Improving Cryo-EM Experimental Data Fit with Automated Multiconformer Modeling for Proteins and Non-proteins
Category
Published on
Type
journal-article
Author
Stephanie Wankowicz and Jessica Flowers and Daniel Hogan and Ashraya Ravikumar and James Fraser
Citation
Wankowicz, S., Flowers, J., Hogan, D., Ravikumar, A., & Fraser, J. (2025). Improving Cryo-EM Experimental Data Fit with Automated Multiconformer Modeling for Proteins and Non-proteins. Structural Dynamics, 12(2_Supplement), A79–A79. https://doi.org/10.1063/4.0000388
Abstract
Biomolecules exchange between multiple conformational states in their folded state, crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, capturing molecules in various conformations. Yet, most models derived from these maps represent only a single conformation, overlooking the complexity of biomolecular structures. The pressing need to accurately reflect the diversity of biomolecular forms by shifting towards modeling structural ensembles that mirror experimental data is complicated by the challenge of distinguishing signal from noise in manual model creation efforts. We have developed qFit, automatic multiconformer modeling software, to model multiconformer models in high-resolution X-ray and cryo-EM structures. Importantly, unlike ensemble models, the multiconformer models produced by qFit can be manually modified in most major model-building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). The advancement of automated multiconformer modeling is poised to transform the interpretation of structural biology data, fostering new hypotheses about the relationship between macromolecular conformational dynamics and their functions, and marking a new era in the prediction of protein structural ensembles.
