AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent at...
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Gespeichert in:

Autor*in:	Roberto Tejero [verfasserIn] Yuanpeng Janet Huang [verfasserIn] Theresa A. Ramelot [verfasserIn] Gaetano T. Montelione [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	protein NMR structure validation AlphaFold protein structure prediction automated structure determination X-ray crystal structure analysis

Übergeordnetes Werk:	In: Frontiers in Molecular Biosciences - Frontiers Media S.A., 2015, 9(2022)
Übergeordnetes Werk:	volume:9 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fmolb.2022.877000

Katalog-ID:	DOAJ028838440

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allfields	10.3389/fmolb.2022.877000 doi (DE-627)DOAJ028838440 (DE-599)DOAJ7d10623df17f4968ad4beb4698dbd1d7 DE-627 ger DE-627 rakwb eng QH301-705.5 Roberto Tejero verfasserin aut AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology. protein NMR structure validation AlphaFold protein structure prediction automated structure determination X-ray crystal structure analysis Biology (General) Yuanpeng Janet Huang verfasserin aut Theresa A. Ramelot verfasserin aut Gaetano T. Montelione verfasserin aut In Frontiers in Molecular Biosciences Frontiers Media S.A., 2015 9(2022) (DE-627)820039691 (DE-600)2814330-9 2296889X nnns volume:9 year:2022 https://doi.org/10.3389/fmolb.2022.877000 kostenfrei https://doaj.org/article/7d10623df17f4968ad4beb4698dbd1d7 kostenfrei https://www.frontiersin.org/articles/10.3389/fmolb.2022.877000/full kostenfrei https://doaj.org/toc/2296-889X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
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allfieldsSound	10.3389/fmolb.2022.877000 doi (DE-627)DOAJ028838440 (DE-599)DOAJ7d10623df17f4968ad4beb4698dbd1d7 DE-627 ger DE-627 rakwb eng QH301-705.5 Roberto Tejero verfasserin aut AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology. protein NMR structure validation AlphaFold protein structure prediction automated structure determination X-ray crystal structure analysis Biology (General) Yuanpeng Janet Huang verfasserin aut Theresa A. Ramelot verfasserin aut Gaetano T. Montelione verfasserin aut In Frontiers in Molecular Biosciences Frontiers Media S.A., 2015 9(2022) (DE-627)820039691 (DE-600)2814330-9 2296889X nnns volume:9 year:2022 https://doi.org/10.3389/fmolb.2022.877000 kostenfrei https://doaj.org/article/7d10623df17f4968ad4beb4698dbd1d7 kostenfrei https://www.frontiersin.org/articles/10.3389/fmolb.2022.877000/full kostenfrei https://doaj.org/toc/2296-889X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2022
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authorswithroles_txt_mv	Roberto Tejero @@aut@@ Yuanpeng Janet Huang @@aut@@ Theresa A. Ramelot @@aut@@ Gaetano T. Montelione @@aut@@
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callnumber-first	Q - Science
author	Roberto Tejero
spellingShingle	Roberto Tejero misc QH301-705.5 misc protein NMR misc structure validation misc AlphaFold misc protein structure prediction misc automated structure determination misc X-ray crystal structure analysis misc Biology (General) AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures
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topic_title	QH301-705.5 AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures protein NMR structure validation AlphaFold protein structure prediction automated structure determination X-ray crystal structure analysis
topic	misc QH301-705.5 misc protein NMR misc structure validation misc AlphaFold misc protein structure prediction misc automated structure determination misc X-ray crystal structure analysis misc Biology (General)
topic_unstemmed	misc QH301-705.5 misc protein NMR misc structure validation misc AlphaFold misc protein structure prediction misc automated structure determination misc X-ray crystal structure analysis misc Biology (General)
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title	AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures
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title_full	AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures
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title_sort	alphafold models of small proteins rival the accuracy of solution nmr structures
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title_auth	AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures
abstract	Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.
abstractGer	Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.
abstract_unstemmed	Recent advances in molecular modeling using deep learning have the potential to revolutionize the field of structural biology. In particular, AlphaFold has been observed to provide models of protein structures with accuracies rivaling medium-resolution X-ray crystal structures, and with excellent atomic coordinate matches to experimental protein NMR and cryo-electron microscopy structures. Here we assess the hypothesis that AlphaFold models of small, relatively rigid proteins have accuracies (based on comparison against experimental data) similar to experimental solution NMR structures. We selected six representative small proteins with structures determined by both NMR and X-ray crystallography, and modeled each of them using AlphaFold. Using several structure validation tools integrated under the Protein Structure Validation Software suite (PSVS), we then assessed how well these models fit to experimental NMR data, including NOESY peak lists (RPF-DP scores), comparisons between predicted rigidity and chemical shift data (ANSURR scores), and 15N-1H residual dipolar coupling data (RDC Q factors) analyzed by software tools integrated in the PSVS suite. Remarkably, the fits to NMR data for the protein structure models predicted with AlphaFold are generally similar, or better, than for the corresponding experimental NMR or X-ray crystal structures. Similar conclusions were reached in comparing AlphaFold2 predictions and NMR structures for three targets from the Critical Assessment of Protein Structure Prediction (CASP). These results contradict the widely held misperception that AlphaFold cannot accurately model solution NMR structures. They also document the value of PSVS for model vs. data assessment of protein NMR structures, and the potential for using AlphaFold models for guiding analysis of experimental NMR data and more generally in structural biology.
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title_short	AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures
url	https://doi.org/10.3389/fmolb.2022.877000 https://doaj.org/article/7d10623df17f4968ad4beb4698dbd1d7 https://www.frontiersin.org/articles/10.3389/fmolb.2022.877000/full https://doaj.org/toc/2296-889X
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up_date	2024-07-03T19:42:25.455Z
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AlphaFold Models of Small Proteins Rival the Accuracy of Solution NMR Structures

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