A data-driven approach towards generation of permeable peptide therapeutics

项目摘要 数量惊人的抗击疾病的潜在靶点属于一种被称为不可用药的类别， 两种常用药物模型不能达到的靶点：抗体和小分子。这些 无法下药的靶点通常驻留在细胞内，无法被抗体访问，抗体太大，不能 穿过细胞膜。他们的平坦表面与常见的深口袋不同，这些口袋经常被小武器击中 分子。为了挖掘这些靶点的潜力，我们需要一种可以针对浅表的治疗方法。 然后穿过细胞膜。多肽的大小范围和组成可能是理想的 选择作为这一替代方式。 尽管努力开发高亲和力的多肽结合剂，但产生可渗透的结合剂一直是 由来已久的挑战。细胞通透性功能多肽的少数例子通常是通过 错误和试验或通过多轮修改和测试。获得渗透性粘结剂的挑战是 主要是由于缺乏高通量的方法来筛选渗透性，这不仅限制了动力 用于获得渗透性粘结剂的基于库的筛选，但也导致了有限数量的 实验数据，人们可以从这些数据中推断出支配多肽渗透性的规则。因此，使用 多肽作为治疗无法用药的药物，在很大程度上仍然不发达。 这项提案使用跨学科的方法来应对这一挑战。我们生成一个网络来 以一种有意义的方式表示多肽空间。然后，我们对该网络进行集群，并选择进行实验 测试一个真正代表多肽空间的集合。我们的测试结果，包括 因此，细菌和哺乳动物细胞中的人工膜分析和细胞摄取可以概括为 整个空间。这一史无前例的数据集将被用作更好地了解 多肽的通透性，并开发实验和计算方法来快速鉴定 渗透性多肽结合剂，可用作治疗开发的先导。我们将使用此数据集 开发一种自动生成算法，通过计算设计可靶向于 感兴趣的目标接口。 这一建议是多肽治疗发展领域的一次飞跃。在此过程中生成的数据集 我们开发的工作和方法将成为许多对药物发现感兴趣的研究人员的垫脚石， 以物理为基础的渗透性模型和多肽疗法。

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.