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Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge

  • Catherine L. Lawson
  • , Andriy Kryshtafovych
  • , Paul D. Adams
  • , Pavel V. Afonine
  • , Matthew L. Baker
  • , Benjamin A. Barad
  • , Paul Bond
  • , Tom Burnley
  • , Renzhi Cao
  • , Jianlin Cheng
  • , Grzegorz Chojnowski
  • , Kevin Cowtan
  • , Ken A. Dill
  • , Frank DiMaio
  • , Daniel P. Farrell
  • , James S. Fraser
  • , Mark A. Herzik
  • , Soon Wen Hoh
  • , Jie Hou
  • , Li Wei Hung
  • Maxim Igaev, Agnel P. Joseph, Daisuke Kihara, Dilip Kumar, Sumit Mittal, Bohdan Monastyrskyy, Mateusz Olek, Colin M. Palmer, Ardan Patwardhan, Alberto Perez, Jonas Pfab, Grigore D. Pintilie, Jane S. Richardson, Peter B. Rosenthal, Daipayan Sarkar, Luisa U. Schäfer, Michael F. Schmid, Gunnar F. Schröder, Mrinal Shekhar, Dong Si, Abishek Singharoy, Genki Terashi, Thomas C. Terwilliger, Andrea Vaiana, Liguo Wang, Zhe Wang, Stephanie A. Wankowicz, Christopher J. Williams, Martyn Winn, Tianqi Wu, Xiaodi Yu, Kaiming Zhang, Helen M. Berman, Wah Chiu
  • Rutgers - The State University of New Jersey, New Brunswick
  • University of California at Davis
  • Lawrence Berkeley National Laboratory
  • University of California at Berkeley
  • University of Texas Health Science Center at Houston
  • Scripps Research Institute
  • University of York
  • Science and Technology Facilities Council
  • Pacific Lutheran University
  • University of Missouri
  • European Molecular Biology Laboratory
  • University of Washington
  • University of California at San Francisco
  • University of California at San Diego
  • Saint Louis University
  • Los Alamos National Laboratory
  • Max Planck Institute for Biophysical Chemistry (Karl Friedrich Bonhoeffer Institute)
  • Purdue University
  • Baylor College of Medicine
  • Arizona State University
  • VIT Bhopal University
  • University of Florida
  • Stanford University
  • Duke University
  • The Francis Crick Institute
  • Jülich Research Centre
  • Heinrich Heine University Düsseldorf
  • Broad Institute
  • New Mexico Consortium
  • Johnson & Johnson
  • University of Southern California

Research output: Contribution to journalArticlepeer-review

85 Scopus citations

Abstract

This paper describes outcomes of the 2019 Cryo-EM Model Challenge. The goals were to (1) assess the quality of models that can be produced from cryogenic electron microscopy (cryo-EM) maps using current modeling software, (2) evaluate reproducibility of modeling results from different software developers and users and (3) compare performance of current metrics used for model evaluation, particularly Fit-to-Map metrics, with focus on near-atomic resolution. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived by 13 participating teams from four benchmark maps, including three forming a resolution series (1.8 to 3.1 Å). The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual experiments and structure data archives such as the Protein Data Bank. We recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed cryo-EM map density.

Original languageEnglish
Pages (from-to)156-164
Number of pages9
JournalNature Methods
Volume18
Issue number2
DOIs
StatePublished - Feb 2021

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