Ensemble Reweighting Using Cryo-EM Particle Images.
Wai Shing TangDavid Silva-SánchezJulian Giraldo-BarretoBob CarpenterSonya M HansonAlex H BarnettErik H ThiedePilar CossioPublished in: The journal of physical chemistry. B (2023)
Cryo-electron microscopy (cryo-EM) has recently become a leading method for obtaining high-resolution structures of biological macromolecules. However, cryo-EM is limited to biomolecular samples with low conformational heterogeneity, where most conformations can be well-sampled at various projection angles. While cryo-EM provides single-molecule data for heterogeneous molecules, most existing reconstruction tools cannot retrieve the ensemble distribution of possible molecular conformations from these data. To overcome these limitations, we build on a previous Bayesian approach and develop an ensemble refinement framework that estimates the ensemble density from a set of cryo-EM particle images by reweighting a prior conformational ensemble, e.g., from molecular dynamics simulations or structure prediction tools. Our work provides a general approach to recovering the equilibrium probability density of the biomolecule directly in conformational space from single-molecule data. To validate the framework, we study the extraction of state populations and free energies for a simple toy model and from synthetic cryo-EM particle images of a simulated protein that explores multiple folded and unfolded conformations.
Keyphrases
- single molecule
- convolutional neural network
- molecular dynamics simulations
- high resolution
- deep learning
- electron microscopy
- atomic force microscopy
- living cells
- electronic health record
- neural network
- big data
- molecular docking
- molecular dynamics
- single cell
- small molecule
- machine learning
- artificial intelligence
- data analysis
- amino acid
- tandem mass spectrometry
- protein protein
- monte carlo