Dr Matteo Degiacomi matteo.t.degiacomi@durham.ac.uk
Associate Professor
On the Effect of Sphere-Overlap on Super Coarse-Grained Models of Protein Assemblies
Degiacomi, Matteo T.
Authors
Abstract
Ion mobility mass spectrometry (IM/MS) can provide structural information on intact protein complexes. Such data, including connectivity and collision cross sections (CCS) of assemblies’ subunits, can in turn be used as a guide to produce representative super coarse-grained models. These models are constituted by ensembles of overlapping spheres, each representing a protein subunit. A model is considered plausible if the CCS and sphere-overlap levels of its subunits fall within predetermined confidence intervals. While the first is determined by experimental error, the latter is based on a statistical analysis on a range of protein dimers. Here, we first propose a new expression to describe the overlap between two spheres. Then we analyze the effect of specific overlap cutoff choices on the precision and accuracy of super coarse-grained models. Finally, we propose a method to determine overlap cutoff levels on a per-case scenario, based on collected CCS data, and show that it can be applied to the characterization of the assembly topology of symmetrical homo-multimers.
Citation
Degiacomi, M. T. (2019). On the Effect of Sphere-Overlap on Super Coarse-Grained Models of Protein Assemblies. Journal of The American Society for Mass Spectrometry, 30(1), 113-117. https://doi.org/10.1007/s13361-018-1974-2
Journal Article Type | Article |
---|---|
Acceptance Date | Apr 18, 2018 |
Online Publication Date | May 7, 2018 |
Publication Date | Jan 31, 2019 |
Deposit Date | May 8, 2018 |
Publicly Available Date | Mar 28, 2024 |
Journal | Journal of The American Society for Mass Spectrometry |
Print ISSN | 1044-0305 |
Electronic ISSN | 1879-1123 |
Publisher | American Chemical Society |
Peer Reviewed | Peer Reviewed |
Volume | 30 |
Issue | 1 |
Pages | 113-117 |
DOI | https://doi.org/10.1007/s13361-018-1974-2 |
Files
Accepted Journal Article
(534 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
© The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Published Journal Article (Advance online version)
(855 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
Advance online version
Published Journal Article
(846 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
You might also like
Course Materials for an Introduction to Data-Driven Chemistry
(2023)
Journal Article
Denoising Diffusion Probabilistic Models on SO(3) for Rotational Alignment
(2022)
Conference Proceeding
Identification of Graphene Dispersion Agents through Molecular Fingerprints
(2022)
Journal Article
Allophycocyanin A is a carbon dioxide receptor in the cyanobacterial phycobilisome
(2022)
Journal Article
Complementing machine learning‐based structure predictions with native mass spectrometry
(2022)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search