Cryo‐electron microscopy targets in CASP13: Overview and evaluation of results

Kryshtafovych, Andriy; Malhotra, Sony; Monastyrskyy, Bohdan; Cragnolini, Tristan; Joseph, A. Merriman; Chiu, Wah; Topf, Maya

doi:10.1002/prot.25817

Cited by 25 publications

(45 citation statements)

References 34 publications

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“…There are EM targets for a total of six complexes (four heteromeric) and one protein monomer. These targets tend to be considerably larger than typical in CASP, but once parsed into evaluation domains are less unusual . A fuller account of the procedures used in CASP is available in Reference .…”

Section: Introductionmentioning

confidence: 99%

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Critical assessment of methods of protein structure prediction (CASP)—Round XIII

et al. 2019

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CASP (critical assessment of structure prediction) assesses the state of the art in modeling protein structure from amino acid sequence. The most recent experiment (CASP13 held in 2018) saw dramatic progress in structure modeling without use of structural templates (historically “ab initio” modeling). Progress was driven by the successful application of deep learning techniques to predict inter‐residue distances. In turn, these results drove dramatic improvements in three‐dimensional structure accuracy: With the proviso that there are an adequate number of sequences known for the protein family, the new methods essentially solve the long‐standing problem of predicting the fold topology of monomeric proteins. Further, the number of sequences required in the alignment has fallen substantially. There is also substantial improvement in the accuracy of template‐based models. Other areas—model refinement, accuracy estimation, and the structure of protein assemblies—have again yielded interesting results. CASP13 placed increased emphasis on the use of sparse data together with modeling and chemical crosslinking, SAXS, and NMR all yielded more mature results. This paper summarizes the key outcomes of CASP13. The special issue of PROTEINS contains papers describing the CASP13 assessments in each modeling category and contributions from the participants.

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Section: Introductionmentioning

confidence: 99%

“…These targets tend to be considerably larger than typical in CASP, but once parsed into evaluation domains are less unusual. 3 A fuller account of the procedures used in CASP is available in Reference 4.…”

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confidence: 99%

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

et al. 2019

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“…Optimized models are shaded blue. Only two groups (28,31) had all peptides correct for all 4 targets. Models illustrated in panels b-d are indicated by labeled boxes.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2021

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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.

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“…A correlation matrix of the different local scores has been obtained for structure and models generated during the CASP13 competition, including SCCC, SMOC, EMRinger and other existing local scores (see Fig. 3 of Kryshtafovych et al, 2019).…”

Section: Local Scoringmentioning

confidence: 99%

TEMPy2: a Python library with improved 3D electron microscopy density-fitting and validation workflows

Cragnolini¹,

Sahota²,

Joseph³

et al. 2021

Acta Crystallogr D Struct Biol

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Cryo‐electron microscopy targets in CASP13: Overview and evaluation of results

Cited by 25 publications

References 34 publications

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

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

TEMPy2: a Python library with improved 3D electron microscopy density-fitting and validation workflows

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