Carbonara: a SAXS-guided seeding framework for exploring protein solution-state dynamics

Abstract

Proteins in solution often populate conformational ensembles that differ from the static states captured by crystallography or AI-based structure prediction. Conventional molecular dynamics (MD) simulations often fail to cross the energy barriers separating these states on accessible timescales, and statistical reweighting cannot recover conformations never sampled. Here we present Carbonara, a framework that uses experimental small-angle X-ray scattering (SAXS) data to predict alternative physically plausible protein conformations. Carbonara builds on Wiggle, a standalone Cα-based SAXS forward model validated against explicit-solvent calculations and experimental benchmarks. Using two case studies, an AI-predicted multi-domain helicase (SMAR-CAL1) and a crystallographic antibody fragment (ChiLob7/4 IgG2), we show how seeding MD simulations from Carbonara conformations enables efficient exploration of solution-state conformational landscapes. In both cases, MD ensembles initiated from available models either fail to match the SAXS data or do so only after discarding nearly all sampled conformations, whereas Carbonara-seeded ensembles reach agreement while retaining the majority of conformations. Our modelling framework provides a route from static structural models of flexible multi-domain proteins and multimeric assemblies to solution-state ensembles.