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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
- Audit Status
- Audits have not yet been run on this file.
- 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
| User Activity | Date |
|---|---|
| User J.J.C. Mckeown has updated Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures [dataset] | 2 days ago |
| User J.J.C. Mckeown has updated Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures [dataset] | 2 days ago |
| User J.J.C. Mckeown has updated Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures [dataset] | 3 days ago |
| User J.J.C. Mckeown has updated Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures [dataset] | 3 days ago |
| User J.J.C. Mckeown has deposited Carbonara_Paper_Data.zip | 3 days ago |