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:

Hura, Greg L. ^[1]; Hodge, Curtis D. ^[2]; Rosenberg, Daniel ^[2]; Guzenko, Dmytro  ^[3];

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Duarte, Jose M.  ^[3];

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Monastyrskyy, Bohdan ^[4]; Grudinin, Sergei  ^[5];

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Kryshtafovych, Andriy  ^[4];

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Tainer, John A.  ^[6];

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Fidelis, Krzysztof  ^[4];

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Tsutakawa, Susan E.  ^[2]

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+ Show Author Affiliations

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:

2019-10-07

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