Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion
This work addresses the problem of efficient structural ensemble generation for macrocyclic peptides, which are important therapeutic molecules, representing an incremental improvement in computational methods for this domain.
The researchers tackled the challenge of accurately sampling diverse 3D conformational ensembles of macrocyclic peptides, which are difficult due to conformational diversity and geometric constraints, by introducing RINGER, a diffusion-based transformer model that generates these ensembles from 2D representations at a fraction of the computational cost while maintaining high-quality and diverse geometries.
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints. Here, we introduce RINGER, a diffusion-based transformer model using a redundant internal coordinate representation that generates three-dimensional conformational ensembles of macrocyclic peptides from their 2D representations. RINGER provides fast backbone and side-chain sampling while respecting key structural invariances of cyclic peptides. Through extensive benchmarking and analysis against gold-standard conformer ensembles of cyclic peptides generated with metadynamics, we demonstrate how RINGER generates both high-quality and diverse geometries at a fraction of the computational cost. Our work lays the foundation for improved sampling of cyclic geometries and the development of geometric learning methods for peptides.