AI-powered chemical proteomics for drug discovery targeting orphan proteins

基于人工智能的化学蛋白质组学，用于针对孤儿蛋白的药物发现

• 批准号: 10651934
• 负责人: Lei Xie
• 金额: $ 46.8万
• 依托单位: HUNTER COLLEGE
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651934
• 关键词: Acceleration; Address; Adopted; Advocate; Affinity; Agonist; Area; Big Data; Binding; Biomedical Research; Chemicals; Clinical; Collaborations; Communities; Complex; Computer Models; Computing Methodologies; Data; Data Set; Disease; Docking; Dopamine Antagonists; Drug Design; Drug Modelings; Drug Targeting; Effectiveness; Failure; Genes; Genome; Genomics; Glioblastoma; Health; Human; Human Genome; In Vitro; Interdisciplinary Study; Knowledge; Laboratories; Lead; Ligands; Link; Machine Learning; Malignant Neoplasms; Marketing; Methodology; Methods; Modernization; Orphan; Outcome; Pharmaceutical Preparations; Pharmacologic Substance; Pharmacology; Phase III Clinical Trials; Process; Proteins; Proteome; Proteomics; Research; Resort; Structure; System; Techniques; Testing; Time; Training; United States National Institutes of Health; Vision; antagonist; anti-cancer; biological systems; cancer therapy; computer framework; computer infrastructure; computerized tools; cost; deep learning; deep learning algorithm; design; drug action; drug candidate; drug development; drug discovery; drug efficacy; drug repurposing; druggable target; experience; functional genomics; genome sequencing; genome wide association study; genome-wide; improved; in vivo; inhibitor; innovation; laboratory experiment; learning strategy; molecular dynamics; new therapeutic target; novel; novel strategies; novel therapeutics; opioid use disorder; precision medicine; programs; screening; side effect; structural genomics; success; translational applications; welfare; whole genome

Abstract Genome-Wide Association Studies, whole-genome sequencing, and high-throughput techniques have generated vast amounts of diverse omics data. However, these sets of data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery. Only 5-10% of druggable proteins are targeted by approved drugs. The undrugged orphan proteins are potential targets of yet-incurable diseases but whose endogenous and exogeneous ligands are unknown. Furthermore, there is a knowledge gap to link drug-target binding affinities to clinical outcomes. We know little if the target is activated or inhibited by the binder (i.e., function activity: agonist vs. antagonist). To date, few experimental and computational tools can determine genome-wide protein-ligand interactions (PLIs) for orphan proteins and ligand-induced functional activities (LIFAs) for both orphan proteins and majority of well-studied proteins. Existing machine learning techniques are mostly unsuccessful in predicting the ligand of orphan proteins due to an out-of-distribution (OOD) problem, i.e., they cannot reliably predict the function of an unseen protein if it is significantly different from the proteins in the training data in terms of sequence and structure. Commonly used computational tools for structure-based drug design, such as protein-ligand docking/scoring and Molecular Dynamics simulations, are neither scalable nor particularly reliable. As a result, we only have a limited capability of compound screening for orphan proteins. This proposal seeks to develop and experimentally validate innovative methods for predicting genome-wide PLIs and LIFAs to address aforementioned challenges. Building on our successful proof-of-concept studies and our close multidisciplinary collaborations between experimental and computational laboratories, we will develop a novel computational framework to model drug actions on a multi-scale by integrating big data from chemical and structural genomics and developing innovative deep learning algorithms. Specifically, we will develop a structure- enhanced deep learning framework to reliably and accurately predict protein-ligand interactions for orphan proteins on a genome-scale. We will integrate functional genomics with chemical genomics to predict ligand- induced functional activity. We will apply the methods developed to design and experimentally test inhibitors of orphan anti-cancer target AVIL and dual antagonists of dopamine receptors for opioid use disorder (OUD). The proposed research offers an innovative concept, methodology, and translational applications. Completing this research will fill a critical knowledge gap in understanding drug actions in a biological system and significantly impact drug discovery for complex diseases, many of which lack effective and safe treatments. The developed methodology and platform will not only immediately impact the NIH’s “Illuminating the Druggable Genome” Program but also has potentially broad applications in other areas of biomedical research.

