Computational Core

计算核心

基本信息

批准号：
10213205
负责人：
Ryan Scott Renslow
金额：
$ 35.73万
依托单位：
BATTELLE PACIFIC NORTHWEST LABORATORIES
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2023-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10213205
关键词：
Algorithms Analytical Chemistry Biochemical Biological Markers Biomedical Research Catalogs Chemicals Clinical Communities Computer software Computing Methodologies Coupling Data Data Analyses Development Disease Engineering Equipment and supply inventories Funding Gases Generations Geometry Goals Ions Isomerism Isotopes Knowledge Laboratories Libraries Machine Learning Measurement Measures Metabolic Methodology Methods Mission Modeling Molecular Molecular Conformation Outcome Pacific Northwest Parents Pattern Performance Phase Predictive Value Probability Procedures Property Proteome Research Resources Sampling Shapes Speed Standardization Thermodynamics Training Translating United States National Institutes of Health Work analytical method base chemical property chemical standard cheminformatics computational chemistry computerized tools dietary experimental analysis experimental study genome sequencing in silico innovation instrumentation metabolome metabolomics molecular dynamics next generation novel programs quantum quantum chemistry simulation small molecule small molecule libraries statistics tool user-friendly

项目摘要

COMPUTATIONAL CORE SUMMARY The capability to chemically identify thousands of metabolites and other chemicals in clinical samples will revolutionize the search for environmental, dietary, and metabolic determinants of disease. Through innovations in computational chemistry, we propose to overcome a significant, long-standing obstacle in the field of metabolomics: the absence of methods for accurate and comprehensive identification of small molecules without relying on data from analysis of authentic chemical standards. A paradigm shift in metabolomics, we will use gas-phase molecular properties, collision cross section, MS/MS spectra, accurate mass, and isotopic distribution that can be both accurately predicted computationally and consistently measured experimentally, and which can thus be used for comprehensive identification of the metabolome. The outcomes of this proposal directly advance the mission and goals of the NIH Common Fund by: (i) accurately calculating chemical properties using an integrated, scalable high-performance computational chemistry pipeline, (ii) generating in silico reference data for an initial target of 500,000 molecules comprising both known and novel metabolites, (iii) developing and validating a multi-property feature matching approach for unambiguous chemical identification in biomedical samples, and (iv) disseminating the computational tools, algorithms, and resources . This work is significant because it enables comprehensive and confident chemical measurement of the metabolome. This work is innovative because it utilizes a high- throughput, high-accuracy, quantum-chemistry-based computational and chemical informatics platform to predict physical- chemical properties of metabolites.

计算核心总结以化学方式识别临床样本中数千种代谢物和其他化学物质的能力将带来革命性的变化寻找疾病的环境、饮食和代谢决定因素。通过计算创新化学，我们建议克服代谢组学领域一个重大的、长期存在的障碍：缺乏准确、全面鉴定小分子的方法，无需依赖真实分析数据化学标准。代谢组学的范式转变，我们将使用气相分子特性、碰撞截面、 MS/MS 谱图、精确质量和同位素分布，可以通过计算和精确预测通过实验进行一致测量，因此可用于代谢组的综合鉴定。这该提案的结果通过以下方式直接推进 NIH 共同基金的使命和目标：(i) 准确计算使用集成的、可扩展的高性能计算化学管道计算化学性质，(ii) 生成包含已知和新代谢物的 500,000 个分子的初始目标的计算机参考数据，(iii) 开发并验证多属性特征匹配方法，以实现生物医学样品中明确的化学物质识别， (iv) 传播计算工具、算法和资源。这项工作意义重大，因为它使对代谢组进行全面且可靠的化学测量。这项工作具有创新性，因为它利用了高高通量、高精度、基于量子化学的计算和化学信息学平台，用于预测物理- 代谢物的化学性质。