Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics

[Display omitted] •AlphaFold2’s pLDDT can differentiate between ordered or disordered residues.•AlphaFold2’s pLDDT does not reflect gradations on residue dynamics.•Biases in the training sets result in predicted rigidity of flexible regions.•Conformational diversity in NMR ensembles is associated wi...

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Vydané v:	Journal of molecular biology Ročník 437; číslo 2; s. 168900
Hlavní autori:	Gavalda-Garcia, Jose, Dixit, Bhawna, Díaz, Adrián, Ghysels, An, Vranken, Wim
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Netherlands Elsevier Ltd 15.01.2025
Predmet:	AlphaFold2 Computational Biology - methods large-scale analysis Magnetic Resonance Spectroscopy - methods Molecular Dynamics Simulation nuclear magnetic resonance Nuclear Magnetic Resonance, Biomolecular - methods Protein Conformation protein dynamics Protein Folding Proteins - chemistry structural biology structural biology nuclear magnetic resonance large-scale analysis protein dynamics AlphaFold2
ISSN:	0022-2836, 1089-8638, 1089-8638
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Abstract	[Display omitted] •AlphaFold2’s pLDDT can differentiate between ordered or disordered residues.•AlphaFold2’s pLDDT does not reflect gradations on residue dynamics.•Biases in the training sets result in predicted rigidity of flexible regions.•Conformational diversity in NMR ensembles is associated with lower pLDDT values. The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic resonance (NMR) is the experimental method of choice to gain insight into protein dynamics at near physiological conditions. Computationally, methods such as molecular dynamics (MD) simulations and Normal Mode Analysis (NMA) allow the estimation of a protein’s intrinsic flexibility based on a single protein structure. This work addresses, on a large scale, the relationships for proteins between the AlphaFold2 pLDDT metric, the observed dynamics in solution from NMR metrics, interpreted MD simulations, and the computed dynamics with NMA from single AlphaFold2 models and NMR ensembles. We observe that these metrics agree well for rigid residues that adopt a single well-defined conformation, which are clearly distinct from residues that exhibit dynamic behavior and adopt multiple conformations. This direct order/disorder categorisation is reflected in the correlations observed between the parameters, but becomes very limited when considering only the likely dynamic residues. The gradations of dynamics observed by NMR in flexible protein regions are therefore not represented by these computational approaches. Our results are interactively available for each protein from https://bio2byte.be/af_nmr_nma/.
AbstractList	The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic resonance (NMR) is the experimental method of choice to gain insight into protein dynamics at near physiological conditions. Computationally, methods such as molecular dynamics (MD) simulations and Normal Mode Analysis (NMA) allow the estimation of a protein's intrinsic flexibility based on a single protein structure. This work addresses, on a large scale, the relationships for proteins between the AlphaFold2 pLDDT metric, the observed dynamics in solution from NMR metrics, interpreted MD simulations, and the computed dynamics with NMA from single AlphaFold2 models and NMR ensembles. We observe that these metrics agree well for rigid residues that adopt a single well-defined conformation, which are clearly distinct from residues that exhibit dynamic behavior and adopt multiple conformations. This direct order/disorder categorisation is reflected in the correlations observed between the parameters, but becomes very limited when considering only the likely dynamic residues. The gradations of dynamics observed by NMR in flexible protein regions are therefore not represented by these computational approaches. Our results are interactively available for each protein from https://bio2byte.be/af_nmr_nma/.The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic resonance (NMR) is the experimental method of choice to gain insight into protein dynamics at near physiological conditions. Computationally, methods such as molecular dynamics (MD) simulations and Normal Mode Analysis (NMA) allow the estimation of a protein's intrinsic flexibility based on a single protein structure. This work addresses, on a large scale, the relationships for proteins between the AlphaFold2 pLDDT metric, the observed dynamics in solution from NMR metrics, interpreted MD simulations, and the computed dynamics with NMA from single AlphaFold2 models and NMR ensembles. We observe that these metrics agree well for rigid residues that adopt a single well-defined conformation, which are clearly distinct from residues that exhibit dynamic behavior and adopt multiple conformations. This direct order/disorder categorisation is reflected in the correlations observed between the parameters, but becomes very limited when considering only the likely dynamic residues. The gradations of dynamics observed by NMR in flexible protein regions are therefore not represented by these computational approaches. Our results are interactively available for each protein from https://bio2byte.be/af_nmr_nma/. [Display omitted] •AlphaFold2’s pLDDT can differentiate between ordered or disordered residues.