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Assessment of prediction methods for protein structures determined by NMR in CASP14: Impact of AlphaFold2.
Huang, Yuanpeng Janet; Zhang, Ning; Bersch, Beate; Fidelis, Krzysztof; Inouye, Masayori; Ishida, Yojiro; Kryshtafovych, Andriy; Kobayashi, Naohiro; Kuroda, Yutaka; Liu, Gaohua; LiWang, Andy; Swapna, G V T; Wu, Nan; Yamazaki, Toshio; Montelione, Gaetano T.
Affiliation
  • Huang YJ; Department of Chemistry and Chemical Biology, Center for Biotechnology and Interdisciplinary Sciences, Rensselaer Polytechnic Institute, Troy, New York, USA.
  • Zhang N; Department of Chemistry and Biochemistry, University of California, Merced, California, USA.
  • Bersch B; Biomolecular NMR Spectroscopy Group, Institut de Biologie Structurale, UMD-5075, CNRS-CEA-UJF, Grenoble, France.
  • Fidelis K; Genome Center, University of California, Davis, California, USA.
  • Inouye M; Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.
  • Ishida Y; Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA.
  • Kryshtafovych A; Department of Biochemistry, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.
  • Kobayashi N; Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, New Jersey, USA.
  • Kuroda Y; Genome Center, University of California, Davis, California, USA.
  • Liu G; NMR Science and Development Division, RSC, RIKEN, Yokohama, Kanagawa, Japan.
  • LiWang A; Department of Biotechnology and Life Science, Graduate School of Engineering, Tokyo University of Agriculture and Technology (TUAT), Tokyo, Japan.
  • Swapna GVT; Nexomics Biosciences, Inc., Rocky Hill, New Jersey, USA.
  • Wu N; Department of Chemistry and Biochemistry, University of California, Merced, California, USA.
  • Yamazaki T; Center for Cellular and Biomolecular Machines and Health Sciences Research Institute, University of California, Merced, California, USA.
  • Montelione GT; Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, New Jersey, USA.
Proteins ; 89(12): 1959-1976, 2021 12.
Article in En | MEDLINE | ID: mdl-34559429
NMR studies can provide unique information about protein conformations in solution. In CASP14, three reference structures provided by solution NMR methods were available (T1027, T1029, and T1055), as well as a fourth data set of NMR-derived contacts for an integral membrane protein (T1088). For the three targets with NMR-based structures, the best prediction results ranged from very good (GDT_TS = 0.90, for T1055) to poor (GDT_TS = 0.47, for T1029). We explored the basis of these results by comparing all CASP14 prediction models against experimental NMR data. For T1027, NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction methods. The analysis of T1029 motivated exploration of a novel method of "inverse structure determination," in which an AlphaFold2 model was used to guide NMR data analysis. NMR data provided to CASP predictor groups for target T1088, a 238-residue integral membrane porin, was also used to assess several NMR-assisted prediction methods. Most groups involved in this exercise generated similar beta-barrel models, with good agreement with the experimental data. However, as was also observed in CASP13, some pure prediction groups that did not use any NMR data generated models for T1088 that better fit the NMR data than the models generated using these experimental data. These results demonstrate the remarkable power of modern methods to predict structures of proteins with accuracies rivaling solution NMR structures, and that it is now possible to reliably use prediction models to guide and complement experimental NMR data analysis.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Protein Conformation / Software / Magnetic Resonance Spectroscopy / Models, Molecular / Membrane Proteins Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Proteins Journal subject: BIOQUIMICA Year: 2021 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Protein Conformation / Software / Magnetic Resonance Spectroscopy / Models, Molecular / Membrane Proteins Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Proteins Journal subject: BIOQUIMICA Year: 2021 Type: Article Affiliation country: United States