Cryo-electron microscopy data are becoming more prevalent
and accessible
at higher resolution levels, leading to the development of new computational
tools to determine the atomic structure of macromolecules. However,
while existing tools adapted from X-ray crystallography are suitable
for the highest-resolution maps, new tools are needed for lower-resolution
levels and to account for map heterogeneity. In this article, we introduce
CryoFold 2.0, an integrative physics-based approach that combines
Bayesian inference and the ability to handle multiple data sources
with the molecular dynamics flexible fitting (MDFF) approach to determine
the structures of macromolecules by using cryo-EM data. CryoFold 2.0
is incorporated into the MELD (modeling employing limited data) plugin,
resulting in a pipeline that is more computationally efficient and
accurate than running MELD or MDFF alone. The approach requires fewer
computational resources and shorter simulation times than the original
CryoFold, and it minimizes manual intervention. We demonstrate the
effectiveness of the approach on eight different systems, highlighting
its various benefits.