Metabolomics Tools for Biomedicine

描述（由申请人提供）：本申请中描述的研究的长期目标是开发可用于快速识别人体生物体液中化合物化学结构的计算、分析和数据库工具。这些计算和分析工具将显著改善代谢组学的使用：a)了解疾病机制，b)允许早期疾病诊断，以及c)提高疾病预后的准确性。我们的创新方法是开发算法来预测PubChem和其他大型数据库中包含的化合物的物理/化学性质。所选择的物理/化学性质是那些可以通过hplc -质谱法对任何未知化合物进行实验测量的性质。化学数据库中预测性质与实验性质最接近的化合物被作为最有可能的未知候选物返回。然后，我们将这些工具应用于失血性休克的动物模型和急性创伤儿童。我们之前的工作和初步数据利用预测保留指数、Ecom50、漂移指数和碰撞诱导解离破碎谱的计算模型描述了这种方法的有效性。基于这些有希望的初步数据，我们提出以下具体目标：具体目标1：优化现有的预测模型（正离子Ecom50和正离子漂移指数），并开发新的预测模型，以便使用MolFind/BioSM对大型数据库进行高效搜索。具体目标2：通过生成KEGG， HMDB, FooDB, DrugBank, HumanCyc, Metlin, PlantCyc， Phenol Explorer和脂质地图数据库中所有化合物的人工I期和II期代谢物，构建bbbb2x106化合物的虚拟生化数据库。具体目标3：优化输入参数，开发数据库辅助结构识别（DASI）工具，该工具将产生合理设计的虚拟候选代谢物。具体目标4：验证MolFind/BioSM在猪失血性休克模型中的非靶向代谢组学应用，以及在急性创伤儿童中假设驱动的靶向代谢组学应用。通过促进临床相关生物体液中化合物的快速结构鉴定，这些创新工具将极大地增强代谢组学研究的能力，以补充和协同其他生物医学研究领域，并最终改善人类健康保健。

DESCRIPTION (provided by applicant): The long-term objective of the research described in this application is to develop computational, analytical and database tools that can be used to rapidly identify the chemical structure of compounds in human biofluids. These computational and analytical tools will significantly improve the use of metabolomics for: a) understanding disease mechanisms, b) allowing earlier disease diagnosis, and, c) enhancing the accuracy of disease prognosis. Our innovative approach is to develop algorithms that predict physical/chemical properties of compounds contained in PubChem and other large databases. The physical/chemical properties chosen are those that can be experimentally measured for any unknown compound by HPLC-mass spectrometry. Compounds in chemical databases whose predicted properties most closely match experimental properties are returned as the most likely candidates for the unknown. We will then apply these tools to an animal model of hemorrhagic shock and in children with acute trauma. Our previous work and preliminary data describe the validity of this approach using computational models developed for predicting retention indices, Ecom50, drift index and collision induced dissociation fragmentation spectra. Based on these promising preliminary data, we propose the following specific aims: Specific Aim 1: Optimize current and develop new predictive models (positive ion Ecom50 and positive ion drift index) to allow efficient searching of large databases using MolFind/BioSM. Specific Aim 2: Build a virtual biochemical database of >2x106 compounds by generating in silico human phase I and phase II metabolites of all compounds in the KEGG, HMDB, FooDB, DrugBank, HumanCyc, Metlin, PlantCyc, Phenol Explorer and Lipid Maps databases. Specific Aim 3: Optimize input parameters to develop a database assisted structure identification (DASI) tool that will produce rationally designed virtual candidate metabolites. Specific Aim 4: Validate the use of MolFind/BioSM for non-targeted metabolomics in a swine model of hemorrhagic shock and for hypothesis driven targeted metabolomics in children with acute trauma. By facilitating the rapid structural identification of chemical compounds in clinically relevant biofluids, these innovative tools will greatly enhance the ability of metabolomics studies to compliment and synergize other areas of biomedical research and ultimately improve human health care.