Benchmarking generative AI and physics based molecular simulation for sampling conformational heterogeneity in T4 Lysozyme
Abstract
Wild-type T4 lysozyme (T4L) is used as a benchmark to evaluate conformational sampling across generative AI, AI–accelerated molecular simulation (AMS), and physics-based enhanced molecular dynamics (EMD). A four-state model: exposed/open, exposed/closed, buried/open, and buried/closed; is defined using physically meaningful collective variables. While generative AI methods (AF–cluster, MSA subsampling of AlphaFold2, ConforFold, AlphaFlow, ESMFlow, ConfRover, BioEmu) largely sample only the exposed/open state, AMS integrating generative ensembles with iterative molecular dynamics, recovering all states and reproducing equilibrium populations similar to EMD and experimental smFRET signatures.
Fields of Study & Subjects
Protein Structure and Dynamics
Machine Learning in Materials Science
Enzyme Structure and Function
Publication details
- Journal / source
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Publication date
- 13 May 2026
- Document type
- Preprint