Compound Cardiovascular Activity Prediction Using Structural and Genomic Features

项目摘要 意外的心血管活动在治疗计划失败导致重大损失中起着重要作用 病人的生活和研究时间。因此，采取措施了解和预测药物是当务之急 心血管活动。随着个性化医学时代的发展，基因表达签名的使用 (GES)已成为描述生物过程的一种新工具。这些GES由数量层面组成 作为扰动的结果，在生物系统中表达的信使核糖核酸；然而，它们缺乏关于 潜在的蛋白质结构和功能。已有研究表明，结构基因表达特征 (SGE)集成了来自GES的蛋白质结构信息，产生了可靠的签名， 捕捉细胞对药物等干扰物的反应。具体地说，初步结果表明 这些SGE捕捉到了将化合物结构与心肌细胞的心脏活动联系起来的潜在模式。 这一初步数据，结合了计算和实验工作，是由杰出的 通过这项提案之间的长期合作培育了科学研究的环境 赞助商和共同赞助商。这项培训的目标是磨练技能，弥合信息学、 台式实验，对人体健康以及发展流畅的科学思维都是可以应用的 在未来的职业生涯中成为内科科学家。这项提案发起人的指导，机会在 ISMMS从不同的合作和经验中学习，以及本文件中概述的科学计划 所有的建议都有助于这一培训计划的力量实现这一目标。具体地说，这个项目将 通过添加从结构和功能派生的多个功能来扩展SSES工具，例如 二级结构和蛋白质紊乱，并将由此产生的信号应用于心血管活动 理解和预测一样重要。最终的SGES工具将在网络服务器上公开提供， 它已经建好了。接下来，将根据化合物的签名为其生成简档， 结构，并在FDALabel数据库中记录心血管活动。这些配置文件的使用将有两个 收牌。第一个将作为集成学习算法的训练数据，该算法将预测药物结构 从而为从疾病中产生从头合成化合物迈出了有价值的第一步 签名。这些曲线的第二个用途将是创建将化学结构与心血管联系起来的地图 活动。根据这些计算目标预测的心血管活动将得到实验验证 以细胞为基础的心脏毒性测定，如HERG试验，这是一种常用的一线筛查 心血管毒性。最终，该项目的完成将导致开发有用的、 经过验证的、公开可用的工具，用于了解和预测心血管活动并准备 调查员以内科科学家的身份进行科学研究。

Project Summary Unexpected cardiovascular activity plays a substantial role in therapeutic program failure leading to major loss of patient life and research time. Therefore, it is imperative that steps be taken to understand and predict drug cardiovascular activity. As the age of personalized medicine advances, the use of Gene Expression Signatures (GES) has emerged as a new tool to describe biological processes. These GES consist of the quantitative levels of mRNA expressed in a biological system as a result of a perturbation; however, they lack information about underlying protein structure and function. It has been shown that Structural Gene Expression Signatures (sGES), which integrate protein structure information derived from GES, produce reliable signatures that capture cellular responses to perturbagens such as drugs. Specifically, preliminary results demonstrate how these sGES capture underlying patterns that link compound structure with cardioactivity in cardyomyocites. This preliminary data, combining computational and experimental work, was made possible by the outstanding environment of scientific inquiry nurtured through the long-standing collaboration between this proposal's sponsor and co-sponsor. The goal of this training is to hone skills that bridge the divide between informatics, bench top experiments, and human health as well as develop fluency in scientific thinking that can be applied to a future career as a physician scientist. The mentorship of this proposal's sponsors, the opportunities at ISMMS to learn from diverse collaborations and experiences, as well as the scientific plan outlined in this proposal all contribute to the strength of this training plan to achieve this goal. Specifically, this project will expand the sGES tool through the addition of multiple features derived from structure and function, such as secondary structure and protein disorder, and apply the resulting signatures to cardiovascular activity understanding as well as prediction. The final sGES tool will be made publically available on a web server, which has already been constructed. Next, profiles will be generated for compounds based on their signature, structure, and recorded cardiovascular activity in the FDALabel database. The use of these profiles will be two fold. The first will be as training data for an ensemble learning algorithm, which will predict drug structure from signature and therefore provide a valuable first step toward generating de novo compounds from disease signatures. The second use of these profiles will be to create a map linking chemical structure to cardiovascular activity. The predicted cardiovascular activities from these computational aims will be experimentally validated with cell based cardiotoxicity assays such as the hERG assay, which is a commonly used as a first line screen for cardiovascular toxicity. Ultimately, the completion of this project will result in the development of useful, validated, publically available tools for understanding as well as predicting cardiovascular activity and prepare the investigator to conduct scientific research as a physician scientist.