Cryo-EM model validation recommendations based on outcomes of the 2019 EMDataResource challenge.
journal contributionposted on 10.02.2021, 11:01 by 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
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.