Nielsen Alexander L, Bartling Christian R O, Zarda Anne, De Sadeleer Nathan, Neeser Rebecca M, Schwaller Phillippe, Strømgaard Kristian, Heinis Christian
Institute of Chemical Sciences and Engineering, School of Basic Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, CH-1015, Switzerland.
Center for Biopharmaceuticals, Department of Drug Design and Pharmacology, Faculty of Health and Medical Sciences, University of Copenhagen, Jagtvej 162, Copenhagen, DK-2100, Denmark.
Angew Chem Int Ed Engl. 2025 Jul;64(27):e202500493. doi: 10.1002/anie.202500493. Epub 2025 Jun 1.
Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far, not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is not trivial. In this work, we assessed the membrane permeability of thioether-cyclized peptides, a widely used format in drug discovery. We developed a strategy for synthesizing hundreds of cyclic peptides carrying a short chloroalkane tag for the bulk quantification of membrane permeability in live cells using the chloroalkane penetration assay. Permeability data for random cyclic peptides established design rules, indicating the probability of peptides entering cells is strongly increasing if the molecular weight is below 800 Da, the polar surface is smaller than 250 Å, or if there are less than six hydrogen bond donors. From this, machine learning could predict the membrane permeability of random peptides with good confidence, facilitating the future development of membrane-permeable cyclic peptide drugs.
环肽因其出色的结合特性以及穿越细胞膜的潜力，在药物研发中颇具吸引力。然而，到目前为止，并非所有环肽都具有细胞渗透性，测量或预测它们的膜渗透性并非易事。在这项工作中，我们评估了硫醚环化肽的膜渗透性，这是药物研发中一种广泛使用的形式。我们开发了一种合成数百种带有短氯代烷烃标签的环肽的策略，以便使用氯代烷烃渗透测定法对活细胞中的膜渗透性进行大量定量分析。随机环肽的渗透性数据确立了设计规则，表明如果分子量低于800 Da、极性表面积小于250 Å，或者氢键供体少于六个，肽进入细胞的可能性会大幅增加。据此，机器学习能够较为可靠地预测随机肽的膜渗透性，从而推动具有膜渗透性的环肽药物的未来发展。
