Understanding and Designing Cyclic Peptides

蛋白质相互作用(PPI)具有许多重要的细胞作用，包括在转录、蛋白质 降解、蛋白质转运、信号转导、分子和囊泡运输。有能力 因此，选择性地调制PPI提供了一种有价值的手段来控制特定的生物过程 治疗性干预。不幸的是，由于它们的平面和大接口，PPI很难瞄准 使用传统的小分子药物。环肽(CP)是一种很有前途的靶向PPI的解决方案。 它们可以直接模拟靶蛋白界面的结合伙伴，并具有增强的生物稳定性和 生物利用度与它们的线性对应物相比。尽管有几个CP成功地用作 治疗药物，包括作为免疫系统抑制剂、抗生素和抗真菌药物，这些例子中的大多数 实际上是天然产品或其衍生品，而不是成功的重生CP的产品 发展。新奇的、功能性的CP很难设计的关键原因之一是我们目前无法 高效可靠地预测CP三维结构。我们的长期目标是合理设计 以感兴趣的特定PPI为目标的功能性CP。在这项建议中，我们的具体目标是：(1)发展一种 应对当前CP结构预测挑战的计算方法；(2)填充大量 关于CP序列-结构关系的知识差距；以及(3)验证平台以合理设计 具有所需结构的CP。 在我们的第一个目标中，我们将开发一种增强的抽样方法，用于CP结构预测，方法是 CPS受约束性质的优势。我们假设CPS只有一组有限的运动 可以用来转换构象，并且CP的这些基本的过渡运动可以被利用来 极大地加快了构象采样，允许在显式溶剂中高效地预测CP结构。在 第二个目标，我们将系统地改变CP序列，并将我们改进的抽样方法应用于 模拟它们的结构。从这项研究中，我们将提取初级氨基酸如何 序列影响CP结构。此外，我们将把这些原理集成到预测CP的算法中 结构和指导CP设计。在我们的第三个目标中，我们将整合我们模拟CP结构和 我们对CP序列-结构关系的知识可用于设计和实验验证CP PPI的热循环。拟议的工作将极大地促进CPS作为调制器的发展 PPI，促进了我们对正常和疾病生物学中如此重要的分子相互作用的理解，以及 提供可能的手段，以针对特定的PPI进行治疗干预。

Protein–protein interactions (PPIs) have many important cellular roles, including in transcription, protein degradation, protein translocation, signal transduction, and molecule and vesicular transport. The ability to selectively modulate PPIs thus provides a valuable means to control specific biological processes for therapeutic intervention. Unfortunately, owing to their flat and large interfaces, PPIs are challenging to target using traditional small molecule drugs. Cyclic peptides (CPs) represent a promising solution to target PPIs – they can directly mimic the binding partners of the target protein interface and have enhanced biostability and bioavailability compared to their linear counterparts. Despite several examples of CPs successfully used as therapeutics, including as immune system suppressants, antibiotics, and antifungals, most of these examples are actually natural products or their derivatives, rather than the products of successful de novo CP development. One of the key reasons that novel, functional CPs are difficult to design is our current inability to efficiently and reliably predict CP three-dimensional structures. Our long-term objective is the rational design of functional CPs to target specific PPIs of interest. In this proposal, our specific aims are to (1) develop a computational method to address current challenges in CP structure prediction; (2) fill the substantial knowledge gap regarding CP sequence–structure relationships; and (3) validate a platform to rationally design CPs with desired structures. In our first aim, we will develop an enhanced sampling method for CP structure prediction by taking advantage of the constrained nature of CPs. We hypothesize that CPs have only a limited set of motions they can use to switch conformations and that these essential transitional motions of CPs can be leveraged to greatly accelerate conformational sampling, allowing efficient CP structure prediction in explicit solvent. In the second aim, we will systematically vary the sequences of CPs and apply our enhanced sampling methods to simulate their structures. From this study, we will extract general principles of how primary amino acid sequences affect CP structures. Moreover, we will integrate these principles into algorithms to predict CP structures and guide CP design. In our third aim, we will integrate our capability to simulate CP structures and our knowledge of CP sequence–structure relationships to design and experimentally validate CPs that mimic hot loops at PPIs. The proposed work will greatly enable the continued development of CPs as modulators of PPIs, advance our understanding of such important molecular interactions in normal and disease biology, and provide possible means to target specific PPIs for therapeutic interventions.