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Assessment of Prediction Methods for Protein Structures Determined by NMR in CASP14: Impact of AlphaFold2
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  • Yuanpeng Janet Huang,
  • Ning Zhang,
  • Beate Bersch,
  • Krzysztof Fidelis,
  • Masayori Inouye,
  • Yojiro Ishida,
  • Andriy Kryshtafovych,
  • Naohiro Kobayashi,
  • Yutaka Kuroda,
  • Gaohua Liu,
  • Andy LiWang,
  • Swapna Gurla,
  • Nan Wu,
  • Toshio Yamazaki,
  • Gaetano Montelione
Yuanpeng Janet Huang
Rensselaer Polytechnic Institute
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Ning Zhang
University of California Merced
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Beate Bersch
Institut de Biologie Structurale
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Krzysztof Fidelis
University of California
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Masayori Inouye
Rutgers University
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Yojiro Ishida
Rutgers University
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Andriy Kryshtafovych
University of California, Davis
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Naohiro Kobayashi
Riken Yokohama Institute
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Yutaka Kuroda
Tokyo University of Agriculture and Technology
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Gaohua Liu
Nexomics Biosciences, Inc
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Andy LiWang
University of California Merced
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Swapna Gurla
Rutgers University
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Nan Wu
Zhengzhou University of Light Industry
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Toshio Yamazaki
Riken Yokohama Institute
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Gaetano Montelione
Rensselaer Polytechnic Institute
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Abstract

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 a 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, the NMR data reveal extensive internal dynamics, presenting a unique challenge for protein structure prediction. The analysis of T1029 motivated exploration of a novel method of “inverse structure determination”, in which an AF2 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 the NMR data generated structures 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.

Peer review status:ACCEPTED

24 Jul 2021Submitted to PROTEINS: Structure, Function, and Bioinformatics
26 Jul 2021Submission Checks Completed
26 Jul 2021Assigned to Editor
27 Jul 2021Reviewer(s) Assigned
24 Aug 2021Review(s) Completed, Editorial Evaluation Pending
25 Aug 2021Editorial Decision: Revise Minor
08 Sep 20211st Revision Received
11 Sep 2021Submission Checks Completed
11 Sep 2021Assigned to Editor
14 Sep 2021Review(s) Completed, Editorial Evaluation Pending
14 Sep 2021Editorial Decision: Accept