RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion

05/30/2023

∙

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 for sequence-conditioned generation of macrocycle structures based on internal coordinates. RINGER provides fast backbone 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.

READ FULL TEXT

RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion

CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning

SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models

Conditional Generation from Unconditional Diffusion Models using Denoiser Representations

Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation

Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI

A Novel GPR-Based Prediction Model for Cylic Backbone Curves of Reinforced Concrete Shear Walls

Generating Initial Conditions for Ensemble Data Assimilation of Large-Eddy Simulations with Latent Diffusion Models

RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion

Related Research

CREMP: Conformer-Rotamer Ensembles of Macrocyclic Peptides for Machine Learning

SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models

Conditional Generation from Unconditional Diffusion Models using Denoiser Representations

Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned Generation

Q-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI

A Novel GPR-Based Prediction Model for Cylic Backbone Curves of Reinforced Concrete Shear Walls

Generating Initial Conditions for Ensemble Data Assimilation of Large-Eddy Simulations with Latent Diffusion Models