A data-driven approach towards generation of permeable peptide therapeutics

生成可渗透肽疗法的数据驱动方法

• 批准号: 10241206
• 负责人: Parisa Hosseinzadeh
• 金额: $ 130.19万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10241206
• 关键词: Address; Affinity; Antibodies; Artificial Membranes; Categories; Cell membrane; Cells; Computational Technique; Computational algorithm; Computing Methodologies; Data; Data Set; Development; Disease; Drug Targeting; Drug usage; Generations; Libraries; Machine Learning; Mammalian Cell; Methods; Modality; Modeling; Modification; Peptides; Permeability; Pharmaceutical Preparations; Physics; Research Personnel; Surface; Testing; Therapeutic; Therapeutic Uses; Work; base; design; drug discovery; falls; interdisciplinary approach; interest; next generation; novel; peptide drug; rapid technique; screening; small molecule; therapeutic development; uptake

Project Summary A staggering number of potential targets for combating diseases fall into a category called undruggable, targets that are not accessible to two commonly used drug modalites: antibodies and small molecules. These undruggable targets often reside inside the cells and cannot be accessed by antibodies, which are too large to cross the cell membrane. Their flat surfaces deviate from the common deep pockets that are often hit by small molecules. To untap the potential of these targets, we need a therapeutic modality that can target shallow pockets and cross the cell membrane. Peptides are at the size range and composition that can be an ideal choice as this alternative modality. Despite efforts in developing high affinity peptide binders, generating permeable binders has been a long-standing challenge. The few examples of cell permeable functional peptides are often developed through error and trial or via many rounds of modification and testing. The challenge in obtaining permeable binders is mainly due to lack of high throughput methods to screen for permeability, which has not only limited the power of library-based screening for obtaining permeable binders, but has also resulted in a limited amount of experimental data from which one can deduce rules that govern a peptide permeable. Thus, the use of peptides as therapeutics to drug the undruggable has remained largely underdeveloped. This proposal uses an interdisciplinary approach to address this challenge. We generate a network to represent the peptide space in a meaningful manner. We then cluster this network and select for experimental testing a set that is truly representative of the peptide space. The results of our tests, which include both artificial membrane analysis and cellular uptake in bacterial and mammalian cells, can thus be generalized to the entire space. This unprecedented dataset will then be used as a starting point to gain better understanding of peptide permeability and to develop experimental and computational methods for rapid identification of permeable peptide binders that can be used as leads for therapeutic development. We will use this dataset to develop an automated generative algorithm that computationally designs permeable peptides that can target a target interface of interest. This proposal is a leap in the field of peptide therapeutic development. The dataset generated during this work and methods we develop will be the stepping stone for many researchers interested in drug discovery, physics-based models of permeability, and peptide therapeutics.

项目摘要 打击疾病的大量潜在目标属于一个名为不可用的类别， 两种常用的药物模源无法访问的靶标：抗体和小分子。这些 不良目标通常位于细胞内部，无法通过抗体来访问，抗体太大而无法 越过细胞膜。它们的平坦表面偏离了常见的深层口袋，这些口袋通常被小击中 分子。为了解开这些目标的潜力，我们需要一种可以靶向浅的治疗方式 口袋并越过细胞膜。肽处于尺寸范围和成分，可以是理想的 选择作为这种替代方式。 尽管在开发高亲和力肽粘合剂方面做出了努力，但产生可渗透的粘合剂一直是 长期挑战。几个可渗透肽的细胞渗透肽的例子通常是通过 错误和试验，或通过许多一轮修改和测试。获得可渗透粘合剂的挑战是 主要是由于缺乏筛选渗透性的高吞吐量方法，这不仅限制了功率 基于图书馆的筛选，以获得可渗透的粘合剂，但也导致了有限的 实验数据可以从中推断出控制肽可渗透的规则。因此，使用 肽作为吸毒的治疗剂，不可用的肽在很大程度上尚未发育不足。 该建议使用跨学科的方法来应对这一挑战。我们生成一个网络 以有意义的方式表示肽空间。然后，我们聚集此网络，然后选择实验 测试真正代表肽空间的集合。我们的测试结果包括 因此，人工膜分析和细菌和哺乳动物细胞中的细胞摄取可以推广到 整个空间。然后，这个空前的数据集将被用作获得更好理解的起点 肽渗透性以及开发实验和计算方法，以快速鉴定 可渗透的肽粘合剂，可用作治疗发育的铅。我们将使用此数据集来 开发一种自动生成算法，该算法在计算上设计可渗透的肽，可以针对 感兴趣的目标接口。 该提议是肽治疗发展领域的飞跃。在此期间生成的数据集 我们开发的工作和方法将是许多对药物发现感兴趣的研究人员的垫脚石， 基于物理学的渗透性模型和肽治疗剂。