Compound Cardiovascular Activity Prediction Using Structural and Genomic Features

使用结构和基因组特征预测复合心血管活动

• 批准号: 10544289
• 负责人: Nicole Zatorski
• 金额: $ 4.66万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10544289
• 关键词: Address; Adverse reactions; Age; Amino Acid Sequence; Biochemical; Bioinformatics; Biological; Biological Assay; Biological Phenomena; Biological Process; Cardiotoxicity; Cardiovascular Agents; Cardiovascular system; Cell physiology; Cells; Chemical Structure; Collaborations; Data; Databases; Development; Disease; Drug Approval; Drug toxicity; Environment; Failure; Future; Gene Expression; Gene Expression Profile; Genes; Genomics; Genotype; Goals; Graph; Health; Human; Informatics; Laboratories; Lead; Learning; Life; Link; Machine Learning; Maps; Mentorship; Messenger RNA; Methods; Molecular; Patients; Pattern; Pharmaceutical Preparations; Pharmacogenomics; Physicians; Play; Process; Program Development; Proteins; Proteome; Protocols documentation; Reproducibility; Research; Research Personnel; Role; Sampling; Scientific Inquiry; Scientist; Structural Genes; Structure; Technology; Therapeutic; Thinking; Time; Toxic effect; Training; Transcript; Withdrawal; Work; base; biological systems; career; cost; drug development; drug structure; experience; experimental study; gene function; improved; insight; learning algorithm; next generation; novel strategies; novel therapeutics; personalized medicine; predictive tools; programs; protein folding; protein structure; protein structure function; response; skills; strength training; three dimensional structure; tool; transcriptomics; web server

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.

项目摘要 意外的心血管活动在治疗程序失败中起着重大作用，导致重大损失 耐心的生活和研究时间。因此，必须采取步骤来理解和预测药物 心血管活动。随着个性化医学的发展，基因表达签名的使用 （GES）已成为描述生物过程的新工具。这些GE由定量水平组成 由于扰动而在生物系统中表达的mRNA；但是，他们缺乏有关的信息 潜在的蛋白质结构和功能。已经表明结构基因表达特征 （SGES）整合从GES得出的蛋白质结构信息的（SGES）产生可靠的特征 捕获对药物等细胞脑扰手的细胞反应。具体而言，初步结果证明了如何 这些SGE捕获了基本模式，将复合结构与心肌岩中的心脏活性联系起来。 出色的 通过该提议的长期合作来培养科学探究的环境 赞助商和共同赞助商。这项培训的目的是磨练技能，使信息学之间的鸿沟弥合鸿沟， 台式实验和人类健康以及可以应用科学思维的流利性 从事医师科学家的未来职业。该提议的赞助商的指导，机会 ISMM可以从各种合作和经验中学习，以及其中概述的科学计划 提案都有助于实现这一目标的实力。具体来说，这个项目将 通过添加来自结构和功能的多个功能来扩展SGES工具，例如 二级结构和蛋白质障碍，并将结果签名应用于心血管活动 理解和预测。最终的SGES工具将在Web服务器上公开可用， 已经构建了。接下来，将根据化合物的签名生成配置文件， FDALABEL数据库中的结构和记录的心血管活动。这些配置文件的使用将是两个 折叠。第一个是作为合奏学习算法的培训数据，该算法将预测药物结构 从签名中，因此提供了从疾病产生从头化合物的宝贵第一步 签名。这些轮廓的第二种用途是创建一个将化学结构与心血管联系起来的地图 活动。这些计算目标的预测心血管活动将经过实验验证 使用基于细胞的心脏毒性测定，例如HERG分析，该测定通常用作第一行屏幕 心血管毒性。最终，该项目的完成将导致有用的发展， 经过验证的公开可用工具，用于理解和预测心血管活动并准备 研究人员作为医师科学家进行科学研究。