Faculty, Staff and Student Publications
Publication Date
2-20-2024
Journal
Biophysical Journal
Abstract
Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achieving near-atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools, which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. Although they offer new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment, and limitations when integrating predictive modeling with cryo-EM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.
Keywords
Models, Molecular, Cryoelectron Microscopy, Proteins, Protein Conformation, Machine Learning, Macromolecular Substances
DOI
10.1016/j.bpj.2024.01.021
PMID
38268190
PMCID
PMC10912932
PubMedCentral® Posted Date
1-23-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes