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Complementing machi...
Complementing machine learning‐based structure predictions with native mass spectrometry
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- Allison, Timothy M. (författare)
- Biomolecular Interaction Centre, School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
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- Degiacomi, Matteo T. (författare)
- Department of Physics, Durham University, Durham, UK
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- Marklund, Erik, Teknologie doktor, 1979- (författare)
- Uppsala universitet,Biokemi,Erik Marklund
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- Jovine, Luca (författare)
- Karolinska Institutet
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- Elofsson, Arne, 1966- (författare)
- Stockholms universitet,Science for Life Laboratory (SciLifeLab),Institutionen för biokemi och biofysik
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- Benesch, Justin L. P. (författare)
- Department of Chemistry, University of Oxford, Oxford, UK
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- Landreh, Michael (författare)
- Karolinska Institutet
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(creator_code:org_t)
- 2022-05-21
- 2022
- Engelska.
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Ingår i: Protein Science. - : John Wiley & Sons. - 0961-8368 .- 1469-896X. ; 31:6
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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 complexes are based on user-provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.
Ämnesord
- NATURVETENSKAP -- Biologi -- Biofysik (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Biophysics (hsv//eng)
- NATURVETENSKAP -- Biologi -- Biokemi och molekylärbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Biochemistry and Molecular Biology (hsv//eng)
- NATURVETENSKAP -- Biologi -- Strukturbiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Structural Biology (hsv//eng)
- NATURVETENSKAP -- Kemi -- Analytisk kemi (hsv//swe)
- NATURAL SCIENCES -- Chemical Sciences -- Analytical Chemistry (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
Nyckelord
- integrative modeling
- machine learning
- protein structure prediction
- structural proteomics
- Kemi med inriktning mot biofysik
- Chemistry with specialization in Biophysics
- Biokemi
- Biochemistry
- Biologi med inriktning mot strukturbiologi
- Biology with specialization in Structural Biology
- Kemi med inriktning mot analytisk kemi
- Chemistry with specialization in Analytical Chemistry
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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