Enabling Rational Design of Drug Targeting Protein-Protein Interactions with Physics-based Computational Modeling

通过基于物理的计算模型合理设计靶向药物的蛋白质-蛋白质相互作用

• 批准号: 10710974
• 负责人: Emiliano Brini
• 金额: $ 18.5万
• 依托单位: ROCHESTER INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710974
• 关键词: Affect; Affinity; Antibiotic Therapy; Antibiotics; Antiviral Agents; Artificial Intelligence; Aspirin; Bacteria; Bacterial Antibiotic Resistance; Bacterial Infections; Beds; Behavior; Benchmarking; Binding; Binding Sites; Cancer Etiology; Cells; Complex; Computer Models; DNA Sequence Alteration; Data; Databases; Development; Diffusion; Disease; Drug Design; Drug Screening; Drug Targeting; Equus caballus; Free Energy; Future; Genetic Diseases; Genetic Predisposition to Disease; Grant; Induced Mutation; Infection; Infection prevention; Inflammation; Knowledge; Life Cycle Stages; Ligands; Machine Learning; Malignant Neoplasms; Measures; Methods; Modeling; Molecular; Molecular Conformation; Mutation; Paxlovid; Peptides; Pharmaceutical Preparations; Pharmacotherapy; Physics; Property; Protein Engineering; Proteins; Protocols documentation; Race; Research; Role; Sampling; Series; Speed; System; Techniques; Testing; Training; Viral; Virus; Virus Diseases; Work; biological systems; cancer therapy; cancer type; computer studies; computerized tools; coronavirus disease; design; dimer; drug discovery; drug-like compound; experimental study; fighting; inhibitor; interest; molecular dynamics; next generation; nirmatrelvir; novel antibiotic class; novel drug class; novel therapeutics; protein folding; protein protein interaction; protein structure prediction; rational design; response; simulation; small molecule; success; tool

Project Summary Enabling Rational Design of Drug Targeting Protein-Protein Interactions with Physics-based Computational Modeling. Current drugs mainly target single proteins; future drugs will modulate the interaction between proteins. Protein- protein interactions (PPIs) are the building blocks of the complex interaction network that regulates cells' and viruses' behaviors and life cycles. PPI-targeting drugs' ability to modulate these networks will provide new tools to fight cancer and genetic diseases, and provide new classes of antibiotics and antivirals. PPI-targeting drugs will act on the imbalance of cells' PPI network caused by cancer-related genetic mutations and block bacterial and viral infections by disrupting PPIs essential for the progression of the infection. To rationally design this new class of drugs, we need to develop computational tools to predict the strength of the interactions between two proteins, and the effects of mutations and drugs on those interactions. Machine learning and artificial intelligence techniques are the basis on which many drug design tools are developed. These techniques rely on databases that can be used to train the models. However, such databases don't exist for PPIs, and might be impossible to build due to the uniqueness of PPI interfaces. Physics-based methods, like molecular dynamics simulations, offer a principled way to develop drug-design tools without relying on the existence of databases. Although the conformational space of biologically relevant systems is typically too vast to be sampled effectively using physics- based simulation techniques, the Modelling Employing Limited Data (MELD) method can overcome this limitation by using external information. MELD has been successfully used to fold proteins, predict drug binding affinities, and predict the structure of protein dimers. In this grant, we propose to leverage the MELD method to create some of the computational tools that are currently missing to design PPI-targeting drugs. In Aim 1, we propose to develop a protocol to quantify the effect that mutations have on proteins' ability to interact. In Aim 2, we propose to develop computational tools to screen drugs based on their effect on PPIs. The benchmark for developing our tools will be small biological systems that have been previously studied using other physics- based approaches. The testing bed for our tools will be the calculation of key properties of biologically relevant size systems that have been studied experimentally, that are available in databases, and that are too big for any currently available computational tools to tackle. The final test for our tools will be the prediction of key properties on systems that our experimental collaborators will help us investigate. At the end of this project, we will have developed and tested the tools to fill the current gap in the rational design of PPI-targeting drugs. In the long term, these tools will allow us to understand the molecular mechanisms of cancer and genetic diseases, and will help the rational design of the next generation of drugs to treat cancer and viral and bacterial infections. These are drugs that will target PPI interactions rather than single proteins. The tools and the knowledge we will acquire from this grant will be the stepping stone for the future research of our group, which aims to study the molecular mechanisms of genetic diseases, including cancer, and design antiviral and antibiotic drugs.

项目摘要 以物理学为基础实现药物靶向蛋白质-蛋白质相互作用的合理设计 计算建模。 目前的药物主要针对单一蛋白质；未来的药物将调节蛋白质之间的相互作用。蛋白质- 蛋白质相互作用(PPI)是复杂的相互作用网络的组成部分，它调节细胞和 病毒的行为和生命周期。PPI靶向药物调节这些网络的能力将提供新的工具 抗击癌症和遗传疾病，并提供新类别的抗生素和抗病毒药物。PPI靶向药物 将作用于癌症相关基因突变引起的细胞PPI网络失衡，并阻断细菌 和病毒感染，通过干扰PPI对感染的进展至关重要。理性地设计这个新的 类药物，我们需要开发计算工具来预测两种药物之间相互作用的强度 蛋白质，以及突变和药物对这些相互作用的影响。机器学习与人工智能 技术是许多药物设计工具开发的基础。这些技术依赖于数据库 可以用来训练模特的东西。然而，这样的数据库不存在于PPI，并且可能不可能 由于PPI接口的唯一性而构建。基于物理的方法，如分子动力学模拟， 提供一种原则性的方法来开发药物设计工具，而不依赖于数据库的存在。尽管 生物相关体系的构象空间通常太大，无法使用物理方法进行有效采样。 基于仿真技术，使用有限数据(MELD)方法的建模可以克服这一限制 通过使用外部信息。MELD已成功地用于折叠蛋白质，预测药物结合亲和力， 并预测蛋白质二聚体的结构。在这笔赠款中，我们建议利用MELD方法来创建 目前设计PPI靶向药物所缺少的一些计算工具。在目标1中，我们建议 开发一种方案来量化突变对蛋白质相互作用能力的影响。在目标2中，我们 建议开发计算工具，根据药物对PPI的影响来筛选药物。的基准 开发我们的工具将是以前使用其他物理研究的小型生物系统- 基于方法。我们工具的测试平台将是计算与生物相关的关键属性 经过实验研究的、可在数据库中使用的大小系统，这些系统太大了 目前可用的计算工具来处理。我们工具的最终测试将是对关键属性的预测 我们的实验合作者将帮助我们研究的系统。在这个项目结束时，我们将拥有 开发并测试了这些工具，以填补目前在合理设计PPI靶向药物方面的空白。在漫长的岁月里 从长远来看，这些工具将使我们能够了解癌症和遗传疾病的分子机制，并将 帮助合理设计下一代治疗癌症以及病毒和细菌感染的药物。这些 是针对PPI相互作用而不是单一蛋白质的药物。我们将获得的工具和知识 这笔资金将为我们小组未来的研究奠定基础，该小组的目标是研究分子 研究包括癌症在内的遗传性疾病的机制，并设计抗病毒和抗生素药物。