High-Accuracy Protein Models Derived from Lower Resolution Data

从较低分辨率数据得出的高精度蛋白质模型

• 批准号: 7304272
• 负责人: Andrzej Kloczkowski
• 金额: $ 24.8万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7304272
• 关键词: Amino Acid Sequence; Area; Arts; Behavior; Bioinformatics; Biology; CASP6 gene; Carbon; Categories; Cellular biology; Cereals; Chemistry; Code; Collaborations; Communicable Diseases; Communities; Computer Simulation; Computers; Consensus; Crystallography; Data; Databases; Detection; Development; Drug Design; Ensure; Enzymes; Evaluation; Foundations; Future; Generations; Genes; Genetic; Genomics; Goals; Guidelines; High Performance Computing; Homology Modeling; Individual; Institutes; International; Internet; Investments; Iowa; Laboratories; Lead; Letters; Machine Learning; Mathematics; Medal; Methodology; Methods; Metric; Mining; Modeling; Molecular; Molecular Biology; Molecular Conformation; Monsters; Motion; Numbers; Organism; Peptide Sequence Determination; Performance; Pharmaceutical Preparations; Physics; Polishes; Principal Investigator; Procedures; Protein Structure Initiative; Proteins; Protocols documentation; Public Health; Purpose; Range; Research; Research Personnel; Research Project Grants; Resolution; Sampling; Score; Site; Solutions; Source; Staging; Standards of Weights and Measures; Structural Models; Structural Protein; Structure; Techniques; Testing; Torsion; Universities; Ursidae Family; Validation; Variant; Wisconsin; Work; base; comparative; computer science; data mining; experience; improved; insertion/deletion mutation; member; method development; molecular mechanics; molecular modeling; network models; novel strategies; numb protein; programs; protein structure; protein structure prediction; quantum; quantum chemistry; reconstruction; research study; restraint; scaffold; simulation; size; software development; structural biology; theories; three dimensional structure; tool

DESCRIPTION (provided by applicant): 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 data-mining to high performance computing. Input from collaborators in NMR and crystallography 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 include 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 their 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.

描述（由申请人提供）：一支优秀的国际跨学科团队已经组建，将带来广泛的专业知识来构建蛋白质模型，汇集来自化学、物理、计算机科学、数学、结构生物学和生物信息学的研究人员。专业知识涵盖从量子化学到机器学习，从数据挖掘到高性能计算。核磁共振和晶体学领域合作者的输入对于验证蛋白质模型至关重要。提高蛋白质建模能力可以通过增强我们对蛋白质行为的基本了解以及促进更有效地选择药物设计的蛋白质靶标来以重要方式影响公共健康。 总体目标是通过开发新方法和结合以前开发的方法来改进各种蛋白质建模方法。具体目标是：(1) 改进现有的比较（同源）建模；(2) 改进通过折叠识别和从头算程序获得的模型，使其可用于分子替换。将会有一些新方法的开发。努力集中在四个领域——数据库、相互作用势、构象采样和组合方法的优化。我们将开发方法来包含使用新的 HIRES 数据库从亚原子分辨率蛋白质结构中挖掘的约束（包括分辨率 <0.85 A 的结构）。这些将包括结构片段数据库以及短程距离分布。这些数据可用于将建模结构与收集的数据进行比较。高分辨率数据的用途将包括选择更高质量的片段来替换模型中的低质量片段，用于挖掘相互作用潜力，以及作为有关蛋白质结构的各种其他高质量信息的来源。 将开发更好的蛋白质结构模型评估，包括评估蛋白质结构内各个片段的质量；开发的新指标将用于评估计算机构建模型、晶体结构和核磁共振结构的质量，并提供整个蛋白质模型及其片段的预期质量指标。将寻求对蛋白质运动进行采样的新方法。结合不同的方法将在蛋白质结构的计算机建模方面取得重大进展。项目的每个阶段和每个部分都将进行广泛的测试和验证，以确保模型准确性的大幅提高。