Complementing machine learning-based structure predictions with native mass spectrometry
The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein compl...
主要な著者: | Allison, TM, Degiacomi, MT, Marklund, EG, Jovine, L, Elofsson, A, Benesch, JLP, Landreh, M |
---|---|
フォーマット: | Journal article |
言語: | English |
出版事項: |
Wiley
2022
|
類似資料
-
Software requirements for the analysis and interpretation of native ion mobility mass spectrometry data
著者:: Allison, TM, 等
出版事項: (2020) -
Computational strategies and challenges for using native ion mobility mass spectrometry in biophysics and structural biology
著者:: Allison, TM, 等
出版事項: (2020) -
Ion mobility-mass spectrometry shows stepwise protein unfolding under alkaline conditions
著者:: Sahin, C, 等
出版事項: (2021) -
Weighing-up protein dynamics: the combination of native mass spectrometry and molecular dynamics simulations
著者:: Marklund, E, 等
出版事項: (2019) -
Native Mass Spectrometry Captures the Conformational Plasticity of Proteins with Low-Complexity Domains
著者:: Hannah Osterholz, 等
出版事項: (2025-01-01)