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Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures [dataset] Open Access

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.

Descriptions

Resource type

Dataset

Contributors

Creator: McKeown, Josh ¹
Bale, Arron ¹
Brown, Cameron ²
Fisher, Hayden ³
Rambo, Robert ⁴
Essex, Jonathan ²
Prior, Christopher ¹
Degiacomi, Matteo ⁵

¹ Durham University
² University of Southampton
³ European Synchrotron Radiation Facility
⁴ Diamond Light Source Ltd
⁵ University of Edinburgh

Funder

Engineering and Physical Sciences Research Council

Research methods

Other description

Keyword

SAXS
AlphaFold3
Molecular dynamics simulations
Protein structure refinement
Solution-state conformations
Carbonara (computational pipeline)
Coarse-grained modeling
Conformational sampling
SMARCAL1 (DNA repair helicase)
ChiLob7/4 antibody
AlphaFold refinement
Structural biology
Computational structural biology
Topological constraints

Subject

Small-angle x-ray scattering
Molecular biology--Mathematical models
Structural bioinformatics
Proteins--Structure--Computer simulation
Computational biology

Location

Language

Cited in

doi:10.21203/rs.3.rs-6447099/v1

Identifier

ark:/32150/r1jq085k049
doi:10.15128/r1jq085k049

Rights

Creative Commons Attribution 4.0 International (CC BY)

Publisher

Durham University

Date Created

File Details

Depositor

J.J.C. Mckeown

Date Uploaded

6 May 2025, 18:05:35

Date Modified

7 May 2025, 15:05:02

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Characterization

File format: zip (ZIP Format)

Mime type: application/zip

File size: 2016527855

Last modified: 2025:05:06 19:04:10+01:00

Filename: Carbonara_Paper_Data.zip

Original checksum: 76c04a351cf6164d11d65102a98fcd07