Predicting adverse drug reactions via networks of drug binding pocket similarity

通过药物结合袋相似性网络预测药物不良反应

• 批准号: 10750556
• 负责人: Kristy Carpenter
• 金额: $ 4.77万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-30 至 2025-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10750556
• 关键词: 3-Dimensional; Acceleration; Address; Adverse reactions; Affinity; Algorithms; Binding; Binding Proteins; Binding Sites; Bioinformatics; Cessation of life; Clinical Trials; Collaborations; Communication; Computing Methodologies; Data; Databases; Detection; Drug Binding Site; Drug Design; Drug Interactions; Drug Targeting; Educational process of instructing; Emergency department visit; Environment; Event; Failure; Goals; Graph; Health; Hospitalization; Hospitals; Human; Human body; Informatics; Knowledge; Learning; Ligand Binding; Ligands; Location; Membrane Proteins; Mentors; Methods; Molecular; Molecular Structure; Network-based; Oral; Pathway Analysis; Patients; Pharmaceutical Preparations; Protein Structure Databases; Proteins; Proteome; Recording of previous events; Research; Research Personnel; Resources; Scanning; Structure; Surface; Therapeutic Effect; Toxic effect; Training; Universities; Visit; Work; Writing; adverse drug reaction; career; deep learning; dosage; drug development; drug repurposing; education resources; experience; graduate student; improved; knowledge base; member; novel; novel therapeutics; pharmacologic; prediction algorithm; protein data bank; protein structure; protein structure prediction; response; small molecule; structural biology; success; symposium; wasting

PROJECT SUMMARY The CDC estimates that adverse drug reactions (ADRs) cause 1.3 million emergency department visits annually in the U.S., and that hundreds of thousands of these patients require hospitalization. ADRs are often caused by drugs binding proteins in the body that were not intended targets. Predicting this off-target binding is difficult. There are methods that use 3D molecular structure to predict if a small molecule can bind a given protein, but the majority of the human proteome does not have an experimentally-solved structure. Recent breakthroughs in protein structure prediction have enabled high confidence prediction of nearly any protein's structure from sequence alone, meaning that we can leverage structure information for the entire human proteome in a way that was impossible two years ago. Additionally, recent advances in structural informatics algorithms have improved our ability to identify locations on a protein surface with high binding propensity; despite this, current ADR prediction algorithms are unable to both leverage binding information of functionally uncharacterized proteins and make interpretable predictions that can guide drug design. I propose to create methods to predict drug binding pockets and ADRs in an interpretable manner at the proteome scale. I will accomplish this by 1) building a graph representation of known and predicted drug-pocket pairs; 2) using this graph to estimate ADRs associated with pockets and drugs; and 3) extending the pocket and ADR prediction methods to predict and explain ADRs caused by proteome-wide off-target binding. Application of the proposed method to the entire human proteome will allow the prediction of a drug's potential ADRs before it is used in humans, improving drug development and reducing the number of ADRs experienced. I will conduct this project in the lab of Dr. Russ Altman at Stanford University, where I am working toward my long-term career goal of becoming an independent researcher developing computational methods that accelerate drug development and aid understanding of drug response at the molecular level. My training environment sets me up well to achieve this goal as Dr. Altman has an excellent track record of mentoring graduate students and Stanford University provides a plethora of educational resources and a highly collaborative research environment. The Altman group has developed algorithms for characterizing protein microenvironments and has a history in both computational structural biology and drug response research, providing me with easy access to experts in domains highly relevant to my proposed work. Beyond the proposed research, my training plan includes attending seminars and conferences, collaborating with other research groups, taking additional coursework, teaching, and oral and written communication of my work.

项目摘要 疾病预防控制中心估计，不良药物反应（ADR）每年130万急诊室访问 在美国，成千上万的患者需要住院。 ADR通常是由 人体中没有预期靶标的药物结合蛋白。很难预测这种脱靶结合。 有一些方法使用3D分子结构来预测小分子是否可以结合给定的蛋白质，但是 大多数人蛋白质组没有实验分解的结构。最近的突破 在蛋白质结构的预测中，几乎可以从任何蛋白质结构中进行高度置信 仅序列，这意味着我们可以以某种方式利用整个人类蛋白质组的结构信息 这是两年前不可能的。此外，结构信息学算法的最新进展具有 提高了我们识别具有高结合倾向的蛋白质表面位置的能力；尽管如此， 当前的ADR预测算法既无法利用功能上的绑定信息 未表征的蛋白质并做出可解释的预测，可以指导药物设计。我建议创建 以蛋白质组量表可解释的方式预测药物结合口袋和ADR的方法。我会 通过1）构建已知和预测的药袋对的图表； 2）使用此 图表以估计与口袋和药物相关的ADR； 3）扩展口袋和ADR预测 预测和解释由全蛋白质组脱靶结合引起的ADR的方法。拟议的应用 整个人类蛋白质组的方法将允许对药物的潜在ADR进行预测 人类，改善药物开发并减少经历的ADR数量。 我将在斯坦福大学的Russ Altman博士的实验室中进行该项目，在那里我正在努力 长期职业目标是成为开发计算方法的独立研究人员 加速药物开发并有助于对分子水平的药物反应理解。我的训练 由于Altman博士拥有指导的出色记录，环境使我很高兴实现这一目标 研究生和斯坦福大学提供了大量的教育资源和高度 协作研究环境。 Altman组开发了用于表征蛋白质的算法 微环境，并且在计算结构生物学和药物反应研究中都有历史， 让我轻松地访问与我建议的工作高度相关的领域专家。超越 拟议的研究，我的培训计划包括参加研讨会和会议，与其他合作 研究小组对我的工作进行其他课程，教学以及口头和书面交流。