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

Greg L. Hura, Curtis D. Hodge, Daniel Rosenberg, Dmytro Guzenko, Jose M. Duarte, Bohdan Monastyrskyy, Sergei Grudinin, Andriy Kryshtafovych, John A. Tainer, Krzysztof Fidelis, Susan E. Tsutakawa

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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, 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.
Original languageEnglish (US)
Pages (from-to)1298-1314
Number of pages17
JournalProteins: Structure, Function, and Bioinformatics
Volume87
Issue number12
DOIs
StatePublished - Oct 7 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): CRG3
Acknowledgements: The authors declare that they have no conflict of interest. For financial support, we thank NIH (PO1CA092584 to J.A.T., G.L.H, M.H., and S.E.T.; R35CA22043 to J.A.T; R01GM110387 to S.T.; and R01GM100482 to K.F.). J.A.T. acknowledges support by a Robert A. Welch Chemistry Chair, the Cancer Prevention and Research Institute of Texas, the University of Texas System Science and Technology Acquisition and Retention. DG and JD were supported by the RCSB PDB, jointly funded by the NSF, the NIH and the DOE (NSF-DBI 1338415; Principal Investigator Stephen K. Burley). This research used resources of the Advanced Light Source, which are DOE Office of Science User Facilities under contract no. DE-AC02-05CH11231. The SIBYLS beamline 12.3.1 and our CASP efforts are supported by the DOE-BER IDAT program, the NIH supported ALS-ENABLE (P30 GM124169) and a CRG3 from KAUST. Molecular graphics and analyses performed with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.

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