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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

基本信息

批准号：
10687235
负责人：
Nicole Zatorski
金额：
$ 4.61万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
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/10687235
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 Marketing Mentorship Messenger RNA Methods Molecular Patients Pattern Pharmaceutical Preparations Pharmacogenomics Physicians Play Process Program Development Proteins Proteome Protocols documentation Reaction Reproducibility Research Research Personnel Role Sampling Scientific Inquiry Scientist Structural Genes Structure Technology Therapeutic Thinking Time Toxic effect Training Transcript Withdrawal Work biological systems career cost drug development drug structure drug withdrawal experience experimental study gene function improved insight learning algorithm next generation novel strategies novel therapeutics personalized medicine predictive tools programs protein folding protein function protein structure response skills strength training three dimensional structure tool transcriptomics web server

项目摘要

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导致重大损失的治疗程序失败中起着重要作用病人的生活和研究时间。因此，必须采取措施来了解和预测药物心血管活动随着个性化医疗时代的发展，基因表达特征的使用 (GES)已经成为描述生物过程的新工具。这些GES包括定量水平作为扰动的结果，在生物系统中表达的mRNA;然而，他们缺乏关于蛋白质的结构和功能。已经表明，结构基因表达特征（sGES），其整合来自GES的蛋白质结构信息，产生可靠的签名，捕捉细胞对药物等干扰物的反应。具体而言，初步结果表明，这些sGES捕获了将化合物结构与心肌细胞中的心脏活性联系起来的潜在模式。这一初步数据，结合计算和实验工作，是由杰出的科学探究的环境，通过这一建议的长期合作，提案国和共同提案国。这项培训的目标是磨练技能，弥合信息学，台式实验，人类健康以及发展流畅的科学思维，可以应用成为一名医生科学家本提案发起人的指导， ISMMS从各种合作和经验中学习，以及本报告中概述的科学计划。所有的建议都有助于本培训计划的力量，以实现这一目标。具体而言，该项目将通过添加源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紊乱，并将产生的签名应用于心血管活动理解和预测。最终的sGES工具将在网络服务器上提供，它已经建成了。接下来，将根据化合物的特征生成化合物的特征，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是两个折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因此为从疾病中重新产生化合物迈出了有价值的第一步签名.这些图谱的第二个用途将是创建一个将化学结构与心血管疾病联系起来的图谱。活动从这些计算目标预测的心血管活动将通过实验验证使用基于细胞的心脏毒性测定，例如hERG测定，其通常用作用于以下的第一线筛选：心血管毒性最终，该项目的完成将导致开发有用的，经验证的，临床上可用的工具，用于理解和预测心血管活动，并准备研究者作为医生科学家进行科学研究。