摘要 全基因组关联研究、全基因组测序和高通量技术 产生了大量不同的组学数据。然而，这些数据集尚未得到充分探索， 提高药物发现的有效性和效率。只有5-10%的可药物蛋白质是药物的目标 批准的药物。未被破坏的孤儿蛋白是尚未治愈的疾病的潜在靶点， 内源和外源配体是未知的。此外，在将药物与靶点联系起来方面存在知识差距， 将亲和力与临床结果结合起来。我们几乎不知道靶标是否被结合剂激活或抑制（即， 功能活性：激动剂对拮抗剂）。到目前为止，很少有实验和计算工具可以确定 孤儿蛋白和配体诱导的功能活性的全基因组蛋白-配体相互作用（PLI） 对于孤儿蛋白和大多数充分研究的蛋白质，可以使用LIFAs。现有的机器学习技术 由于分布外（OOD）问题，即， 他们不能可靠地预测一个看不见的蛋白质的功能，如果它是显着不同的蛋白质， 在序列和结构方面训练数据。常用的基于结构的药物计算工具 设计，如蛋白质-配体对接/评分和分子动力学模拟，既不可扩展， 特别可靠。因此，我们对孤儿蛋白的化合物筛选能力有限。 该提案旨在开发和实验验证预测全基因组PLIs的创新方法 和LIFAs来应对上述挑战。基于我们成功的概念验证研究和我们的 实验和计算实验室之间的密切多学科合作，我们将开发一个 一种新的计算框架，通过整合来自化学和生物医学领域的大数据， 结构基因组学和开发创新的深度学习算法。具体来说，我们将建立一个结构- 增强的深度学习框架可以可靠准确地预测孤儿的蛋白质-配体相互作用 蛋白质的基因组规模。我们将整合功能基因组学和化学基因组学来预测配体- 诱导功能活动。我们将应用开发的方法来设计和实验测试抑制剂的 孤儿抗癌靶点AVIL和阿片类药物使用障碍（OUD）的多巴胺受体双重拮抗剂。的 拟议的研究提供了一个创新的概念，方法和翻译应用。完成本 研究将填补在理解药物在生物系统中的作用方面的关键知识空白， 影响复杂疾病的药物发现，其中许多缺乏有效和安全的治疗方法。发达 方法和平台不仅会立即影响美国国立卫生研究院的“阐明可药用基因组” 计划，而且在生物医学研究的其他领域也有潜在的广泛应用。

项目成果

DeepREAL: a deep learning powered multi-scale modeling framework for predicting out-of-distribution ligand-induced GPCR activity.

• DOI: 10.1093/bioinformatics/btac154
• 发表时间: 2022-04-28
• 期刊: Bioinformatics (Oxford, England)

A universal framework for accurate and efficient geometric deep learning of molecular systems.

• DOI: 10.1038/s41598-023-46382-8
• 发表时间: 2023-11-06
• 期刊: Scientific reports
• 影响因子: 4.6

ANTENNA, a Multi-Rank, Multi-Layered Recommender System for Inferring Reliable Drug-Gene-Disease Associations: Repurposing Diazoxide as a Targeted Anti-Cancer Therapy.

• DOI: 10.1109/tcbb.2018.2812189
• 发表时间: 2018-11
• 期刊: IEEE/ACM transactions on computational biology and bioinformatics
• 作者: Wang A;Lim H;Cheng SY;Xie L
• 通讯作者: Xie L

Small molecule modulation of microbiota: a systems pharmacology perspective.

• DOI: 10.1186/s12859-022-04941-2
• 发表时间: 2022-09-29
• 期刊: BMC BIOINFORMATICS
• 影响因子: 3
• 作者: Liu, Qiao;Lee, Bohyun;Xie, Lei
• 通讯作者: Xie, Lei

Mining FDA resources to compute population-specific frequencies of adverse drug reactions.

• DOI: 10.1109/bibm.2017.8217935
• 发表时间: 2017-11
• 期刊: Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
• 作者: Poleksic A;Turner C;Dalal R;Gray P;Xie L
• 通讯作者: Xie L