Benchmarking generative AI and physics based molecular simulation for sampling conformational heterogeneity in T4 Lysozyme

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Authors: Soumendranath Bhakat
Cited by: 1

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