Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Generative machine learning models for protein structure prediction are primarily trained on X-ray crystallography data, which captures proteins in crystal lattices that can deviate significantly from their native solution conformations. Biological small angle X-ray scattering (bioSAXS) offers valuable solution-state insights, but creating atomic models that rationalise this data remains challenging. Here we present Carbonara, a rapid computational pipeline that combines coarse-grained sampling with experimental constraints to efficiently identify solution-state conformations from an initial atomic model. We demonstrate Carbonara's effectiveness by refining an AlphaFold-predicted model of the DNA repair helicase, SMARCAL1, and a crystallographically determined structure of the antigen binding domains of the anti-hCD40 monoclonal antibody, ChiLob7/4, a clinically relevant immunostimulatory antibody. In both cases, Carbonara identifies physiologically relevant solution-state conformations separated from crystal-like predictions by large energy barriers, achieving in minutes what traditional MD simulations might not accomplish in weeks.