Publications by authors named "D Juergens"
The development of macrocyclic binders to therapeutic proteins typically relies on large-scale screening methods that are resource-intensive and provide little control over binding mode. Despite considerable progress in physics-based methods for peptide design and deep-learning methods for protein design, there are currently no robust approaches for design of protein-binding macrocycles. Here, we introduce RFpeptides, a denoising diffusion-based pipeline for designing macrocyclic peptide binders against protein targets of interest.
View Article and Find Full Text PDFEnzymes that proceed through multistep reaction mechanisms often utilize complex, polar active sites positioned with sub-angstrom precision to mediate distinct chemical steps, which makes their de novo construction extremely challenging. We sought to overcome this challenge using the classic catalytic triad and oxyanion hole of serine hydrolases as a model system. We used RFdiffusion to generate proteins housing catalytic sites of increasing complexity and varying geometry, and a newly developed ensemble generation method called ChemNet to assess active site geometry and preorganization at each step of the reaction.
View Article and Find Full Text PDFFrancis Crick's global parameterization of coiled coil geometry has been widely useful for guiding design of new protein structures and functions. However, design guided by similar global parameterization of beta barrel structures has been less successful, likely due to the deviations from ideal barrel geometry required to maintain inter-strand hydrogen bonding without introducing backbone strain. Instead, beta barrels have been designed using 2D structural blueprints; while this approach has successfully generated new fluorescent proteins, transmembrane nanopores, and other structures, it requires expert knowledge and provides only indirect control over the global shape.
View Article and Find Full Text PDFArticle Synopsis
- - We developed a method to create small proteins that can bind strongly to specific molecules, using advanced deep learning techniques to design their shapes based on repeating structural units.
- - We test these designs by docking various small molecules into the optimal binding sites and then experimentally validate which designs have the highest binding affinity.
- - Our successful designs include binders for diverse molecules like methotrexate and thyroxine, and we also used our designs to create systems for chemical dimerization and sensitive nanopore sensors that reassemble when a molecule is added.
Article Synopsis
- * The approach involves creating customizable binding pockets, or pseudocycles, that can adapt to different small molecule targets by adjusting their size and shape for high affinity interactions.
- * The researchers successfully designed protein binders for various molecules, including polar flexible ones like methotrexate and thyroxine, achieving strong binding affinities, and demonstrating the application of these designs in low noise nanopore sensors.