Insights into Conformational Ensembles of Compositionally Identical Disordered Peptidomimetics

While the conformational ensembles of disordered peptides and peptidomimetics are complex and challenging to characterize, they are a critical component in the paradigm connecting macromolecule sequence, structure, and function. In molecules that do not adopt a single predominant conformation, the conformational ensemble contains rich structural information that, if accessible, can provide fundamental understanding related to desirable functions such as cell penetration of a therapeutic or the generation of tunable enzyme-mimetic architecture. To address the fundamental challenge of describing broad conformational ensembles, we developed a model system of peptidomimetics comprised of polar glycine and hydrophobic N-butylglycine to characterize using a suite of analytical techniques, including replica exchange molecular dynamics atomistic simulations and liquid chromatography coupled to ion mobility spectrometry, which allowed us to distinguish the conformations of compositionally identical model sequences. However, differences between these model sequences were more challenging to resolve with characterization tools developed for intrinsically disordered proteins and polymers. These tools include double electron-electron resonance (DEER) spectroscopy and diffusion ordered spectroscopy (DOSY) NMR. Finally, we introduce a facile colorimetric assay that employs immobilized sequences leveraging a solvatochromic probe, Reichardt’s dye, to visually reveal conformational trends consistent with the experimental and computational analysis, thus providing a rapid and complementary method to characterize macromolecular disorder and unravel the complexity of conformational ensembles, either as an isolated or multiplexed technique

A High-Throughput Workflow to Analyze Sequence-Conformation Relationships and Explore Hydrophobic Patterning in Disordered Peptoids

Understanding how a macromolecule’s primary sequence governs its conformational landscape is crucial for elucidating its function, yet these design principles are still emerging for macromolecules with intrinsic disorder. While parameters describing subsets of disordered proteins and synthetic materials have been established, they are often tailored to specific chemical interactions and monomer classes, limiting their broader applicability. To address this gap, we present a high-throughput workflow that implements a versatile colorimetric conformational assay, introduces a semi-automated sequencing protocol using MALDI-MS/MS, and pioneers a data-driven sequence parameterization methodology that integrates into a predictive algorithm. Using a model system consisting of two-component peptidomimetics (20mer peptoids) containing polar glycine and hydrophobic N-butylglycine residues in a one-bead one-compound (OBOC) library, we visually identified nine classifications of conformational disorder. From this library, we identified 122 unique sequences across varied compositions and conformations, and we developed an image analysis tool that ultimately characterized an order of magnitude larger fraction of the complete library. Low-throughput techniques, atomistic simulations and ion mobility spectrometry coupled with liquid chromatography and mass spectrometry separations (LC-IMS-MS) of purified peptoids, yielded quantitative descriptors of the conformational ensembles formed by three compositionally identical sequences selected from the library. Finally, a data-driven technique was developed that exploits ‘motifs’ within the 20mer sequences to inform a gradient-boosted tree machine learning algorithm towards conformation prediction. This multifaceted approach enhances our understanding of sequence-conformation relationships and offers a powerful tool for accelerating the discovery and development of advanced materials with precise conformational control