Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.
Daniel D Richman1, Jessica Karaguesian1, Carl-Mikael Suomivuori1
1Stanford University.
Arxiv
|December 11, 2025
Summary
ConforMix enhances protein conformational sampling using diffusion models, improving the discovery of diverse protein structures without needing prior knowledge of specific movements. This computational method aids in understanding protein dynamics and function.
Area of Science:
- Computational Biology
- Structural Biology
- Biophysics
Background:
- Protein function is intrinsically linked to their dynamic conformational changes.
- Predicting the full spectrum of protein conformations computationally remains a significant challenge.
- Existing methods often struggle to capture the complete conformational landscape.
Purpose of the Study:
- To introduce ConforMix, an inference-time algorithm for enhanced conformational sampling.
- To improve the efficiency of discovering protein conformational variability.
- To enable better understanding of protein dynamics without prior knowledge of degrees of freedom.
Main Methods:
- Utilizes classifier guidance, filtering, and free energy estimation.
- Applies to pre-trained diffusion models for structure prediction or conformational generation.
- Operates at inference time, enhancing existing models.
Main Results:
- ConforMix successfully enhances sampling of conformational distributions.
- It captures biologically relevant structural dynamics like domain motion and flexibility.
- Demonstrates accuracy and scalability on biologically critical proteins, avoiding unphysical states.
Conclusions:
- ConforMix offers a powerful, generalizable approach to explore protein conformational space.
- The method is orthogonal to model pretraining and improves upon existing diffusion models.
- Enables more efficient and accurate computational prediction of protein dynamics.
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