; Heart; Homology Modeling; Investments; Knowledge; Lead; Length; Letters; Libraries; Life; Machine Learning; Methodology; Mining; Modeling; Molecular Conformation; Numbers; Object Attachment; Online Systems; Other Resources; Pattern; Peptides; Personal Satisfaction; Pharmaceutical Preparations; Process; Protein Conformation; Protein Structure Initiative; Proteins; Public Health; Research Personnel; Resolution; Resources; Risk; Roentgen Rays; Side; Structure; Technology; Time; Torsion; Translations; United States National Institutes of Health; Validation; Variant; Vertebral column; Work; X-Ray Crystallography; base; comparative; cost; design; disease-causing mutation; falls; inhibitor/antagonist; innovation; insight; knowledge base; molecular mechanics; novel; predictive modeling; preference; programs; protein structure; protein structure prediction; software development; structural biology; structural genomics

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.

详细而准确地了解蛋白质的结构是现代生物医学研究的基石之一，NIH的明确目标是通过准确的实验测定或比较模型构建来定义所有蛋白质的结构。最成功的结构预测方法采用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--最著名的是单残基？分布、主链依赖性侧链旋转异构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征是假设骨干具有固定的理想几何形状。这一提议背后的驱动假设是，在骨架键角和肽平面性中存在很大程度上未被认识到但真实的、系统的、显著的和普遍的变化，其作为骨架扭转角的函数发生，并且需要适当地解释这种变化以实现比较模型的X射线晶体结构质量。这项工作的总体目标是为这种共价变异产生准确的经验值，这将导致通过比较建模和从头结构预测以及X射线晶体学产生的结构的准确性的切实改善。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，开发和提供一个灵活的可搜索数据库，其中包含所有已知结构的键长，键角和扭转角，分辨率优于1.75 <$s（目前约500，000残留物）; 2）使用常规查询-基于现代机器学习方法，从数据库中获得关于局部构象与共价几何结构变化的系统相关性的准确经验信息;以及3）创建模块化构象依赖性预期共价几何形状库，并促进其并入用于比较和晶体学蛋白质结构建模的主要应用中。随着现在已知的超高分辨率晶体结构数量的急剧增加，构建蛋白质几何数据库的时机已经成熟，该数据库将提供轻松访问大量关于蛋白质结构的可靠和详细的经验信息的宝库。要做好这项工作，需要对细节的细致关注，并熟悉晶体学精炼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，因为他有20多年的高质量晶体结构测定记录，结合对蛋白质结构的更一般见解的贡献，其中包括作为该项目基础的共价几何构型依赖性变化的开创性表征。与世界领先的结构预测、晶体学优化和结构验证以及基于知识的库开发团队的合作，确保了将收集到的信息快速有效地转化为蛋白质建模的改进。公共卫生相关性：蛋白质负责执行大多数生命过程，它们的功能精确地取决于它们的结构，即使是最微小的结构细节。因此，确定蛋白质的精确结构是现代生物医学研究的基石。这项工作旨在普遍提高蛋白质结构构建的准确性。