•AlphaFold2’s pLDDT does not reflect gradations on residue dynamics.•Biases in the training sets result in predicted rigidity of flexible regions.•Conformational diversity in NMR ensembles is associated with lower pLDDT values. The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic resonance (NMR) is the experimental method of choice to gain insight into protein dynamics at near physiological conditions. Computationally, methods such as molecular dynamics (MD) simulations and Normal Mode Analysis (NMA) allow the estimation of a protein’s intrinsic flexibility based on a single protein structure. This work addresses, on a large scale, the relationships for proteins between the AlphaFold2 pLDDT metric, the observed dynamics in solution from NMR metrics, interpreted MD simulations, and the computed dynamics with NMA from single AlphaFold2 models and NMR ensembles. We observe that these metrics agree well for rigid residues that adopt a single well-defined conformation, which are clearly distinct from residues that exhibit dynamic behavior and adopt multiple conformations. This direct order/disorder categorisation is reflected in the correlations observed between the parameters, but becomes very limited when considering only the likely dynamic residues. The gradations of dynamics observed by NMR in flexible protein regions are therefore not represented by these computational approaches. Our results are interactively available for each protein from https://bio2byte.be/af_nmr_nma/. The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their design. However, these methods are mainly trained on protein structures determined with X-ray diffraction, where the protein is packed in crystals at often cryogenic temperatures. They can therefore only reliably cover well-folded parts of proteins that experience few, if any, conformational changes. Experimentally, solution nuclear magnetic resonance (NMR) is the experimental method of choice to gain insight into protein dynamics at near physiological conditions. Computationally, methods such as molecular dynamics (MD) simulations and Normal Mode Analysis (NMA) allow the estimation of a protein's intrinsic flexibility based on a single protein structure. This work addresses, on a large scale, the relationships for proteins between the AlphaFold2 pLDDT metric, the observed dynamics in solution from NMR metrics, interpreted MD simulations, and the computed dynamics with NMA from single AlphaFold2 models and NMR ensembles. We observe that these metrics agree well for rigid residues that adopt a single well-defined conformation, which are clearly distinct from residues that exhibit dynamic behavior and adopt multiple conformations. This direct order/disorder categorisation is reflected in the correlations observed between the parameters, but becomes very limited when considering only the likely dynamic residues. The gradations of dynamics observed by NMR in flexible protein regions are therefore not represented by these computational approaches. Our results are interactively available for each protein from https://bio2byte.be/af_nmr_nma/.
ArticleNumber	168900
Author	Vranken, Wim Dixit, Bhawna Díaz, Adrián Ghysels, An Gavalda-Garcia, Jose
Author_xml	– sequence: 1 givenname: Jose surname: Gavalda-Garcia fullname: Gavalda-Garcia, Jose organization: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium – sequence: 2 givenname: Bhawna surname: Dixit fullname: Dixit, Bhawna organization: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium – sequence: 3 givenname: Adrián surname: Díaz fullname: Díaz, Adrián organization: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium – sequence: 4 givenname: An surname: Ghysels fullname: Ghysels, An organization: IBiTech - BioMMedA group, Ghent University, Belgium – sequence: 5 givenname: Wim surname: Vranken fullname: Vranken, Wim email: wim.vranken@vub.be organization: Interuniversity Institute of Bioinformatics in Brussels, ULB-VUB, Brussels, Belgium
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Snippet	[Display omitted] •AlphaFold2’s pLDDT can differentiate between ordered or disordered residues.•AlphaFold2’s pLDDT does not reflect gradations on residue... The advent of accurate methods to predict the fold of proteins initiated by AlphaFold2 is rapidly changing our understanding of proteins and helping their...
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SubjectTerms	AlphaFold2 Computational Biology - methods large-scale analysis Magnetic Resonance Spectroscopy - methods Molecular Dynamics Simulation nuclear magnetic resonance Nuclear Magnetic Resonance, Biomolecular - methods Protein Conformation protein dynamics Protein Folding Proteins - chemistry structural biology
Title	Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics
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