A computational pipeline for generating SAXS-consistent atomistic protein ensembles - dataset 
Author: Cameron Brown
Supervisors: Prof Jonathan Essex, Dr Robert Rambo


Dataset DOI: 10.5258/SOTON/D3902

ReadMe Author: CAMERON BROWN, University of Southampton http://orcid.org/0000-0001-8991-245X

This dataset supports the thesis entitled AWARDED BY: University of Southampton DATE OF AWARD: 2026

Date of data collection: September 2021 - September 2025

Information about geographic location of data collection: Southampton

Licence: CC-BY

Related projects/Funders: Diamond Light Source, EPSRC


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Description:

This repo contains sample input and output files, analysis scripts, and relevent code associated with the thesis 'A computational pipeline for generating SAXS-consistent protein ensembles'.

Note: MD trajectories are not included due to file size. Instead, pdbs used to seed simulations, as well as selected output (e.g. tpr/gro/crysol_summary.txt) are provided.  

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Directories:

Chapter3_Pipeline
- AutoMD-SAXS: This is the finished computational pipeline used to generate and analyse the majority of the simulation data in this thesis. Git: https://github.com/camlbrown/AutoMD-SAXS
- GROMACS-calculated_MD_Metrics
- CLoNe clustering
- Carbonara: Version of Carbonara used to generate all Carbonara-related data in the thesis, apart from the '20-seed' models - these were generated with the latest version of Carbonara, 
  which can be found here: https://github.com/Prior-Lab-Durham-University/carbonara

Chapter4_Rift_valley
- MD: AMBER and CHARMM simulation data with analysis scripts
- SAXS data
- Dimer: AF3, pydocksaxs, pydocksaxs MD data, Carbonara data

Chapter5_IgG2
- Carbonara models and their modeller variations
- Analysis scripts  
- Original crystal structures
- Experimental SAXS data
- MD data for each modeller variant  

Chapter6_IgG3
- Analysis scripts
- Models
- SAXS data 
- MD data: glycosims, saxs-guided, metadynamics, hinge simulations, amber, charmm 

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References:

Carbonara: 
Original: https://pubmed.ncbi.nlm.nih.gov/32023061/
Latest pre-print: https://www.researchgate.net/publication/391422815_Carbonara_A_Rapid_Method_for_SAXS-Based_Refinement_of_Protein_Structures
github: https://github.com/Prior-Lab-Durham-University/carbonara

If you use Carbonara, please cite:
@article{carbonara2025,
  title={Carbonara: A Rapid Method for SAXS-Based Refinement of Protein Structures},
  author={McKeown, J. and Bale, A. and Brown, C. and Fisher, H. and Rambo, R. and Essex, J. and Degiacomi, M. and Prior, C.},
  journal={ResearchSquare},
  year={2025},
  doi={10.21203/rs.3.rs-6447099/v1},
  url={https://doi.org/10.21203/rs.3.rs-6447099/v1}
}


This dataset contains modified code from external sources, detailed below:

- Bayesian/Maximum Entropy (BME): 

BME, used in Chapters 5 and 6, was run using modified scripts. The originals are provided here: https://github.com/KULL-Centre/BME 

Latex bib reference:
@incollection{bottaro2020integrating,
title={Integrating molecular simulation and experimental data: a Bayesian/maximum entropy reweighting approach},
author={Bottaro, Sandro and Bengtsen, Tone and Lindorff-Larsen, Kresten},
booktitle={Structural Bioinformatics},
pages={219--240},
year={2020},
publisher={Springer}
}

- CLoNe clustering:

CLoNe clustering, used in the AutoMD-SAXS pipeline in Chapter 3, was run using modified scripts. Original code found here: https://github.com/LBM-EPFL/CLoNe

@article{10.1093/bioinformatics/btaa742,
author = {Träger, Sylvain and Tamò, Giorgio and Aydin, Deniz and Fonti, Giulia and Audagnotto, Martina and Dal Peraro, Matteo},
title = {CLoNe: automated clustering based on local density neighborhoods for application to biomolecular structural ensembles},
journal = {Bioinformatics},
volume = {37},
number = {7},
pages = {921-928},
year = {2020},
month = {08},
issn = {1367-4803},
doi = {10.1093/bioinformatics/btaa742},
url = {https://doi.org/10.1093/bioinformatics/btaa742},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/37/7/921/50341061/btaa742.pdf},
}


- Ramachandran Plotter:
Ramachandran plots used in Chapters 5 and 6 were generated using modified scripts from an external source. Original code found here: https://github.com/Joseph-Ellaway/Ramachandran_Plotter

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THE LICENSE FOR THIS DATASET DOES NOT COVER CARBONARA, BME, CLONE CLUSTERING, OR RAMACHANDRAN PLOTTER 
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