| Literature DB >> 33542514 |
Catherine L Lawson1, Andriy Kryshtafovych2, Paul D Adams3,4, Pavel V Afonine3, Matthew L Baker5, Benjamin A Barad6, Paul Bond7, Tom Burnley8, Renzhi Cao9, Jianlin Cheng10, Grzegorz Chojnowski11, Kevin Cowtan7, Ken A Dill12, Frank DiMaio13, Daniel P Farrell13, James S Fraser14, Mark A Herzik15, Soon Wen Hoh7, Jie Hou16, Li-Wei Hung17, Maxim Igaev18, Agnel P Joseph8, Daisuke Kihara19,20, Dilip Kumar21, Sumit Mittal22,23, Bohdan Monastyrskyy2, Mateusz Olek7, Colin M Palmer8, Ardan Patwardhan24, Alberto Perez25, Jonas Pfab26, Grigore D Pintilie27, Jane S Richardson28, Peter B Rosenthal29, Daipayan Sarkar19,22, Luisa U Schäfer30, Michael F Schmid31, Gunnar F Schröder30,32, Mrinal Shekhar22,33, Dong Si26, Abishek Singharoy22, Genki Terashi18, Thomas C Terwilliger34, Andrea Vaiana18, Liguo Wang35, Zhe Wang24, Stephanie A Wankowicz14,36, Christopher J Williams28, Martyn Winn8, Tianqi Wu10, Xiaodi Yu37, Kaiming Zhang27, Helen M Berman38,39, Wah Chiu40,41.
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.Entities:
Mesh:
Substances:
Year: 2021 PMID: 33542514 PMCID: PMC7864804 DOI: 10.1038/s41592-020-01051-w
Source DB: PubMed Journal: Nat Methods ISSN: 1548-7091 Impact factor: 28.547