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Improving Modeling by Learning from Details of High Accuracy Protein Structures

通过学习高精度蛋白质结构的细节来改进建模

基本信息

批准号：
8895978
负责人：
Paul Andrew KARPLUS
金额：
$ 20.68万
依托单位：
OREGON STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-08-01 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8895978
关键词：
Accounting Automobile Driving Behavior Benchmarking Biomedical Research Catalysis Cells Computers Crystallography Databases Development Drug Formulations Environment Enzymes Experimental Models Funding Generations Geometry Glean Goals Homologous Gene Investments Learning Libraries Life Machine Learning Maintenance Methodology Methods Mining Modeling Molecular Conformation Peptides Pharmaceutical Preparations Process Protein Conformation Protein Structure Initiative Proteins Research Personnel Resolution Resources Risk Side Source Structure Technology Testing Time Torsion United States National Institutes of Health Validation Variant Vertebral column Work base cost design disease-causing mutation falls improved inhibitor/antagonist innovation knowledge base model building molecular mechanics next generation predictive modeling protein function protein structure protein structure prediction tool trend ultra high resolution

项目摘要

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.

描述（由申请人提供）：蛋白质的功能非常依赖于它们的结构，0.1级的细节影响酶催化、致病突变和药物识别。因此，拥有详细而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了蛋白质结构倡议，其目标是精确地获得每种蛋白质结构的模型