Understanding and designing cyclic peptides

一种强大的选择性调节蛋白质-蛋白质相互作用(PPI)的能力将提供有价值的 控制特定生物过程以进行治疗干预的手段。不幸的是，由于他们的公寓和 大界面、PPI是传统小分子药物具有挑战性的靶点。环肽代表一种 有希望的针对PPI的解决方案他们可以直接模拟PPI的绑定功能，并具有 与线性制剂相比，提高了生物稳定性和生物利用度。然而，只有~50个周期 多肽类药物。大多数只是天然产品或其衍生品，而不是从成功的创新中衍生出来的。 环肽的发展。新奇的功能性环肽的设计证明了 困难的是需要同时优化环肽的多种药物相关性质，例如结合 亲和力、水溶性和膜透过性。因为环肽通常有≤12残基，并且 环状连接在一起，即使改变一种氨基酸也能显著改变环肽的性质。 因此，改变环肽序列以优化一种性质往往会对其他性质产生负面影响。 机器学习(ML)现在被广泛用于建立药物性质的预测模型，具有巨大的潜力 指导成功的环肽设计。不幸的是，ML模型预测环肽的几次尝试 房地产的表现相当糟糕。核心挑战是大多数环肽，包括目前的环肽 多肽药物，在水中采用多种构象。因此，ML模型很难解释它是如何 序列修改影响环肽的复杂结构集合，进而影响 他们的财产。如果我们能为ML模型提供这种缺失的结构信息，我们将大大改进 它们在预测环肽性质方面的表现。 由于没有可靠的实验方法来表征和量化分子中的构象 环肽结构系综，计算化学代表了一种合乎逻辑的选择。虽然是最近的 研究表明，显式溶剂分子动力学(MD)模拟能够提供高质量的 对于环肽的结构预测，它太慢了，无法规模化使用。另一方面，计算性 为环肽结构系综提供快速预测的方法是不准确的。 我们的长期目标是为计算设计提供一个可靠、健壮和用户友好的平台 有效的，生物可利用的环肽靶向PPI。在我们的第一个目标中，我们使用不同的显式溶剂MD结果 序列作为训练数据集来构建可以预测环肽结构集成的新颖的ML模型， 既保持了准确性又保持了速度。在我们的第二个目标中，我们使用这些高质量的结构合奏作为 用于ML输入的新描述符，使得能够训练高性能的ML模型来预测环肽 属性。在我们的第三个目标中，我们演示了这些新的计算平台允许我们开发出强大的 环肽蛋白结合剂和PPI抑制剂。

A robust ability to selectively modulate protein–protein interactions (PPIs) would provide a valuable means to control specific biological processes for therapeutic intervention. Unfortunately, owing to their flat and large interfaces, PPIs are challenging targets for traditional small molecule drugs. Cyclic peptides represent a promising solution to target PPIsthey can directly mimic the binding functionalities at the PPIs and have enhanced biostability and bioavailability compared to their linear counterparts. However, there are only ~50 cyclic peptide drugs. Most are simply natural products or their derivatives, rather than deriving from successful de novo cyclic peptide development. A major reason why the design of novel, functional cyclic peptides has proven difficult is the need to simultaneously optimize multiple drug-related properties of cyclic peptides, e.g., binding affinity, water solubility, and membrane permeability. Because cyclic peptides often have ≤12 residues and are connected in a ring, even changing one amino acid can dramatically alter the properties of cyclic peptides. Hence, changing cyclic peptide sequences to optimize for one property often negatively impacts other properties. Machine learning (ML), now widely used to build predictive models for drug properties, holds enormous potential to guide successful cyclic peptide design. Unfortunately, the few attempts at ML models to predict cyclic peptide properties perform quite poorly. The core challenge is that most cyclic peptides, including the current cyclic peptide drugs, adopt multiple conformations in water. It is, therefore, difficult for ML models to decipher how sequence modifications impact the complicated structural ensembles of cyclic peptides, which in turn influence their properties. If we can provide the ML models with this missing structural information, we will greatly improve their performance in predicting cyclic peptide properties. Since no robust experimental methods are available to characterize and quantify the conformations in a cyclic peptide structural ensemble, computational chemistry represents a logical alternative. Although recent work has revealed that explicit-solvent molecular dynamics (MD) simulation is capable of providing high-quality structural predictions of cyclic peptides, it is far too slow to be used at scale. On the other hand, computational methods that provide speedy predictions for cyclic peptide structural ensembles are inaccurate. Our long-term objective is a reliable, robust, and user-friendly platform for the computational design of potent, bioavailable cyclic peptides targeting PPIs. In our first aim, we use explicit-solvent MD results of diverse sequences as training datasets to build novel ML models that can predict cyclic peptide structural ensembles, preserving both accuracy and speed. In our second aim, we use these high-quality structural ensembles as a new descriptor for ML inputs to enable the training of high-performing ML models to predict cyclic peptide properties. In our third aim, we demonstrate that these new computational platforms allow us to develop potent cyclic peptide protein binders and PPI inhibitors.