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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要基于物理原理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工具对抗癌症和遗传疾病，并提供新型抗生素和抗病毒药物。 PPI靶向药物作用于癌症相关基因突变引起的细胞PPI网络失衡，阻断细菌通过破坏感染进展所必需的 PPI 来预防病毒感染。为了合理设计这个新对于一类药物，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。机器学习和人工智能技术是开发许多药物设计工具的基础。这些技术依赖于数据库可用于训练模型。然而，这样的 PPI 数据库并不存在，而且可能不可能由于 PPI 接口的独特性而构建。基于物理的方法，如分子动力学模拟，提供了一种在不依赖数据库的情况下开发药物设计工具的原则性方法。虽然生物相关系统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，采用有限数据建模（MELD）方法可以克服这一限制通过使用外部信息。 MELD 已成功用于折叠蛋白质、预测药物结合亲和力、并预测蛋白质二聚体的结构。在本次资助中，我们建议利用 MELD 方法来创建目前设计 PPI 靶向药物时缺少一些计算工具。在目标 1 中，我们建议开发一个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。基准为开发我们的工具将是之前使用其他物理学研究过的小型生物系统—— 基于的方法。我们工具的测试平台将是计算生物学相关的关键属性已经过实验研究的尺寸系统，可在数据库中使用，并且对于任何系统来说都太大了目前可用的计算工具可以解决。我们工具的最终测试将是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，以填补当前 PPI 靶向药物合理设计方面的空白。在漫长的从长远来看，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赠款将成为我们小组未来研究的垫脚石，该研究旨在研究分子遗传疾病（包括癌症）的机制，并设计抗病毒和抗生素药物。