Integrated Prediction and Validation of Protein Structures

蛋白质结构的综合预测和验证

基本信息

批准号：
9119094
负责人：
Jianlin Cheng
金额：
$ 32.59万
依托单位：
UNIVERSITY OF MISSOURI-COLUMBIA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-06-01 至 2019-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9119094
关键词：
Address Amino Acid Sequence Amino Acids Benchmarking Biochemical Bioinformatics Biological Biological Assay Biological Process Biomedical Research Collaborations Communities Computational Biology Computer Simulation Computing Methodologies Data Databases Disease Enzymes Epilepsy Feedback Foundations Future Genes Genomics Hybrids Inherited Investigation Knowledge Learning Link Machine Learning Manuals Maps Metabolic Diseases Methods Missense Mutation Modeling Molecular Molecular Conformation Mutagenesis Mutate Mutation NMR Spectroscopy Nuclear Magnetic Resonance Output Peptide Sequence Determination Positioning Attribute Probability Protein Conformation Protein Engineering Proteins Resolution Roentgen Rays Sampling Science Site Software Tools Space Models Statistical Models Structure Study models Techniques Technology Tertiary Protein Structure Testing Time Training Validation Vitamin B6 X-Ray Crystallography aldehyde dehydrogenases base cost data mining design drug discovery engineering design flexibility improved innovation learning network markov model novel protein folding protein protein interaction protein structure protein structure function protein structure prediction public health relevance research study structural biology success tool user-friendly web services web site

项目摘要

DESCRIPTION (provided by applicant): Knowledge of three-dimensional protein structure is indispensable in biomedical research. Protein structure and function are intimately linked, and thus structure facilitates drug discovery, aids investigations of protein-protein interactions, informs mutagenesis analysis, guides protein engineering and the design of new proteins, and provides a foundation for understanding the molecular basis of disease. However, the number of protein sequences available in the genomic era far exceeds the capacity of the main experimental structure determination techniques of X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, resulting in a substantial sequence- structure gap. We address this ever-widening gap by developing and disseminating novel protein structure modeling tools. This renewal project is a new collaboration between experts in computational modeling (Cheng) and experimental structural biology (Tanner). We plan to develop innovative, integrated machine learning (e.g., deep learning), data mining and statistical modeling methods to address major challenges in both template-based structure modeling and template-free (ab initio) structure modeling. We will apply these tools to enzymes in the aldehyde dehydrogenase (ALDH) superfamily, a group of enzymes that are involved in numerous important biological processes and implicated in many diseases due to mutations. The ALDH models will be experimentally validated using X-ray crystallography and biochemical assays. Furthermore, we will combine the modeling power of our structural Input-Output hidden Markov model with experimental small- angle X-ray scattering (SAXS) to predict the tertiary structures of large multi-domain proteins. The integration of computational and experimental sciences in this project positions us uniquely in structure modeling space.

描述（由申请人提供）：三维蛋白质结构的知识在生物医学研究中是必不可少的。蛋白质结构和功能密切相关，因此结构有助于药物发现，帮助研究蛋白质-蛋白质相互作用，为突变分析提供信息，指导蛋白质工程。以及新蛋白质的设计，并为理解疾病的分子基础提供了基础。然而，基因组时代可用的蛋白质序列数量远远超过了X射线晶体学主要实验结构测定技术的能力。和核磁共振（NMR）光谱，导致了巨大的序列结构差距，我们通过开发和传播新型蛋白质结构建模工具来解决这一不断扩大的差距，这个更新项目是计算建模专家之间的新合作。我们计划开发创新的、集成的机器学习（例如深度学习）、数据挖掘和统计建模方法，以解决基于模板的结构建模和无模板（ab initio）结构中的主要挑战。我们会建模。将这些工具应用于乙醛脱氢酶 (ALDH) 超家族中的酶，该家族涉及许多重要的生物过程，并因突变而与许多疾病有关。ALDH 模型将使用 X 射线晶体学和生化测定进行实验验证。此外，我们将把结构输入输出隐马尔可夫模型的建模能力与实验性小角度 X 射线散射 (SAXS) 相结合，以预测大型结构的三级结构。该项目中计算科学和实验科学的整合使我们在结构建模领域处于独特地位。