SFCalculator: connecting deep generative models and crystallography.

ABSTRACT: Proteins drive biochemical transformations by transitioning through distinct conformational states. Understanding these states is essential for modulating protein function. Although X-ray crystallography has enabled revolutionary advances in protein structure prediction by machine learning, this connection was made at the level of atomic models, not the underlying data. This lack of connection to crystallographic data limits the potential for further advances in both the accuracy of protein structure prediction and the application of machine learning to experimental structure determination. Here, we present SFCalculator, a differentiable pipeline that generates crystallographic observables from atomistic molecular structures with bulk solvent correction, bridging crystallographic data and

SUBMITTER: Li M 

PROVIDER: S-EPMC11760793 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature