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Title: Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences

Abstract

Small angle X-ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS-assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography-SAXS (SEC-SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS-assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXSmore » data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology.« less

Authors:
 [1];  [2];  [2]; ORCiD logo [3]; ORCiD logo [3];  [4]; ORCiD logo [5]; ORCiD logo [4]; ORCiD logo [6]; ORCiD logo [4]; ORCiD logo [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Santa Cruz, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. of California, San Diego, CA (United States)
  4. Univ. of California, Davis, CA (United States)
  5. Univ. of Grenoble (France)
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of Houston, TX (United States)
Publication Date:
Research Org.:
Rutgers Univ., New Brunswick, NJ (United States); Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER); National Institute of General Medical Sciences; National Cancer Institute (NCI); National Science Foundation (NSF); Welch Foundation
OSTI Identifier:
1601673
Alternate Identifier(s):
OSTI ID: 1570547; OSTI ID: 1756323
Grant/Contract Number:  
SC0019749; AC02‐05CH11231; DBI-1338415
Resource Type:
Accepted Manuscript
Journal Name:
Proteins
Additional Journal Information:
Journal Volume: 87; Journal Issue: 12; Journal ID: ISSN 0887-3585
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; complexes; disorder; experimental restraints; flexibility; modeling; SAS; SAXS; solution scattering; structure prediction; unstructured regions

Citation Formats

Hura, Greg L., Hodge, Curtis D., Rosenberg, Daniel, Guzenko, Dmytro, Duarte, Jose M., Monastyrskyy, Bohdan, Grudinin, Sergei, Kryshtafovych, Andriy, Tainer, John A., Fidelis, Krzysztof, and Tsutakawa, Susan E. Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences. United States: N. p., 2019. Web. https://doi.org/10.1002/prot.25827.
Hura, Greg L., Hodge, Curtis D., Rosenberg, Daniel, Guzenko, Dmytro, Duarte, Jose M., Monastyrskyy, Bohdan, Grudinin, Sergei, Kryshtafovych, Andriy, Tainer, John A., Fidelis, Krzysztof, & Tsutakawa, Susan E. Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences. United States. https://doi.org/10.1002/prot.25827
Hura, Greg L., Hodge, Curtis D., Rosenberg, Daniel, Guzenko, Dmytro, Duarte, Jose M., Monastyrskyy, Bohdan, Grudinin, Sergei, Kryshtafovych, Andriy, Tainer, John A., Fidelis, Krzysztof, and Tsutakawa, Susan E. Mon . "Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences". United States. https://doi.org/10.1002/prot.25827. https://www.osti.gov/servlets/purl/1601673.
@article{osti_1601673,
title = {Small angle X-ray scattering-assisted protein structure prediction in CASP13 and emergence of solution structure differences},
author = {Hura, Greg L. and Hodge, Curtis D. and Rosenberg, Daniel and Guzenko, Dmytro and Duarte, Jose M. and Monastyrskyy, Bohdan and Grudinin, Sergei and Kryshtafovych, Andriy and Tainer, John A. and Fidelis, Krzysztof and Tsutakawa, Susan E.},
abstractNote = {Small angle X-ray scattering (SAXS) measures comprehensive distance information on a protein's structure, which can constrain and guide computational structure prediction algorithms. Here, we evaluate structure predictions of 11 monomeric and oligomeric proteins for which SAXS data were collected and provided to predictors in the 13th round of the Critical Assessment of protein Structure Prediction (CASP13). The category for SAXS-assisted predictions made gains in certain areas for CASP13 compared to CASP12. Improvements included higher quality data with size exclusion chromatography-SAXS (SEC-SAXS) and better selection of targets and communication of results by CASP organizers. In several cases, we can track improvements in model accuracy with use of SAXS data. For hard multimeric targets where regular folding algorithms were unsuccessful, SAXS data helped predictors to build models better resembling the global shape of the target. For most models, however, no significant improvement in model accuracy at the domain level was registered from use of SAXS data, when rigorously comparing SAXS-assisted models to the best regular server predictions. To promote future progress in this category, we identify successes, challenges, and opportunities for improved strategies in prediction, assessment, and communication of SAXS data to predictors. An important observation is that, for many targets, SAXS data were inconsistent with crystal structures, suggesting that these proteins adopt different conformation(s) in solution. This CASP13 result, if representative of PDB structures and future CASP targets, may have substantive implications for the structure training databases used for machine learning, CASP, and use of prediction models for biology.},
doi = {10.1002/prot.25827},
journal = {Proteins},
number = 12,
volume = 87,
place = {United States},
year = {2019},
month = {10}
}

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    Works referencing / citing this record:

    Assessment of protein assembly prediction in CASP13
    journal, August 2019

    • Guzenko, Dmytro; Lafita, Aleix; Monastyrskyy, Bohdan
    • Proteins: Structure, Function, and Bioinformatics, Vol. 87, Issue 12
    • DOI: 10.1002/prot.25795